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

Connectivity Predicts Deep Brain Stimulation Outcome in Parkinson Disease

PubMed Central

Horn, Andreas; Reich, Martin; Vorwerk, Johannes; Li, Ningfei; Wenzel, Gregor; Fang, Qianqian; Schmitz-Hübsch, Tanja; Nickl, Robert; Kupsch, Andreas; Volkmann, Jens; Kühn, Andrea A.; Fox, Michael D.

2018-01-01

Objective The benefit of deep brain stimulation (DBS) for Parkinson disease (PD) may depend on connectivity between the stimulation site and other brain regions, but which regions and whether connectivity can predict outcome in patients remain unknown. Here, we identify the structural and functional connectivity profile of effective DBS to the subthalamic nucleus (STN) and test its ability to predict outcome in an independent cohort. Methods A training dataset of 51 PD patients with STN DBS was combined with publicly available human connectome data (diffusion tractography and resting state functional connectivity) to identify connections reliably associated with clinical improvement (motor score of the Unified Parkinson Disease Rating Scale [UPDRS]). This connectivity profile was then used to predict outcome in an independent cohort of 44 patients from a different center. Results In the training dataset, connectivity between the DBS electrode and a distributed network of brain regions correlated with clinical response including structural connectivity to supplementary motor area and functional anticorrelation to primary motor cortex (p<0.001). This same connectivity profile predicted response in an independent patient cohort (p<0.01). Structural and functional connectivity were independent predictors of clinical improvement (p<0.001) and estimated response in individual patients with an average error of 15% UPDRS improvement. Results were similar using connectome data from normal subjects or a connectome age, sex, and disease matched to our DBS patients. Interpretation Effective STN DBS for PD is associated with a specific connectivity profile that can predict clinical outcome across independent cohorts. This prediction does not require specialized imaging in PD patients themselves. PMID:28586141

Extracting physicochemical features to predict protein secondary structure.

PubMed

Huang, Yin-Fu; Chen, Shu-Ying

2013-01-01

We propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q 3 reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60; all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features in predicting protein secondary structure would exhibit better performances.

Extracting Physicochemical Features to Predict Protein Secondary Structure

PubMed Central

Chen, Shu-Ying

2013-01-01

We propose a protein secondary structure prediction method based on position-specific scoring matrix (PSSM) profiles and four physicochemical features including conformation parameters, net charges, hydrophobic, and side chain mass. First, the SVM with the optimal window size and the optimal parameters of the kernel function is found. Then, we train the SVM using the PSSM profiles generated from PSI-BLAST and the physicochemical features extracted from the CB513 data set. Finally, we use the filter to refine the predicted results from the trained SVM. For all the performance measures of our method, Q 3 reaches 79.52, SOV94 reaches 86.10, and SOV99 reaches 74.60; all the measures are higher than those of the SVMpsi method and the SVMfreq method. This validates that considering these physicochemical features in predicting protein secondary structure would exhibit better performances. PMID:23766688

Modelling the Tox21 10 K chemical profiles for in vivo toxicity prediction and mechanism characterization

PubMed Central

Huang, Ruili; Xia, Menghang; Sakamuru, Srilatha; Zhao, Jinghua; Shahane, Sampada A.; Attene-Ramos, Matias; Zhao, Tongan; Austin, Christopher P.; Simeonov, Anton

2016-01-01

Target-specific, mechanism-oriented in vitro assays post a promising alternative to traditional animal toxicology studies. Here we report the first comprehensive analysis of the Tox21 effort, a large-scale in vitro toxicity screening of chemicals. We test ∼10,000 chemicals in triplicates at 15 concentrations against a panel of nuclear receptor and stress response pathway assays, producing more than 50 million data points. Compound clustering by structure similarity and activity profile similarity across the assays reveals structure–activity relationships that are useful for the generation of mechanistic hypotheses. We apply structural information and activity data to build predictive models for 72 in vivo toxicity end points using a cluster-based approach. Models based on in vitro assay data perform better in predicting human toxicity end points than animal toxicity, while a combination of structural and activity data results in better models than using structure or activity data alone. Our results suggest that in vitro activity profiles can be applied as signatures of compound mechanism of toxicity and used in prioritization for more in-depth toxicological testing. PMID:26811972

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.

Statistical potential-based amino acid similarity matrices for aligning distantly related protein sequences.

PubMed

Tan, Yen Hock; Huang, He; Kihara, Daisuke

2006-08-15

Aligning distantly related protein sequences is a long-standing problem in bioinformatics, and a key for successful protein structure prediction. Its importance is increasing recently in the context of structural genomics projects because more and more experimentally solved structures are available as templates for protein structure modeling. Toward this end, recent structure prediction methods employ profile-profile alignments, and various ways of aligning two profiles have been developed. More fundamentally, a better amino acid similarity matrix can improve a profile itself; thereby resulting in more accurate profile-profile alignments. Here we have developed novel amino acid similarity matrices from knowledge-based amino acid contact potentials. Contact potentials are used because the contact propensity to the other amino acids would be one of the most conserved features of each position of a protein structure. The derived amino acid similarity matrices are tested on benchmark alignments at three different levels, namely, the family, the superfamily, and the fold level. Compared to BLOSUM45 and the other existing matrices, the contact potential-based matrices perform comparably in the family level alignments, but clearly outperform in the fold level alignments. The contact potential-based matrices perform even better when suboptimal alignments are considered. Comparing the matrices themselves with each other revealed that the contact potential-based matrices are very different from BLOSUM45 and the other matrices, indicating that they are located in a different basin in the amino acid similarity matrix space.

[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.

Experimental verification, and domain definition, of structural alerts for protein binding: epoxides, lactones, nitroso, nitros, aldehydes and ketones.

PubMed

Nelms, M D; Cronin, M T D; Schultz, T W; Enoch, S J

2013-01-01

This study outlines how a combination of in chemico and Tetrahymena pyriformis data can be used to define the applicability domain of selected structural alerts within the profilers of the OECD QSAR Toolbox. Thirty-three chemicals were profiled using the OECD and OASIS profilers, enabling the applicability domain of six structural alerts to be defined, the alerts being: epoxides, lactones, nitrosos, nitros, aldehydes and ketones. Analysis of the experimental data showed the applicability domains for the epoxide, nitroso, aldehyde and ketone structural alerts to be well defined. In contrast, the data showed the applicability domains for the lactone and nitro structural alerts needed modifying. The accurate definition of the applicability domain for structural alerts within in silico profilers is important due to their use in the chemical category in predictive and regulatory toxicology. This study highlights the importance of utilizing multiple profilers in category formation.

(PS)2: protein structure prediction server version 3.0.

PubMed

Huang, Tsun-Tsao; Hwang, Jenn-Kang; Chen, Chu-Huang; Chu, Chih-Sheng; Lee, Chi-Wen; Chen, Chih-Chieh

2015-07-01

Protein complexes are involved in many biological processes. Examining coupling between subunits of a complex would be useful to understand the molecular basis of protein function. Here, our updated (PS)(2) web server predicts the three-dimensional structures of protein complexes based on comparative modeling; furthermore, this server examines the coupling between subunits of the predicted complex by combining structural and evolutionary considerations. The predicted complex structure could be indicated and visualized by Java-based 3D graphics viewers and the structural and evolutionary profiles are shown and compared chain-by-chain. For each subunit, considerations with or without the packing contribution of other subunits cause the differences in similarities between structural and evolutionary profiles, and these differences imply which form, complex or monomeric, is preferred in the biological condition for the subunit. We believe that the (PS)(2) server would be a useful tool for biologists who are interested not only in the structures of protein complexes but also in the coupling between subunits of the complexes. The (PS)(2) is freely available at http://ps2v3.life.nctu.edu.tw/. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Prediction of protein long-range contacts using an ensemble of genetic algorithm classifiers with sequence profile centers.

PubMed

Chen, Peng; Li, Jinyan

2010-05-17

Prediction of long-range inter-residue contacts is an important topic in bioinformatics research. It is helpful for determining protein structures, understanding protein foldings, and therefore advancing the annotation of protein functions. In this paper, we propose a novel ensemble of genetic algorithm classifiers (GaCs) to address the long-range contact prediction problem. Our method is based on the key idea called sequence profile centers (SPCs). Each SPC is the average sequence profiles of residue pairs belonging to the same contact class or non-contact class. GaCs train on multiple but different pairs of long-range contact data (positive data) and long-range non-contact data (negative data). The negative data sets, having roughly the same sizes as the positive ones, are constructed by random sampling over the original imbalanced negative data. As a result, about 21.5% long-range contacts are correctly predicted. We also found that the ensemble of GaCs indeed makes an accuracy improvement by around 5.6% over the single GaC. Classifiers with the use of sequence profile centers may advance the long-range contact prediction. In line with this approach, key structural features in proteins would be determined with high efficiency and accuracy.

MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

PubMed

Fang, Chao; Shang, Yi; Xu, Dong

2018-05-01

Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neural network architecture, named the Deep inception-inside-inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD-SS. The input to MUFOLD-SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, and HHBlits profile. MUFOLD-SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. The architecture of MUFOLD-SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD-SS outperformed the best existing methods and other deep neural networks significantly. MUFold-SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html. © 2018 Wiley Periodicals, Inc.

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.

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.

PSS-3D1D: an improved 3D1D profile method of protein fold recognition for the annotation of twilight zone sequences.

PubMed

Ganesan, K; Parthasarathy, S

2011-12-01

Annotation of any newly determined protein sequence depends on the pairwise sequence identity with known sequences. However, for the twilight zone sequences which have only 15-25% identity, the pair-wise comparison methods are inadequate and the annotation becomes a challenging task. Such sequences can be annotated by using methods that recognize their fold. Bowie et al. described a 3D1D profile method in which the amino acid sequences that fold into a known 3D structure are identified by their compatibility to that known 3D structure. We have improved the above method by using the predicted secondary structure information and employ it for fold recognition from the twilight zone sequences. In our Protein Secondary Structure 3D1D (PSS-3D1D) method, a score (w) for the predicted secondary structure of the query sequence is included in finding the compatibility of the query sequence to the known fold 3D structures. In the benchmarks, the PSS-3D1D method shows a maximum of 21% improvement in predicting correctly the α + β class of folds from the sequences with twilight zone level of identity, when compared with the 3D1D profile method. Hence, the PSS-3D1D method could offer more clues than the 3D1D method for the annotation of twilight zone sequences. The web based PSS-3D1D method is freely available in the PredictFold server at http://bioinfo.bdu.ac.in/servers/ .

Analytical and Experimental Characterization of Gravity Induced Deformations In Subscale Gossamer Structures

NASA Technical Reports Server (NTRS)

Johnston, John D.; Blandino, Joseph R.; McEvoy, Kiley C.

2004-01-01

The development of gossamer space structures such as solar sails and sunshields presents many challenges due to their large size and extreme flexibility. The post-deployment structural geometry exhibited during ground testing may significantly depart from the in-space configuration due to the presence of gravity-induced deformations (gravity sag) of lightly preloaded membranes. This paper describes a study carried out to characterize gravity sag in two subscale gossamer structures: a single quadrant from a 2 m, 4 quadrant square solar sail and a 1.7 m membrane layer from a multi-layer sunshield The behavior of the test articles was studied over a range of preloads and in several orientations with respect to gravity. An experimental study was carried out to measure the global surface profiles using photogrammetry, and nonlinear finite element analysis was used to predict the behavior of the test articles. Comparison of measured and predicted surface profiles shows that the finite dement analysis qualitatively predicts deformed shapes comparable to those observed in the laboratory. Quantitatively, finite element analysis predictions for peak gravity-induced deformations in both test articles were within 10% of measured values. Results from this study provide increased insight into gravity sag behavior in gossamer structures, and demonstrates the potential to analytically predict gravity-induced deformations to within reasonable accuracy.

CPHmodels-3.0--remote homology modeling using structure-guided sequence profiles.

PubMed

Nielsen, Morten; Lundegaard, Claus; Lund, Ole; Petersen, Thomas Nordahl

2010-07-01

CPHmodels-3.0 is a web server predicting protein 3D structure by use of single template homology modeling. The server employs a hybrid of the scoring functions of CPHmodels-2.0 and a novel remote homology-modeling algorithm. A query sequence is first attempted modeled using the fast CPHmodels-2.0 profile-profile scoring function suitable for close homology modeling. The new computational costly remote homology-modeling algorithm is only engaged provided that no suitable PDB template is identified in the initial search. CPHmodels-3.0 was benchmarked in the CASP8 competition and produced models for 94% of the targets (117 out of 128), 74% were predicted as high reliability models (87 out of 117). These achieved an average RMSD of 4.6 A when superimposed to the 3D structure. The remaining 26% low reliably models (30 out of 117) could superimpose to the true 3D structure with an average RMSD of 9.3 A. These performance values place the CPHmodels-3.0 method in the group of high performing 3D prediction tools. Beside its accuracy, one of the important features of the method is its speed. For most queries, the response time of the server is <20 min. The web server is available at http://www.cbs.dtu.dk/services/CPHmodels/.

Rational selection of experimental readout and intervention sites for reducing uncertainties in computational model predictions.

PubMed

Flassig, Robert J; Migal, Iryna; der Zalm, Esther van; Rihko-Struckmann, Liisa; Sundmacher, Kai

2015-01-16

Understanding the dynamics of biological processes can substantially be supported by computational models in the form of nonlinear ordinary differential equations (ODE). Typically, this model class contains many unknown parameters, which are estimated from inadequate and noisy data. Depending on the ODE structure, predictions based on unmeasured states and associated parameters are highly uncertain, even undetermined. For given data, profile likelihood analysis has been proven to be one of the most practically relevant approaches for analyzing the identifiability of an ODE structure, and thus model predictions. In case of highly uncertain or non-identifiable parameters, rational experimental design based on various approaches has shown to significantly reduce parameter uncertainties with minimal amount of effort. In this work we illustrate how to use profile likelihood samples for quantifying the individual contribution of parameter uncertainty to prediction uncertainty. For the uncertainty quantification we introduce the profile likelihood sensitivity (PLS) index. Additionally, for the case of several uncertain parameters, we introduce the PLS entropy to quantify individual contributions to the overall prediction uncertainty. We show how to use these two criteria as an experimental design objective for selecting new, informative readouts in combination with intervention site identification. The characteristics of the proposed multi-criterion objective are illustrated with an in silico example. We further illustrate how an existing practically non-identifiable model for the chlorophyll fluorescence induction in a photosynthetic organism, D. salina, can be rendered identifiable by additional experiments with new readouts. Having data and profile likelihood samples at hand, the here proposed uncertainty quantification based on prediction samples from the profile likelihood provides a simple way for determining individual contributions of parameter uncertainties to uncertainties in model predictions. The uncertainty quantification of specific model predictions allows identifying regions, where model predictions have to be considered with care. Such uncertain regions can be used for a rational experimental design to render initially highly uncertain model predictions into certainty. Finally, our uncertainty quantification directly accounts for parameter interdependencies and parameter sensitivities of the specific prediction.

Regularized Moment Equations and Shock Waves for Rarefied Granular Gas

NASA Astrophysics Data System (ADS)

Reddy, Lakshminarayana; Alam, Meheboob

2016-11-01

It is well-known that the shock structures predicted by extended hydrodynamic models are more accurate than the standard Navier-Stokes model in the rarefied regime, but they fail to predict continuous shock structures when the Mach number exceeds a critical value. Regularization or parabolization is one method to obtain smooth shock profiles at all Mach numbers. Following a Chapman-Enskog-like method, we have derived the "regularized" version 10-moment equations ("R10" moment equations) for inelastic hard-spheres. In order to show the advantage of R10 moment equations over standard 10-moment equations, the R10 moment equations have been employed to solve the Riemann problem of plane shock waves for both molecular and granular gases. The numerical results are compared between the 10-moment and R10-moment models and it is found that the 10-moment model fails to produce continuous shock structures beyond an upstream Mach number of 1 . 34 , while the R10-moment model predicts smooth shock profiles beyond the upstream Mach number of 1 . 34 . The density and granular temperature profiles are found to be asymmetric, with their maxima occurring within the shock-layer.

Stargate GTM: Bridging Descriptor and Activity Spaces.

PubMed

Gaspar, Héléna A; Baskin, Igor I; Marcou, Gilles; Horvath, Dragos; Varnek, Alexandre

2015-11-23

Predicting the activity profile of a molecule or discovering structures possessing a specific activity profile are two important goals in chemoinformatics, which could be achieved by bridging activity and molecular descriptor spaces. In this paper, we introduce the "Stargate" version of the Generative Topographic Mapping approach (S-GTM) in which two different multidimensional spaces (e.g., structural descriptor space and activity space) are linked through a common 2D latent space. In the S-GTM algorithm, the manifolds are trained simultaneously in two initial spaces using the probabilities in the 2D latent space calculated as a weighted geometric mean of probability distributions in both spaces. S-GTM has the following interesting features: (1) activities are involved during the training procedure; therefore, the method is supervised, unlike conventional GTM; (2) using molecular descriptors of a given compound as input, the model predicts a whole activity profile, and (3) using an activity profile as input, areas populated by relevant chemical structures can be detected. To assess the performance of S-GTM prediction models, a descriptor space (ISIDA descriptors) of a set of 1325 GPCR ligands was related to a B-dimensional (B = 1 or 8) activity space corresponding to pKi values for eight different targets. S-GTM outperforms conventional GTM for individual activities and performs similarly to the Lasso multitask learning algorithm, although it is still slightly less accurate than the Random Forest method.

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models.

PubMed

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com .

TargetNet: a web service for predicting potential drug-target interaction profiling via multi-target SAR models

NASA Astrophysics Data System (ADS)

Yao, Zhi-Jiang; Dong, Jie; Che, Yu-Jing; Zhu, Min-Feng; Wen, Ming; Wang, Ning-Ning; Wang, Shan; Lu, Ai-Ping; Cao, Dong-Sheng

2016-05-01

Drug-target interactions (DTIs) are central to current drug discovery processes and public health fields. Analyzing the DTI profiling of the drugs helps to infer drug indications, adverse drug reactions, drug-drug interactions, and drug mode of actions. Therefore, it is of high importance to reliably and fast predict DTI profiling of the drugs on a genome-scale level. Here, we develop the TargetNet server, which can make real-time DTI predictions based only on molecular structures, following the spirit of multi-target SAR methodology. Naïve Bayes models together with various molecular fingerprints were employed to construct prediction models. Ensemble learning from these fingerprints was also provided to improve the prediction ability. When the user submits a molecule, the server will predict the activity of the user's molecule across 623 human proteins by the established high quality SAR model, thus generating a DTI profiling that can be used as a feature vector of chemicals for wide applications. The 623 SAR models related to 623 human proteins were strictly evaluated and validated by several model validation strategies, resulting in the AUC scores of 75-100 %. We applied the generated DTI profiling to successfully predict potential targets, toxicity classification, drug-drug interactions, and drug mode of action, which sufficiently demonstrated the wide application value of the potential DTI profiling. The TargetNet webserver is designed based on the Django framework in Python, and is freely accessible at http://targetnet.scbdd.com.

Protein Structure and Function Prediction Using I-TASSER

PubMed Central

Yang, Jianyi; Zhang, Yang

2016-01-01

I-TASSER is a hierarchical protocol for automated protein structure prediction and structure-based function annotation. Starting from the amino acid sequence of target proteins, I-TASSER first generates full-length atomic structural models from multiple threading alignments and iterative structural assembly simulations followed by atomic-level structure refinement. The biological functions of the protein, including ligand-binding sites, enzyme commission number, and gene ontology terms, are then inferred from known protein function databases based on sequence and structure profile comparisons. I-TASSER is freely available as both an on-line server and a stand-alone package. This unit describes how to use the I-TASSER protocol to generate structure and function prediction and how to interpret the prediction results, as well as alternative approaches for further improving the I-TASSER modeling quality for distant-homologous and multi-domain protein targets. PMID:26678386

Systematic Evaluation of Wajima Superposition (Steady-State Concentration to Mean Residence Time) in the Estimation of Human Intravenous Pharmacokinetic Profile.

PubMed

Lombardo, Franco; Berellini, Giuliano; Labonte, Laura R; Liang, Guiqing; Kim, Sean

2016-03-01

We present a systematic evaluation of the Wajima superpositioning method to estimate the human intravenous (i.v.) pharmacokinetic (PK) profile based on a set of 54 marketed drugs with diverse structure and range of physicochemical properties. We illustrate the use of average of "best methods" for the prediction of clearance (CL) and volume of distribution at steady state (VDss) as described in our earlier work (Lombardo F, Waters NJ, Argikar UA, et al. J Clin Pharmacol. 2013;53(2):178-191; Lombardo F, Waters NJ, Argikar UA, et al. J Clin Pharmacol. 2013;53(2):167-177). These methods provided much more accurate prediction of human PK parameters, yielding 88% and 70% of the prediction within 2-fold error for VDss and CL, respectively. The prediction of human i.v. profile using Wajima superpositioning of rat, dog, and monkey time-concentration profiles was tested against the observed human i.v. PK using fold error statistics. The results showed that 63% of the compounds yielded a geometric mean of fold error below 2-fold, and an additional 19% yielded a geometric mean of fold error between 2- and 3-fold, leaving only 18% of the compounds with a relatively poor prediction. Our results showed that good superposition was observed in any case, demonstrating the predictive value of the Wajima approach, and that the cause of poor prediction of human i.v. profile was mainly due to the poorly predicted CL value, while VDss prediction had a minor impact on the accuracy of human i.v. profile prediction. Copyright © 2016. Published by Elsevier Inc.

Amazon forest carbon dynamics predicted by profiles of canopy leaf area and light environment

Treesearch

S. C. Stark; V. Leitold; J. L. Wu; M. O. Hunter; C. V. de Castilho; F. R. C. Costa; S. M. McMahon; G. G. Parker; M. Takako Shimabukuro; M. A. Lefsky; M. Keller; L. F. Alves; J. Schietti; Y. E. Shimabukuro; D. O. Brandao; T. K. Woodcock; N. Higuchi; P. B de Camargo; R. C. de Oliveira; S. R. Saleska

2012-01-01

Tropical forest structural variation across heterogeneous landscapes may control above-ground carbon dynamics. We tested the hypothesis that canopy structure (leaf area and light availability) – remotely estimated from LiDAR – control variation in above-ground coarse wood production (biomass growth). Using a statistical model, these factors predicted biomass growth...

Predicting drug-target interactions by dual-network integrated logistic matrix factorization

NASA Astrophysics Data System (ADS)

Hao, Ming; Bryant, Stephen H.; Wang, Yanli

2017-01-01

In this work, we propose a dual-network integrated logistic matrix factorization (DNILMF) algorithm to predict potential drug-target interactions (DTI). The prediction procedure consists of four steps: (1) inferring new drug/target profiles and constructing profile kernel matrix; (2) diffusing drug profile kernel matrix with drug structure kernel matrix; (3) diffusing target profile kernel matrix with target sequence kernel matrix; and (4) building DNILMF model and smoothing new drug/target predictions based on their neighbors. We compare our algorithm with the state-of-the-art method based on the benchmark dataset. Results indicate that the DNILMF algorithm outperforms the previously reported approaches in terms of AUPR (area under precision-recall curve) and AUC (area under curve of receiver operating characteristic) based on the 5 trials of 10-fold cross-validation. We conclude that the performance improvement depends on not only the proposed objective function, but also the used nonlinear diffusion technique which is important but under studied in the DTI prediction field. In addition, we also compile a new DTI dataset for increasing the diversity of currently available benchmark datasets. The top prediction results for the new dataset are confirmed by experimental studies or supported by other computational research.

SVM-PB-Pred: SVM based protein block prediction method using sequence profiles and secondary structures.

PubMed

Suresh, V; Parthasarathy, S

2014-01-01

We developed a support vector machine based web server called SVM-PB-Pred, to predict the Protein Block for any given amino acid sequence. The input features of SVM-PB-Pred include i) sequence profiles (PSSM) and ii) actual secondary structures (SS) from DSSP method or predicted secondary structures from NPS@ and GOR4 methods. There were three combined input features PSSM+SS(DSSP), PSSM+SS(NPS@) and PSSM+SS(GOR4) used to test and train the SVM models. Similarly, four datasets RS90, DB433, LI1264 and SP1577 were used to develop the SVM models. These four SVM models developed were tested using three different benchmarking tests namely; (i) self consistency, (ii) seven fold cross validation test and (iii) independent case test. The maximum possible prediction accuracy of ~70% was observed in self consistency test for the SVM models of both LI1264 and SP1577 datasets, where PSSM+SS(DSSP) input features was used to test. The prediction accuracies were reduced to ~53% for PSSM+SS(NPS@) and ~43% for PSSM+SS(GOR4) in independent case test, for the SVM models of above two same datasets. Using our method, it is possible to predict the protein block letters for any query protein sequence with ~53% accuracy, when the SP1577 dataset and predicted secondary structure from NPS@ server were used. The SVM-PB-Pred server can be freely accessed through http://bioinfo.bdu.ac.in/~svmpbpred.

Relationship of carbohydrates and lignin molecular structure spectral profiles to nutrient profile in newly developed oats cultivars and barley grain

NASA Astrophysics Data System (ADS)

Prates, Luciana Louzada; Refat, Basim; Lei, Yaogeng; Louzada-Prates, Mariana; Yu, Peiqiang

2018-01-01

The objectives of this study were to quantify the chemical profile and the magnitude of differences in the oat and barley grain varieties developed by Crop Development Centre (CDC) in terms of Cornell Net Carbohydrate Protein System (CNCPS) carbohydrate sub-fractions: CA4 (sugars), CB1 (starch), CB2 (soluble fibre), CB3 (available neutral detergent fibre - NDF), and CC (unavailable carbohydrate); to estimate the energy values; to detect the lignin and carbohydrate (CHO) molecular structure profiles in CDC Nasser and CDC Seabiscuit oat and CDC Meredith barley grains by using Fourier transform infrared attenuated total reflectance (FTIR-ATR); to develop a model to predict nutrient supply based on CHO molecular profile. Results showed that NDF, ADF and CHO were greater (P < 0.05) in oat than in barley. The starch content was greater (P < 0.05) in barley than in oat. The CDC Meredith showed greater total rumen degradable carbohydrate (RDC), intestinal digestible fraction carbohydrate (FC) and lower total rumen undegradable carbohydrate (RUC). However, the estimated milk production did not differ for CDC Nasser oat and CDC Meredith barley. Lignin peak area and peak height did not differ (P > 0.05) for oat and barley grains as well as non-structural CHO. However, cellulosic compounds peak area and height were greater (P < 0.05) in oat than barley grains. Multiple regressions were determined to predict nutrient supply by using lignin and CHO molecular profiles. It was concluded that although there were some differences between oat and barley grains, CDC Nasser and CDC Meredith presented similarities related to chemical and molecular profiles, indicating that CDC Meredith barley could be replaced for CDC Nasser as ruminant feed. The FTIR was able to identify functional groups related to CHO molecular spectral in oat and barley grains and FTIR-ATR results could be used to predict nutrient supply in ruminant livestock systems.

fRMSDPred: Predicting Local RMSD Between Structural Fragments Using Sequence Information

DTIC Science & Technology

2007-04-04

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

Validation of a coupled core-transport, pedestal-structure, current-profile and equilibrium model

NASA Astrophysics Data System (ADS)

Meneghini, O.

2015-11-01

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

Structural features based genome-wide characterization and prediction of nucleosome organization

PubMed Central

2012-01-01

Background Nucleosome distribution along chromatin dictates genomic DNA accessibility and thus profoundly influences gene expression. However, the underlying mechanism of nucleosome formation remains elusive. Here, taking a structural perspective, we systematically explored nucleosome formation potential of genomic sequences and the effect on chromatin organization and gene expression in S. cerevisiae. Results We analyzed twelve structural features related to flexibility, curvature and energy of DNA sequences. The results showed that some structural features such as DNA denaturation, DNA-bending stiffness, Stacking energy, Z-DNA, Propeller twist and free energy, were highly correlated with in vitro and in vivo nucleosome occupancy. Specifically, they can be classified into two classes, one positively and the other negatively correlated with nucleosome occupancy. These two kinds of structural features facilitated nucleosome binding in centromere regions and repressed nucleosome formation in the promoter regions of protein-coding genes to mediate transcriptional regulation. Based on these analyses, we integrated all twelve structural features in a model to predict more accurately nucleosome occupancy in vivo than the existing methods that mainly depend on sequence compositional features. Furthermore, we developed a novel approach, named DLaNe, that located nucleosomes by detecting peaks of structural profiles, and built a meta predictor to integrate information from different structural features. As a comparison, we also constructed a hidden Markov model (HMM) to locate nucleosomes based on the profiles of these structural features. The result showed that the meta DLaNe and HMM-based method performed better than the existing methods, demonstrating the power of these structural features in predicting nucleosome positions. Conclusions Our analysis revealed that DNA structures significantly contribute to nucleosome organization and influence chromatin structure and gene expression regulation. The results indicated that our proposed methods are effective in predicting nucleosome occupancy and positions and that these structural features are highly predictive of nucleosome organization. The implementation of our DLaNe method based on structural features is available online. PMID:22449207

An EQT-cDFT approach to determine thermodynamic properties of confined fluids.

PubMed

Mashayak, S Y; Motevaselian, M H; Aluru, N R

2015-06-28

We present a continuum-based approach to predict the structure and thermodynamic properties of confined fluids at multiple length-scales, ranging from a few angstroms to macro-meters. The continuum approach is based on the empirical potential-based quasi-continuum theory (EQT) and classical density functional theory (cDFT). EQT is a simple and fast approach to predict inhomogeneous density and potential profiles of confined fluids. We use EQT potentials to construct a grand potential functional for cDFT. The EQT-cDFT-based grand potential can be used to predict various thermodynamic properties of confined fluids. In this work, we demonstrate the EQT-cDFT approach by simulating Lennard-Jones fluids, namely, methane and argon, confined inside slit-like channels of graphene. We show that the EQT-cDFT can accurately predict the structure and thermodynamic properties, such as density profiles, adsorption, local pressure tensor, surface tension, and solvation force, of confined fluids as compared to the molecular dynamics simulation results.

Through-Thickness Residual Stress Profiles in Austenitic Stainless Steel Welds: A Combined Experimental and Prediction Study

NASA Astrophysics Data System (ADS)

Mathew, J.; Moat, R. J.; Paddea, S.; Francis, J. A.; Fitzpatrick, M. E.; Bouchard, P. J.

2017-12-01

Economic and safe management of nuclear plant components relies on accurate prediction of welding-induced residual stresses. In this study, the distribution of residual stress through the thickness of austenitic stainless steel welds has been measured using neutron diffraction and the contour method. The measured data are used to validate residual stress profiles predicted by an artificial neural network approach (ANN) as a function of welding heat input and geometry. Maximum tensile stresses with magnitude close to the yield strength of the material were observed near the weld cap in both axial and hoop direction of the welds. Significant scatter of more than 200 MPa was found within the residual stress measurements at the weld center line and are associated with the geometry and welding conditions of individual weld passes. The ANN prediction is developed in an attempt to effectively quantify this phenomenon of `innate scatter' and to learn the non-linear patterns in the weld residual stress profiles. Furthermore, the efficacy of the ANN method for defining through-thickness residual stress profiles in welds for application in structural integrity assessments is evaluated.

SNBRFinder: A Sequence-Based Hybrid Algorithm for Enhanced Prediction of Nucleic Acid-Binding Residues.

PubMed

Yang, Xiaoxia; Wang, Jia; Sun, Jun; Liu, Rong

2015-01-01

Protein-nucleic acid interactions are central to various fundamental biological processes. Automated methods capable of reliably identifying DNA- and RNA-binding residues in protein sequence are assuming ever-increasing importance. The majority of current algorithms rely on feature-based prediction, but their accuracy remains to be further improved. Here we propose a sequence-based hybrid algorithm SNBRFinder (Sequence-based Nucleic acid-Binding Residue Finder) by merging a feature predictor SNBRFinderF and a template predictor SNBRFinderT. SNBRFinderF was established using the support vector machine whose inputs include sequence profile and other complementary sequence descriptors, while SNBRFinderT was implemented with the sequence alignment algorithm based on profile hidden Markov models to capture the weakly homologous template of query sequence. Experimental results show that SNBRFinderF was clearly superior to the commonly used sequence profile-based predictor and SNBRFinderT can achieve comparable performance to the structure-based template methods. Leveraging the complementary relationship between these two predictors, SNBRFinder reasonably improved the performance of both DNA- and RNA-binding residue predictions. More importantly, the sequence-based hybrid prediction reached competitive performance relative to our previous structure-based counterpart. Our extensive and stringent comparisons show that SNBRFinder has obvious advantages over the existing sequence-based prediction algorithms. The value of our algorithm is highlighted by establishing an easy-to-use web server that is freely accessible at http://ibi.hzau.edu.cn/SNBRFinder.

Precision cosmology with baryons: non-radiative hydrodynamics of galaxy groups

NASA Astrophysics Data System (ADS)

Rabold, Manuel; Teyssier, Romain

2017-05-01

The effect of baryons on the matter power spectrum is likely to have an observable effect for future galaxy surveys, like Euclid or Large Synoptic Survey Telescope (LSST). As a first step towards a fully predictive theory, we investigate the effect of non-radiative hydrodynamics on the structure of galaxy groups sized haloes, which contribute the most to the weak-lensing power spectrum. We perform high-resolution (more than one million particles per halo and one kilo-parsec resolution) non-radiative hydrodynamical zoom-in simulations of a sample of 16 haloes, comparing the profiles to popular analytical models. We find that the total mass profile is well fitted by a Navarro, Frenk & White model, with parameters slightly modified from the dark matter only simulation. We also find that the Komatsu & Seljak hydrostatic solution provides a good fit to the gas profiles, with however significant deviations, arising from strong turbulent mixing in the core and from non-thermal, turbulent pressure support in the outskirts. The turbulent energy follows a shallow, rising linear profile with radius, and correlates with the halo formation time. Using only three main structural halo parameters as variables (total mass, concentration parameter and central gas density), we can predict, with an accuracy better than 20 per cent, the individual gas density and temperature profiles. For the average total mass profile, which is relevant for power spectrum calculations, we even reach an accuracy of 1 per cent. The robustness of these predictions has been tested against resolution effects, different types of initial conditions and hydrodynamical schemes.

Prediction of polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space.

PubMed

Cheng, Feixiong; Li, Weihua; Wu, Zengrui; Wang, Xichuan; Zhang, Chen; Li, Jie; Liu, Guixia; Tang, Yun

2013-04-22

Prediction of polypharmacological profiles of drugs enables us to investigate drug side effects and further find their new indications, i.e. drug repositioning, which could reduce the costs while increase the productivity of drug discovery. Here we describe a new computational framework to predict polypharmacological profiles of drugs by the integration of chemical, side effect, and therapeutic space. On the basis of our previous developed drug side effects database, named MetaADEDB, a drug side effect similarity inference (DSESI) method was developed for drug-target interaction (DTI) prediction on a known DTI network connecting 621 approved drugs and 893 target proteins. The area under the receiver operating characteristic curve was 0.882 ± 0.011 averaged from 100 simulated tests of 10-fold cross-validation for the DSESI method, which is comparative with drug structural similarity inference and drug therapeutic similarity inference methods. Seven new predicted candidate target proteins for seven approved drugs were confirmed by published experiments, with the successful hit rate more than 15.9%. Moreover, network visualization of drug-target interactions and off-target side effect associations provide new mechanism-of-action of three approved antipsychotic drugs in a case study. The results indicated that the proposed methods could be helpful for prediction of polypharmacological profiles of drugs.

Mode Profiles in Waveguide-Coupled Resonators

NASA Technical Reports Server (NTRS)

Hunt, William D.; Cameron, Tom; Saw, John C. B.; Kim, Yoonkee

1993-01-01

Surface acoustic wave (SAW) waveguide-coupled resonators are of considerable interest for narrow-band filter applications, though to date there has been very little published on the acoustic details of their operation. As in any resonator, one must fully understand its mode structure and herein we study the SAW mode profiles in these devices. Transverse mode profiles in the resonant cavity of the device were measured at various frequencies of interest using a knife-edge laser probe. In addition we predict the mode profiles for the device structure by two independent methods. One is a stack-matrix approach adapted from integrated optics and the other is a conventional analytical eigenmode analysis of the Helmholtz equation. Both modeling techniques are in good agreement with the measured results.

Model-based analysis of N-glycosylation in Chinese hamster ovary cells

PubMed Central

Krambeck, Frederick J.; Bennun, Sandra V.; Betenbaugh, Michael J.

2017-01-01

The Chinese hamster ovary (CHO) cell is the gold standard for manufacturing of glycosylated recombinant proteins for production of biotherapeutics. The similarity of its glycosylation patterns to the human versions enable the products of this cell line favorable pharmacokinetic properties and lower likelihood of causing immunogenic responses. Because glycan structures are the product of the concerted action of intracellular enzymes, it is difficult to predict a priori how the effects of genetic manipulations alter glycan structures of cells and therapeutic properties. For that reason, quantitative models able to predict glycosylation have emerged as promising tools to deal with the complexity of glycosylation processing. For example, an earlier version of the same model used in this study was used by others to successfully predict changes in enzyme activities that could produce a desired change in glycan structure. In this study we utilize an updated version of this model to provide a comprehensive analysis of N-glycosylation in ten Chinese hamster ovary (CHO) cell lines that include a wild type parent and nine mutants of CHO, through interpretation of previously published mass spectrometry data. The updated N-glycosylation mathematical model contains up to 50,605 glycan structures. Adjusting the enzyme activities in this model to match N-glycan mass spectra produces detailed predictions of the glycosylation process, enzyme activity profiles and complete glycosylation profiles of each of the cell lines. These profiles are consistent with biochemical and genetic data reported previously. The model-based results also predict glycosylation features of the cell lines not previously published, indicating more complex changes in glycosylation enzyme activities than just those resulting directly from gene mutations. The model predicts that the CHO cell lines possess regulatory mechanisms that allow them to adjust glycosylation enzyme activities to mitigate side effects of the primary loss or gain of glycosylation function known to exist in these mutant cell lines. Quantitative models of CHO cell glycosylation have the potential for predicting how glycoengineering manipulations might affect glycoform distributions to improve the therapeutic performance of glycoprotein products. PMID:28486471

Modeling and life prediction methodology for Titanium Matrix Composites subjected to mission profiles

NASA Technical Reports Server (NTRS)

Mirdamadi, M.; Johnson, W. S.

1994-01-01

Titanium matrix composites (TMC) are being evaluated as structural materials for elevated temperature applications in future generation hypersonic vehicles. In such applications, TMC components are subjected to complex thermomechanical loading profiles at various elevated temperatures. Therefore, thermomechanical fatigue (TMF) testing, using a simulated mission profile, is essential for evaluation and development of life prediction methodologies. The objective of the research presented in this paper was to evaluate the TMF response of the (0/90)2s SCS-6/Timetal-21S subjected to a generic hypersonic flight profile and its portions with a temperature ranging from -130 C to 816 C. It was found that the composite modulus, prior to rapid degradation, had consistent values for all the profiles tested. A micromechanics based analysis was used to predict the stress-strain response of the laminate and of the constituents in each ply during thermomechanical loading conditions by using only constituent properties as input. The fiber was modeled as elastic with transverse orthotropic and temperature dependent properties. The matrix was modeled using a thermoviscoplastic constitutive relation. In the analysis, the composite modulus degradation was assumed to result from matrix cracking and was modeled by reducing the matrix modulus. Fatigue lives of the composite subjected to the complex generic hypersonic flight profile were well correlated using the predicted stress in 0 degree fibers.

Quantitative Understanding of SHAPE Mechanism from RNA Structure and Dynamics Analysis.

PubMed

Hurst, Travis; Xu, Xiaojun; Zhao, Peinan; Chen, Shi-Jie

2018-05-10

The selective 2'-hydroxyl acylation analyzed by primer extension (SHAPE) method probes RNA local structural and dynamic information at single nucleotide resolution. To gain quantitative insights into the relationship between nucleotide flexibility, RNA 3D structure, and SHAPE reactivity, we develop a 3D Structure-SHAPE Relationship model (3DSSR) to rebuild SHAPE profiles from 3D structures. The model starts from RNA structures and combines nucleotide interaction strength and conformational propensity, ligand (SHAPE reagent) accessibility, and base-pairing pattern through a composite function to quantify the correlation between SHAPE reactivity and nucleotide conformational stability. The 3DSSR model shows the relationship between SHAPE reactivity and RNA structure and energetics. Comparisons between the 3DSSR-predicted SHAPE profile and the experimental SHAPE data show correlation, suggesting that the extracted analytical function may have captured the key factors that determine the SHAPE reactivity profile. Furthermore, the theory offers an effective method to sieve RNA 3D models and exclude models that are incompatible with experimental SHAPE data.

Fluids density functional theory and initializing molecular dynamics simulations of block copolymers

NASA Astrophysics Data System (ADS)

Brown, Jonathan R.; Seo, Youngmi; Maula, Tiara Ann D.; Hall, Lisa M.

2016-03-01

Classical, fluids density functional theory (fDFT), which can predict the equilibrium density profiles of polymeric systems, and coarse-grained molecular dynamics (MD) simulations, which are often used to show both structure and dynamics of soft materials, can be implemented using very similar bead-based polymer models. We aim to use fDFT and MD in tandem to examine the same system from these two points of view and take advantage of the different features of each methodology. Additionally, the density profiles resulting from fDFT calculations can be used to initialize the MD simulations in a close to equilibrated structure, speeding up the simulations. Here, we show how this method can be applied to study microphase separated states of both typical diblock and tapered diblock copolymers in which there is a region with a gradient in composition placed between the pure blocks. Both methods, applied at constant pressure, predict a decrease in total density as segregation strength or the length of the tapered region is increased. The predictions for the density profiles from fDFT and MD are similar across materials with a wide range of interfacial widths.

Predictive Validity of Career Decision-Making Profiles over Time among Chinese College Students

ERIC Educational Resources Information Center

Tian, Lin; Guan, Yanjun; Chen, Sylvia Xiaohua; Levin, Nimrod; Cai, Zijun; Chen, Pei; Zhu, Chengfeng; Fu, Ruchunyi; Wang, Yang; Zhang, Shu

2014-01-01

Two studies were conducted to validate the Chinese version of the Career Decision-Making Profiles (CDMP) questionnaire, a multidimensional measure of the way individuals make career decisions. Results of Study 1 showed that after dropping 1 item from the original CDMP scale, the 11-factor structure was supported among Chinese college students (N =…

Local atomic and electronic structure of oxide/GaAs and SiO2/Si interfaces using high-resolution XPS

NASA Technical Reports Server (NTRS)

Grunthaner, F. J.; Grunthaner, P. J.; Vasquez, R. P.; Lewis, B. F.; Maserjian, J.; Madhukar, A.

1979-01-01

The chemical structures of thin SiO2 films, thin native oxides of GaAs (20-30 A), and the respective oxide-semiconductor interfaces, have been investigated using high-resolution X-ray photoelectron spectroscopy. Depth profiles of these structures have been obtained using argon ion bombardment and wet chemical etching techniques. The chemical destruction induced by the ion profiling method is shown by direct comparison of these methods for identical samples. Fourier transform data-reduction methods based on linear prediction with maximum entropy constraints are used to analyze the discrete structure in oxides and substrates. This discrete structure is interpreted by means of a structure-induced charge-transfer model.

The Structure of Vertical Wind Shear in Tropical Cyclone Environments: Implications for Forecasting and Predictability

NASA Astrophysics Data System (ADS)

Finocchio, Peter M.

The vertical wind shear measured between 200 and 850 hPa is commonly used to diagnose environmental interactions with a tropical cyclone (TC) and to forecast the storm's intensity and structural evolution. More often than not, stronger vertical shear within this deep layer prohibits the intensification of TCs and leads to predictable asymmetries in precipitation. But such bulk measures of vertical wind shear can occasionally mislead the forecaster. In the first part of this dissertation, we use a series of idealized numerical simulations to examine how a TC responds to changing the structure of unidirectional vertical wind shear while fixing the 200-850-hPa shear magnitude. These simulations demonstrate a significant intensity response, in which shear concentrated in shallow layers of the lower troposphere prevents vortex intensification. We attribute the arrested development of TCs in lower-level shear to the intrusion of mid-level environmental air over the surface vortex early in the simulations. Convection developing on the downshear side of the storm interacts with the intruding air so as to enhance the downward flux of low-entropy air into the boundary layer. We also construct a two-dimensional intensity response surface from a set of simulations that sparsely sample the joint shear height-depth parameter space. This surface reveals regions of the two-parameter space for which TC intensity is particularly sensitive. We interpret these parameter ranges as those which lead to reduced intensity predictability. Despite the robust response to changing the shape of a sheared wind profile in idealized simulations, we do not encounter such sensitivity within a large set of reanalyzed TCs in the Northern Hemisphere. Instead, there is remarkable consistency in the structure of reanalyzed wind profiles around TCs. This is evident in the distributions of two new parameters describing the height and depth of vertical wind shear, which highlight a clear preference for shallow layers of upper-level shear. Many of the wind profiles tested in the idealized simulations have shear height or depth values on the tails of these distributions, suggesting that the environmental wind profiles around real TCs do not exhibit enough structural variability to have the clear statistical relationship to intensity change that we expected. In the final part of this dissertation, we use the reanalyzed TC environments to initialize ensembles of idealized simulations. Using a new modeling technique that allows for time-varying environments, these simulations examine the predictability implications of exposing a TC to different structures and magnitudes of vertical wind shear during its life cycle. We find that TCs in more deeply distributed vertical wind shear environments have a more uncertain intensity evolution than TCs exposed to shallower layers of upper-level shear. This higher uncertainty arises from a more marginal boundary layer environment that the deeply distributed shear establishes, which enhances the TC sensitivity to the magnitude of deep-layer shear. Simulated radar reflectivity also appears to evolve in a more uncertain fashion in environments with deeply distributed vertical shear. However, structural predictability timescales, computed as the time it takes for errors in the amplitude or phase of azimuthal asymmetries of reflectivity to saturate, are similar for wind profiles with shallow upper-level shear and deeply distributed shear. Both ensembles demonstrate predictability timescales of two to three days for the lowest azimuthal wavenumbers of amplitude and phase. As the magnitude of vertical wind shear increases to universally destructive levels, structural and intensity errors begin to decrease. Shallow upper-level shear primes the TC for a more pronounced recovery in the predictability of the wavenumber-one precipitation structure in stronger shear. The recovered low-wavenumber predictability of TC precipitation structure and the collapse in intensity spread in strong shear suggests that vertical wind shear is most effective at reducing TC predictability when its magnitude is near the threshold between favorable and unfavorable values and when it is deeply distributed through the troposphere. By isolating the effect of the environmental flow, the simulations and analyses in this dissertation offer a unique understanding of how vertical wind shear affects TCs. In particular, the results have important implications for designing and implementing future environmental observing strategies that will be critical for improving forecasts of these destructive storms.

Global Quantitative Modeling of Chromatin Factor Interactions

PubMed Central

Zhou, Jian; Troyanskaya, Olga G.

2014-01-01

Chromatin is the driver of gene regulation, yet understanding the molecular interactions underlying chromatin factor combinatorial patterns (or the “chromatin codes”) remains a fundamental challenge in chromatin biology. Here we developed a global modeling framework that leverages chromatin profiling data to produce a systems-level view of the macromolecular complex of chromatin. Our model ultilizes maximum entropy modeling with regularization-based structure learning to statistically dissect dependencies between chromatin factors and produce an accurate probability distribution of chromatin code. Our unsupervised quantitative model, trained on genome-wide chromatin profiles of 73 histone marks and chromatin proteins from modENCODE, enabled making various data-driven inferences about chromatin profiles and interactions. We provided a highly accurate predictor of chromatin factor pairwise interactions validated by known experimental evidence, and for the first time enabled higher-order interaction prediction. Our predictions can thus help guide future experimental studies. The model can also serve as an inference engine for predicting unknown chromatin profiles — we demonstrated that with this approach we can leverage data from well-characterized cell types to help understand less-studied cell type or conditions. PMID:24675896

Automated Protocol for Large-Scale Modeling of Gene Expression Data.

PubMed

Hall, Michelle Lynn; Calkins, David; Sherman, Woody

2016-11-28

With the continued rise of phenotypic- and genotypic-based screening projects, computational methods to analyze, process, and ultimately make predictions in this field take on growing importance. Here we show how automated machine learning workflows can produce models that are predictive of differential gene expression as a function of a compound structure using data from A673 cells as a proof of principle. In particular, we present predictive models with an average accuracy of greater than 70% across a highly diverse ∼1000 gene expression profile. In contrast to the usual in silico design paradigm, where one interrogates a particular target-based response, this work opens the opportunity for virtual screening and lead optimization for desired multitarget gene expression profiles.

Structure-related clustering of gene expression fingerprints of thp-1 cells exposed to smaller polycyclic aromatic hydrocarbons.

PubMed

Wan, B; Yarbrough, J W; Schultz, T W

2008-01-01

This study was undertaken to test the hypothesis that structurally similar PAHs induce similar gene expression profiles. THP-1 cells were exposed to a series of 12 selected PAHs at 50 microM for 24 hours and gene expressions profiles were analyzed using both unsupervised and supervised methods. Clustering analysis of gene expression profiles revealed that the 12 tested chemicals were grouped into five clusters. Within each cluster, the gene expression profiles are more similar to each other than to the ones outside the cluster. One-methylanthracene and 1-methylfluorene were found to have the most similar profiles; dibenzothiophene and dibenzofuran were found to share common profiles with fluorine. As expression pattern comparisons were expanded, similarity in genomic fingerprint dropped off dramatically. Prediction analysis of microarrays (PAM) based on the clustering pattern generated 49 predictor genes that can be used for sample discrimination. Moreover, a significant analysis of Microarrays (SAM) identified 598 genes being modulated by tested chemicals with a variety of biological processes, such as cell cycle, metabolism, and protein binding and KEGG pathways being significantly (p < 0.05) affected. It is feasible to distinguish structurally different PAHs based on their genomic fingerprints, which are mechanism based.

A chemogenomic analysis of the human proteome: application to enzyme families.

PubMed

Bernasconi, Paul; Chen, Min; Galasinski, Scott; Popa-Burke, Ioana; Bobasheva, Anna; Coudurier, Louis; Birkos, Steve; Hallam, Rhonda; Janzen, William P

2007-10-01

Sequence-based phylogenies (SBP) are well-established tools for describing relationships between proteins. They have been used extensively to predict the behavior and sensitivity toward inhibitors of enzymes within a family. The utility of this approach diminishes when comparing proteins with little sequence homology. Even within an enzyme family, SBPs must be complemented by an orthogonal method that is independent of sequence to better predict enzymatic behavior. A chemogenomic approach is demonstrated here that uses the inhibition profile of a 130,000 diverse molecule library to uncover relationships within a set of enzymes. The profile is used to construct a semimetric additive distance matrix. This matrix, in turn, defines a sequence-independent phylogeny (SIP). The method was applied to 97 enzymes (kinases, proteases, and phosphatases). SIP does not use structural information from the molecules used for establishing the profile, thus providing a more heuristic method than the current approaches, which require knowledge of the specific inhibitor's structure. Within enzyme families, SIP shows a good overall correlation with SBP. More interestingly, SIP uncovers distances within families that are not recognizable by sequence-based methods. In addition, SIP allows the determination of distance between enzymes with no sequence homology, thus uncovering novel relationships not predicted by SBP. This chemogenomic approach, used in conjunction with SBP, should prove to be a powerful tool for choosing target combinations for drug discovery programs as well as for guiding the selection of profiling and liability targets.

OCT structure, COB location and magmatic type of the SE Brazilian & S Angolan margins from integrated quantitative analysis of deep seismic reflection and gravity anomaly data

NASA Astrophysics Data System (ADS)

Cowie, L.; Kusznir, N. J.; Horn, B.

2013-12-01

Knowledge of ocean-continent transition (OCT) structure, continent-ocean boundary (COB) location and magmatic type are of critical importance for understanding rifted continental margin formation processes and in evaluating petroleum systems in deep-water frontier oil and gas exploration. The OCT structure, COB location and magmatic type of the SE Brazilian and S Angolan rifted continental margins are much debated; exhumed and serpentinised mantle have been reported at these margins. Integrated quantitative analysis using deep seismic reflection data and gravity inversion have been used to determine OCT structure, COB location and magmatic type for the SE Brazilian and S Angolan margins. Gravity inversion has been used to determine Moho depth, crustal basement thickness and continental lithosphere thinning. Residual Depth Anomaly (RDA) analysis has been used to investigate OCT bathymetric anomalies with respect to expected oceanic bathymetries and subsidence analysis has been used to determine the distribution of continental lithosphere thinning. These techniques have been validated on the Iberian margin for profiles IAM9 and ISE-01. In addition a joint inversion technique using deep seismic reflection and gravity anomaly data has been applied to the ION-GXT BS1-575 SE Brazil and ION-GXT CS1-2400 S Angola. The joint inversion method solves for coincident seismic and gravity Moho in the time domain and calculates the lateral variations in crustal basement densities and velocities along profile. Gravity inversion, RDA and subsidence analysis along the S Angolan ION-GXT CS1-2400 profile has been used to determine OCT structure and COB location. Analysis suggests that exhumed mantle, corresponding to a magma poor margin, is absent beneath the allochthonous salt. The thickness of earliest oceanic crust, derived from gravity and deep seismic reflection data is approximately 7km. The joint inversion predicts crustal basement densities and seismic velocities which are slightly less than expected for 'normal' oceanic crust. The difference between the sediment corrected RDA and that predicted from gravity inversion crustal thickness variation implies that this margin is experiencing ~300m of anomalous uplift attributed to mantle dynamic uplift. Gravity inversion, RDA and subsidence analysis have also been used to determine OCT structure and COB location along the ION-GXT BS1-575 profile, crossing the Sao Paulo Plateau and Florianopolis Ridge of the SE Brazilian margin. Gravity inversion, RDA and subsidence analysis predict the COB to be located SE of the Florianopolis Ridge. Analysis shows no evidence for exhumed mantle on this margin profile. The joint inversion technique predicts normal oceanic basement seismic velocities and densities and beneath the Sao Paulo Plateau and Florianopolis Ridge predicts crustal basement thicknesses between 10-15km. The Sao Paulo Plateau and Florianopolis Ridge are separated by a thin region of crustal basement beneath the salt interpreted as a regional transtensional structure. Sediment corrected RDAs and gravity derived 'synthetic' RDAs are of a similar magnitude on oceanic crust, implying negligible mantle dynamic topography.

Fourier-based classification of protein secondary structures.

PubMed

Shu, Jian-Jun; Yong, Kian Yan

2017-04-15

The correct prediction of protein secondary structures is one of the key issues in predicting the correct protein folded shape, which is used for determining gene function. Existing methods make use of amino acids properties as indices to classify protein secondary structures, but are faced with a significant number of misclassifications. The paper presents a technique for the classification of protein secondary structures based on protein "signal-plotting" and the use of the Fourier technique for digital signal processing. New indices are proposed to classify protein secondary structures by analyzing hydrophobicity profiles. The approach is simple and straightforward. Results show that the more types of protein secondary structures can be classified by means of these newly-proposed indices. Copyright © 2017 Elsevier Inc. All rights reserved.

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields.

PubMed

Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

2016-01-11

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

Protein Secondary Structure Prediction Using Deep Convolutional Neural Fields

NASA Astrophysics Data System (ADS)

Wang, Sheng; Peng, Jian; Ma, Jianzhu; Xu, Jinbo

2016-01-01

Protein secondary structure (SS) prediction is important for studying protein structure and function. When only the sequence (profile) information is used as input feature, currently the best predictors can obtain ~80% Q3 accuracy, which has not been improved in the past decade. Here we present DeepCNF (Deep Convolutional Neural Fields) for protein SS prediction. DeepCNF is a Deep Learning extension of Conditional Neural Fields (CNF), which is an integration of Conditional Random Fields (CRF) and shallow neural networks. DeepCNF can model not only complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent SS labels, so it is much more powerful than CNF. Experimental results show that DeepCNF can obtain ~84% Q3 accuracy, ~85% SOV score, and ~72% Q8 accuracy, respectively, on the CASP and CAMEO test proteins, greatly outperforming currently popular predictors. As a general framework, DeepCNF can be used to predict other protein structure properties such as contact number, disorder regions, and solvent accessibility.

Predicting vertical phase segregation in polymer-fullerene bulk heterojunction solar cells by free energy analysis.

PubMed

Clark, Michael D; Jespersen, Michael L; Patel, Romesh J; Leever, Benjamin J

2013-06-12

Blends of poly(3-hexylthiophene) (P3HT) and C61-butyric acid methyl ester (PCBM) are widely used as a model system for bulk heterojunction active layers developed for solution-processable, flexible solar cells. In this work, vertical concentration profiles within the P3HT:PCBM active layer are predicted based on a thermodynamic analysis of the constituent materials and typical solvents. Surface energies of the active layer components and a common transport interlayer blend, poly(3,4-ethylenedioxythiophene) poly(styrenesulfonate) (PEDOT:PSS), are first extracted using contact angle measurements coupled with the acid-base model. From this data, intra- and interspecies interaction free energies are calculated, which reveal that the thermodynamically favored arrangement consists of a uniformly blended "bulk" structure capped with a P3HT-rich air interface and a slightly PCBM-rich buried interface. Although the "bulk" composition is solely determined by P3HT:PCBM ratio, composition near the buried interface is dependent on both the blend ratio and interaction free energy difference between solvated P3HT and PCBM deposition onto PEDOT:PSS. In contrast, the P3HT-rich overlayer is independent of processing conditions, allowing kinetic formation of a PCBM-rich sublayer during film casting due to limitations in long-range species diffusion. These thermodynamic calculations are experimentally validated by angle-resolved X-ray photoelectron spectroscopy (XPS) and low energy XPS depth profiling, which show that the actual composition profiles of the cast and annealed films closely match the predicted behavior. These experimentally derived profiles provide clear evidence that typical bulk heterojunction active layers are predominantly characterized by thermodynamically stable composition profiles. Furthermore, the predictive capabilities of the comprehensive free energy approach are demonstrated, which will enable investigation of structurally integrated devices and novel active layer systems including low band gap polymers, ternary systems, and small molecule blends.

Structural features of microRNA (miRNA) precursors and their relevance to miRNA biogenesis and small interfering RNA/short hairpin RNA design.

PubMed

Krol, Jacek; Sobczak, Krzysztof; Wilczynska, Urszula; Drath, Maria; Jasinska, Anna; Kaczynska, Danuta; Krzyzosiak, Wlodzimierz J

2004-10-01

We have established the structures of 10 human microRNA (miRNA) precursors using biochemical methods. Eight of these structures turned out to be different from those that were computer-predicted. The differences localized in the terminal loop region and at the opposite side of the precursor hairpin stem. We have analyzed the features of these structures from the perspectives of miRNA biogenesis and active strand selection. We demonstrated the different thermodynamic stability profiles for pre-miRNA hairpins harboring miRNAs at their 5'- and 3'-sides and discussed their functional implications. Our results showed that miRNA prediction based on predicted precursor structures may give ambiguous results, and the success rate is significantly higher for the experimentally determined structures. On the other hand, the differences between the predicted and experimentally determined structures did not affect the stability of termini produced through "conceptual dicing." This result confirms the value of thermodynamic analysis based on mfold as a predictor of strand section by RNAi-induced silencing complex (RISC).

RaptorX-Property: a web server for protein structure property prediction.

PubMed

Wang, Sheng; Li, Wei; Liu, Shiwang; Xu, Jinbo

2016-07-08

RaptorX Property (http://raptorx2.uchicago.edu/StructurePropertyPred/predict/) is a web server predicting structure property of a protein sequence without using any templates. It outperforms other servers, especially for proteins without close homologs in PDB or with very sparse sequence profile (i.e. carries little evolutionary information). This server employs a powerful in-house deep learning model DeepCNF (Deep Convolutional Neural Fields) to predict secondary structure (SS), solvent accessibility (ACC) and disorder regions (DISO). DeepCNF not only models complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent property labels. Our experimental results show that, tested on CASP10, CASP11 and the other benchmarks, this server can obtain ∼84% Q3 accuracy for 3-state SS, ∼72% Q8 accuracy for 8-state SS, ∼66% Q3 accuracy for 3-state solvent accessibility, and ∼0.89 area under the ROC curve (AUC) for disorder prediction. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

The 13C-Pocket Structure In AGB Models: Constraints From Zirconium Isotope Abundances In Single Mainstream SiC Grains

DOE PAGES

Liu, Nan; Gallino, Roberto; Bisterzo, Sara; ...

2014-06-04

In this paper, we present postprocess asymptotic giant branch (AGB) nucleosynthesis models with different 13C-pocket internal structures to better explain zirconium isotope measurements in mainstream presolar SiC grains by Nicolussi et al. and Barzyk et al. We show that higher-than-solar 92Zr/ 94Zr ratios can be predicted by adopting a 13C-pocket with a flat 13C profile, instead of the previous decreasing-with-depth 13C profile. Finally, the improved agreement between grain data for zirconium isotopes and AGB models provides additional support for a recent proposal of a flat 13C profile based on barium isotopes in mainstream SiC grains by Liu et al.

Protein 8-class secondary structure prediction using conditional neural fields.

PubMed

Wang, Zhiyong; Zhao, Feng; Peng, Jian; Xu, Jinbo

2011-10-01

Compared with the protein 3-class secondary structure (SS) prediction, the 8-class prediction gains less attention and is also much more challenging, especially for proteins with few sequence homologs. This paper presents a new probabilistic method for 8-class SS prediction using conditional neural fields (CNFs), a recently invented probabilistic graphical model. This CNF method not only models the complex relationship between sequence features and SS, but also exploits the interdependency among SS types of adjacent residues. In addition to sequence profiles, our method also makes use of non-evolutionary information for SS prediction. Tested on the CB513 and RS126 data sets, our method achieves Q8 accuracy of 64.9 and 64.7%, respectively, which are much better than the SSpro8 web server (51.0 and 48.0%, respectively). Our method can also be used to predict other structure properties (e.g. solvent accessibility) of a protein or the SS of RNA. Copyright © 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Unprecedently Large-Scale Kinase Inhibitor Set Enabling the Accurate Prediction of Compound–Kinase Activities: A Way toward Selective Promiscuity by Design?

PubMed Central

2016-01-01

Drug discovery programs frequently target members of the human kinome and try to identify small molecule protein kinase inhibitors, primarily for cancer treatment, additional indications being increasingly investigated. One of the challenges is controlling the inhibitors degree of selectivity, assessed by in vitro profiling against panels of protein kinases. We manually extracted, compiled, and standardized such profiles published in the literature: we collected 356 908 data points corresponding to 482 protein kinases, 2106 inhibitors, and 661 patents. We then analyzed this data set in terms of kinome coverage, results reproducibility, popularity, and degree of selectivity of both kinases and inhibitors. We used the data set to create robust proteochemometric models capable of predicting kinase activity (the ligand–target space was modeled with an externally validated RMSE of 0.41 ± 0.02 log units and R02 0.74 ± 0.03), in order to account for missing or unreliable measurements. The influence on the prediction quality of parameters such as number of measurements, Murcko scaffold frequency or inhibitor type was assessed. Interpretation of the models enabled to highlight inhibitors and kinases properties correlated with higher affinities, and an analysis in the context of kinases crystal structures was performed. Overall, the models quality allows the accurate prediction of kinase-inhibitor activities and their structural interpretation, thus paving the way for the rational design of compounds with a targeted selectivity profile. PMID:27482722

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

PubMed

Kirschner, Andreas; Frishman, Dmitrij

2008-10-01

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

Cheminformatics Analysis of EPA ToxCast Chemical Libraries to Identify Domains of Applicability for Predictive Toxicity Models and Prioritize Compounds for Toxicity Testing

EPA Science Inventory

An important goal of toxicology research is the development of robust methods that use in vitro and chemical structure information to predict in vivo toxicity endpoints. The US EPA ToxCast program is addressing this goal using ~600 in vitro assays to create bioactivity profiles o...

Head losses prediction and analysis in a bulb turbine draft tube under different operating conditions using unsteady simulations

NASA Astrophysics Data System (ADS)

Wilhelm, S.; Balarac, G.; Métais, O.; Ségoufin, C.

2016-11-01

Flow prediction in a bulb turbine draft tube is conducted for two operating points using Unsteady RANS (URANS) simulations and Large Eddy Simulations (LES). The inlet boundary condition of the draft tube calculation is a rotating two dimensional velocity profile exported from a RANS guide vane- runner calculation. Numerical results are compared with experimental data in order to validate the flow field and head losses prediction. Velocity profiles prediction is improved with LES in the center of the draft tube compared to URANS results. Moreover, more complex flow structures are obtained with LES. A local analysis of the predicted flow field using the energy balance in the draft tube is then introduced in order to detect the hydrodynamic instabilities responsible for head losses in the draft tube. In particular, the production of turbulent kinetic energy next to the draft tube wall and in the central vortex structure is found to be responsible for a large part of the mean kinetic energy dissipation in the draft tube and thus for head losses. This analysis is used in order to understand the differences in head losses for different operating points. The numerical methodology could then be improved thanks to an in-depth understanding of the local flow topology.

Recovering actives in multi-antitarget and target design of analogs of the myosin II inhibitor blebbistatin

NASA Astrophysics Data System (ADS)

Roman, Bart I.; Guedes, Rita C.; Stevens, Christian V.; García-Sosa, Alfonso T.

2018-05-01

In multitarget drug design, it is critical to identify active and inactive compounds against a variety of targets and antitargets. Multitarget strategies thus test the limits of available technology, be that in screening large databases of compounds versus a large number of targets, or in using in silico methods for understanding and reliably predicting these pharmacological outcomes. In this paper, we have evaluated the potential of several in silico approaches to predict the target, antitarget and physicochemical profile of (S)-blebbistatin, the best-known myosin II ATPase inhibitor, and a series of analogs thereof. Standard and augmented structure-based design techniques could not recover the observed activity profiles. A ligand-based method using molecular fingerprints was, however, able to select actives for myosin II inhibition. Using further ligand- and structure-based methods, we also evaluated toxicity through androgen receptor binding, affinity for an array of antitargets and the ADME profile (including assay-interfering compounds) of the series. In conclusion, in the search for (S)-blebbistatin analogs, the dissimilarity distance of molecular fingerprints to known actives and the computed antitarget and physicochemical profile of the molecules can be used for compound design for molecules with potential as tools for modulating myosin II and motility-related diseases.

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.

FastRNABindR: Fast and Accurate Prediction of Protein-RNA Interface Residues.

PubMed

El-Manzalawy, Yasser; Abbas, Mostafa; Malluhi, Qutaibah; Honavar, Vasant

2016-01-01

A wide range of biological processes, including regulation of gene expression, protein synthesis, and replication and assembly of many viruses are mediated by RNA-protein interactions. However, experimental determination of the structures of protein-RNA complexes is expensive and technically challenging. Hence, a number of computational tools have been developed for predicting protein-RNA interfaces. Some of the state-of-the-art protein-RNA interface predictors rely on position-specific scoring matrix (PSSM)-based encoding of the protein sequences. The computational efforts needed for generating PSSMs severely limits the practical utility of protein-RNA interface prediction servers. In this work, we experiment with two approaches, random sampling and sequence similarity reduction, for extracting a representative reference database of protein sequences from more than 50 million protein sequences in UniRef100. Our results suggest that random sampled databases produce better PSSM profiles (in terms of the number of hits used to generate the profile and the distance of the generated profile to the corresponding profile generated using the entire UniRef100 data as well as the accuracy of the machine learning classifier trained using these profiles). Based on our results, we developed FastRNABindR, an improved version of RNABindR for predicting protein-RNA interface residues using PSSM profiles generated using 1% of the UniRef100 sequences sampled uniformly at random. To the best of our knowledge, FastRNABindR is the only protein-RNA interface residue prediction online server that requires generation of PSSM profiles for query sequences and accepts hundreds of protein sequences per submission. Our approach for determining the optimal BLAST database for a protein-RNA interface residue classification task has the potential of substantially speeding up, and hence increasing the practical utility of, other amino acid sequence based predictors of protein-protein and protein-DNA interfaces.

JNSViewer—A JavaScript-based Nucleotide Sequence Viewer for DNA/RNA secondary structures

PubMed Central

Dong, Min; Graham, Mitchell; Yadav, Nehul

2017-01-01

Many tools are available for visualizing RNA or DNA secondary structures, but there is scarce implementation in JavaScript that provides seamless integration with the increasingly popular web computational platforms. We have developed JNSViewer, a highly interactive web service, which is bundled with several popular tools for DNA/RNA secondary structure prediction and can provide precise and interactive correspondence among nucleotides, dot-bracket data, secondary structure graphs, and genic annotations. In JNSViewer, users can perform RNA secondary structure predictions with different programs and settings, add customized genic annotations in GFF format to structure graphs, search for specific linear motifs, and extract relevant structure graphs of sub-sequences. JNSViewer also allows users to choose a transcript or specific segment of Arabidopsis thaliana genome sequences and predict the corresponding secondary structure. Popular genome browsers (i.e., JBrowse and BrowserGenome) were integrated into JNSViewer to provide powerful visualizations of chromosomal locations, genic annotations, and secondary structures. In addition, we used StructureFold with default settings to predict some RNA structures for Arabidopsis by incorporating in vivo high-throughput RNA structure profiling data and stored the results in our web server, which might be a useful resource for RNA secondary structure studies in plants. JNSViewer is available at http://bioinfolab.miamioh.edu/jnsviewer/index.html. PMID:28582416

Effects of TT8 and HB12 Silencing on the Relations between the Molecular Structures of Alfalfa ( Medicago sativa) Plants and Their Nutritional Profiles and In Vitro Gas Production.

PubMed

Lei, Yaogeng; Hannoufa, Abdelali; Prates, Luciana Louzada; Shi, Haitao; Wang, Yuxi; Biligetu, Bill; Christensen, David; Yu, Peiqiang

2018-06-06

The objective of this study was to investigate the effects of silencing the TT8 and HB12 genes on the nutritive profiles and in vitro gas production of alfalfa in relation to the spectral molecular structures of alfalfa. TT8-silenced (TT8i, n = 5) and HB12-silenced (HB12i, n = 11) alfalfa were generated by RNA interference (RNAi) and grown with nontransgenic wild type controls (WT, n = 4) in a greenhouse. Alfalfa plants were harvested at early-to-mid vegetative stage. Samples were analyzed for their chemical compositions, CNCPS fractions, and in vitro gas production. Correlations and regressions of the nutritional profiles and in vitro gas production with the molecular spectral structures were also determined. The results showed that the transformed alfalfa had higher digestible fiber and lower crude protein with higher proportions of indigestible protein than WT. HB12 RNAi had lower gas production compared with those of the others. Some chemical, CNCPS, and gas-production profiles were closely correlated with spectral structures and could be well predicted from spectral parameters. In conclusion, the RNAi silencing of TT8 and HB12 in alfalfa altered the chemical, CNCPS and gas-production profiles of alfalfa, and such alterations were closely correlated with the inherent spectral structures of alfalfa.

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.

UQlust: combining profile hashing with linear-time ranking for efficient clustering and analysis of big macromolecular data.

PubMed

Adamczak, Rafal; Meller, Jarek

2016-12-28

Advances in computing have enabled current protein and RNA structure prediction and molecular simulation methods to dramatically increase their sampling of conformational spaces. The quickly growing number of experimentally resolved structures, and databases such as the Protein Data Bank, also implies large scale structural similarity analyses to retrieve and classify macromolecular data. Consequently, the computational cost of structure comparison and clustering for large sets of macromolecular structures has become a bottleneck that necessitates further algorithmic improvements and development of efficient software solutions. uQlust is a versatile and easy-to-use tool for ultrafast ranking and clustering of macromolecular structures. uQlust makes use of structural profiles of proteins and nucleic acids, while combining a linear-time algorithm for implicit comparison of all pairs of models with profile hashing to enable efficient clustering of large data sets with a low memory footprint. In addition to ranking and clustering of large sets of models of the same protein or RNA molecule, uQlust can also be used in conjunction with fragment-based profiles in order to cluster structures of arbitrary length. For example, hierarchical clustering of the entire PDB using profile hashing can be performed on a typical laptop, thus opening an avenue for structural explorations previously limited to dedicated resources. The uQlust package is freely available under the GNU General Public License at https://github.com/uQlust . uQlust represents a drastic reduction in the computational complexity and memory requirements with respect to existing clustering and model quality assessment methods for macromolecular structure analysis, while yielding results on par with traditional approaches for both proteins and RNAs.

PSSP-RFE: accurate prediction of protein structural class by recursive feature extraction from PSI-BLAST profile, physical-chemical property and functional annotations.

PubMed

Li, Liqi; Cui, Xiang; Yu, Sanjiu; Zhang, Yuan; Luo, Zhong; Yang, Hua; Zhou, Yue; Zheng, Xiaoqi

2014-01-01

Protein structure prediction is critical to functional annotation of the massively accumulated biological sequences, which prompts an imperative need for the development of high-throughput technologies. As a first and key step in protein structure prediction, protein structural class prediction becomes an increasingly challenging task. Amongst most homological-based approaches, the accuracies of protein structural class prediction are sufficiently high for high similarity datasets, but still far from being satisfactory for low similarity datasets, i.e., below 40% in pairwise sequence similarity. Therefore, we present a novel method for accurate and reliable protein structural class prediction for both high and low similarity datasets. This method is based on Support Vector Machine (SVM) in conjunction with integrated features from position-specific score matrix (PSSM), PROFEAT and Gene Ontology (GO). A feature selection approach, SVM-RFE, is also used to rank the integrated feature vectors through recursively removing the feature with the lowest ranking score. The definitive top features selected by SVM-RFE are input into the SVM engines to predict the structural class of a query protein. To validate our method, jackknife tests were applied to seven widely used benchmark datasets, reaching overall accuracies between 84.61% and 99.79%, which are significantly higher than those achieved by state-of-the-art tools. These results suggest that our method could serve as an accurate and cost-effective alternative to existing methods in protein structural classification, especially for low similarity datasets.

Prediction of pi-turns in proteins using PSI-BLAST profiles and secondary structure information.

PubMed

Wang, Yan; Xue, Zhi-Dong; Shi, Xiao-Hong; Xu, Jin

2006-09-01

Due to the structural and functional importance of tight turns, some methods have been proposed to predict gamma-turns, beta-turns, and alpha-turns in proteins. In the past, studies of pi-turns were made, but not a single prediction approach has been developed so far. It will be useful to develop a method for identifying pi-turns in a protein sequence. In this paper, the support vector machine (SVM) method has been introduced to predict pi-turns from the amino acid sequence. The training and testing of this approach is performed with a newly collected data set of 640 non-homologous protein chains containing 1931 pi-turns. Different sequence encoding schemes have been explored in order to investigate their effects on the prediction performance. With multiple sequence alignment and predicted secondary structure, the final SVM model yields a Matthews correlation coefficient (MCC) of 0.556 by a 7-fold cross-validation. A web server implementing the prediction method is available at the following URL: http://210.42.106.80/piturn/.

Distinguishing between tectonic and lithologic controls on bedrock channel longitudinal profiles using cosmogenic 10Be erosion rates and channel steepness index

USGS Publications Warehouse

Cyr, Andrew J.; Granger, Darryl E.; Olivetti, Valerio; Molin, Paola

2014-01-01

Knickpoints in fluvial channel longitudinal profiles and channel steepness index values derived from digital elevation data can be used to detect tectonic structures and infer spatial patterns of uplift. However, changes in lithologic resistance to channel incision can also influence the morphology of longitudinal profiles. We compare the spatial patterns of both channel steepness index and cosmogenic 10Be-determined erosion rates from four landscapes in Italy, where the geology and tectonics are well constrained, to four theoretical predictions of channel morphologies, which can be interpreted as the result of primarily tectonic or lithologic controls. These data indicate that longitudinal profile forms controlled by unsteady or nonuniform tectonics can be distinguished from those controlled by nonuniform lithologic resistance. In each landscape the distribution of channel steepness index and erosion rates is consistent with model predictions and demonstrates that cosmogenic nuclide methods can be applied to distinguish between these two controlling factors.

Profiles of neurological outcome prediction among intensivists.

PubMed

Racine, Eric; Dion, Marie-Josée; Wijman, Christine A C; Illes, Judy; Lansberg, Maarten G

2009-12-01

Advances in intensive care medicine have increased survival rates of patients with critical neurological conditions. The focus of prognostication for such patients is therefore shifting from predicting chances of survival to meaningful neurological recovery. This study assessed the variability in long-term outcome predictions among physicians and aimed to identify factors that may account for this variability. Based on a clinical vignette describing a comatose patient suffering from post-anoxic brain injury intensivists were asked in a semi-structured interview about the patient's specific neurological prognosis and about prognostication in general. Qualitative research methods were used to identify areas of variability in prognostication and to classify physicians according to specific prognostication profiles. Quantitative statistics were used to assess for associations between prognostication profiles and physicians' demographic and practice characteristics. Eighteen intensivists participated. Functional outcome predictions varied along an evaluative dimension (fair/good-poor) and a confidence dimension (certain-uncertain). More experienced physicians tended to be more pessimistic about the patient's functional outcome and more certain of their prognosis. Attitudes toward quality of life varied along an evaluative dimension (good-poor) and a "style" dimension (objective-subjective). Older and more experienced physicians were more likely to express objective judgments of quality of life and to predict a worse quality of life for the patient than their younger and less experienced counterparts. Various prognostication profiles exist among intensivists. These may be dictated by factors such as physicians' age and clinical experience. Awareness of these associations may be a first step to more uniform prognostication.

Proposing Novel MAO-B Hit Inhibitors Using Multidimensional Molecular Modeling Approaches and Application of Binary QSAR Models for Prediction of Their Therapeutic Activity, Pharmacokinetic and Toxicity Properties.

PubMed

Is, Yusuf Serhat; Durdagi, Serdar; Aksoydan, Busecan; Yurtsever, Mine

2018-05-07

Monoamine oxidase (MAO) enzymes MAO-A and MAO-B play a critical role in the metabolism of monoamine neurotransmitters. Hence, MAO inhibitors are very important for the treatment of several neurodegenerative diseases such as Parkinson's disease (PD), Alzheimer's disease (AD), and amyotrophic lateral sclerosis (ALS). In this study, 256 750 molecules from Otava Green Chemical Collection were virtually screened for their binding activities as MAO-B inhibitors. Two hit molecules were identified after applying different filters such as high docking scores and selectivity to MAO-B, desired pharmacokinetic profile predictions with binary quantitative structure-activity relationship (QSAR) models. Therapeutic activity prediction as well as pharmacokinetic and toxicity profiles were investigated using MetaCore/MetaDrug platform which is based on a manually curated database of molecular interactions, molecular pathways, gene-disease associations, chemical metabolism, and toxicity information. Particular therapeutic activity and toxic effect predictions are based on the ChemTree ability to correlate structural descriptors to that property using recursive partitioning algorithm. Molecular dynamics (MD) simulations were also performed to make more detailed assessments beyond docking studies. All these calculations were made not only to determine if studied molecules possess the potential to be a MAO-B inhibitor but also to find out whether they carry MAO-B selectivity versus MAO-A. The evaluation of docking results and pharmacokinetic profile predictions together with the MD simulations enabled us to identify one hit molecule (ligand 1, Otava ID: 3463218) which displayed higher selectivity toward MAO-B than a positive control selegiline which is a commercially used drug for PD therapeutic purposes.

Insights into an original pocket-ligand pair classification: a promising tool for ligand profile prediction.

PubMed

Pérot, Stéphanie; Regad, Leslie; Reynès, Christelle; Spérandio, Olivier; Miteva, Maria A; Villoutreix, Bruno O; Camproux, Anne-Claude

2013-01-01

Pockets are today at the cornerstones of modern drug discovery projects and at the crossroad of several research fields, from structural biology to mathematical modeling. Being able to predict if a small molecule could bind to one or more protein targets or if a protein could bind to some given ligands is very useful for drug discovery endeavors, anticipation of binding to off- and anti-targets. To date, several studies explore such questions from chemogenomic approach to reverse docking methods. Most of these studies have been performed either from the viewpoint of ligands or targets. However it seems valuable to use information from both ligands and target binding pockets. Hence, we present a multivariate approach relating ligand properties with protein pocket properties from the analysis of known ligand-protein interactions. We explored and optimized the pocket-ligand pair space by combining pocket and ligand descriptors using Principal Component Analysis and developed a classification engine on this paired space, revealing five main clusters of pocket-ligand pairs sharing specific and similar structural or physico-chemical properties. These pocket-ligand pair clusters highlight correspondences between pocket and ligand topological and physico-chemical properties and capture relevant information with respect to protein-ligand interactions. Based on these pocket-ligand correspondences, a protocol of prediction of clusters sharing similarity in terms of recognition characteristics is developed for a given pocket-ligand complex and gives high performances. It is then extended to cluster prediction for a given pocket in order to acquire knowledge about its expected ligand profile or to cluster prediction for a given ligand in order to acquire knowledge about its expected pocket profile. This prediction approach shows promising results and could contribute to predict some ligand properties critical for binding to a given pocket, and conversely, some key pocket properties for ligand binding.

Insights into an Original Pocket-Ligand Pair Classification: A Promising Tool for Ligand Profile Prediction

PubMed Central

Reynès, Christelle; Spérandio, Olivier; Miteva, Maria A.; Villoutreix, Bruno O.; Camproux, Anne-Claude

2013-01-01

Pockets are today at the cornerstones of modern drug discovery projects and at the crossroad of several research fields, from structural biology to mathematical modeling. Being able to predict if a small molecule could bind to one or more protein targets or if a protein could bind to some given ligands is very useful for drug discovery endeavors, anticipation of binding to off- and anti-targets. To date, several studies explore such questions from chemogenomic approach to reverse docking methods. Most of these studies have been performed either from the viewpoint of ligands or targets. However it seems valuable to use information from both ligands and target binding pockets. Hence, we present a multivariate approach relating ligand properties with protein pocket properties from the analysis of known ligand-protein interactions. We explored and optimized the pocket-ligand pair space by combining pocket and ligand descriptors using Principal Component Analysis and developed a classification engine on this paired space, revealing five main clusters of pocket-ligand pairs sharing specific and similar structural or physico-chemical properties. These pocket-ligand pair clusters highlight correspondences between pocket and ligand topological and physico-chemical properties and capture relevant information with respect to protein-ligand interactions. Based on these pocket-ligand correspondences, a protocol of prediction of clusters sharing similarity in terms of recognition characteristics is developed for a given pocket-ligand complex and gives high performances. It is then extended to cluster prediction for a given pocket in order to acquire knowledge about its expected ligand profile or to cluster prediction for a given ligand in order to acquire knowledge about its expected pocket profile. This prediction approach shows promising results and could contribute to predict some ligand properties critical for binding to a given pocket, and conversely, some key pocket properties for ligand binding. PMID:23840299

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

Grierson, B. A.; Wang, W. X.; Ethier, S.

Intrinsic toroidal rotation of the deuterium main ions in the core of the DIII-D tokamak is observed to transition from flat to hollow, forming an off-axis peak, above a threshold level of direct electron heating. Nonlinear gyrokinetic simulations show that the residual stress associated with electrostatic ion temperature gradient turbulence possesses the correct radial location and stress structure to cause the observed hollow rotation profile. Residual stress momentum flux in the gyrokinetic simulations is balanced by turbulent momentum diffusion, with negligible contributions from turbulent pinch. Finally, the prediction of the velocity profile by integrating the momentum balance equation produces amore » rotation profile that qualitatively and quantitatively agrees with the measured main-ion profile, demonstrating that fluctuation-induced residual stress can drive the observed intrinsic velocity profile.« less

Prediction of cis/trans isomerization in proteins using PSI-BLAST profiles and secondary structure information.

PubMed

Song, Jiangning; Burrage, Kevin; Yuan, Zheng; Huber, Thomas

2006-03-09

The majority of peptide bonds in proteins are found to occur in the trans conformation. However, for proline residues, a considerable fraction of Prolyl peptide bonds adopt the cis form. Proline cis/trans isomerization is known to play a critical role in protein folding, splicing, cell signaling and transmembrane active transport. Accurate prediction of proline cis/trans isomerization in proteins would have many important applications towards the understanding of protein structure and function. In this paper, we propose a new approach to predict the proline cis/trans isomerization in proteins using support vector machine (SVM). The preliminary results indicated that using Radial Basis Function (RBF) kernels could lead to better prediction performance than that of polynomial and linear kernel functions. We used single sequence information of different local window sizes, amino acid compositions of different local sequences, multiple sequence alignment obtained from PSI-BLAST and the secondary structure information predicted by PSIPRED. We explored these different sequence encoding schemes in order to investigate their effects on the prediction performance. The training and testing of this approach was performed on a newly enlarged dataset of 2424 non-homologous proteins determined by X-Ray diffraction method using 5-fold cross-validation. Selecting the window size 11 provided the best performance for determining the proline cis/trans isomerization based on the single amino acid sequence. It was found that using multiple sequence alignments in the form of PSI-BLAST profiles could significantly improve the prediction performance, the prediction accuracy increased from 62.8% with single sequence to 69.8% and Matthews Correlation Coefficient (MCC) improved from 0.26 with single local sequence to 0.40. Furthermore, if coupled with the predicted secondary structure information by PSIPRED, our method yielded a prediction accuracy of 71.5% and MCC of 0.43, 9% and 0.17 higher than the accuracy achieved based on the singe sequence information, respectively. A new method has been developed to predict the proline cis/trans isomerization in proteins based on support vector machine, which used the single amino acid sequence with different local window sizes, the amino acid compositions of local sequence flanking centered proline residues, the position-specific scoring matrices (PSSMs) extracted by PSI-BLAST and the predicted secondary structures generated by PSIPRED. The successful application of SVM approach in this study reinforced that SVM is a powerful tool in predicting proline cis/trans isomerization in proteins and biological sequence analysis.

Three-dimensional (3D) structure prediction of the American and African oil-palms β-ketoacyl-[ACP] synthase-II protein by comparative modelling

PubMed Central

Wang, Edina; Chinni, Suresh; Bhore, Subhash Janardhan

2014-01-01

Background: The fatty-acid profile of the vegetable oils determines its properties and nutritional value. Palm-oil obtained from the African oil-palm [Elaeis guineensis Jacq. (Tenera)] contains 44% palmitic acid (C16:0), but, palm-oil obtained from the American oilpalm [Elaeis oleifera] contains only 25% C16:0. In part, the b-ketoacyl-[ACP] synthase II (KASII) [EC: 2.3.1.179] protein is responsible for the high level of C16:0 in palm-oil derived from the African oil-palm. To understand more about E. guineensis KASII (EgKASII) and E. oleifera KASII (EoKASII) proteins, it is essential to know its structures. Hence, this study was undertaken. Objective: The objective of this study was to predict three-dimensional (3D) structure of EgKASII and EoKASII proteins using molecular modelling tools. Materials and Methods: The amino-acid sequences for KASII proteins were retrieved from the protein database of National Center for Biotechnology Information (NCBI), USA. The 3D structures were predicted for both proteins using homology modelling and ab-initio technique approach of protein structure prediction. The molecular dynamics (MD) simulation was performed to refine the predicted structures. The predicted structure models were evaluated and root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values were calculated. Results: The homology modelling showed that EgKASII and EoKASII proteins are 78% and 74% similar with Streptococcus pneumonia KASII and Brucella melitensis KASII, respectively. The EgKASII and EoKASII structures predicted by using ab-initio technique approach shows 6% and 9% deviation to its structures predicted by homology modelling, respectively. The structure refinement and validation confirmed that the predicted structures are accurate. Conclusion: The 3D structures for EgKASII and EoKASII proteins were predicted. However, further research is essential to understand the interaction of EgKASII and EoKASII proteins with its substrates. PMID:24748752

Three-dimensional (3D) structure prediction of the American and African oil-palms β-ketoacyl-[ACP] synthase-II protein by comparative modelling.

PubMed

Wang, Edina; Chinni, Suresh; Bhore, Subhash Janardhan

2014-01-01

The fatty-acid profile of the vegetable oils determines its properties and nutritional value. Palm-oil obtained from the African oil-palm [Elaeis guineensis Jacq. (Tenera)] contains 44% palmitic acid (C16:0), but, palm-oil obtained from the American oilpalm [Elaeis oleifera] contains only 25% C16:0. In part, the b-ketoacyl-[ACP] synthase II (KASII) [EC: 2.3.1.179] protein is responsible for the high level of C16:0 in palm-oil derived from the African oil-palm. To understand more about E. guineensis KASII (EgKASII) and E. oleifera KASII (EoKASII) proteins, it is essential to know its structures. Hence, this study was undertaken. The objective of this study was to predict three-dimensional (3D) structure of EgKASII and EoKASII proteins using molecular modelling tools. The amino-acid sequences for KASII proteins were retrieved from the protein database of National Center for Biotechnology Information (NCBI), USA. The 3D structures were predicted for both proteins using homology modelling and ab-initio technique approach of protein structure prediction. The molecular dynamics (MD) simulation was performed to refine the predicted structures. The predicted structure models were evaluated and root mean square deviation (RMSD) and root mean square fluctuation (RMSF) values were calculated. The homology modelling showed that EgKASII and EoKASII proteins are 78% and 74% similar with Streptococcus pneumonia KASII and Brucella melitensis KASII, respectively. The EgKASII and EoKASII structures predicted by using ab-initio technique approach shows 6% and 9% deviation to its structures predicted by homology modelling, respectively. The structure refinement and validation confirmed that the predicted structures are accurate. The 3D structures for EgKASII and EoKASII proteins were predicted. However, further research is essential to understand the interaction of EgKASII and EoKASII proteins with its substrates.

Probing the hammerhead ribozyme structure with ribonucleases.

PubMed Central

Hodgson, R A; Shirley, N J; Symons, R H

1994-01-01

Susceptibility to RNase digestion has been used to probe the conformation of the hammerhead ribozyme structure prepared from chemically synthesised RNAs. Less than about 1.5% of the total sample was digested to obtain a profile of RNase digestion sites. The observed digestion profiles confirmed the predicted base-paired secondary structure for the hammerhead. Digestion profiles of both cis and trans hammerhead structures were nearly identical which indicated that the structural interactions leading to self-cleavage were similar for both systems. Furthermore, the presence or absence of Mg2+ did not affect the RNase digestion profiles, thus indicating that Mg2+ did not modify the hammerhead structure significantly to induce self-cleavage. The base-paired stems I and II in the hammerhead structure were stable whereas stem III, which was susceptible to digestion, appeared to be an unstable region. The single strand domains separating the stems were susceptible to digestion with the exception of sites adjacent to guanosines; GL2.1 in the stem II loop and G12 in the conserved GAAAC sequence, which separates stems II and III. The absence of digestion at GL2.1 in the stem II hairpin loop of the hammerhead complex was maintained in uncomplexed ribozyme and in short oligonucleotides containing only the stem II hairpin region. In contrast, the G12 site became susceptible when the ribozyme was not complexed with its substrate. Overall the results are consistent with the role of Mg2+ in the hammerhead self-cleavage reaction being catalytic and not structural. Images PMID:8202361

Modeling Pair Distribution Functions of Rare-Earth Phosphate Glasses Using Principal Component Analysis.

PubMed

Cole, Jacqueline M; Cheng, Xie; Payne, Michael C

2016-11-07

The use of principal component analysis (PCA) to statistically infer features of local structure from experimental pair distribution function (PDF) data is assessed on a case study of rare-earth phosphate glasses (REPGs). Such glasses, codoped with two rare-earth ions (R and R') of different sizes and optical properties, are of interest to the laser industry. The determination of structure-property relationships in these materials is an important aspect of their technological development. Yet, realizing the local structure of codoped REPGs presents significant challenges relative to their singly doped counterparts; specifically, R and R' are difficult to distinguish in terms of establishing relative material compositions, identifying atomic pairwise correlation profiles in a PDF that are associated with each ion, and resolving peak overlap of such profiles in PDFs. This study demonstrates that PCA can be employed to help overcome these structural complications, by statistically inferring trends in PDFs that exist for a restricted set of experimental data on REPGs, and using these as training data to predict material compositions and PDF profiles in unknown codoped REPGs. The application of these PCA methods to resolve individual atomic pairwise correlations in t(r) signatures is also presented. The training methods developed for these structural predictions are prevalidated by testing their ability to reproduce known physical phenomena, such as the lanthanide contraction, on PDF signatures of the structurally simpler singly doped REPGs. The intrinsic limitations of applying PCA to analyze PDFs relative to the quality control of source data, data processing, and sample definition, are also considered. While this case study is limited to lanthanide-doped REPGs, this type of statistical inference may easily be extended to other inorganic solid-state materials and be exploited in large-scale data-mining efforts that probe many t(r) functions.

ON PREDICTING INFRAGRAVITY ENERGY IN THE SURF ZONE.

USGS Publications Warehouse

Sallenger,, Asbury H.; Holman, Robert A.; Edge, Billy L.

1985-01-01

Flow data were obtained in the surf zone across a barred profile during a storm. RMS cross-shore velocities due to waves in the intragravity band (wave periods greater than 20 s) had maxima in excess of 0. 5 m/s over the bar crest. For comparison to measured spectra, synthetic spectra of cross-shore flow were computed using measured nearshore profiles. The structure, in the infragravity band, of these synthetic spectra corresponded reasonably well with the structure of the measured spectra. Total variances of measured cross-shore flow within the infragravity band were nondimensionalized by dividing by total infragravity variances of synthetic spectra. These nondimensional variances were independent of distance offshore and increased with the square of the breaker height. Thus, cross-shore flow due to infragravity waves can be estimated with knowledge of the nearshore profile and incident wave conditions. Refs.

Knowledge-based prediction of protein backbone conformation using a structural alphabet.

PubMed

Vetrivel, Iyanar; Mahajan, Swapnil; Tyagi, Manoj; Hoffmann, Lionel; Sanejouand, Yves-Henri; Srinivasan, Narayanaswamy; de Brevern, Alexandre G; Cadet, Frédéric; Offmann, Bernard

2017-01-01

Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks, is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of Protein Blocks. Predicting the local structure of a protein in terms of protein blocks is the general objective of this work. A new approach, PB-kPRED is proposed towards this aim. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) applying a knowledge-based algorithm that does not rely on any secondary structure predictions and/or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures. Though PB-kPRED uses the structural information from homologues in preference, if available. The predictions were evaluated rigorously on 15,544 query proteins representing a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. We have shown that the kPRED method was able to achieve mean accuracies ranging from 40.8% to 66.3% depending on the availability of homologues. The impact of the different strategies for scanning the database on the prediction was evaluated and is discussed. Our results highlight the usefulness of the method in the context of proteins without any known structural homologues. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.

SVM-Fold: a tool for discriminative multi-class protein fold and superfamily recognition

PubMed Central

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

2007-01-01

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

Internal structure of shock waves in disparate mass mixtures

NASA Technical Reports Server (NTRS)

Chung, Chan-Hong; De Witt, Kenneth J.; Jeng, Duen-Ren; Penko, Paul F.

1992-01-01

The detailed flow structure of a normal shock wave for a gas mixture is investigated using the direct-simulation Monte Carlo method. A variable diameter hard-sphere (VDHS) model is employed to investigate the effect of different viscosity temperature exponents (VTE) for each species in a gas mixture. Special attention is paid to the irregular behavior in the density profiles which was previously observed in a helium-xenon experiment. It is shown that the VTE can have substantial effects in the prediction of the structure of shock waves. The variable hard-sphere model of Bird shows good agreement, but with some limitations, with the experimental data if a common VTE is chosen properly for each case. The VDHS model shows better agreement with the experimental data without adjusting the VTE. The irregular behavior of the light-gas component in shock waves of disparate mass mixtures is observed not only in the density profile, but also in the parallel temperature profile. The strength of the shock wave, the type of molecular interactions, and the mole fraction of heavy species have substantial effects on the existence and structure of the irregularities.

Static Aeroelasticity in Combat Aircraft.

DTIC Science & Technology

1986-01-01

stiffness scaled beam machined along a predicted elastic axis, and load iola- tion cuts forward and aft of the beam, has proved to be most successful...aircraft components. Many papers deal with the activities in the field of structural optimization.’ 4sing fiber composites , a new design technique...Supersonic Design Composite Structures Fly - by - Wire Thin Profiles Aeroelastic Tailoring Unstable Aircraft V Variable Camber Lght Weight Pilot Handling

Surface Composition of NiPd Alloys

NASA Technical Reports Server (NTRS)

Noebe, Ronald D.; Khalil, Joe; Bozzolo, Guillermo; Gray, Hugh R. (Technical Monitor)

2002-01-01

Surface segregation in Ni-Pd alloys has been studied using the BFS method for alloys. Not only does the method predict an oscillatory segregation profile but it also indicates that the number of Pd-enriched surface planes can vary as a function of orientation. The segregation profiles were computed as a function of temperature, crystal face, and composition. Pd enrichment of the first layer is observed in (111) and (100) surfaces, and enrichment of the top two layers occurs for (110) surfaces. In all cases, the segregation profile shows oscillations that are actually related to weak ordering tendencies in the bulk. An atom-by-atom analysis was performed to identify the competing mechanisms leading to the observed surface behaviors. Large-scale atomistic simulations were also performed to investigate the temperature dependence of the segregation profiles as well as for analysis of the bulk structures. Finally, the observed surface behaviors are discussed in relation to the bulk phase structure of Ni-Pd alloys, which exhibit a tendency to weakly order.

Study on numerical simulation of asymmetric structure aluminum profile extrusion based on ALE method

NASA Astrophysics Data System (ADS)

Chen, Kun; Qu, Yuan; Ding, Siyi; Liu, Changhui; Yang, Fuyong

2018-05-01

Using the HyperXtrude module based on the Arbitrary Lagrangian-Eulerian (ALE) finite element method, the paper simulates the steady extrusion process of the asymmetric structure aluminum die successfully. A verification experiment is carried out to verify the simulation results. Having obtained and analyzed the stress-strain field, temperature field and extruded velocity of the metal, it confirms that the simulation prediction results and the experimental schemes are consistent. The scheme of the die correction and optimization are discussed at last. By adjusting the bearing length and core thickness, adopting the structure of feeder plate protection, short shunt bridge in the upper die and three-level bonding container in the lower die to control the metal flowing, the qualified aluminum profile can be obtained.

Academic Self-Handicapping and Achievement Goals: A Further Examination.

PubMed

Midgley, Carol; Urdan, Tim

2001-01-01

This study extends previous research on the relations among students' personal achievement goals, perceptions of the classroom goal structure, and reports of the use of self-handicapping strategies. Surveys, specific to the math domain, were given to 484 7th-grade students in nine middle schools. Personal performance-avoid goals positively predicted handicapping, whereas personal performance-approach goals did not. Personal task goals negatively predicted handicapping. Perceptions of a performance goal structure positively predicted handicapping, and perceptions of a task goal structure negatively predicted handicapping, independent of personal goals. Median splits used to examine multiple goal profiles revealed that students high in performance-avoid goals used handicapping more than did those low in performance-avoid goals regardless of the level of task goals. Students low in performance-avoid goals and high in task goals handicapped less than those low in both goals. Level of performance-approach goals had little effect on the relation between task goals and handicapping. Copyright 2001 Academic Press.

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

How mesoscopic staircases condense to macroscopic barriers in confined plasma turbulence

NASA Astrophysics Data System (ADS)

Ashourvan, Arash; Diamond, P. H.

2016-11-01

This Rapid Communication sets forth the mechanism by which mesoscale staircase structures condense to form macroscopic states of enhanced confinement. Density, vorticity, and turbulent potential enstrophy are the variables for this model. Formation of the staircase structures is due to inhomogeneous mixing of (generalized) potential vorticity (PV). Such mixing results in the local sharpening of density and vorticity gradients. When PV gradients steepen, the density staircase structure develops into a lattice of mesoscale "jumps" and "steps," which are, respectively, regions of local gradient steepening and flattening. The jumps then merge and migrate in radius, leading to the emergence of a new macroscale profile structure, so indicating that profile self-organization is a global process, which may be described by a local, but nonlinear model. This work predicts and demonstrates how mesoscale condensation of staircases leads to global states of enhanced confinement.

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.

Carbohydrate and lipid spectroscopic molecular structures of different alfalfa hay and their relationship with nutrient availability in ruminants

PubMed Central

Yari, Mojtaba; Valizadeh, Reza; Nnaserian, Abbas Ali; Jonker, Arjan; Yu, Peiqiang

2017-01-01

Objective This study was conducted to determine molecular structures related to carbohydrates and lipid in alfalfa hay cut at early bud, late bud and early flower and in the afternoon and next morning using Fourier transform infrared spectroscopy (FT/IR) and to determine their relationship with alfalfa hay nutrient profile and availability in ruminants. Methods Chemical composition analysis, carbohydrate fractionation, in situ ruminal degradability, and DVE/OEB model were used to measure nutrient profile and availability of alfalfa hay. Univariate analysis, hierarchical cluster analysis (CLA) and principal components analysis (PCA) were conducted to identify FT/IR spectra differences. Results The FT/IR non-structural carbohydrate (NSCHO) to total carbohydrates and NSCHO to structural carbohydrate ratios decreased (p<0.05), while lignin to NSCHO and lipid CH3 symmetric to CH2 symmetric ratios increased with advancing maturity (p<0.05). The FT/IR spectra related to structural carbohydrates, lignin and lipids were distinguished for alfalfa hay at three maturities by PCA and CLA, while FT/IR molecular structures related to carbohydrates and lipids were similar between alfalfa hay cut in the morning and afternoon when analyzed by PCA and CLA analysis. Positive correlations were found for FT/IR NSCHO to total carbohydrate and NSCHO to structural carbohydrate ratios with non-fiber carbohydrate (by wet chemistry), ruminal fast and intermediately degradable carbohydrate fractions and total ruminal degradability of carbohydrates and predicted intestinal nutrient availability in dairy cows (r≥0.60; p<0.05) whereas FT/IR lignin to NSCHO and CH3 to CH2 symmetric stretching ratio had negative correlation with predicted ruminal and intestinal nutrient availability of alfalfa hay in dairy cows (r≥−0.60; p<0.05). Conclusion FT/IR carbohydrate and lipid molecular structures in alfalfa hay changed with advancing maturity from early bud to early flower, but not during the day, and these molecular structures correlated with predicted nutrient supply of alfalfa hay in ruminants. PMID:28335093

Thermal-structural modeling of polymer Bragg grating waveguides illuminated by a light emitting diode.

PubMed

Joon Kim, Kyoung; Bar-Cohen, Avram; Han, Bongtae

2012-02-20

This study reports both analytical and numerical thermal-structural models of polymer Bragg grating (PBG) waveguides illuminated by a light emitting diode (LED). A polymethyl methacrylate (PMMA) Bragg grating (BG) waveguide is chosen as an analysis vehicle to explore parametric effects of incident optical powers and substrate materials on the thermal-structural behavior of the BG. Analytical models are verified by comparing analytically predicted average excess temperatures, and thermally induced axial strains and stresses with numerical predictions. A parametric study demonstrates that the PMMA substrate induces more adverse effects, such as higher excess temperatures, complex axial temperature profiles, and greater and more complicated thermally induced strains in the BG compared with the Si substrate. © 2012 Optical Society of America

Biochemical Activities of 320 ToxCast Chemicals Evaluated Across 239 Functional Targets

EPA Science Inventory

EPA’s ToxCast research program is profiling chemical bioactivity in order to generate predictive signatures of toxicity. The present study evaluated 320 chemicals across 239 biochemical assays. ToxCast phase I chemicals include 309 unique structures, most of which are pesticide ...

Modeling Techniques for Shipboard Manning: A Review and Plan for Development

DTIC Science & Technology

1993-02-01

manning levels. Once manning models have been created, experiments can be conducted to show how changes in the manning structure might affect ship safety...these predictions, users of the manning models can evaluate how changes in crew configurations, manning levels, and voyage profiles affect ship safety...mitigate emergency situations would provide crucial information on how changes in manning structure would affect overall ship safety. Like emergency

Exploring Polypharmacology Using a ROCS-Based Target Fishing Approach

DTIC Science & Technology

2012-01-01

target representatives. Target profiles were then generated for a given query molecule by computing maximal shape/ chemistry overlap between the query...molecule and the drug sets assigned to each protein target. The overlap was computed using the program ROCS (Rapid Overlay of Chemical Structures ). We...approaches in off-target prediction has been reviewed.9,10 Many structure -based target fishing (SBTF) approaches, such as INVDOCK11 and Target Fishing Dock

Main-ion intrinsic toroidal rotation profile driven by residual stress torque from ion temperature gradient turbulence in the DIII-D tokamak

DOE PAGES

Grierson, B. A.; Wang, W. X.; Ethier, S.; ...

2017-01-06

Intrinsic toroidal rotation of the deuterium main ions in the core of the DIII-D tokamak is observed to transition from flat to hollow, forming an off-axis peak, above a threshold level of direct electron heating. Nonlinear gyrokinetic simulations show that the residual stress associated with electrostatic ion temperature gradient turbulence possesses the correct radial location and stress structure to cause the observed hollow rotation profile. Residual stress momentum flux in the gyrokinetic simulations is balanced by turbulent momentum diffusion, with negligible contributions from turbulent pinch. Finally, the prediction of the velocity profile by integrating the momentum balance equation produces amore » rotation profile that qualitatively and quantitatively agrees with the measured main-ion profile, demonstrating that fluctuation-induced residual stress can drive the observed intrinsic velocity profile.« less

Concurrent and prognostic utility of subtyping anorexia nervosa along dietary and negative affect dimensions.

PubMed

Forbush, Kelsie T; Hagan, Kelsey E; Salk, Rachel H; Wildes, Jennifer E

2017-03-01

Bulimia nervosa can be reliably classified into subtypes based on dimensions of dietary restraint and negative affect. Community and clinical studies have shown that dietary-negative affect subtypes have greater test-retest reliability and concurrent and predictive validity compared to subtypes based on the Diagnostic and Statistical Manual of Mental Disorders (DSM). Although dietary-negative affect subtypes have shown utility for characterizing eating disorders that involve binge eating, this framework may have broader implications for understanding restrictive eating disorders. The purpose of this study was to test the concurrent and predictive validity of dietary-negative affect subtypes among patients with anorexia nervosa (AN; N = 194). Latent profile analysis was used to identify subtypes of AN based on dimensions of dietary restraint and negative affect. Chi-square and multivariate analysis of variance were used to characterize baseline differences between identified subtypes. Structural equation modeling was used to test whether dietary-negative affect subtypes would outperform DSM categories in predicting clinically relevant outcomes. Results supported a 2-profile model that replicated dietary-negative affect subtypes: Latent Profile 1 (n = 68) had clinically elevated scores on restraint only; Latent Profile 2 (n = 126) had elevated scores on both restraint and negative affect. Validation analyses showed that membership in the dietary-negative affect profile was associated with greater lifetime psychiatric comorbidity and psychosocial impairment compared to the dietary class. Dietary-negative affect subtypes only outperformed DSM categories in predicting quality-of-life impairment at 1-year follow-up. Findings highlight the clinical utility of subtyping AN based on dietary restraint and negative affect for informing future treatment-matching or personalized medicine strategies. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Computational flow field in energy efficient engine (EEE)

NASA Astrophysics Data System (ADS)

Miki, Kenji; Moder, Jeff; Liou, Meng-Sing

2016-11-01

In this paper, preliminary results for the recently-updated Open National Combustor Code (Open NCC) as applied to the EEE are presented. The comparison between two different numerical schemes, the standard Jameson-Schmidt-Turkel (JST) scheme and the advection upstream splitting method (AUSM), is performed for the cold flow and the reacting flow calculations using the RANS. In the cold flow calculation, the AUSM scheme predicts a much stronger reverse flow in the central recirculation zone. In the reacting flow calculation, we test two cases: gaseous fuel injection and liquid spray injection. In the gaseous fuel injection case, the overall flame structures of the two schemes are similar to one another, in the sense that the flame is attached to the main nozzle, but is detached from the pilot nozzle. However, in the exit temperature profile, the AUSM scheme shows a more uniform profile than that of the JST scheme, which is close to the experimental data. In the liquid spray injection case, we expect different flame structures in this scenario. We will give a brief discussion on how two numerical schemes predict the flame structures inside the Eusing different ways to introduce the fuel injection. Supported by NASA's Transformational Tools and Technologies project.

Computational Flow Field in Energy Efficient Engine (EEE)

NASA Technical Reports Server (NTRS)

Miki, Kenji; Moder, Jeff; Liou, Meng-Sing

2016-01-01

In this paper, preliminary results for the recently-updated Open National Combustion Code (Open NCC) as applied to the EEE are presented. The comparison between two different numerical schemes, the standard Jameson-Schmidt-Turkel (JST) scheme and the advection upstream splitting method (AUSM), is performed for the cold flow and the reacting flow calculations using the RANS. In the cold flow calculation, the AUSM scheme predicts a much stronger reverse flow in the central recirculation zone. In the reacting flow calculation, we test two cases: gaseous fuel injection and liquid spray injection. In the gaseous fuel injection case, the overall flame structures of the two schemes are similar to one another, in the sense that the flame is attached to the main nozzle, but is detached from the pilot nozzle. However, in the exit temperature profile, the AUSM scheme shows a more uniform profile than that of the JST scheme, which is close to the experimental data. In the liquid spray injection case, we expect different flame structures in this scenario. We will give a brief discussion on how two numerical schemes predict the flame structures inside the EEE using different ways to introduce the fuel injection.

Evaluation of an atmospheric model with surface and ABL meteorological data for energy applications in structured areas

NASA Astrophysics Data System (ADS)

Triantafyllou, A. G.; Kalogiros, J.; Krestou, A.; Leivaditou, E.; Zoumakis, N.; Bouris, D.; Garas, S.; Konstantinidis, E.; Wang, Q.

2018-03-01

This paper provides the performance evaluation of the meteorological component of The Air Pollution Model (TAPM), a nestable prognostic model, in predicting meteorological variables in urban areas, for both its surface layer and atmospheric boundary layer (ABL) turbulence parameterizations. The model was modified by incorporating four urban land surface types, replacing the existing single urban surface. Control runs were carried out over the wider area of Kozani, an urban area in NW Greece. The model was evaluated for both surface and ABL meteorological variables by using measurements of near-surface and vertical profiles of wind and temperature. The data were collected by using monitoring surface stations in selected sites as well as an acoustic sounder (SOnic Detection And Ranging (SODAR), up to 300 m above ground) and a radiometer profiler (up to 600 m above ground). The results showed the model demonstrated good performance in predicting the near-surface meteorology in the Kozani region for both a winter and a summer month. In the ABL, the comparison showed that the model's forecasts generally performed well with respect to the thermal structure (temperature profiles and ABL height) but overestimated wind speed at the heights of comparison (mostly below 200 m) up to 3-4 ms-1.

Density functional study on the structural and thermodynamic properties of aqueous DNA-electrolyte solution in the framework of cell model.

PubMed

Wang, Ke; Yu, Yang-Xin; Gao, Guang-Hua

2008-05-14

A density functional theory (DFT) in the framework of cell model is proposed to calculate the structural and thermodynamic properties of aqueous DNA-electrolyte solution with finite DNA concentrations. The hard-sphere contribution to the excess Helmholtz energy functional is derived from the modified fundamental measure theory, and the electrostatic interaction is evaluated through a quadratic functional Taylor expansion around a uniform fluid. The electroneutrality in the cell leads to a variational equation with a constraint. Since the reference fluid is selected to be a bulk phase, the Lagrange multiplier proves to be the potential drop across the cell boundary (Donnan potential). The ion profiles and electrostatic potential profiles in the cell are calculated from the present DFT-cell model. Our DFT-cell model gives better prediction of ion profiles than the Poisson-Boltzmann (PB)- or modified PB-cell models when compared to the molecular simulation data. The effects of polyelectrolyte concentration, ion size, and added-salt concentration on the electrostatic potential difference between the DNA surface and the cell boundary are investigated. The expression of osmotic coefficient is derived from the general formula of grand potential. The osmotic coefficients predicted by the DFT are lower than the PB results and are closer to the simulation results and experimental data.

Associations between soil bacterial community structure and nutrient cycling functions in long-term organic farm soils following cover crop and organic fertilizer amendment.

PubMed

Fernandez, Adria L; Sheaffer, Craig C; Wyse, Donald L; Staley, Christopher; Gould, Trevor J; Sadowsky, Michael J

2016-10-01

Agricultural management practices can produce changes in soil microbial populations whose functions are crucial to crop production and may be detectable using high-throughput sequencing of bacterial 16S rRNA. To apply sequencing-derived bacterial community structure data to on-farm decision-making will require a better understanding of the complex associations between soil microbial community structure and soil function. Here 16S rRNA sequencing was used to profile soil bacterial communities following application of cover crops and organic fertilizer treatments in certified organic field cropping systems. Amendment treatments were hairy vetch (Vicia villosa), winter rye (Secale cereale), oilseed radish (Raphanus sativus), buckwheat (Fagopyrum esculentum), beef manure, pelleted poultry manure, Sustane(®) 8-2-4, and a no-amendment control. Enzyme activities, net N mineralization, soil respiration, and soil physicochemical properties including nutrient levels, organic matter (OM) and pH were measured. Relationships between these functional and physicochemical parameters and soil bacterial community structure were assessed using multivariate methods including redundancy analysis, discriminant analysis, and Bayesian inference. Several cover crops and fertilizers affected soil functions including N-acetyl-β-d-glucosaminidase and β-glucosidase activity. Effects, however, were not consistent across locations and sampling timepoints. Correlations were observed among functional parameters and relative abundances of individual bacterial families and phyla. Bayesian analysis inferred no directional relationships between functional activities, bacterial families, and physicochemical parameters. Soil functional profiles were more strongly predicted by location than by treatment, and differences were largely explained by soil physicochemical parameters. Composition of soil bacterial communities was predictive of soil functional profiles. Differences in soil function were better explained using both soil physicochemical test values and bacterial community structure data than using soil tests alone. Pursuing a better understanding of bacterial community composition and how it is affected by farming practices is a promising avenue for increasing our ability to predict the impact of management practices on important soil functions. Copyright © 2016. Published by Elsevier B.V.

Application of renormalization group theory to the large-eddy simulation of transitional boundary layers

NASA Technical Reports Server (NTRS)

Piomelli, Ugo; Zang, Thomas A.; Speziale, Charles G.; Lund, Thomas S.

1990-01-01

An eddy viscosity model based on the renormalization group theory of Yakhot and Orszag (1986) is applied to the large-eddy simulation of transition in a flat-plate boundary layer. The simulation predicts with satisfactory accuracy the mean velocity and Reynolds stress profiles, as well as the development of the important scales of motion. The evolution of the structures characteristic of the nonlinear stages of transition is also predicted reasonably well.

GlycoPP: A Webserver for Prediction of N- and O-Glycosites in Prokaryotic Protein Sequences

PubMed Central

Chauhan, Jagat S.; Bhat, Adil H.; Raghava, Gajendra P. S.; Rao, Alka

2012-01-01

Glycosylation is one of the most abundant post-translational modifications (PTMs) required for various structure/function modulations of proteins in a living cell. Although elucidated recently in prokaryotes, this type of PTM is present across all three domains of life. In prokaryotes, two types of protein glycan linkages are more widespread namely, N- linked, where a glycan moiety is attached to the amide group of Asn, and O- linked, where a glycan moiety is attached to the hydroxyl group of Ser/Thr/Tyr. For their biologically ubiquitous nature, significance, and technology applications, the study of prokaryotic glycoproteins is a fast emerging area of research. Here we describe new Support Vector Machine (SVM) based algorithms (models) developed for predicting glycosylated-residues (glycosites) with high accuracy in prokaryotic protein sequences. The models are based on binary profile of patterns, composition profile of patterns, and position-specific scoring matrix profile of patterns as training features. The study employ an extensive dataset of 107 N-linked and 116 O-linked glycosites extracted from 59 experimentally characterized glycoproteins of prokaryotes. This dataset includes validated N-glycosites from phyla Crenarchaeota, Euryarchaeota (domain Archaea), Proteobacteria (domain Bacteria) and validated O-glycosites from phyla Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria (domain Bacteria). In view of the current understanding that glycosylation occurs on folded proteins in bacteria, hybrid models have been developed using information on predicted secondary structures and accessible surface area in various combinations with training features. Using these models, N-glycosites and O-glycosites could be predicted with an accuracy of 82.71% (MCC 0.65) and 73.71% (MCC 0.48), respectively. An evaluation of the best performing models with 28 independent prokaryotic glycoproteins confirms the suitability of these models in predicting N- and O-glycosites in potential glycoproteins from aforementioned organisms, with reasonably high confidence. A web server GlycoPP, implementing these models is available freely at http:/www.imtech.res.in/raghava/glycopp/. PMID:22808107

Impact of source collinearity in simulated PM 2.5 data on the PMF receptor model solution

NASA Astrophysics Data System (ADS)

Habre, Rima; Coull, Brent; Koutrakis, Petros

2011-12-01

Positive Matrix Factorization (PMF) is a factor analytic model used to identify particle sources and to estimate their contributions to PM 2.5 concentrations observed at receptor sites. Collinearity in source contributions due to meteorological conditions introduces uncertainty in the PMF solution. We simulated datasets of speciated PM 2.5 concentrations associated with three ambient particle sources: "Motor Vehicle" (MV), "Sodium Chloride" (NaCl), and "Sulfur" (S), and we varied the correlation structure between their mass contributions to simulate collinearity. We analyzed the datasets in PMF using the ME-2 multilinear engine. The Pearson correlation coefficients between the simulated and PMF-predicted source contributions and profiles are denoted by " G correlation" and " F correlation", respectively. In sensitivity analyses, we examined how the means or variances of the source contributions affected the stability of the PMF solution with collinearity. The % errors in predicting the average source contributions were 23, 80 and 23% for MV, NaCl, and S, respectively. On average, the NaCl contribution was overestimated, while MV and S contributions were underestimated. The ability of PMF to predict the contributions and profiles of the three sources deteriorated significantly as collinearity in their contributions increased. When the mean of NaCl or variance of NaCl and MV source contributions was increased, the deterioration in G correlation with increasing collinearity became less significant, and the ability of PMF to predict the NaCl and MV loading profiles improved. When the three factor profiles were simulated to share more elements, the decrease in G and F correlations became non-significant. Our findings agree with previous simulation studies reporting that correlated sources are predicted with higher error and bias. Consequently, the power to detect significant concentration-response estimates in health effect analyses weakens.

A low-complexity add-on score for protein remote homology search with COMER.

PubMed

Margelevicius, Mindaugas

2018-06-15

Protein sequence alignment forms the basis for comparative modeling, the most reliable approach to protein structure prediction, among many other applications. Alignment between sequence families, or profile-profile alignment, represents one of the most, if not the most, sensitive means for homology detection but still necessitates improvement. We aim at improving the quality of profile-profile alignments and the sensitivity induced by them by refining profile-profile substitution scores. We have developed a new score that represents an additional component of profile-profile substitution scores. A comprehensive evaluation shows that the new add-on score statistically significantly improves both the sensitivity and the alignment quality of the COMER method. We discuss why the score leads to the improvement and its almost optimal computational complexity that makes it easily implementable in any profile-profile alignment method. An implementation of the add-on score in the open-source COMER software and data are available at https://sourceforge.net/projects/comer. The COMER software is also available on Github at https://github.com/minmarg/comer and as a Docker image (minmar/comer). Supplementary data are available at Bioinformatics online.

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.

Profile of capillary bridges between two vertically stacked cylindrical fibers under gravitational effect

NASA Astrophysics Data System (ADS)

Sun, Xiaohang; Lee, Hoon Joo; Michielsen, Stephen; Wilusz, Eugene

2018-05-01

Although profiles of axisymmetric capillary bridges between two cylindrical fibers have been extensively studied, little research has been reported on capillary bridges under external forces such as the gravitational force. This is because external forces add significant complications to the Laplace-Young equation, making it difficult to predict drop profiles based on analytical approaches. In this paper, simulations of capillary bridges between two vertically stacked cylindrical fibers with gravitational effect taken into consideration are studied. The asymmetrical structure of capillary bridges that are hard to predict based on analytical approaches was studied via a numerical approach based on Surface Evolver (SE). The axial and the circumferential spreading of liquids on two identical fibers in the presence of gravitational effects are predicted to determine when the gravitational effects are significant or can be neglected. The effect of liquid volume, equilibrium contact angle, the distance between two fibers and fiber radii. The simulation results were verified by comparing them with experimental measurements. Based on SE simulations, curves representing the spreading of capillary bridges along the two cylindrical fibers were obtained. The gravitational effect was scaled based on the difference of the spreading on upper and lower fibers.

Seismic profile analysis of the Kangra and Dehradun re-entrant of NW Himalayan Foreland thrust belt, India: A new approach to delineate subsurface geometry

NASA Astrophysics Data System (ADS)

Dey, Joyjit; Perumal, R. Jayangonda; Sarkar, Subham; Bhowmik, Anamitra

2017-08-01

In the NW Sub-Himalayan frontal thrust belt in India, seismic interpretation of subsurface geometry of the Kangra and Dehradun re-entrant mismatch with the previously proposed models. These procedures lack direct quantitative measurement on the seismic profile required for subsurface structural architecture. Here we use a predictive angular function for establishing quantitative geometric relationships between fault and fold shapes with `Distance-displacement method' (D-d method). It is a prognostic straightforward mechanism to probe the possible structural network from a seismic profile. Two seismic profiles Kangra-2 and Kangra-4 of Kangra re-entrant, Himachal Pradesh (India), are investigated for the fault-related folds associated with the Balh and Paror anticlines. For Paror anticline, the final cut-off angle β =35{°} was obtained by transforming the seismic time profile into depth profile to corroborate the interpreted structures. Also, the estimated shortening along the Jawalamukhi Thrust and Jhor Fault, lying between the Himalayan Frontal Thrust (HFT) and the Main Boundary Thrust (MBT) in the frontal fold-thrust belt, were found to be 6.06 and 0.25 km, respectively. Lastly, the geometric method of fold-fault relationship has been exercised to document the existence of a fault-bend fold above the Himalayan Frontal Thrust (HFT). Measurement of shortening along the fault plane is employed as an ancillary tool to prove the multi-bending geometry of the blind thrust of the Dehradun re-entrant.

Accounting for observed small angle X-ray scattering profile in the protein-protein docking server ClusPro.

PubMed

Xia, Bing; Mamonov, Artem; Leysen, Seppe; Allen, Karen N; Strelkov, Sergei V; Paschalidis, Ioannis Ch; Vajda, Sandor; Kozakov, Dima

2015-07-30

The protein-protein docking server ClusPro is used by thousands of laboratories, and models built by the server have been reported in over 300 publications. Although the structures generated by the docking include near-native ones for many proteins, selecting the best model is difficult due to the uncertainty in scoring. Small angle X-ray scattering (SAXS) is an experimental technique for obtaining low resolution structural information in solution. While not sufficient on its own to uniquely predict complex structures, accounting for SAXS data improves the ranking of models and facilitates the identification of the most accurate structure. Although SAXS profiles are currently available only for a small number of complexes, due to its simplicity the method is becoming increasingly popular. Since combining docking with SAXS experiments will provide a viable strategy for fairly high-throughput determination of protein complex structures, the option of using SAXS restraints is added to the ClusPro server. © 2015 Wiley Periodicals, Inc. © 2015 Wiley Periodicals, Inc.

Numerical modeling of field-assisted ion-exchanged channel waveguides by the explicit consideration of space-charge buildup.

PubMed

Mrozek, Piotr

2011-08-01

A numerical model explicitly considering the space-charge density evolved both under the mask and in the region of optical structure formation was used to predict the profiles of Ag concentration during field-assisted Ag(+)-Na(+) ion exchange channel waveguide fabrication. The influence of the unequal values of diffusion constants and mobilities of incoming and outgoing ions, the value of a correlation factor (Haven ratio), and particularly space-charge density induced during the ion exchange, on the resulting profiles of Ag concentration was analyzed and discussed. It was shown that the incorporation into the numerical model of a small quantity of highly mobile ions other than exclusively Ag(+) and Na(+) may considerably affect the range and shape of calculated Ag profiles in the multicomponent glass. The Poisson equation was used to predict the electric field spread evolution in the glass substrate. The results of the numerical analysis were verified by the experimental data of Ag concentration in a channel waveguide fabricated using a field-assisted process.

Distance matrix-based approach to protein structure prediction.

PubMed

Kloczkowski, Andrzej; Jernigan, Robert L; Wu, Zhijun; Song, Guang; Yang, Lei; Kolinski, Andrzej; Pokarowski, Piotr

2009-03-01

Much structural information is encoded in the internal distances; a distance matrix-based approach can be used to predict protein structure and dynamics, and for structural refinement. Our approach is based on the square distance matrix D = [r(ij)(2)] containing all square distances between residues in proteins. This distance matrix contains more information than the contact matrix C, that has elements of either 0 or 1 depending on whether the distance r (ij) is greater or less than a cutoff value r (cutoff). We have performed spectral decomposition of the distance matrices D = sigma lambda(k)V(k)V(kT), in terms of eigenvalues lambda kappa and the corresponding eigenvectors v kappa and found that it contains at most five nonzero terms. A dominant eigenvector is proportional to r (2)--the square distance of points from the center of mass, with the next three being the principal components of the system of points. By predicting r (2) from the sequence we can approximate a distance matrix of a protein with an expected RMSD value of about 7.3 A, and by combining it with the prediction of the first principal component we can improve this approximation to 4.0 A. We can also explain the role of hydrophobic interactions for the protein structure, because r is highly correlated with the hydrophobic profile of the sequence. Moreover, r is highly correlated with several sequence profiles which are useful in protein structure prediction, such as contact number, the residue-wise contact order (RWCO) or mean square fluctuations (i.e. crystallographic temperature factors). We have also shown that the next three components are related to spatial directionality of the secondary structure elements, and they may be also predicted from the sequence, improving overall structure prediction. We have also shown that the large number of available HIV-1 protease structures provides a remarkable sampling of conformations, which can be viewed as direct structural information about the dynamics. After structure matching, we apply principal component analysis (PCA) to obtain the important apparent motions for both bound and unbound structures. There are significant similarities between the first few key motions and the first few low-frequency normal modes calculated from a static representative structure with an elastic network model (ENM) that is based on the contact matrix C (related to D), strongly suggesting that the variations among the observed structures and the corresponding conformational changes are facilitated by the low-frequency, global motions intrinsic to the structure. Similarities are also found when the approach is applied to an NMR ensemble, as well as to atomic molecular dynamics (MD) trajectories. Thus, a sufficiently large number of experimental structures can directly provide important information about protein dynamics, but ENM can also provide a similar sampling of conformations. Finally, we use distance constraints from databases of known protein structures for structure refinement. We use the distributions of distances of various types in known protein structures to obtain the most probable ranges or the mean-force potentials for the distances. We then impose these constraints on structures to be refined or include the mean-force potentials directly in the energy minimization so that more plausible structural models can be built. This approach has been successfully used by us in 2006 in the CASPR structure refinement (http://predictioncenter.org/caspR).

The potential of high-frequency profiling to assess vertical and seasonal patterns of phytoplankton dynamics in lakes: An extension of the Plankton Ecology Group (PEG) model

USGS Publications Warehouse

Brentrup, Jennifer A.; Williamson, Craig E.; Colom-Montero, William; Eckert, Werner; de Eyto, Elvira; Grossart, Hans-Peter; Huot, Yannick; Isles, Peter D. F.; Knoll, Lesley B.; Leach, Taylor H.; McBride, Christopher G.; Pierson, Don; Pomati, Francesco; Read, Jordan S.; Rose, Kevin C.; Samal, Nihar R.; Staehr, Peter A.; Winslow, Luke A.

2016-01-01

The use of high-frequency sensors on profiling buoys to investigate physical, chemical, and biological processes in lakes is increasing rapidly. Profiling buoys with automated winches and sensors that collect high-frequency chlorophyll fluorescence (ChlF) profiles in 11 lakes in the Global Lake Ecological Observatory Network (GLEON) allowed the study of the vertical and temporal distribution of ChlF, including the formation of subsurface chlorophyll maxima (SSCM). The effectiveness of 3 methods for sampling phytoplankton distributions in lakes, including (1) manual profiles, (2) single-depth buoys, and (3) profiling buoys were assessed. High-frequency ChlF surface data and profiles were compared to predictions from the Plankton Ecology Group (PEG) model. The depth-integrated ChlF dynamics measured by the profiling buoy data revealed a greater complexity that neither conventional sampling nor the generalized PEG model captured. Conventional sampling techniques would have missed SSCM in 7 of 11 study lakes. Although surface-only ChlF data underestimated average water column ChlF, at times by nearly 2-fold in 4 of the lakes, overall there was a remarkable similarity between surface and mean water column data. Contrary to the PEG model’s proposed negligible role for physical control of phytoplankton during the growing season, thermal structure and light availability were closely associated with ChlF seasonal depth distribution. Thus, an extension of the PEG model is proposed, with a new conceptual framework that explicitly includes physical metrics to better predict SSCM formation in lakes and highlight when profiling buoys are especially informative.

Sensitivity of Latent Heating Profiles to Environmental Conditions: Implications for TRMM and Climate Research

NASA Technical Reports Server (NTRS)

Shepherd, J. Marshall; Einaudi, Franco (Technical Monitor)

2000-01-01

The Tropical Rainfall Measuring Mission (TRMM) as a part of NASA's Earth System Enterprise is the first mission dedicated to measuring tropical rainfall through microwave and visible sensors, and includes the first spaceborne rain radar. Tropical rainfall comprises two-thirds of global rainfall. It is also the primary distributor of heat through the atmosphere's circulation. It is this circulation that defines Earth's weather and climate. Understanding rainfall and its variability is crucial to understanding and predicting global climate change. Weather and climate models need an accurate assessment of the latent heating released as tropical rainfall occurs. Currently, cloud model-based algorithms are used to derive latent heating based on rainfall structure. Ultimately, these algorithms can be applied to actual data from TRMM. This study investigates key underlying assumptions used in developing the latent heating algorithms. For example, the standard algorithm is highly dependent on a system's rainfall amount and structure. It also depends on an a priori database of model-derived latent heating profiles based on the aforementioned rainfall characteristics. Unanswered questions remain concerning the sensitivity of latent heating profiles to environmental conditions (both thermodynamic and kinematic), regionality, and seasonality. This study investigates and quantifies such sensitivities and seeks to determine the optimal latent heating profile database based on the results. Ultimately, the study seeks to produce an optimized latent heating algorithm based not only on rainfall structure but also hydrometeor profiles.

Damping profile of standing kink oscillations observed by SDO/AIA

NASA Astrophysics Data System (ADS)

Pascoe, D. J.; Goddard, C. R.; Nisticò, G.; Anfinogentov, S.; Nakariakov, V. M.

2016-01-01

Aims: Strongly damped standing and propagating kink oscillations are observed in the solar corona. This can be understood in terms of mode coupling, which causes the wave energy to be converted from the bulk transverse oscillation to localised, unresolved azimuthal motions. The damping rate can provide information about the loop structure, and theory predicts two possible damping profiles. Methods: We used the recently compiled catalogue of decaying standing kink oscillations of coronal loops to search for examples with high spatial and temporal resolution and sufficient signal quality to allow the damping profile to be examined. The location of the loop axis was tracked, detrended, and fitted with sinusoidal oscillations with Gaussian and exponential damping profiles. Results: Using the highest quality data currently available, we find that for the majority of our cases a Gaussian profile describes the damping behaviour at least as well as an exponential profile, which is consistent with the recently developed theory for the damping profile due to mode coupling.

Schlieren image velocimetry measurements in a rocket engine exhaust plume

NASA Astrophysics Data System (ADS)

Morales, Rudy; Peguero, Julio; Hargather, Michael

2017-11-01

Schlieren image velocimetry (SIV) measures velocity fields by tracking the motion of naturally-occurring turbulent flow features in a compressible flow. Here the technique is applied to measuring the exhaust velocity profile of a liquid rocket engine. The SIV measurements presented include discussion of visibility of structures, image pre-processing for structure visibility, and ability to process resulting images using commercial particle image velocimetry (PIV) codes. The small-scale liquid bipropellant rocket engine operates on nitrous oxide and ethanol as propellants. Predictions of the exhaust velocity are obtained through NASA CEA calculations and simple compressible flow relationships, which are compared against the measured SIV profiles. Analysis of shear layer turbulence along the exhaust plume edge is also presented.

Perspective on computational and structural aspects of kinase discovery from IPK2014.

PubMed

Martin, Eric; Knapp, Stefan; Engh, Richard A; Moebitz, Henrik; Varin, Thibault; Roux, Benoit; Meiler, Jens; Berdini, Valerio; Baumann, Alexander; Vieth, Michal

2015-10-01

Recent advances in understanding the activity and selectivity of kinase inhibitors and their relationships to protein structure are presented. Conformational selection in kinases is studied from empirical, data-driven and simulation approaches. Ligand binding and its affinity are, in many cases, determined by the predetermined active and inactive conformation of kinases. Binding affinity and selectivity predictions highlight the current state of the art and advances in computational chemistry as it applies to kinase inhibitor discovery. Kinome wide inhibitor profiling and cell panel profiling lead to a better understanding of selectivity and allow for target validation and patient tailoring hypotheses. This article is part of a Special Issue entitled: Inhibitors of Protein Kinases. Copyright © 2015 Elsevier B.V. All rights reserved.

In silico models for the prediction of dose-dependent human hepatotoxicity

NASA Astrophysics Data System (ADS)

Cheng, Ailan; Dixon, Steven L.

2003-12-01

The liver is extremely vulnerable to the effects of xenobiotics due to its critical role in metabolism. Drug-induced hepatotoxicity may involve any number of different liver injuries, some of which lead to organ failure and, ultimately, patient death. Understandably, liver toxicity is one of the most important dose-limiting considerations in the drug development cycle, yet there remains a serious shortage of methods to predict hepatotoxicity from chemical structure. We discuss our latest findings in this area and present a new, fully general in silico model which is able to predict the occurrence of dose-dependent human hepatotoxicity with greater than 80% accuracy. Utilizing an ensemble recursive partitioning approach, the model classifies compounds as toxic or non-toxic and provides a confidence level to indicate which predictions are most likely to be correct. Only 2D structural information is required and predictions can be made quite rapidly, so this approach is entirely appropriate for data mining applications and for profiling large synthetic and/or virtual libraries.

Biochemical profiling in silico--predicting substrate specificities of large enzyme families.

PubMed

Tyagi, Sadhna; Pleiss, Juergen

2006-06-25

A general high-throughput method for in silico biochemical profiling of enzyme families has been developed based on covalent docking of potential substrates into the binding sites of target enzymes. The method has been tested by systematically docking transition state--analogous intermediates of 12 substrates into the binding sites of 20 alpha/beta hydrolases from 15 homologous families. To evaluate the effect of side chain orientations to the docking results, 137 crystal structures were included in the analysis. A good substrate must fulfil two criteria: it must bind in a productive geometry with four hydrogen bonds between the substrate and the catalytic histidine and the oxyanion hole, and a high affinity of the enzyme-substrate complex as predicted by a high docking score. The modelling results in general reproduce experimental data on substrate specificity and stereoselectivity: the differences in substrate specificity of cholinesterases toward acetyl- and butyrylcholine, the changes of activity of lipases and esterases upon the size of the acid moieties, activity of lipases and esterases toward tertiary alcohols, and the stereopreference of lipases and esterases toward chiral secondary alcohols. Rigidity of the docking procedure was the major reason for false positive and false negative predictions, as the geometry of the complex and docking score may sensitively depend on the orientation of individual side chains. Therefore, appropriate structures have to be identified. In silico biochemical profiling provides a time efficient and cost saving protocol for virtual screening to identify the potential substrates of the members of large enzyme family from a library of molecules.

Modeling pair distribution functions of rare-earth phosphate glasses using principal component analysis

DOE PAGES

Cole, Jacqueline M.; Cheng, Xie; Payne, Michael C.

2016-10-18

The use of principal component analysis (PCA) to statistically infer features of local structure from experimental pair distribution function (PDF) data is assessed on a case study of rare-earth phosphate glasses (REPGs). Such glasses, co-doped with two rare-earth ions (R and R’) of different sizes and optical properties, are of interest to the laser industry. The determination of structure-property relationships in these materials is an important aspect of their technological development. Yet, realizing the local structure of co-doped REPGs presents significant challenges relative to their singly-doped counterparts; specifically, R and R’ are difficult to distinguish in terms of establishing relativemore » material compositions, identifying atomic pairwise correlation profiles in a PDF that are associated with each ion, and resolving peak overlap of such profiles in PDFs. This study demonstrates that PCA can be employed to help overcome these structural complications, by statistically inferring trends in PDFs that exist for a restricted set of experimental data on REPGs, and using these as training data to predict material compositions and PDF profiles in unknown co-doped REPGs. The application of these PCA methods to resolve individual atomic pairwise correlations in t(r) signatures is also presented. The training methods developed for these structural predictions are pre-validated by testing their ability to reproduce known physical phenomena, such as the lanthanide contraction, on PDF signatures of the structurally simpler singly-doped REPGs. The intrinsic limitations of applying PCA to analyze PDFs relative to the quality control of source data, data processing, and sample definition, are also considered. Furthermore, while this case study is limited to lanthanide-doped REPGs, this type of statistical inference may easily be extended to other inorganic solid-state materials, and be exploited in large-scale data-mining efforts that probe many t(r) functions.« less

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.

Universal Profile of the Vortex Condensate in Two-Dimensional Turbulence

NASA Astrophysics Data System (ADS)

Laurie, Jason; Boffetta, Guido; Falkovich, Gregory; Kolokolov, Igor; Lebedev, Vladimir

2014-12-01

An inverse turbulent cascade in a restricted two-dimensional periodic domain creates a condensate—a pair of coherent system-size vortices. We perform extensive numerical simulations of this system and carry out theoretical analysis based on momentum and energy exchanges between the turbulence and the vortices. We show that the vortices have a universal internal structure independent of the type of small-scale dissipation, small-scale forcing, and boundary conditions. The theory predicts not only the vortex inner region profile, but also the amplitude, which both perfectly agree with the numerical data.

Integration of QUARK and I-TASSER for ab initio protein structure prediction in CASP11

PubMed Central

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

2015-01-01

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

Integration of QUARK and I-TASSER for Ab Initio Protein Structure Prediction in CASP11.

PubMed

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

2016-09-01

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

Interfacial layering and capillary roughness in immiscible liquids.

PubMed

Geysermans, P; Pontikis, V

2010-08-21

The capillary roughness and the atomic density profiles of extended interfaces between immiscible liquids are determined as a function of the interface area by using molecular dynamics and Lennard-Jones (12-6) potentials. We found that with increasing area, the interface roughness diverges logarithmically, thus fitting the theoretical mean-field prediction. In systems small enough for the interfacial roughness not to blur the structural details, atomic density profiles across the fluid interface are layered with correlation length in the range of molecular correlations in liquids. On increasing the system size, the amplitude of the thermally excited position fluctuations of the interface increases, thus causing layering to rapidly vanish, if density profiles are computed without special care. In this work, we present and validate a simple method, operating in the direct space, for extracting from molecular dynamics trajectories the "intrinsic" structure of a fluid interface that is the local density profile of the interface cleaned from capillary wave effects. Estimated values of interfacial properties such as the tension, the intrinsic width, and the lower wavelength limit of position fluctuations are in agreement with results collected from the literature.

Analytical modeling and tolerance analysis of a linear variable filter for spectral order sorting.

PubMed

Ko, Cheng-Hao; Chang, Kuei-Ying; Huang, You-Min

2015-02-23

This paper proposes an innovative method to overcome the low production rate of current linear variable filter (LVF) fabrication. During the fabrication process, a commercial coater is combined with a local mask on a substrate. The proposed analytical thin film thickness model, which is based on the geometry of the commercial coater, is developed to more effectively calculate the profiles of LVFs. Thickness tolerance, LVF zone width, thin film layer structure, transmission spectrum and the effects of variations in critical parameters of the coater are analyzed. Profile measurements demonstrate the efficacy of local mask theory in the prediction of evaporation profiles with a high degree of accuracy.

Compilation of mRNA Polyadenylation Signals in Arabidopsis Revealed a New Signal Element and Potential Secondary Structures1[w

PubMed Central

Loke, Johnny C.; Stahlberg, Eric A.; Strenski, David G.; Haas, Brian J.; Wood, Paul Chris; Li, Qingshun Quinn

2005-01-01

Using a novel program, SignalSleuth, and a database containing authenticated polyadenylation [poly(A)] sites, we analyzed the composition of mRNA poly(A) signals in Arabidopsis (Arabidopsis thaliana), and reevaluated previously described cis-elements within the 3′-untranslated (UTR) regions, including near upstream elements and far upstream elements. As predicted, there are absences of high-consensus signal patterns. The AAUAAA signal topped the near upstream elements patterns and was found within the predicted location to only approximately 10% of 3′-UTRs. More importantly, we identified a new set, named cleavage elements, of poly(A) signals flanking both sides of the cleavage site. These cis-elements were not previously revealed by conventional mutagenesis and are contemplated as a cluster of signals for cleavage site recognition. Moreover, a single-nucleotide profile scan on the 3′-UTR regions unveiled a distinct arrangement of alternate stretches of U and A nucleotides, which led to a prediction of the formation of secondary structures. Using an RNA secondary structure prediction program, mFold, we identified three main types of secondary structures on the sequences analyzed. Surprisingly, these observed secondary structures were all interrupted in previously constructed mutations in these regions. These results will enable us to revise the current model of plant poly(A) signals and to develop tools to predict 3′-ends for gene annotation. PMID:15965016

Injury profile SIMulator, a Qualitative aggregative modelling framework to predict injury profile as a function of cropping practices, and abiotic and biotic environment. II. Proof of concept: design of IPSIM-wheat-eyespot.

PubMed

Robin, Marie-Hélène; Colbach, Nathalie; Lucas, Philippe; Montfort, Françoise; Cholez, Célia; Debaeke, Philippe; Aubertot, Jean-Noël

2013-01-01

IPSIM (Injury Profile SIMulator) is a generic modelling framework presented in a companion paper. It aims at predicting a crop injury profile as a function of cropping practices and abiotic and biotic environment. IPSIM's modelling approach consists of designing a model with an aggregative hierarchical tree of attributes. In order to provide a proof of concept, a model, named IPSIM-Wheat-Eyespot, has been developed with the software DEXi according to the conceptual framework of IPSIM to represent final incidence of eyespot on wheat. This paper briefly presents the pathosystem, the method used to develop IPSIM-Wheat-Eyespot using IPSIM's modelling framework, simulation examples, an evaluation of the predictive quality of the model with a large dataset (526 observed site-years) and a discussion on the benefits and limitations of the approach. IPSIM-Wheat-Eyespot proved to successfully represent the annual variability of the disease, as well as the effects of cropping practices (Efficiency = 0.51, Root Mean Square Error of Prediction = 24%; bias = 5.0%). IPSIM-Wheat-Eyespot does not aim to precisely predict the incidence of eyespot on wheat. It rather aims to rank cropping systems with regard to the risk of eyespot on wheat in a given production situation through ex ante evaluations. IPSIM-Wheat-Eyespot can also help perform diagnoses of commercial fields. Its structure is simple and permits to combine available knowledge in the scientific literature (data, models) and expertise. IPSIM-Wheat-Eyespot is now available to help design cropping systems with a low risk of eyespot on wheat in a wide range of production situations, and can help perform diagnoses of commercial fields. In addition, it provides a proof of concept with regard to the modelling approach of IPSIM. IPSIM-Wheat-Eyespot will be a sub-model of IPSIM-Wheat, a model that will predict injury profile on wheat as a function of cropping practices and the production situation.

Electrical resistivity structure of the Great Slave Lake shear zone, northwest Canada: implications for tectonic history

NASA Astrophysics Data System (ADS)

Yin, Yaotian; Unsworth, Martyn; Liddell, Mitch; Pana, Dinu; Craven, James A.

2014-10-01

Three magnetotelluric (MT) profiles in northwestern Canada cross the central and western segments of Great Slave Lake shear zone (GSLsz), a continental scale strike-slip structure active during the Slave-Rae collision in the Proterozoic. Dimensionality analysis indicates that (i) the resistivity structure is approximately 2-D with a geoelectric strike direction close to the dominant geological strike of N45°E and that (ii) electrical anisotropy may be present in the crust beneath the two southernmost profiles. Isotropic and anisotropic 2-D inversion and isotropic 3-D inversions show different resistivity structures on different segments of the shear zone. The GSLsz is imaged as a high resistivity zone (>5000 Ω m) that is at least 20 km wide and extends to a depth of at least 50 km on the northern profile. On the southern two profiles, the resistive zone is confined to the upper crust and pierces an east-dipping crustal conductor. Inversions show that this dipping conductor may be anisotropic, likely caused by conductive materials filling a network of fractures with a preferred spatial orientation. These conductive regions would have been disrupted by strike-slip, ductile deformation on the GSLsz that formed granulite to greenschist facies mylonite belts. The pre-dominantly granulite facies mylonites are resistive and explain why the GSLsz appears as a resistive structure piercing the east-dipping anisotropic layer. The absence of a dipping anisotropic/conductive layer on the northern MT profile, located on the central segment of the GSLsz, is consistent with the lack of subduction at this location as predicted by geological and tectonic models.

The effects of profiles on supersonic jet noise

NASA Technical Reports Server (NTRS)

Tiwari, S. N.; Bhat, T. R. S.

1994-01-01

The effect of velocity profiles on supersonic jet noise are studied by using stability calculations made for a shock-free coannular jet, with both the inner and outer flows supersonic. The Mach wave emission process is modeled as the noise generated by the large scale turbulent structures or the instability waves in the mixing region. Both the vortex-sheet and the realistic finite thickness shear layer models are considered. The stability calculations were performed for both inverted and normal velocity profiles. Comparisons are made with the results for an equivalent single jet, based on equal thrust, mass flow rate and exit area to that of the coannular jet. The advantages and disadvantages of these velocity profiles as far as noise radiation is concerned are discussed. It is shown that the Rayleigh's model prediction of the merits and demerits of different velocity profiles are in good agreement with the experimental data.

Mapping the pharmaceutical design space by amorphous ionic liquid strategies.

PubMed

Wiest, Johannes; Saedtler, Marco; Balk, Anja; Merget, Benjamin; Widmer, Toni; Bruhn, Heike; Raccuglia, Marc; Walid, Elbast; Picard, Franck; Stopper, Helga; Dekant, Wolfgang; Lühmann, Tessa; Sotriffer, Christoph; Galli, Bruno; Holzgrabe, Ulrike; Meinel, Lorenz

2017-12-28

Poor water solubility of drugs fuels complex formulations and jeopardizes patient access to medication. Simplifying these complexities we systematically synthesized a library of 36 sterically demanding counterions and mapped the pharmaceutical design space for amorphous ionic liquid strategies for Selurampanel, a poorly water soluble drug used against migraine. Patients would benefit from a rapid uptake after oral administration to alleviate migraine symptoms. Therefore, we probed the ionic liquids for the flux, supersaturation period and hygroscopicity leading to algorithms linking molecular counterion descriptors to predicted pharmaceutical outcome. By that, 30- or 800-fold improvements of the supersaturation period and fluxes were achieved as were immediate to sustained release profiles through structural counterions' optimization compared to the crystalline free acid of Selurampanel. Guided by ionic liquid structure, in vivo profiles ranged from rapid bioavailability and high maximal plasma concentrations to sustained patterns. In conclusion, the study outlined and predicted the accessible pharmaceutical design space of amorphous ionic liquid based and excipient-free formulations pointing to the enormous pharmaceutical potential of ionic liquid designs. Copyright © 2017 Elsevier B.V. All rights reserved.

Mid-infrared spectral characteristics of lipid molecular structures in Brassica carinata seeds: relationship to oil content, fatty acid and glucosinolate profiles, polyphenols, and condensed tannins.

PubMed

Xin, Hangshu; Khan, Nazir A; Falk, Kevin C; Yu, Peiqiang

2014-08-13

The objectives of this study were to quantify lipid-related inherent molecular structures using a Fourier transform infrared spectroscopy (FT-IR) technique and determine their relationship to oil content, fatty acid and glucosinolate profile, total polyphenols, and condensed tannins in seeds from newly developed yellow-seeded and brown-seeded Brassica carinata lines. Canola seeds were used as a reference. The lipid-related molecular spectral band intensities were strongly correlated to the contents of oil, fatty acids, glucosinolates, and polyphenols. The regression equations gave relatively high predictive power for the estimation of oil (R² = 0.99); all measured fatty acids (R² > 0.80), except C14:0, C20:3n-3, C22:2n-9, and C22:2n-6; 3-butenyl, 2-OH-3-butenyl, 4-OH-3-CH3-indolyl, and total glucosinolates (R² > 0.686); and total polyphenols (R² = 0.935). However, further study is required to obtain predictive equations based on large numbers of samples from diverse sources to illustrate the general applicability of these regression equations.

Human intestinal transporter database: QSAR modeling and virtual profiling of drug uptake, efflux and interactions.

PubMed

Sedykh, Alexander; Fourches, Denis; Duan, Jianmin; Hucke, Oliver; Garneau, Michel; Zhu, Hao; Bonneau, Pierre; Tropsha, Alexander

2013-04-01

Membrane transporters mediate many biological effects of chemicals and play a major role in pharmacokinetics and drug resistance. The selection of viable drug candidates among biologically active compounds requires the assessment of their transporter interaction profiles. Using public sources, we have assembled and curated the largest, to our knowledge, human intestinal transporter database (>5,000 interaction entries for >3,700 molecules). This data was used to develop thoroughly validated classification Quantitative Structure-Activity Relationship (QSAR) models of transport and/or inhibition of several major transporters including MDR1, BCRP, MRP1-4, PEPT1, ASBT, OATP2B1, OCT1, and MCT1. QSAR models have been developed with advanced machine learning techniques such as Support Vector Machines, Random Forest, and k Nearest Neighbors using Dragon and MOE chemical descriptors. These models afforded high external prediction accuracies of 71-100% estimated by 5-fold external validation, and showed hit retrieval rates with up to 20-fold enrichment in the virtual screening of DrugBank compounds. The compendium of predictive QSAR models developed in this study can be used for virtual profiling of drug candidates and/or environmental agents with the optimal transporter profiles.

Predicting fiber refractive index from a measured preform index profile

NASA Astrophysics Data System (ADS)

Kiiveri, P.; Koponen, J.; Harra, J.; Novotny, S.; Husu, H.; Ihalainen, H.; Kokki, T.; Aallos, V.; Kimmelma, O.; Paul, J.

2018-02-01

When producing fiber lasers and amplifiers, silica glass compositions consisting of three to six different materials are needed. Due to the varying needs of different applications, substantial number of different glass compositions are used in the active fiber structures. Often it is not possible to find material parameters for theoretical models to estimate thermal and mechanical properties of those glass compositions. This makes it challenging to predict accurately fiber core refractive index values, even if the preform index profile is measured. Usually the desired fiber refractive index value is achieved experimentally, which is expensive. To overcome this problem, we analyzed statistically the changes between the measured preform and fiber index values. We searched for correlations that would help to predict the Δn-value change from preform to fiber in a situation where we don't know the values of the glass material parameters that define the change. Our index change models were built using the data collected from preforms and fibers made by the Direct Nanoparticle Deposition (DND) technology.

The Reliability and Predictive Validity of the Stalking Risk Profile.

PubMed

McEwan, Troy E; Shea, Daniel E; Daffern, Michael; MacKenzie, Rachel D; Ogloff, James R P; Mullen, Paul E

2018-03-01

This study assessed the reliability and validity of the Stalking Risk Profile (SRP), a structured measure for assessing stalking risks. The SRP was administered at the point of assessment or retrospectively from file review for 241 adult stalkers (91% male) referred to a community-based forensic mental health service. Interrater reliability was high for stalker type, and moderate-to-substantial for risk judgments and domain scores. Evidence for predictive validity and discrimination between stalking recidivists and nonrecidivists for risk judgments depended on follow-up duration. Discrimination was moderate (area under the curve = 0.66-0.68) and positive and negative predictive values good over the full follow-up period ( Mdn = 170.43 weeks). At 6 months, discrimination was better than chance only for judgments related to stalking of new victims (area under the curve = 0.75); however, high-risk stalkers still reoffended against their original victim(s) 2 to 4 times as often as low-risk stalkers. Implications for the clinical utility and refinement of the SRP are discussed.

Investigation of charge weight and shock factor effect on non-linear transient structural response of rectangular plates subjected to underwater explosion (UNDEX) shock loading

NASA Astrophysics Data System (ADS)

Demir, Ozgur; Sahin, Abdurrahman; Yilmaz, Tamer

2012-09-01

Underwater explosion induced shock loads are capable of causing considerable structural damage. Investigations of the underwater explosion (UNDEX) effects on structures have seen continuous developments because of security risks. Most of the earlier experimental investigations were performed by military since the World War I. Subsequently; Cole [1] established mathematical relations for modeling underwater explosion shock loading, which were the outcome of many experimental investigations This study predicts and establishes the transient responses of a panel structure to underwater explosion shock loads using non-linear finite element code Ls-Dyna. Accordingly, in this study a new MATLAB code has been developed for predicting shock loading profile for different weight of explosive and different shock factors. Numerical analysis was performed for various test conditions and results are compared with Ramajeyathilagam's experimental study [8].

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.

Classification of Chemicals Based On Structured Toxicity ...

EPA Pesticide Factsheets

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

DIMA 3.0: Domain Interaction Map.

PubMed

Luo, Qibin; Pagel, Philipp; Vilne, Baiba; Frishman, Dmitrij

2011-01-01

Domain Interaction MAp (DIMA, available at http://webclu.bio.wzw.tum.de/dima) is a database of predicted and known interactions between protein domains. It integrates 5807 structurally known interactions imported from the iPfam and 3did databases and 46,900 domain interactions predicted by four computational methods: domain phylogenetic profiling, domain pair exclusion algorithm correlated mutations and domain interaction prediction in a discriminative way. Additionally predictions are filtered to exclude those domain pairs that are reported as non-interacting by the Negatome database. The DIMA Web site allows to calculate domain interaction networks either for a domain of interest or for entire organisms, and to explore them interactively using the Flash-based Cytoscape Web software.

Investigation of the plasma shaping effects on the H-mode pedestal structure using coupled kinetic neoclassical/MHD stability simulations

NASA Astrophysics Data System (ADS)

Pankin, A. Y.; Rafiq, T.; Kritz, A. H.; Park, G. Y.; Snyder, P. B.; Chang, C. S.

2017-06-01

The effects of plasma shaping on the H-mode pedestal structure are investigated. High fidelity kinetic simulations of the neoclassical pedestal dynamics are combined with the magnetohydrodynamic (MHD) stability conditions for triggering edge localized mode (ELM) instabilities that limit the pedestal width and height in H-mode plasmas. The neoclassical kinetic XGC0 code [Chang et al., Phys. Plasmas 11, 2649 (2004)] is used in carrying out a scan over plasma elongation and triangularity. As plasma profiles evolve, the MHD stability limits of these profiles are analyzed with the ideal MHD ELITE code [Snyder et al., Phys. Plasmas 9, 2037 (2002)]. Simulations with the XGC0 code, which includes coupled ion-electron dynamics, yield predictions for both ion and electron pedestal profiles. The differences in the predicted H-mode pedestal width and height for the DIII-D discharges with different elongation and triangularities are discussed. For the discharges with higher elongation, it is found that the gradients of the plasma profiles in the H-mode pedestal reach semi-steady states. In these simulations, the pedestal slowly continues to evolve to higher pedestal pressures and bootstrap currents until the peeling-ballooning stability conditions are satisfied. The discharges with lower elongation do not reach the semi-steady state, and ELM crashes are triggered at earlier times. The plasma elongation is found to have a stronger stabilizing effect than the plasma triangularity. For the discharges with lower elongation and lower triangularity, the ELM frequency is large, and the H-mode pedestal evolves rapidly. It is found that the temperature of neutrals in the scrape-off-layer (SOL) region can affect the dynamics of the H-mode pedestal buildup. However, the final pedestal profiles are nearly independent of the neutral temperature. The elongation and triangularity affect the pedestal widths of plasma density and electron temperature profiles differently. This provides a new mechanism of controlling the pedestal bootstrap current and the pedestal stability.

Investigation of the plasma shaping effects on the H-mode pedestal structure using coupled kinetic neoclassical/MHD stability simulations

DOE PAGES

Pankin, A. Y.; Rafiq, T.; Kritz, A. H.; ...

2017-06-08

The effects of plasma shaping on the H-mode pedestal structure are investigated. High fidelity kinetic simulations of the neoclassical pedestal dynamics are combined with the magnetohydrodynamic (MHD) stability conditions for triggering edge localized mode (ELM) instabilities that limit the pedestal width and height in H-mode plasmas. We use the neoclassical kinetic XGC0 code [Chang et al., Phys. Plasmas 11, 2649 (2004)] to carry out a scan over plasma elongation and triangularity. As plasma profiles evolve, the MHD stability limits of these profiles are analyzed with the ideal MHD ELITE code [Snyder et al., Phys. Plasmas 9, 2037 (2002)]. In simulationsmore » with the XGC0 code, which includes coupled ion-electron dynamics, yield predictions for both ion and electron pedestal profiles. The differences in the predicted H-mode pedestal width and height for the DIII-D discharges with different elongation and triangularities are discussed. For the discharges with higher elongation, it is found that the gradients of the plasma profiles in the H-mode pedestal reach semi-steady states. In these simulations, the pedestal slowly continues to evolve to higher pedestal pressures and bootstrap currents until the peeling-ballooning stability conditions are satisfied. The discharges with lower elongation do not reach the semi-steady state, and ELM crashes are triggered at earlier times. The plasma elongation is found to have a stronger stabilizing effect than the plasma triangularity. For the discharges with lower elongation and lower triangularity, the ELM frequency is large, and the H-mode pedestal evolves rapidly. It is found that the temperature of neutrals in the scrape-off-layer (SOL) region can affect the dynamics of the H-mode pedestal buildup. But the final pedestal profiles are nearly independent of the neutral temperature. The elongation and triangularity affect the pedestal widths of plasma density and electron temperature profiles differently. This provides a new mechanism of controlling the pedestal bootstrap current and the pedestal stability.« less

Investigation of the plasma shaping effects on the H-mode pedestal structure using coupled kinetic neoclassical/MHD stability simulations

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

Pankin, A. Y.; Rafiq, T.; Kritz, A. H.

The effects of plasma shaping on the H-mode pedestal structure are investigated. High fidelity kinetic simulations of the neoclassical pedestal dynamics are combined with the magnetohydrodynamic (MHD) stability conditions for triggering edge localized mode (ELM) instabilities that limit the pedestal width and height in H-mode plasmas. We use the neoclassical kinetic XGC0 code [Chang et al., Phys. Plasmas 11, 2649 (2004)] to carry out a scan over plasma elongation and triangularity. As plasma profiles evolve, the MHD stability limits of these profiles are analyzed with the ideal MHD ELITE code [Snyder et al., Phys. Plasmas 9, 2037 (2002)]. In simulationsmore » with the XGC0 code, which includes coupled ion-electron dynamics, yield predictions for both ion and electron pedestal profiles. The differences in the predicted H-mode pedestal width and height for the DIII-D discharges with different elongation and triangularities are discussed. For the discharges with higher elongation, it is found that the gradients of the plasma profiles in the H-mode pedestal reach semi-steady states. In these simulations, the pedestal slowly continues to evolve to higher pedestal pressures and bootstrap currents until the peeling-ballooning stability conditions are satisfied. The discharges with lower elongation do not reach the semi-steady state, and ELM crashes are triggered at earlier times. The plasma elongation is found to have a stronger stabilizing effect than the plasma triangularity. For the discharges with lower elongation and lower triangularity, the ELM frequency is large, and the H-mode pedestal evolves rapidly. It is found that the temperature of neutrals in the scrape-off-layer (SOL) region can affect the dynamics of the H-mode pedestal buildup. But the final pedestal profiles are nearly independent of the neutral temperature. The elongation and triangularity affect the pedestal widths of plasma density and electron temperature profiles differently. This provides a new mechanism of controlling the pedestal bootstrap current and the pedestal stability.« less

Modelling the structure of Zr-rich Pb(Zr1-xTix)O3, x = 0.4 by a multiphase approach.

PubMed

Bogdanov, Alexander; Mysovsky, Andrey; Pickard, Chris J; Kimmel, Anna V

2016-10-12

Solid solution perovskite Pb(Zr 1-x Ti x )O 3 (PZT) is an industrially important material. Despite the long history of experimental and theoretical studies, the structure of this material is still under intensive discussion. In this work, we have applied structure searching coupled with density functional theory methods to provide a multiphase description of this material at x = 0.4. We demonstrate that the permutational freedom of B-site cations leads to the stabilisation of a variety of local phases reflecting a relatively flat energy landscape of PZT. Using a set of predicted local phases we reproduce the experimental pair distribution function (PDF) profile with high accuracy. We introduce a complex multiphase picture of the structure of PZT and show that additional monoclinic and rhombohedral phases account for a better description of the experimental PDF profile. We propose that such a multiphase picture reflects the entropy reached in the sample during the preparation process.

Atmospheric studies from the Mars Science Laboratory Entry, Descent and Landing atmospheric structure reconstruction

NASA Astrophysics Data System (ADS)

Holstein-Rathlou, C.; Maue, A.; Withers, P.

2016-01-01

The Mars Science Laboratory (MSL) entered the martian atmosphere on Aug. 6, 2012 landing in Gale crater (4.6°S, 137.4°E) in the local mid-afternoon. Aerodynamic accelerations were measured during descent and atmospheric density, pressure and temperature profiles have been calculated from this data. Using an averaging technique developed for the NASA Phoenix Mars mission, the profiles are extended to 134.1 km, twice that of the engineering reconstruction. Large-scale temperature oscillations in the MSL temperature profile are suggestive of thermal tides. Comparing the MSL temperature profile with measured Mars Climate Sounder temperature profiles and Mars Climate Database model output highlights the presence of diurnal tides. Derived vertical wavelengths for the diurnal migrating tide are larger than predicted from idealized tidal theory, indicating an added presence of nonmigrating diurnal tides. Sub-CO2 condensation mesospheric temperatures, very similar to the Pathfinder temperature profile, allude to the possibility of CO2 clouds. This is however not supported by recent observations and models.

MOSAIC: a chemical-genetic interaction data repository and web resource for exploring chemical modes of action.

PubMed

Nelson, Justin; Simpkins, Scott W; Safizadeh, Hamid; Li, Sheena C; Piotrowski, Jeff S; Hirano, Hiroyuki; Yashiroda, Yoko; Osada, Hiroyuki; Yoshida, Minoru; Boone, Charles; Myers, Chad L

2018-04-01

Chemical-genomic approaches that map interactions between small molecules and genetic perturbations offer a promising strategy for functional annotation of uncharacterized bioactive compounds. We recently developed a new high-throughput platform for mapping chemical-genetic (CG) interactions in yeast that can be scaled to screen large compound collections, and we applied this system to generate CG interaction profiles for more than 13 000 compounds. When integrated with the existing global yeast genetic interaction network, CG interaction profiles can enable mode-of-action prediction for previously uncharacterized compounds as well as discover unexpected secondary effects for known drugs. To facilitate future analysis of these valuable data, we developed a public database and web interface named MOSAIC. The website provides a convenient interface for querying compounds, bioprocesses (Gene Ontology terms) and genes for CG information including direct CG interactions, bioprocesses and gene-level target predictions. MOSAIC also provides access to chemical structure information of screened molecules, chemical-genomic profiles and the ability to search for compounds sharing structural and functional similarity. This resource will be of interest to chemical biologists for discovering new small molecule probes with specific modes-of-action as well as computational biologists interested in analysing CG interaction networks. MOSAIC is available at http://mosaic.cs.umn.edu. hisyo@riken.jp, yoshidam@riken.jp, charlie.boone@utoronto.ca or chadm@umn.edu. Supplementary data are available at Bioinformatics online.

Proteome-level interplay between folding and aggregation propensities of proteins.

PubMed

Tartaglia, Gian Gaetano; Vendruscolo, Michele

2010-10-08

With the advent of proteomics, there is an increasing need of tools for predicting the properties of large numbers of proteins by using the information provided by their amino acid sequences, even in the absence of the knowledge of their structures. One of the most important types of predictions concerns whether proteins will fold or aggregate. Here, we study the competition between these two processes by analyzing the relationship between the folding and aggregation propensity profiles for the human and Escherichia coli proteomes. These profiles are calculated, respectively, using the CamFold method, which we introduce in this work, and the Zyggregator method. Our results indicate that the kinetic behavior of proteins is, to a large extent, determined by the interplay between regions of low folding and high aggregation propensities. Copyright © 2010. Published by Elsevier Ltd.

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences.

PubMed

Mizianty, Marcin J; Kurgan, Lukasz

2009-12-13

Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at http://biomine.ece.ualberta.ca/MODAS/.

Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

PubMed Central

2009-01-01

Background Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. Results The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop Support Vector Machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. Conclusions The improved predictions stem from the novel features that express collocation of the secondary structure segments in the protein sequence and that combine evolutionary and secondary structure information. Our work demonstrates that conservation and arrangement of the secondary structure segments predicted along the protein chain can successfully predict structural classes which are defined based on the spatial arrangement of the secondary structures. A web server is available at http://biomine.ece.ualberta.ca/MODAS/. PMID:20003388

Study of neoclassical effects on the pedestal structure in ELMy H-mode plasmas

NASA Astrophysics Data System (ADS)

Pankin, A. Y.; Bateman, G.; Kritz, A. H.; Rafiq, T.; Park, G. Y.; Ku, S.; Chang, C. S.; Snyder, P. B.

2009-11-01

The neoclassical effects on the H-mode pedestal structure are investigated in this study. First principles' kinetic simulations of the neoclassical pedestal dynamics are combined with the MHD stability conditions for triggering ELM crashes that limit the pedestal width and height in H-mode plasmas. The neoclassical kinetic XGC0 code [1] is used to produce systematic scans over plasma parameters including plasma current, elongation, and triangularity. As plasma profiles evolve, the MHD stability limits of these profiles are analyzed with the ideal MHD stability ELITE code [2]. The scalings of the pedestal width and height are presented as a function of the scanned plasma parameters. Simulations with the XGC0 code, which include coupled ion-electron dynamics, yield predictions for both ion and electron pedestal profiles. Differences in the electron and ion pedestal scalings are investigated. [1] C.S. Chang et al, Phys. Plasmas 11 (2004) 2649. [2] P.B. Snyder et al, Phys. Plasmas, 9 (2002) 2037.

Spatial Evolution of the Thickness Variations over a CFRP Laminated Structure

NASA Astrophysics Data System (ADS)

Davila, Yves; Crouzeix, Laurent; Douchin, Bernard; Collombet, Francis; Grunevald, Yves-Henri

2017-10-01

Ply thickness is one of the main drivers of the structural performance of a composite part. For stress analysis calculations (e.g., finite element analysis), composite plies are commonly considered to have a constant thickness compared to the reality (coefficients of variation up to 9% of the mean ply thickness). Unless this variability is taken into account reliable property predictions cannot be made. A modelling approach of such variations is proposed using parameters obtained from a 16-ply quasi-isotropic CFRP plate cured in an autoclave. A discrete Fourier transform algorithm is used to analyse the frequency response of the observed ply and plate thickness profiles. The model inputs, obtained by a mathematical representation of the ply thickness profiles, permit the generation of a representative stratification considering the spatial continuity of the thickness variations that are in good agreement with the real ply profiles spread over the composite part. A residual deformation FE model of the composite plate is used to illustrate the feasibility of the approach.

Fast Geometric Consensus Approach for Protein Model Quality Assessment

PubMed Central

Adamczak, Rafal; Pillardy, Jaroslaw; Vallat, Brinda K.

2011-01-01

Abstract Model quality assessment (MQA) is an integral part of protein structure prediction methods that typically generate multiple candidate models. The challenge lies in ranking and selecting the best models using a variety of physical, knowledge-based, and geometric consensus (GC)-based scoring functions. In particular, 3D-Jury and related GC methods assume that well-predicted (sub-)structures are more likely to occur frequently in a population of candidate models, compared to incorrectly folded fragments. While this approach is very successful in the context of diversified sets of models, identifying similar substructures is computationally expensive since all pairs of models need to be superimposed using MaxSub or related heuristics for structure-to-structure alignment. Here, we consider a fast alternative, in which structural similarity is assessed using 1D profiles, e.g., consisting of relative solvent accessibilities and secondary structures of equivalent amino acid residues in the respective models. We show that the new approach, dubbed 1D-Jury, allows to implicitly compare and rank N models in O(N) time, as opposed to quadratic complexity of 3D-Jury and related clustering-based methods. In addition, 1D-Jury avoids computationally expensive 3D superposition of pairs of models. At the same time, structural similarity scores based on 1D profiles are shown to correlate strongly with those obtained using MaxSub. In terms of the ability to select the best models as top candidates 1D-Jury performs on par with other GC methods. Other potential applications of the new approach, including fast clustering of large numbers of intermediate structures generated by folding simulations, are discussed as well. PMID:21244273

Protein Folding and Structure Prediction from the Ground Up: The Atomistic Associative Memory, Water Mediated, Structure and Energy Model.

PubMed

Chen, Mingchen; Lin, Xingcheng; Zheng, Weihua; Onuchic, José N; Wolynes, Peter G

2016-08-25

The associative memory, water mediated, structure and energy model (AWSEM) is a coarse-grained force field with transferable tertiary interactions that incorporates local in sequence energetic biases using bioinformatically derived structural information about peptide fragments with locally similar sequences that we call memories. The memory information from the protein data bank (PDB) database guides proper protein folding. The structural information about available sequences in the database varies in quality and can sometimes lead to frustrated free energy landscapes locally. One way out of this difficulty is to construct the input fragment memory information from all-atom simulations of portions of the complete polypeptide chain. In this paper, we investigate this approach first put forward by Kwac and Wolynes in a more complete way by studying the structure prediction capabilities of this approach for six α-helical proteins. This scheme which we call the atomistic associative memory, water mediated, structure and energy model (AAWSEM) amounts to an ab initio protein structure prediction method that starts from the ground up without using bioinformatic input. The free energy profiles from AAWSEM show that atomistic fragment memories are sufficient to guide the correct folding when tertiary forces are included. AAWSEM combines the efficiency of coarse-grained simulations on the full protein level with the local structural accuracy achievable from all-atom simulations of only parts of a large protein. The results suggest that a hybrid use of atomistic fragment memory and database memory in structural predictions may well be optimal for many practical applications.

SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines

PubMed Central

2014-01-01

Background It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. Results We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. Conclusion SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:24776231

SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines.

PubMed

Cao, Renzhi; Wang, Zheng; Wang, Yiheng; Cheng, Jianlin

2014-04-28

It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/.

Evaluation of the Community Multiscale Air Quality Model for Simulating Winter Ozone Formation in the Uinta Basin

NASA Astrophysics Data System (ADS)

Matichuk, Rebecca; Tonnesen, Gail; Luecken, Deborah; Gilliam, Rob; Napelenok, Sergey L.; Baker, Kirk R.; Schwede, Donna; Murphy, Ben; Helmig, Detlev; Lyman, Seth N.; Roselle, Shawn

2017-12-01

The Weather Research and Forecasting (WRF) and Community Multiscale Air Quality (CMAQ) models were used to simulate a 10 day high-ozone episode observed during the 2013 Uinta Basin Winter Ozone Study (UBWOS). The baseline model had a large negative bias when compared to ozone (O3) and volatile organic compound (VOC) measurements across the basin. Contrary to other wintertime Uinta Basin studies, predicted nitrogen oxides (NOx) were typically low compared to measurements. Increases to oil and gas VOC emissions resulted in O3 predictions closer to observations, and nighttime O3 improved when reducing the deposition velocity for all chemical species. Vertical structures of these pollutants were similar to observations on multiple days. However, the predicted surface layer VOC mixing ratios were generally found to be underestimated during the day and overestimated at night. While temperature profiles compared well to observations, WRF was found to have a warm temperature bias and too low nighttime mixing heights. Analyses of more realistic snow heat capacity in WRF to account for the warm bias and vertical mixing resulted in improved temperature profiles, although the improved temperature profiles seldom resulted in improved O3 profiles. While additional work is needed to investigate meteorological impacts, results suggest that the uncertainty in the oil and gas emissions contributes more to the underestimation of O3. Further, model adjustments based on a single site may not be suitable across all sites within the basin.

Distribution and prediction of catalytic domains in 2-oxoglutarate dependent dioxygenases

PubMed Central

2012-01-01

Background The 2-oxoglutarate dependent superfamily is a diverse group of non-haem dioxygenases, and is present in prokaryotes, eukaryotes, and archaea. The enzymes differ in substrate preference and reaction chemistry, a factor that precludes their classification by homology studies and electronic annotation schemes alone. In this work, I propose and explore the rationale of using substrates to classify structurally similar alpha-ketoglutarate dependent enzymes. Findings Differential catalysis in phylogenetic clades of 2-OG dependent enzymes, is determined by the interactions of a subset of active-site amino acids. Identifying these with existing computational methods is challenging and not feasible for all proteins. A clustering protocol based on validated mechanisms of catalysis of known molecules, in tandem with group specific hidden markov model profiles is able to differentiate and sequester these enzymes. Access to this repository is by a web server that compares user defined unknown sequences to these pre-defined profiles and outputs a list of predicted catalytic domains. The server is free and is accessible at the following URL ( http://comp-biol.theacms.in/H2OGpred.html). Conclusions The proposed stratification is a novel attempt at classifying and predicting 2-oxoglutarate dependent function. In addition, the server will provide researchers with a tool to compare their data to a comprehensive list of HMM profiles of catalytic domains. This work, will aid efforts by investigators to screen and characterize putative 2-OG dependent sequences. The profile database will be updated at regular intervals. PMID:22862831

OCT structure, COB location and magmatic type of the S Angolan & SE Brazilian margins from integrated quantitative analysis of deep seismic reflection and gravity anomaly data

NASA Astrophysics Data System (ADS)

Cowie, Leanne; Kusznir, Nick; Horn, Brian

2014-05-01

Integrated quantitative analysis using deep seismic reflection data and gravity inversion have been applied to the S Angolan and SE Brazilian margins to determine OCT structure, COB location and magmatic type. Knowledge of these margin parameters are of critical importance for understanding rifted continental margin formation processes and in evaluating petroleum systems in deep-water frontier oil and gas exploration. The OCT structure, COB location and magmatic type of the S Angolan and SE Brazilian rifted continental margins are much debated; exhumed and serpentinised mantle have been reported at these margins. Gravity anomaly inversion, incorporating a lithosphere thermal gravity anomaly correction, has been used to determine Moho depth, crustal basement thickness and continental lithosphere thinning. Residual Depth Anomaly (RDA) analysis has been used to investigate OCT bathymetric anomalies with respect to expected oceanic bathymetries and subsidence analysis has been used to determine the distribution of continental lithosphere thinning. These techniques have been validated for profiles Lusigal 12 and ISE-01 on the Iberian margin. In addition a joint inversion technique using deep seismic reflection and gravity anomaly data has been applied to the ION-GXT BS1-575 SE Brazil and ION-GXT CS1-2400 S Angola deep seismic reflection lines. The joint inversion method solves for coincident seismic and gravity Moho in the time domain and calculates the lateral variations in crustal basement densities and velocities along the seismic profiles. Gravity inversion, RDA and subsidence analysis along the ION-GXT BS1-575 profile, which crosses the Sao Paulo Plateau and Florianopolis Ridge of the SE Brazilian margin, predict the COB to be located SE of the Florianopolis Ridge. Integrated quantitative analysis shows no evidence for exhumed mantle on this margin profile. The joint inversion technique predicts oceanic crustal thicknesses of between 7 and 8 km thickness with normal oceanic basement seismic velocities and densities. Beneath the Sao Paulo Plateau and Florianopolis Ridge, joint inversion predicts crustal basement thicknesses between 10-15km with high values of basement density and seismic velocities under the Sao Paulo Plateau which are interpreted as indicating a significant magmatic component within the crustal basement. The Sao Paulo Plateau and Florianopolis Ridge are separated by a thin region of crustal basement beneath the salt interpreted as a regional transtensional structure. Sediment corrected RDAs and gravity derived "synthetic" RDAs are of a similar magnitude on oceanic crust, implying negligible mantle dynamic topography. Gravity inversion, RDA and subsidence analysis along the S Angolan ION-GXT CS1-2400 profile suggests that exhumed mantle, corresponding to a magma poor margin, is absent..The thickness of earliest oceanic crust, derived from gravity and deep seismic reflection data, is approximately 7km consistent with the global average oceanic crustal thicknesses. The joint inversion predicts a small difference between oceanic and continental crustal basement density and seismic velocity, with the change in basement density and velocity corresponding to the COB independently determined from RDA and subsidence analysis. The difference between the sediment corrected RDA and that predicted from gravity inversion crustal thickness variation implies that this margin is experiencing approximately 500m of anomalous uplift attributed to mantle dynamic uplift.

Profile control simulations and experiments on TCV: a controller test environment and results using a model-based predictive controller

NASA Astrophysics Data System (ADS)

Maljaars, E.; Felici, F.; Blanken, T. C.; Galperti, C.; Sauter, O.; de Baar, M. R.; Carpanese, F.; Goodman, T. P.; Kim, D.; Kim, S. H.; Kong, M.; Mavkov, B.; Merle, A.; Moret, J. M.; Nouailletas, R.; Scheffer, M.; Teplukhina, A. A.; Vu, N. M. T.; The EUROfusion MST1-team; The TCV-team

2017-12-01

The successful performance of a model predictive profile controller is demonstrated in simulations and experiments on the TCV tokamak, employing a profile controller test environment. Stable high-performance tokamak operation in hybrid and advanced plasma scenarios requires control over the safety factor profile (q-profile) and kinetic plasma parameters such as the plasma beta. This demands to establish reliable profile control routines in presently operational tokamaks. We present a model predictive profile controller that controls the q-profile and plasma beta using power requests to two clusters of gyrotrons and the plasma current request. The performance of the controller is analyzed in both simulation and TCV L-mode discharges where successful tracking of the estimated inverse q-profile as well as plasma beta is demonstrated under uncertain plasma conditions and the presence of disturbances. The controller exploits the knowledge of the time-varying actuator limits in the actuator input calculation itself such that fast transitions between targets are achieved without overshoot. A software environment is employed to prepare and test this and three other profile controllers in parallel in simulations and experiments on TCV. This set of tools includes the rapid plasma transport simulator RAPTOR and various algorithms to reconstruct the plasma equilibrium and plasma profiles by merging the available measurements with model-based predictions. In this work the estimated q-profile is merely based on RAPTOR model predictions due to the absence of internal current density measurements in TCV. These results encourage to further exploit model predictive profile control in experiments on TCV and other (future) tokamaks.

Compositional control of continuously graded anode functional layer

NASA Astrophysics Data System (ADS)

McCoppin, J.; Barney, I.; Mukhopadhyay, S.; Miller, R.; Reitz, T.; Young, D.

2012-10-01

In this work, solid oxide fuel cells (SOFC's) are fabricated with linear-compositionally graded anode functional layers (CGAFL) using a computer-controlled compound aerosol deposition (CCAD) system. Cells with different CGAFL thicknesses (30 um and 50 um) are prepared with a continuous compositionally graded interface deposited between the electrolyte and anode support current collecting regions. The compositional profile was characterized using energy dispersive X-ray spectroscopic mapping. An analytical model of the compound aerosol deposition was developed. The model predicted compositional profiles for both samples that closely matched the measured profiles, suggesting that aerosol-based deposition methods are capable of creating functional gradation on length scales suitable for solid oxide fuel cell structures. The electrochemical performances of the two cells are analyzed using electrochemical impedance spectroscopy (EIS).

A Statistical Approach for Determining Subsurface Thermal Structure from Sea Surface Temperature in the Northeast Pacific Ocean.

DTIC Science & Technology

1983-06-01

DE ERMIuIATIC1N OF SUBSUEFACZE THERMAL STRUCTURE * The study of the oceans by satellites has become a sajc: *arena for sc-intific scrutiny and...between *satellite- de ~ived sea surface temperatu-res and vsrt.-cal *temperature profiles, then the areas of acoust-ical oceanicg- raphy and naval...based on dynamical principles and will ulti-mately provide the basis for pred-icting ocear,-c processes. Emp rical mq4thods have been de -termined i n the

tRNA modification profiles of the fast-proliferating cancer cells

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

Dong, Chao; Niu, Leilei; Song, Wei

Despite the recent progress in RNA modification study, a comprehensive modification profile is still lacking for mammalian cells. Using a quantitative HPLC/MS/MS assay, we present here a study where RNA modifications are examined in term of the major RNA species. With paired slow- and fast-proliferating cell lines, distinct RNA modification profiles are first revealed for diverse RNA species. Compared to mRNAs, increased ribose and nucleobase modifications are shown for the highly-structured tRNAs and rRNAs, lending support to their contribution to the formation of high-order structures. This study also reveals a dynamic tRNA modification profile in the fast-proliferating cells. In additionmore » to cultured cells, this unique tRNA profile has been further confirmed with endometrial cancers and their adjacent normal tissues. Taken together, the results indicate that tRNA is a actively regulated RNA species in the fast-proliferating cancer cells, and suggest that they may play a more active role in biological process than expected. -- Highlights: •RNA modifications were first examined in term of the major RNA species. •A dynamic tRNA modifications was characterized for the fast-proliferating cells. •The unique tRNA profile was confirmed with endometrial cancers and their adjacent normal tissues. •tRNA was predicted as an actively regulated RNA species in the fast-proliferating cancer cells.« less

Pattern sampling for etch model calibration

NASA Astrophysics Data System (ADS)

Weisbuch, François; Lutich, Andrey; Schatz, Jirka

2017-06-01

Successful patterning requires good control of the photolithography and etch processes. While compact litho models, mainly based on rigorous physics, can predict very well the contours printed in photoresist, pure empirical etch models are less accurate and more unstable. Compact etch models are based on geometrical kernels to compute the litho-etch biases that measure the distance between litho and etch contours. The definition of the kernels as well as the choice of calibration patterns is critical to get a robust etch model. This work proposes to define a set of independent and anisotropic etch kernels -"internal, external, curvature, Gaussian, z_profile" - designed to capture the finest details of the resist contours and represent precisely any etch bias. By evaluating the etch kernels on various structures it is possible to map their etch signatures in a multi-dimensional space and analyze them to find an optimal sampling of structures to train an etch model. The method was specifically applied to a contact layer containing many different geometries and was used to successfully select appropriate calibration structures. The proposed kernels evaluated on these structures were combined to train an etch model significantly better than the standard one. We also illustrate the usage of the specific kernel "z_profile" which adds a third dimension to the description of the resist profile.

Depth profiling of marker layers using x-ray waveguide structures

NASA Astrophysics Data System (ADS)

Gupta, Ajay; Rajput, Parasmani; Saraiya, Amit; Reddy, V. R.; Gupta, Mukul; Bernstorff, Sigrid; Amenitsch, H.

2005-08-01

It is demonstrated that x-ray waveguide structures can be used for depth profiling of a marker layer inside the guiding layer with an accuracy of better than 0.2 nm. A combination of x-ray fluorescence and x-ray reflectivity measurements can provide detailed information about the structure of the guiding layer. The position and thickness of the marker layer affect different aspects of the angle-dependent x-ray fluorescence pattern, thus making it possible to determine the structure of the marker layer in an unambiguous manner. As an example, effects of swift heavy ion irradiation on a Si/M/Si trilayer ( M=Fe , W), forming the cavity of the waveguide structure, have been studied. It is found that in accordance with the prediction of thermal spike model, Fe is much more sensitive to swift heavy ion induced modifications as compared to W, even in thin film form. However, a clear evidence of movement of the Fe marker layer towards the surface is observed after irradiation, which cannot be understood in terms of the thermal spike model alone.

Behavioral Profile Predicts Dominance Status in Mountain Chickadees.

PubMed

Fox, Rebecca A; Ladage, Lara D; Roth, Timothy C; Pravosudov, Vladimir V

2009-06-01

Individual variation in stable behavioral traits may explain variation in ecologically-relevant behaviors such as foraging, dispersal, anti-predator behavior, and dominance. We investigated behavioral variation in mountain chickadees (Poecile gambeli), a North American parid that lives in dominance-structured winter flocks, using two common measures of behavioral profile: exploration of a novel room and novel object exploration. We related those behavioral traits to dominance status in male chickadees following brief, pair-wise encounters. Low-exploring birds (birds that visited less than four locations in the novel room) were significantly more likely to become dominant in brief, pairwise encounters with high-exploring birds (i.e., birds that visited all perching locations within a novel room). On the other hand, there was no relationship between novel object exploration and dominance. Interestingly, novel room exploration was also not correlated with novel object exploration. These results suggest that behavioral profile may predict the social status of group-living individuals. Moreover, our results contradict the idea that novel object exploration and novel room exploration are always interchangeable measures of individuals' sensitivity to environmental novelty.

Tractable flux-driven temperature, density, and rotation profile evolution with the quasilinear gyrokinetic transport model QuaLiKiz

NASA Astrophysics Data System (ADS)

Citrin, J.; Bourdelle, C.; Casson, F. J.; Angioni, C.; Bonanomi, N.; Camenen, Y.; Garbet, X.; Garzotti, L.; Görler, T.; Gürcan, O.; Koechl, F.; Imbeaux, F.; Linder, O.; van de Plassche, K.; Strand, P.; Szepesi, G.; Contributors, JET

2017-12-01

Quasilinear turbulent transport models are a successful tool for prediction of core tokamak plasma profiles in many regimes. Their success hinges on the reproduction of local nonlinear gyrokinetic fluxes. We focus on significant progress in the quasilinear gyrokinetic transport model QuaLiKiz (Bourdelle et al 2016 Plasma Phys. Control. Fusion 58 014036), which employs an approximated solution of the mode structures to significantly speed up computation time compared to full linear gyrokinetic solvers. Optimisation of the dispersion relation solution algorithm within integrated modelling applications leads to flux calculations × {10}6-7 faster than local nonlinear simulations. This allows tractable simulation of flux-driven dynamic profile evolution including all transport channels: ion and electron heat, main particles, impurities, and momentum. Furthermore, QuaLiKiz now includes the impact of rotation and temperature anisotropy induced poloidal asymmetry on heavy impurity transport, important for W-transport applications. Application within the JETTO integrated modelling code results in 1 s of JET plasma simulation within 10 h using 10 CPUs. Simultaneous predictions of core density, temperature, and toroidal rotation profiles for both JET hybrid and baseline experiments are presented, covering both ion and electron turbulence scales. The simulations are successfully compared to measured profiles, with agreement mostly in the 5%-25% range according to standard figures of merit. QuaLiKiz is now open source and available at www.qualikiz.com.

Graph wavelet alignment kernels for drug virtual screening.

PubMed

Smalter, Aaron; Huan, Jun; Lushington, Gerald

2009-06-01

In this paper, we introduce a novel statistical modeling technique for target property prediction, with applications to virtual screening and drug design. In our method, we use graphs to model chemical structures and apply a wavelet analysis of graphs to summarize features capturing graph local topology. We design a novel graph kernel function to utilize the topology features to build predictive models for chemicals via Support Vector Machine classifier. We call the new graph kernel a graph wavelet-alignment kernel. We have evaluated the efficacy of the wavelet-alignment kernel using a set of chemical structure-activity prediction benchmarks. Our results indicate that the use of the kernel function yields performance profiles comparable to, and sometimes exceeding that of the existing state-of-the-art chemical classification approaches. In addition, our results also show that the use of wavelet functions significantly decreases the computational costs for graph kernel computation with more than ten fold speedup.

Communication: Theoretical prediction of free-energy landscapes for complex self-assembly

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

Jacobs, William M.; Reinhardt, Aleks; Frenkel, Daan

2015-01-14

We present a technique for calculating free-energy profiles for the nucleation of multicomponent structures that contain as many species as building blocks. We find that a key factor is the topology of the graph describing the connectivity of the target assembly. By considering the designed interactions separately from weaker, incidental interactions, our approach yields predictions for the equilibrium yield and nucleation barriers. These predictions are in good agreement with corresponding Monte Carlo simulations. We show that a few fundamental properties of the connectivity graph determine the most prominent features of the assembly thermodynamics. Surprisingly, we find that polydispersity in themore » strengths of the designed interactions stabilizes intermediate structures and can be used to sculpt the free-energy landscape for self-assembly. Finally, we demonstrate that weak incidental interactions can preclude assembly at equilibrium due to the combinatorial possibilities for incorrect association.« less

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.

Multiscale 2D Inversions of Active-source First-arrival Times in Taiwan

NASA Astrophysics Data System (ADS)

Lin, Y. P.; Zhao, L.; Hung, S. H.

2015-12-01

In this study, we make use of the active-source records collected by the TAIGER (TAiwan Integrated GEodynamics Research) project in 2008 at nearly 1400 locations on the island of Taiwan and the surrounding ocean bottom. We manually picked the first-arrival times from the waveform records to obtain a set of highly accurate P-wave traveltimes. Among the 1400 receivers, more than 1000 were deployed along four almost linear cross-island profiles with inter-seismometer spacing down to 200 m. This ground-truth dataset provides strong constrains on the structure between the exactly known active sources and densely distributed receivers, which can be used to calibrate the seismic structure in the upper crust in Taiwan. In this study, we use this dataset to image the two-dimensional P-wave structure along the four linear profiles. A wavelet parameterization of the model is adopted to achieve an objective and data-adaptive multiscale resolution to the 2D structures. Rigorous estimations of resolution lengths were also conducted to quantify the spatial resolutions of the tomography inversions. The resulting 2D models yield first-arrival time predictions that are in excellent agreement with the observations. The seismic structures along the 2D profiles display strong lateral variations (up to 80% relative to regional average) with more realistic amplitudes of velocity perturbations and spatial patterns consistent with geological zonations of Taiwan

Structure-based design and profiling of novel 17β-HSD14 inhibitors.

PubMed

Braun, Florian; Bertoletti, Nicole; Möller, Gabriele; Adamski, Jerzy; Frotscher, Martin; Guragossian, Nathalie; Madeira Gírio, Patrícia Alexandra; Le Borgne, Marc; Ettouati, Laurent; Falson, Pierre; Müller, Sebastian; Vollmer, Günther; Heine, Andreas; Klebe, Gerhard; Marchais-Oberwinkler, Sandrine

2018-05-22

The human enzyme 17β-hydroxysteroid dehydrogenase 14 (17β-HSD14) oxidizes the hydroxyl group at position 17 of estradiol and 5-androstenediol using NAD + as cofactor. However, the physiological role of the enzyme remains unclear. We recently described the first class of nonsteroidal inhibitors for this enzyme with compound 1 showing a high 17β-HSD14 inhibitory activity. Its crystal structure was used as starting point for a structure-based optimization in this study. The goal was to develop a promising chemical probe to further investigate the enzyme. The newly designed compounds revealed mostly very high inhibition of the enzyme and for seven of them the crystal structures of the corresponding inhibitor-enzyme complexes were resolved. The crystal structures disclosed that a small change in the substitution pattern of the compounds resulted in an alternative binding mode for one inhibitor. The profiling of a set of the most potent inhibitors identified 13 (K i  = 9 nM) with a good selectivity profile toward three 17β-HSDs and the estrogen receptor alpha. This inhibitor displayed no cytotoxicity, good solubility, and auspicious predicted bioavailability. Overall, 13 is a highly interesting 17β-HSD14 inhibitor, which might be used as chemical probe for further investigation of the target enzyme. Copyright © 2018 Elsevier Masson SAS. All rights reserved.

Injury Profile SIMulator, a Qualitative Aggregative Modelling Framework to Predict Injury Profile as a Function of Cropping Practices, and Abiotic and Biotic Environment. II. Proof of Concept: Design of IPSIM-Wheat-Eyespot

PubMed Central

Robin, Marie-Hélène; Colbach, Nathalie; Lucas, Philippe; Montfort, Françoise; Cholez, Célia; Debaeke, Philippe; Aubertot, Jean-Noël

2013-01-01

IPSIM (Injury Profile SIMulator) is a generic modelling framework presented in a companion paper. It aims at predicting a crop injury profile as a function of cropping practices and abiotic and biotic environment. IPSIM's modelling approach consists of designing a model with an aggregative hierarchical tree of attributes. In order to provide a proof of concept, a model, named IPSIM-Wheat-Eyespot, has been developed with the software DEXi according to the conceptual framework of IPSIM to represent final incidence of eyespot on wheat. This paper briefly presents the pathosystem, the method used to develop IPSIM-Wheat-Eyespot using IPSIM's modelling framework, simulation examples, an evaluation of the predictive quality of the model with a large dataset (526 observed site-years) and a discussion on the benefits and limitations of the approach. IPSIM-Wheat-Eyespot proved to successfully represent the annual variability of the disease, as well as the effects of cropping practices (Efficiency = 0.51, Root Mean Square Error of Prediction = 24%; bias = 5.0%). IPSIM-Wheat-Eyespot does not aim to precisely predict the incidence of eyespot on wheat. It rather aims to rank cropping systems with regard to the risk of eyespot on wheat in a given production situation through ex ante evaluations. IPSIM-Wheat-Eyespot can also help perform diagnoses of commercial fields. Its structure is simple and permits to combine available knowledge in the scientific literature (data, models) and expertise. IPSIM-Wheat-Eyespot is now available to help design cropping systems with a low risk of eyespot on wheat in a wide range of production situations, and can help perform diagnoses of commercial fields. In addition, it provides a proof of concept with regard to the modelling approach of IPSIM. IPSIM-Wheat-Eyespot will be a sub-model of IPSIM-Wheat, a model that will predict injury profile on wheat as a function of cropping practices and the production situation. PMID:24146783

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.

Predictive value of dysregulation profile trajectories in childhood for symptoms of ADHD, anxiety and depression in late adolescence.

PubMed

Wang, B; Brueni, L G; Isensee, C; Meyer, T; Bock, N; Ravens-Sieberer, U; Klasen, F; Schlack, R; Becker, A; Rothenberger, A

2018-06-01

We examined whether there are certain dysregulation profile trajectories in childhood that may predict an elevated risk for mental disorders in later adolescence. Participants (N = 554) were drawn from a representative community sample of German children, 7-11 years old, who were followed over four measurement points (baseline, 1, 2 and 6 years later). Dysregulation profile, derived from the parent report of the Strengths and Difficulties Questionnaire, was measured at the first three measurement points, while symptoms of attention deficit hyperactivity disorder (ADHD), anxiety and depression were assessed at the fourth measurement point. We used latent class growth analysis to investigate developmental trajectories in the development of the dysregulation profile. The predictive value of dysregulation profile trajectories for later ADHD, anxiety and depression was examined by linear regression. For descriptive comparison, the predictive value of a single measurement (baseline) was calculated. Dysregulation profile was a stable trait during childhood. Boys and girls had similar levels of dysregulation profile over time. Two developmental subgroups were identified, namely the low dysregulation profile and the high dysregulation profile trajectory. The group membership in the high dysregulation profile trajectory (n = 102) was best predictive of later ADHD, regardless of an individual's gender and age. It explained 11% of the behavioural variance. For anxiety this was 8.7% and for depression 5.6%, including some gender effects. The single-point measurement was less predictive. An enduring high dysregulation profile in childhood showed some predictive value for psychological functioning 4 years later. Hence, it might be helpful in the preventive monitoring of children at risk.

Childhood CBCL Bipolar Profile and Adolescent/Young Adult Personality Disorders: A 9-year Follow-up

PubMed Central

Halperin, Jeffrey M.; Rucklidge, Julia J.; Powers, Robyn L.; Miller, Carlin J.; Newcorn, Jeffrey H.

2010-01-01

Background To assess the late adolescent psychiatric outcomes associated with a positive Child Behavior Checklist – Juvenile Bipolar Disorder Phenotype (CBCL-JBD) in children diagnosed with ADHD and followed over a 9-year period. Methods Parents of 152 children diagnosed as ADHD (ages 7–11 years) completed the CBCL. Ninety of these parents completed it again 9 years later as part of a comprehensive evaluation of Axis I and II diagnoses as assessed using semi-structured interviews. As previously proposed, the CBCL-JBD phenotype was defined as T-scores of 70 or greater on the Attention Problems, Aggression, and Anxiety/Depression subscales. Results The CBCL-JBD phenotype was found in 31% of those followed but only 4.9% of the sample continued to meet the phenotype criteria at follow up. Only two of the sample developed Bipolar Disorder by late adolescence and only one of those had the CBCL-JBD profile in childhood. The proxy did not predict any Axis I disorders. However, the CBCL-JBD proxy was highly predictive of later personality disorders. Limitations Only a subgroup of the original childhood sample was followed. Given this sample was confined to children with ADHD, it is not known whether the prediction of personality disorders from CBCL scores would generalize to a wider community or clinical population Conclusions A positive CBCL-JBD phenotype profile in childhood does not predict Axis I Disorders in late adolescence; however, it may be prognostic of the emergence of personality disorders. PMID:21056910

Recent advances in the in silico modelling of UDP glucuronosyltransferase substrates.

PubMed

Sorich, Michael J; Smith, Paul A; Miners, John O; Mackenzie, Peter I; McKinnon, Ross A

2008-01-01

UDP glucurononosyltransferases (UGT) are a superfamily of enzymes that catalyse the conjugation of a range of structurally diverse drugs, environmental and endogenous chemicals with glucuronic acid. This process plays a significant role in the clearance and detoxification of many chemicals. Over the last decade the regulation and substrate profiles of UGT isoforms have been increasingly characterised. The resulting data has facilitated the prototyping of ligand based in silico models capable of predicting, and gaining insights into, binding affinity and the substrate- and regio- selectivity of glucuronidation by UGT isoforms. Pharmacophore modelling has produced particularly insightful models and quantitative structure-activity relationships based on machine learning algorithms result in accurate predictions. Simple structural chemical descriptors were found to capture much of the chemical information relevant to UGT metabolism. However, quantum chemical properties of molecules and the nucleophilic atoms in the molecule can enhance both the predictivity and chemical intuitiveness of structure-activity models. Chemical diversity analysis of known substrates has shown some bias towards chemicals with aromatic and aliphatic hydroxyl groups. Future progress in in silico development will depend on larger and more diverse high quality metabolic datasets. Furthermore, improved protein structure data on UGTs will enable the application of structural modelling techniques likely leading to greater insight into the binding and reactive processes of UGT catalysed glucuronidation.

Prediction of redox-sensitive cysteines using sequential distance and other sequence-based features.

PubMed

Sun, Ming-An; Zhang, Qing; Wang, Yejun; Ge, Wei; Guo, Dianjing

2016-08-24

Reactive oxygen species can modify the structure and function of proteins and may also act as important signaling molecules in various cellular processes. Cysteine thiol groups of proteins are particularly susceptible to oxidation. Meanwhile, their reversible oxidation is of critical roles for redox regulation and signaling. Recently, several computational tools have been developed for predicting redox-sensitive cysteines; however, those methods either only focus on catalytic redox-sensitive cysteines in thiol oxidoreductases, or heavily depend on protein structural data, thus cannot be widely used. In this study, we analyzed various sequence-based features potentially related to cysteine redox-sensitivity, and identified three types of features for efficient computational prediction of redox-sensitive cysteines. These features are: sequential distance to the nearby cysteines, PSSM profile and predicted secondary structure of flanking residues. After further feature selection using SVM-RFE, we developed Redox-Sensitive Cysteine Predictor (RSCP), a SVM based classifier for redox-sensitive cysteine prediction using primary sequence only. Using 10-fold cross-validation on RSC758 dataset, the accuracy, sensitivity, specificity, MCC and AUC were estimated as 0.679, 0.602, 0.756, 0.362 and 0.727, respectively. When evaluated using 10-fold cross-validation with BALOSCTdb dataset which has structure information, the model achieved performance comparable to current structure-based method. Further validation using an independent dataset indicates it is robust and of relatively better accuracy for predicting redox-sensitive cysteines from non-enzyme proteins. In this study, we developed a sequence-based classifier for predicting redox-sensitive cysteines. The major advantage of this method is that it does not rely on protein structure data, which ensures more extensive application compared to other current implementations. Accurate prediction of redox-sensitive cysteines not only enhances our understanding about the redox sensitivity of cysteine, it may also complement the proteomics approach and facilitate further experimental investigation of important redox-sensitive cysteines.

Application of the NASA A-Train to Evaluate Clouds Simulated by the Weather Research and Forecast Model

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Jedlovec, Gary J.; Lapenta, William M.

2008-01-01

The CloudSat Mission, part of the NASA A-Train, is providing the first global survey of cloud profiles and cloud physical properties, observing seasonal and geographical variations that are pertinent to evaluating the way clouds are parameterized in weather and climate forecast models. CloudSat measures the vertical structure of clouds and precipitation from space through the Cloud Profiling Radar (CPR), a 94 GHz nadir-looking radar measuring the power backscattered by clouds as a function of distance from the radar. One of the goals of the CloudSat mission is to evaluate the representation of clouds in forecast models, thereby contributing to improved predictions of weather, climate and the cloud-climate feedback problem. This paper highlights potential limitations in cloud microphysical schemes currently employed in the Weather Research and Forecast (WRF) modeling system. The horizontal and vertical structure of explicitly simulated cloud fields produced by the WRF model at 4-km resolution are being evaluated using CloudSat observations in concert with products derived from MODIS and AIRS. A radiative transfer model is used to produce simulated profiles of radar reflectivity given WRF input profiles of hydrometeor mixing ratios and ambient atmospheric conditions. The preliminary results presented in the paper will compare simulated and observed reflectivity fields corresponding to horizontal and vertical cloud structures associated with midlatitude cyclone events.

Prediction of mean flow data for adiabatic 2-D compressible turbulent boundary layers

NASA Astrophysics Data System (ADS)

Motallebi, Fariborz

1995-02-01

This report presents a method for the prediction of mean flow data (i.e. , skin friction, velocity profile, and shape parameter) for adiabatic two-dimensional compressible turbulent boundary layers at zero pressure gradient. The transformed law of the wall, law of the wake, the van Driest model for the complete inner region, and a correlation between the Reynolds number based on the boundary layer integral length scale (Re(sub Delta*)) and the Reynolds number based on the boundary layer momentum thickness (Re(sub theta)) were used to predict the mean flow quantities. The results for skin friction coefficient show good agreement with a number of existing theories including those of van Driest and Huang et al. Comparison with a large number of experimental data suggests that at least for transonic and supersonic flows, the velocity profile as described by van Driest and Coles is Reynolds number dependent and should not be presumed universal. Extra information or perhaps a better physical approach to the formulation of the mean structure of compressible turbulent boundary layers, even in zero pressure gradient and adiabatic condition, is required in order to achieve complete (physical and mathematical) convergence when it is applied in any prediction methods.

DPDR-CPI, a server that predicts Drug Positioning and Drug Repositioning via Chemical-Protein Interactome.

PubMed

Luo, Heng; Zhang, Ping; Cao, Xi Hang; Du, Dizheng; Ye, Hao; Huang, Hui; Li, Can; Qin, Shengying; Wan, Chunling; Shi, Leming; He, Lin; Yang, Lun

2016-11-02

The cost of developing a new drug has increased sharply over the past years. To ensure a reasonable return-on-investment, it is useful for drug discovery researchers in both industry and academia to identify all the possible indications for early pipeline molecules. For the first time, we propose the term computational "drug candidate positioning" or "drug positioning", to describe the above process. It is distinct from drug repositioning, which identifies new uses for existing drugs and maximizes their value. Since many therapeutic effects are mediated by unexpected drug-protein interactions, it is reasonable to analyze the chemical-protein interactome (CPI) profiles to predict indications. Here we introduce the server DPDR-CPI, which can make real-time predictions based only on the structure of the small molecule. When a user submits a molecule, the server will dock it across 611 human proteins, generating a CPI profile of features that can be used for predictions. It can suggest the likelihood of relevance of the input molecule towards ~1,000 human diseases with top predictions listed. DPDR-CPI achieved an overall AUROC of 0.78 during 10-fold cross-validations and AUROC of 0.76 for the independent validation. The server is freely accessible via http://cpi.bio-x.cn/dpdr/.

Full-coverage film cooling: 3-dimensional measurements of turbulence structure and prediction of recovery region hydrodynamics

NASA Technical Reports Server (NTRS)

Yavuzkurt, S.; Moffat, R. J.; Kays, W. M.

1979-01-01

Hydrodynamic measurements were made with a triaxial hot-wire in the full-coverage region and the recovery region following an array of injection holes inclined downstream, at 30 degrees to the surface. The data were taken under isothermal conditions at ambient temperature and pressure for two blowing ratios: M = 0.9 and M = 0.4. Profiles of the three main velocity components and the six Reynolds stresses were obtained at several spanwise positions at each of the five locations down the test plate. A one-equation model of turbulence (using turbulent kinetic energy with an algebraic mixing length) was used in a two-dimensional computer program to predict the mean velocity and turbulent kinetic energy profiles in the recovery region. A new real-time hotwire scheme was developed to make measurements in the three-dimensional turbulent boundary layer over the full-coverage surface.

Wind Turbine Gust Prediction Using Remote Sensing Data

NASA Astrophysics Data System (ADS)

Towers, Paul; Jones, Bryn

2013-11-01

Offshore wind energy is a growing energy source as governments around the world look for environmentally friendly solutions to potential future energy shortages. In order to capture more energy from the wind, larger turbines are being designed, leading to the structures becoming increasingly vulnerable to damage caused by violent gusts of wind. Advance knowledge of such gusts will enable turbine control systems to take preventative action, reducing turbine maintenance costs. We present a system which can accurately forecast the velocity profile of an oncoming wind, given only limited spatial measurements from light detection and ranging (LiDAR) units, which are currently operational in industry. Our method combines nonlinear state estimation techniques with low-order models of atmospheric boundary-layer flows to generate flow-field estimates. We discuss the accuracy of our velocity profile predictions by direct comparison to data derived from large eddy simulations of the atmospheric boundary layer.

A novel adjuvant to the resident selection process: the hartman value profile.

PubMed

Cone, Jeffrey D; Byrum, C Stephen; Payne, Wyatt G; Smith, David J

2012-01-01

The goal of resident selection is twofold: (1) select candidates who will be successful residents and eventually successful practitioners and (2) avoid selecting candidates who will be unsuccessful residents and/or eventually unsuccessful practitioners. Traditional tools used to select residents have well-known limitations. The Hartman Value Profile (HVP) is a proven adjuvant tool to predicting future performance in candidates for advanced positions in the corporate setting. No literature exists to indicate use of the HVP for resident selection. The HVP evaluates the structure and the dynamics of an individual value system. Given the potential impact, we implemented its use beginning in 2007 as an adjuvant tool to the traditional selection process. Experience gained from incorporating the HVP into the residency selection process suggests that it may add objectivity and refinement in predicting resident performance. Further evaluation is warranted with longer follow-up times.

A Novel Adjuvant to the Resident Selection Process: the Hartman Value Profile

PubMed Central

Cone, Jeffrey D.; Byrum, C. Stephen; Payne, Wyatt G.; Smith, David J.

2012-01-01

Objectives: The goal of resident selection is twofold: (1) select candidates who will be successful residents and eventually successful practitioners and (2) avoid selecting candidates who will be unsuccessful residents and/or eventually unsuccessful practitioners. Traditional tools used to select residents have well-known limitations. The Hartman Value Profile (HVP) is a proven adjuvant tool to predicting future performance in candidates for advanced positions in the corporate setting. Methods: No literature exists to indicate use of the HVP for resident selection. Results: The HVP evaluates the structure and the dynamics of an individual value system. Given the potential impact, we implemented its use beginning in 2007 as an adjuvant tool to the traditional selection process. Conclusions: Experience gained from incorporating the HVP into the residency selection process suggests that it may add objectivity and refinement in predicting resident performance. Further evaluation is warranted with longer follow-up times. PMID:22720114

Bioinformatics and peptidomics approaches to the discovery and analysis of food-derived bioactive peptides.

PubMed

Agyei, Dominic; Tsopmo, Apollinaire; Udenigwe, Chibuike C

2018-06-01

There are emerging advancements in the strategies used for the discovery and development of food-derived bioactive peptides because of their multiple food and health applications. Bioinformatics and peptidomics are two computational and analytical techniques that have the potential to speed up the development of bioactive peptides from bench to market. Structure-activity relationships observed in peptides form the basis for bioinformatics and in silico prediction of bioactive sequences encrypted in food proteins. Peptidomics, on the other hand, relies on "hyphenated" (liquid chromatography-mass spectrometry-based) techniques for the detection, profiling, and quantitation of peptides. Together, bioinformatics and peptidomics approaches provide a low-cost and effective means of predicting, profiling, and screening bioactive protein hydrolysates and peptides from food. This article discuses the basis, strengths, and limitations of bioinformatics and peptidomics approaches currently used for the discovery and analysis of food-derived bioactive peptides.

Spin-Imbalanced Quasi-Two-Dimensional Fermi Gases

NASA Astrophysics Data System (ADS)

Ong, W.; Cheng, Chingyun; Arakelyan, I.; Thomas, J. E.

2015-03-01

We measure the density profiles for a Fermi gas of Li 6 containing N1 spin-up atoms and N2 spin-down atoms, confined in a quasi-two-dimensional geometry. The spatial profiles are measured as a function of spin imbalance N2/N1 and interaction strength, which is controlled by means of a collisional (Feshbach) resonance. The measured cloud radii and central densities are in disagreement with mean-field Bardeen-Cooper-Schrieffer theory for a true two-dimensional system. We find that the data for normal-fluid mixtures are reasonably well fit by a simple two-dimensional polaron model of the free energy. Not predicted by the model is a phase transition to a spin-balanced central core, which is observed above a critical value of N2/N1. Our observations provide important benchmarks for predictions of the phase structure of quasi-two-dimensional Fermi gases.

Using convolutional neural networks to explore the microbiome.

PubMed

Reiman, Derek; Metwally, Ahmed; Yang Dai

2017-07-01

The microbiome has been shown to have an impact on the development of various diseases in the host. Being able to make an accurate prediction of the phenotype of a genomic sample based on its microbial taxonomic abundance profile is an important problem for personalized medicine. In this paper, we examine the potential of using a deep learning framework, a convolutional neural network (CNN), for such a prediction. To facilitate the CNN learning, we explore the structure of abundance profiles by creating the phylogenetic tree and by designing a scheme to embed the tree to a matrix that retains the spatial relationship of nodes in the tree and their quantitative characteristics. The proposed CNN framework is highly accurate, achieving a 99.47% of accuracy based on the evaluation on a dataset 1967 samples of three phenotypes. Our result demonstrated the feasibility and promising aspect of CNN in the classification of sample phenotype.

Utilization of Gastrointestinal Simulator, an in Vivo Predictive Dissolution Methodology, Coupled with Computational Approach To Forecast Oral Absorption of Dipyridamole.

PubMed

Matsui, Kazuki; Tsume, Yasuhiro; Takeuchi, Susumu; Searls, Amanda; Amidon, Gordon L

2017-04-03

Weakly basic drugs exhibit a pH-dependent dissolution profile in the gastrointestinal (GI) tract, which makes it difficult to predict their oral absorption profile. The aim of this study was to investigate the utility of the gastrointestinal simulator (GIS), a novel in vivo predictive dissolution (iPD) methodology, in predicting the in vivo behavior of the weakly basic drug dipyridamole when coupled with in silico analysis. The GIS is a multicompartmental dissolution apparatus, which represents physiological gastric emptying in the fasted state. Kinetic parameters for drug dissolution and precipitation were optimized by fitting a curve to the dissolved drug amount-time profiles in the United States Pharmacopeia apparatus II and GIS. Optimized parameters were incorporated into mathematical equations to describe the mass transport kinetics of dipyridamole in the GI tract. By using this in silico model, intraluminal drug concentration-time profile was simulated. The predicted profile of dipyridamole in the duodenal compartment adequately captured observed data. In addition, the plasma concentration-time profile was also predicted using pharmacokinetic parameters following intravenous administration. On the basis of the comparison with observed data, the in silico approach coupled with the GIS successfully predicted in vivo pharmacokinetic profiles. Although further investigations are still required to generalize, these results indicated that incorporating GIS data into mathematical equations improves the predictability of in vivo behavior of weakly basic drugs like dipyridamole.

Genome-scale characterization of RNA tertiary structures and their functional impact by RNA solvent accessibility prediction.

PubMed

Yang, Yuedong; Li, Xiaomei; Zhao, Huiying; Zhan, Jian; Wang, Jihua; Zhou, Yaoqi

2017-01-01

As most RNA structures are elusive to structure determination, obtaining solvent accessible surface areas (ASAs) of nucleotides in an RNA structure is an important first step to characterize potential functional sites and core structural regions. Here, we developed RNAsnap, the first machine-learning method trained on protein-bound RNA structures for solvent accessibility prediction. Built on sequence profiles from multiple sequence alignment (RNAsnap-prof), the method provided robust prediction in fivefold cross-validation and an independent test (Pearson correlation coefficients, r, between predicted and actual ASA values are 0.66 and 0.63, respectively). Application of the method to 6178 mRNAs revealed its positive correlation to mRNA accessibility by dimethyl sulphate (DMS) experimentally measured in vivo (r = 0.37) but not in vitro (r = 0.07), despite the lack of training on mRNAs and the fact that DMS accessibility is only an approximation to solvent accessibility. We further found strong association across coding and noncoding regions between predicted solvent accessibility of the mutation site of a single nucleotide variant (SNV) and the frequency of that variant in the population for 2.2 million SNVs obtained in the 1000 Genomes Project. Moreover, mapping solvent accessibility of RNAs to the human genome indicated that introns, 5' cap of 5' and 3' cap of 3' untranslated regions, are more solvent accessible, consistent with their respective functional roles. These results support conformational selections as the mechanism for the formation of RNA-protein complexes and highlight the utility of genome-scale characterization of RNA tertiary structures by RNAsnap. The server and its stand-alone downloadable version are available at http://sparks-lab.org. © 2016 Yang et al.; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Conformation guides molecular efficacy in docking screens of activated β-2 adrenergic G protein coupled receptor.

PubMed

Weiss, Dahlia R; Ahn, SeungKirl; Sassano, Maria F; Kleist, Andrew; Zhu, Xiao; Strachan, Ryan; Roth, Bryan L; Lefkowitz, Robert J; Shoichet, Brian K

2013-05-17

A prospective, large library virtual screen against an activated β2-adrenergic receptor (β2AR) structure returned potent agonists to the exclusion of inverse-agonists, providing the first complement to the previous virtual screening campaigns against inverse-agonist-bound G protein coupled receptor (GPCR) structures, which predicted only inverse-agonists. In addition, two hits recapitulated the signaling profile of the co-crystal ligand with respect to the G protein and arrestin mediated signaling. This functional fidelity has important implications in drug design, as the ability to predict ligands with predefined signaling properties is highly desirable. However, the agonist-bound state provides an uncertain template for modeling the activated conformation of other GPCRs, as a dopamine D2 receptor (DRD2) activated model templated on the activated β2AR structure returned few hits of only marginal potency.

The implication of DNA bending energy for nucleosome positioning and sliding.

PubMed

Liu, Guoqing; Xing, Yongqiang; Zhao, Hongyu; Cai, Lu; Wang, Jianying

2018-06-11

Nucleosome not only directly affects cellular processes, such as DNA replication, recombination, and transcription, but also severs as a fundamentally important target of epigenetic modifications. Our previous study indicated that the bending property of DNA is important in nucleosome formation, particularly in predicting the dyad positions of nucleosomes on a DNA segment. Here, we investigated the role of bending energy in nucleosome positioning and sliding in depth to decipher sequence-directed mechanism. The results show that bending energy is a good physical index to predict the free energy in the process of nucleosome reconstitution in vitro. Our data also imply that there are at least 20% of the nucleosomes in budding yeast do not adopt canonical positioning, in which underlying sequences wrapped around histones are structurally symmetric. We also revealed distinct patterns of bending energy profile for distinctly organized chromatin structures, such as well-positioned nucleosomes, fuzzy nucleosomes, and linker regions and discussed nucleosome sliding in terms of bending energy. We proposed that the stability of a nucleosome is positively correlated with the strength of the bending anisotropy of DNA segment, and both accessibility and directionality of nucleosome sliding is likely to be modulated by diverse patterns of DNA bending energy profile.

Synthesis of line profiles from models of structured winds

NASA Technical Reports Server (NTRS)

Puls, J.; Feldmeier, A.; Springmann, U. W. E.; Owocki, S. P.; Fullerton, A. W.

1994-01-01

On the basis of a careful analysis of resonance line formation (both for singlets and doublets) in structured winds, present time dependent models of the line driven winds of hot stars are shown to be able to explain a number of observational features with respect to variability and structure: they are (in principle) able to reproduce the black and broad troughs (without any artificial 'turbulence velocity') and the 'blue edge variability' observed in saturate resonance lines: they might explain the 'long lived narrow absorption components' often observed in unsaturated lines at high velocities; they predict a relation between the 'edge velocity' of UV-lines and the radiation temperature of the observed X-ray emission. As a first example of the extent to which theoretical models can be constrained by comparisons between observations and profiles calculated by spectrum synthesis from structured winds, we show here that models with deep-seated onset of structure formation (approximately greater than 1.1 R(sub *)) produce resonance lines which agree qualitatively with observational findings; in contrast, the here presented models with structure formation only well out in the wind (approximately greater than 1.6 R(sub *) fail in this respect.

Temporal lobe volume predicts Wada memory test performance in patients with mesial temporal sclerosis.

PubMed

Ding, Kan; Gong, Yunhua; Modur, Pradeep N; Diaz-Arrastia, Ramon; Agostini, Mark; Gupta, Puneet; McColl, Roderick; Hays, Ryan; Van Ness, Paul

2016-02-01

The Wada test is widely used in the presurgical evaluation of potential temporal lobectomy patients to predict postoperative memory function. Expected asymmetry (EA), defined as Wada memory lateralized to the nonsurgical hemisphere, or a higher score after injection of the surgical hemisphere would be considered favorable in terms of postoperative memory outcome. However, in some cases, nonlateralized memory (NM) results, with no appreciable asymmetry, may occur because of impaired scores after both injections, often leading to denial of surgery. The reason for such nonlateralized Wada memory in patients with intractable temporal lobe epilepsy (TLE) remains unclear. Given that quantitative morphometric magnetic resonance imaging studies in TLE patients have shown bilateral regional atrophy in temporal and extratemporal structures, we hypothesized that the volume loss in contralateral temporal structures could contribute to nonlateralized Wada memory performance. To investigate this, we examined the relationship between the volume changes of temporal structures and Wada memory scores in patients with intractable TLE with mesial temporal sclerosis (MTS) using an age- and gender-matched control group. Memory was considered nonlateralized if the absolute difference in the total correct recall scores between ipsilateral and contralateral injections was <11%. Among 21 patients, Wada memory was lateralized in 15 and nonlateralized in 6 patients, with all the nonlateralized scores being observed in left TLE. The recall scores after ipsilateral injection were significantly lower in patients with an NM profile than an EA profile (23 ± 14% vs. 59 ± 18% correct recall, p ≤ 0.001). However, the recall scores after contralateral injection were low but similar between the two groups (25 ± 17% vs. 25 ± 15% correct recall, p=0.97). Compared to controls, all the patients showed greater volume loss in the temporal regions. However, patients with a NM profile showed significantly more volume loss than those with a lateralized memory profile in both contralateral and ipsilateral temporal regions (p<0.05). Left hemispheric Wada memory performance correlated positively with the size of the left mesial and neocortical temporal structures (r=0.49-0.63, p=0.005-0.04). Our study suggests that volume loss in the nonsurgical temporal structures is associated with nonlateralized Wada memory results in patients with intractable TLE. Copyright © 2015 Elsevier B.V. All rights reserved.

GDAP: a web tool for genome-wide protein disulfide bond prediction.

PubMed

O'Connor, Brian D; Yeates, Todd O

2004-07-01

The Genomic Disulfide Analysis Program (GDAP) provides web access to computationally predicted protein disulfide bonds for over one hundred microbial genomes, including both bacterial and achaeal species. In the GDAP process, sequences of unknown structure are mapped, when possible, to known homologous Protein Data Bank (PDB) structures, after which specific distance criteria are applied to predict disulfide bonds. GDAP also accepts user-supplied protein sequences and subsequently queries the PDB sequence database for the best matches, scans for possible disulfide bonds and returns the results to the client. These predictions are useful for a variety of applications and have previously been used to show a dramatic preference in certain thermophilic archaea and bacteria for disulfide bonds within intracellular proteins. Given the central role these stabilizing, covalent bonds play in such organisms, the predictions available from GDAP provide a rich data source for designing site-directed mutants with more stable thermal profiles. The GDAP web application is a gateway to this information and can be used to understand the role disulfide bonds play in protein stability both in these unusual organisms and in sequences of interest to the individual researcher. The prediction server can be accessed at http://www.doe-mbi.ucla.edu/Services/GDAP.

THE FUTURE OF TOXICOLOGY-PREDICTIVE TOXICOLOGY ...

EPA Pesticide Factsheets

A chemistry approach to predictive toxicology relies on structure−activity relationship (SAR) modeling to predict biological activity from chemical structure. Such approaches have proven capabilities when applied to well-defined toxicity end points or regions of chemical space. These approaches are less well-suited, however, to the challenges of global toxicity prediction, i.e., to predicting the potential toxicity of structurally diverse chemicals across a wide range of end points of regulatory and pharmaceutical concern. New approaches that have the potential to significantly improve capabilities in predictive toxicology are elaborating the “activity” portion of the SAR paradigm. Recent advances in two areas of endeavor are particularly promising. Toxicity data informatics relies on standardized data schema, developed for particular areas of toxicological study, to facilitate data integration and enable relational exploration and mining of data across both historical and new areas of toxicological investigation. Bioassay profiling refers to large-scale high-throughput screening approaches that use chemicals as probes to broadly characterize biological response space, extending the concept of chemical “properties” to the biological activity domain. The effective capture and representation of legacy and new toxicity data into mineable form and the large-scale generation of new bioassay data in relation to chemical toxicity, both employing chemical stru

TANGLE: Two-Level Support Vector Regression Approach for Protein Backbone Torsion Angle Prediction from Primary Sequences

PubMed Central

Song, Jiangning; Tan, Hao; Wang, Mingjun; Webb, Geoffrey I.; Akutsu, Tatsuya

2012-01-01

Protein backbone torsion angles (Phi) and (Psi) involve two rotation angles rotating around the Cα-N bond (Phi) and the Cα-C bond (Psi). Due to the planarity of the linked rigid peptide bonds, these two angles can essentially determine the backbone geometry of proteins. Accordingly, the accurate prediction of protein backbone torsion angle from sequence information can assist the prediction of protein structures. In this study, we develop a new approach called TANGLE (Torsion ANGLE predictor) to predict the protein backbone torsion angles from amino acid sequences. TANGLE uses a two-level support vector regression approach to perform real-value torsion angle prediction using a variety of features derived from amino acid sequences, including the evolutionary profiles in the form of position-specific scoring matrices, predicted secondary structure, solvent accessibility and natively disordered region as well as other global sequence features. When evaluated based on a large benchmark dataset of 1,526 non-homologous proteins, the mean absolute errors (MAEs) of the Phi and Psi angle prediction are 27.8° and 44.6°, respectively, which are 1% and 3% respectively lower than that using one of the state-of-the-art prediction tools ANGLOR. Moreover, the prediction of TANGLE is significantly better than a random predictor that was built on the amino acid-specific basis, with the p-value<1.46e-147 and 7.97e-150, respectively by the Wilcoxon signed rank test. As a complementary approach to the current torsion angle prediction algorithms, TANGLE should prove useful in predicting protein structural properties and assisting protein fold recognition by applying the predicted torsion angles as useful restraints. TANGLE is freely accessible at http://sunflower.kuicr.kyoto-u.ac.jp/~sjn/TANGLE/. PMID:22319565

Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties.

PubMed

Batke, Monika; Gütlein, Martin; Partosch, Falko; Gundert-Remy, Ursula; Helma, Christoph; Kramer, Stefan; Maunz, Andreas; Seeland, Madeleine; Bitsch, Annette

2016-01-01

Interest is increasing in the development of non-animal methods for toxicological evaluations. These methods are however, particularly challenging for complex toxicological endpoints such as repeated dose toxicity. European Legislation, e.g., the European Union's Cosmetic Directive and REACH, demands the use of alternative methods. Frameworks, such as the Read-across Assessment Framework or the Adverse Outcome Pathway Knowledge Base, support the development of these methods. The aim of the project presented in this publication was to develop substance categories for a read-across with complex endpoints of toxicity based on existing databases. The basic conceptual approach was to combine structural similarity with shared mechanisms of action. Substances with similar chemical structure and toxicological profile form candidate categories suitable for read-across. We combined two databases on repeated dose toxicity, RepDose database, and ELINCS database to form a common database for the identification of categories. The resulting database contained physicochemical, structural, and toxicological data, which were refined and curated for cluster analyses. We applied the Predictive Clustering Tree (PCT) approach for clustering chemicals based on structural and on toxicological information to detect groups of chemicals with similar toxic profiles and pathways/mechanisms of toxicity. As many of the experimental toxicity values were not available, this data was imputed by predicting them with a multi-label classification method, prior to clustering. The clustering results were evaluated by assessing chemical and toxicological similarities with the aim of identifying clusters with a concordance between structural information and toxicity profiles/mechanisms. From these chosen clusters, seven were selected for a quantitative read-across, based on a small ratio of NOAEL of the members with the highest and the lowest NOAEL in the cluster (< 5). We discuss the limitations of the approach. Based on this analysis we propose improvements for a follow-up approach, such as incorporation of metabolic information and more detailed mechanistic information. The software enables the user to allocate a substance in a cluster and to use this information for a possible read- across. The clustering tool is provided as a free web service, accessible at http://mlc-reach.informatik.uni-mainz.de.

Analysis of Ribosome Stalling and Translation Elongation Dynamics by Deep Learning.

PubMed

Zhang, Sai; Hu, Hailin; Zhou, Jingtian; He, Xuan; Jiang, Tao; Zeng, Jianyang

2017-09-27

Ribosome stalling is manifested by the local accumulation of ribosomes at specific codon positions of mRNAs. Here, we present ROSE, a deep learning framework to analyze high-throughput ribosome profiling data and estimate the probability of a ribosome stalling event occurring at each genomic location. Extensive validation tests on independent data demonstrated that ROSE possessed higher prediction accuracy than conventional prediction models, with an increase in the area under the receiver operating characteristic curve by up to 18.4%. In addition, genome-wide statistical analyses showed that ROSE predictions can be well correlated with diverse putative regulatory factors of ribosome stalling. Moreover, the genome-wide ribosome stalling landscapes of both human and yeast computed by ROSE recovered the functional interplays between ribosome stalling and cotranslational events in protein biogenesis, including protein targeting by the signal recognition particles and protein secondary structure formation. Overall, our study provides a novel method to complement the ribosome profiling techniques and further decipher the complex regulatory mechanisms underlying translation elongation dynamics encoded in the mRNA sequence. Copyright © 2017 Elsevier Inc. All rights reserved.

Structural and functional effects of nucleotide variation on the human TB drug metabolizing enzyme arylamine N-acetyltransferase 1.

PubMed

Cloete, Ruben; Akurugu, Wisdom A; Werely, Cedric J; van Helden, Paul D; Christoffels, Alan

2017-08-01

The human arylamine N-acetyltransferase 1 (NAT1) enzyme plays a vital role in determining the duration of action of amine-containing drugs such as para-aminobenzoic acid (PABA) by influencing the balance between detoxification and metabolic activation of these drugs. Recently, four novel single nucleotide polymorphisms (SNPs) were identified within a South African mixed ancestry population. Modeling the effects of these SNPs within the structural protein was done to assess possible structure and function changes in the enzyme. The use of molecular dynamics simulations and stability predictions indicated less thermodynamically stable protein structures containing E264K and V231G, while the N245I change showed a stabilizing effect. Coincidently the N245I change displayed a similar free energy landscape profile to the known R64W amino acid substitution (slow acetylator), while the R242M displayed a similar profile to the published variant, I263V (proposed fast acetylator), and the wild type protein structure. Similarly, principal component analysis indicated that two amino acid substitutions (E264K and V231G) occupied less conformational clusters of folded states as compared to the WT and were found to be destabilizing (may affect protein function). However, two of the four novel SNPs that result in amino acid changes: (V231G and N245I) were predicted by both SIFT and POLYPHEN-2 algorithms to affect NAT1 protein function, while two other SNPs that result in R242M and E264K substitutions showed contradictory results based on SIFT and POLYPHEN-2 analysis. In conclusion, the structural methods were able to verify that two non-synonymous substitutions (E264K and V231G) can destabilize the protein structure, and are in agreement with mCSM predictions, and should therefore be experimentally tested for NAT1 activity. These findings could inform a strategy of incorporating genotypic data (i.e., functional SNP alleles) with phenotypic information (slow or fast acetylator) to better prescribe effective treatment using drugs metabolized by NAT1. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Semi-analytical model for a static sheath including a weakly collisional presheath

NASA Astrophysics Data System (ADS)

Shirafuji, Tatsuru; Denpoh, Kazuki

2018-06-01

A semi-analytical static sheath (SASS) model can provide a spatial potential profile on a biased surface with microstructures, which can be used for predicting ion trajectories on the surface. However, two- or three-dimensional SASS models require a search procedure for a sheath edge equipotential profile, at which ions have the Bohm velocity, as the starting positions for calculating ion trajectories. This procedure can be troublesome when surface microstructures have complex structures. This difficulty is due to the fact that the SASS model cannot handle a presheath region. In this work, we propose a modified SASS model that can handle a presheath region. By using the modified SASS model, ion trajectories can be calculated from edges with arbitrary geometry without searching for the equipotential profile corresponding to sheath edges.

Structural imprints in vivo decode RNA regulatory mechanisms.

PubMed

Spitale, Robert C; Flynn, Ryan A; Zhang, Qiangfeng Cliff; Crisalli, Pete; Lee, Byron; Jung, Jong-Wha; Kuchelmeister, Hannes Y; Batista, Pedro J; Torre, Eduardo A; Kool, Eric T; Chang, Howard Y

2015-03-26

Visualizing the physical basis for molecular behaviour inside living cells is a great challenge for biology. RNAs are central to biological regulation, and the ability of RNA to adopt specific structures intimately controls every step of the gene expression program. However, our understanding of physiological RNA structures is limited; current in vivo RNA structure profiles include only two of the four nucleotides that make up RNA. Here we present a novel biochemical approach, in vivo click selective 2'-hydroxyl acylation and profiling experiment (icSHAPE), which enables the first global view, to our knowledge, of RNA secondary structures in living cells for all four bases. icSHAPE of the mouse embryonic stem cell transcriptome versus purified RNA folded in vitro shows that the structural dynamics of RNA in the cellular environment distinguish different classes of RNAs and regulatory elements. Structural signatures at translational start sites and ribosome pause sites are conserved from in vitro conditions, suggesting that these RNA elements are programmed by sequence. In contrast, focal structural rearrangements in vivo reveal precise interfaces of RNA with RNA-binding proteins or RNA-modification sites that are consistent with atomic-resolution structural data. Such dynamic structural footprints enable accurate prediction of RNA-protein interactions and N(6)-methyladenosine (m(6)A) modification genome wide. These results open the door for structural genomics of RNA in living cells and reveal key physiological structures controlling gene expression.

Life extension of Structural Repairs – A statistical approach towards efficiency improvement

NASA Astrophysics Data System (ADS)

Deepashri, N. V.; Kalaiyappan, Mohan

2018-05-01

The life extension program of aircraft is taken up whenever aircraft’s intended life reaches close to its DSG (Design Service Goal). The Extended Service Goal (ESG) of an aircraft, in general, and structural repairs, in particular, is arrived at on the basis of F&DT (Fatigue & Damage Tolerance) analysis. Life extension program of aircraft consists of assessment of remaining life of all parts of the aircrafts including structural, mechanical, and electrical and avionics equipment and structural repairs. For life extension of stringer repair, as an example, it is required to re-assess the fatigue life of stringer in the presence of coupling under modified load spectrum. This is achieved by assessing the fatigue life of Web and Outer Flange (OF) part of stringers separately as per F&DT justification philosophy. Assessment of the fatigue life requires determination of stress concentration factor (Kt) for different combination of width, pitch, stringer thickness, coupling thickness and pad-up thickness of all stringer profiles available in different sections of fuselage. Determination of stress concentration factor for Web and Outer Flange of stringer profile covering entire ranges involves substantial number of Finite Element (FE) analysis. In order to optimise the number of FE runs, stress concentration factor is determined under worst repair factors combination (max. plate width; max. thickness; max. pitch; min. rivet dia.; and min. No. of rivets) resulting in conservative value. A parametric study of Web and Outer Flange data across stringer profiles were carried out and proven statistical techniques were used to find the optimal equation to predict stress concentration factor. This in turn reduced number of FE runs substantially for a given range of width, pitch, stringer thickness and so on. The use of optimal equation obtained through regression analysis is able to predict Kt within reasonable accuracy for a given range of inputs.

Prediction of human population responses to toxic compounds by a collaborative competition.

PubMed

Eduati, Federica; Mangravite, Lara M; Wang, Tao; Tang, Hao; Bare, J Christopher; Huang, Ruili; Norman, Thea; Kellen, Mike; Menden, Michael P; Yang, Jichen; Zhan, Xiaowei; Zhong, Rui; Xiao, Guanghua; Xia, Menghang; Abdo, Nour; Kosyk, Oksana; Friend, Stephen; Dearry, Allen; Simeonov, Anton; Tice, Raymond R; Rusyn, Ivan; Wright, Fred A; Stolovitzky, Gustavo; Xie, Yang; Saez-Rodriguez, Julio

2015-09-01

The ability to computationally predict the effects of toxic compounds on humans could help address the deficiencies of current chemical safety testing. Here, we report the results from a community-based DREAM challenge to predict toxicities of environmental compounds with potential adverse health effects for human populations. We measured the cytotoxicity of 156 compounds in 884 lymphoblastoid cell lines for which genotype and transcriptional data are available as part of the Tox21 1000 Genomes Project. The challenge participants developed algorithms to predict interindividual variability of toxic response from genomic profiles and population-level cytotoxicity data from structural attributes of the compounds. 179 submitted predictions were evaluated against an experimental data set to which participants were blinded. Individual cytotoxicity predictions were better than random, with modest correlations (Pearson's r < 0.28), consistent with complex trait genomic prediction. In contrast, predictions of population-level response to different compounds were higher (r < 0.66). The results highlight the possibility of predicting health risks associated with unknown compounds, although risk estimation accuracy remains suboptimal.

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

Fielding, E.J.; Barazangi, M.; Isacks, B.L.

Topography and heterogeneous crustal structure have major effects on the propagation of regional seismic phases. We are collecting topographical, geological, and geophysical datasets for Eurasia into an information system that can be accessed via Internet connections. Now available are digital topography, satellite imagery, and data on sedimentary basins and crustal structure thicknesses. New datasets for Eurasia include maps of depth to Moho beneath Europe and Scandinavia. We have created regularly spaced grids of the crustal thickness values from these maps that can be used to create profiles of crustal structure. These profiles can be compared by an analyst or anmore » automatic program with the crustal seismic phases received along the propagation path to better understand and predict the path effects on phase amplitudes, a key to estimating magnitudes and yields, and for understanding variations in travel-time delays for phases such as Pn, important for improving regional event locations. The gridded data could also be used to model propagation of crustal phases in three dimensions. Digital elevation models, Satellite imagery, Geographic information systems, Lg Propagation, Moho, Geology, Crustal structure, Topographic relief.« less

Gene Expression Profiling Predicts the Development of Oral Cancer

PubMed Central

Saintigny, Pierre; Zhang, Li; Fan, You-Hong; El-Naggar, Adel K.; Papadimitrakopoulou, Vali; Feng, Lei; Lee, J. Jack; Kim, Edward S.; Hong, Waun Ki; Mao, Li

2011-01-01

Patients with oral preneoplastic lesion (OPL) have high risk of developing oral cancer. Although certain risk factors such as smoking status and histology are known, our ability to predict oral cancer risk remains poor. The study objective was to determine the value of gene expression profiling in predicting oral cancer development. Gene expression profile was measured in 86 of 162 OPL patients who were enrolled in a clinical chemoprevention trial that used the incidence of oral cancer development as a prespecified endpoint. The median follow-up time was 6.08 years and 35 of the 86 patients developed oral cancer over the course. Gene expression profiles were associated with oral cancer-free survival and used to develope multivariate predictive models for oral cancer prediction. We developed a 29-transcript predictive model which showed marked improvement in terms of prediction accuracy (with 8% predicting error rate) over the models using previously known clinico-pathological risk factors. Based on the gene expression profile data, we also identified 2182 transcripts significantly associated with oral cancer risk associated genes (P-value<0.01, single variate Cox proportional hazards model). Functional pathway analysis revealed proteasome machinery, MYC, and ribosomes components as the top gene sets associated with oral cancer risk. In multiple independent datasets, the expression profiles of the genes can differentiate head and neck cancer from normal mucosa. Our results show that gene expression profiles may improve the prediction of oral cancer risk in OPL patients and the significant genes identified may serve as potential targets for oral cancer chemoprevention. PMID:21292635

Atomically informed nonlocal semi-discrete variational Peierls-Nabarro model for planar core dislocations

PubMed Central

Liu, Guisen; Cheng, Xi; Wang, Jian; Chen, Kaiguo; Shen, Yao

2017-01-01

Prediction of Peierls stress associated with dislocation glide is of fundamental concern in understanding and designing the plasticity and mechanical properties of crystalline materials. Here, we develop a nonlocal semi-discrete variational Peierls-Nabarro (SVPN) model by incorporating the nonlocal atomic interactions into the semi-discrete variational Peierls framework. The nonlocal kernel is simplified by limiting the nonlocal atomic interaction in the nearest neighbor region, and the nonlocal coefficient is directly computed from the dislocation core structure. Our model is capable of accurately predicting the displacement profile, and the Peierls stress, of planar-extended core dislocations in face-centered cubic structures. Our model could be extended to study more complicated planar-extended core dislocations, such as <110> {111} dislocations in Al-based and Ti-based intermetallic compounds. PMID:28252102

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

Modeling a simple coronal streamer during whole sun month

NASA Technical Reports Server (NTRS)

Gibson, S. E.; Bagenal, F.; Biesecker, D.; Guhathakurta, M.; Hoeksema, J. T.; Thompson, B. J.

1997-01-01

The solar minimum streamer structure observed during the whole sun month was modeled. The Van de Hulst inversion was used in order to determine the coronal electron density profiles and scale-height temperature profiles. The axisymmetric magnetostatic model of Gibson, Bagenal and Low was also used. The density, temperature, and magnetic field distribution were quantified using both coronal white light data and photospheric magnetic field data from the Wilcox Solar Observatory. The densities and temperatures obtained by the Van de Hulst and magnetostatic models are compared to the magnetic field predicted by the magnetostatic model to a potential field extrapolated from the photosphere.

Analysis and test of low profile aluminum aerospace tank dome

NASA Astrophysics Data System (ADS)

Ahmed, R.; Wilhelm, J. M.

1993-12-01

In order to increase the structural performance of cryogenic tanks, the aerospace industry is beginning to employ low-profile bulkheads in new generation launch vehicle designs. This report details the analysis and test of one such dome made from 2219 aluminum. Such domes have two potential failure modes under internal pressure, general tensile failure and hoop compression buckling (in regions near the equator). The test determined the buckling load and ultimate tensile load of the hardware and showed that both compared well with the analysis predictions. This effort was conducted under the auspices of NASA and the General Dynamics Cryogenic Tank Technology Program (CTTP).

Analysis and test of low profile aluminum aerospace tank dome

NASA Technical Reports Server (NTRS)

Ahmed, R.; Wilhelm, J. M.

1993-01-01

In order to increase the structural performance of cryogenic tanks, the aerospace industry is beginning to employ low-profile bulkheads in new generation launch vehicle designs. This report details the analysis and test of one such dome made from 2219 aluminum. Such domes have two potential failure modes under internal pressure, general tensile failure and hoop compression buckling (in regions near the equator). The test determined the buckling load and ultimate tensile load of the hardware and showed that both compared well with the analysis predictions. This effort was conducted under the auspices of NASA and the General Dynamics Cryogenic Tank Technology Program (CTTP).

Liquid chromatography/mass spectrometry-based plasma metabolic profiling study of escitalopram in subjects with major depressive disorder.

PubMed

Bandu, Raju; Lee, Hyun Jeong; Lee, Hyeong Min; Ha, Tae Hyon; Lee, Heon-Jeong; Kim, Se Joo; Ha, Kyooseob; Kim, Kwang Pyo

2018-05-01

Liquid chromatography-mass spectrometry (LC-MS) method revealed the plasma metabolite profiles in major depressive disorder patients treated with escitalopram (ECTP) (n = 7). Depression severity was assessed according to the 17-item Hamilton Depression Rating Scale. Metabolic profiles were derived from major depressive disorder subject blood samples collected after ECTP treatment. Blood plasma was separated and processed in order to effectively extract metabolites, which were then analyzed using LC-MS. We identified 19 metabolites and elucidated their structures using LC-tandem MS (LC-MS/MS) combined with elemental compositions derived from accurate mass measurements. We further used online H/D exchange experiments to verify the structural elucidations of each metabolite. Identifying molecular metabolites may provide critical insights into the pharmacological and clinical effects of ECTP treatment and may also provide useful information informing the development of new antidepressant treatments. These detailed plasma metabolite analyses may also be used to identify optimal dose concentrations in psychopharmacotherapeutic treatment through drug monitoring, as well as forming the basis for response predictions in depressed subjects. Copyright © 2018 John Wiley & Sons, Ltd.

An integrated view of the 1987 Australian monsoon and its mesoscale convective systems. II - Vertical structure

NASA Technical Reports Server (NTRS)

Mapes, Brian; Houze, Robert A., Jr.

1993-01-01

The vertical structure of monsoon thermal forcing by precipitating convection is diagnosed in terms of horizontal divergence. Airborne Doppler-radar divergence profiles from nine diverse mesoscale convective systems (MCSs) are presented. The MCSs consisted of multicellular convective elements which in time gave rise to areas of stratiform precipitation. Each of the three basic building blocks of the MCSs - convective, intermediary, and stratiform precipitation areas - has a consistent, characteristic divergence profile. Convective areas have low-level convergence, with its peak at 2-4 km altitude, and divergence above 6 km. Intermediary areas have convergence aloft, peaked near 10 km, feeding into mean ascent high in the upper troposphere. Stratiform areas have mid-level convergence, indicating a mesoscale downdraught below the melting level, and a mesoscale updraught aloft. Rawinsonde composite divergence profiles agree with the Doppler data in at least one important respect: the lower-tropospheric convergence into the MCSs peaks 2-4-km above the surface. Rawinsonde vorticity profiles show that monsoonal tropical cyclones spin-up at these elevated levels first, then later descend to the surface. Rawinsonde observations on a larger, continental scale demonstrate that at large horizontal scales only the 'gravest vertical mode' of MCS heating is felt, while the effects of shallower components of the heating (or divergence) profiles are trapped near the heating, as predicted by geostrophic adjustment theory.

On the emergence of macroscopic transport barriers from staircase structures

NASA Astrophysics Data System (ADS)

Ashourvan, Arash; Diamond, P. H.

2017-01-01

This paper presents a theory for the formation and evolution of coupled density staircases and zonal shear profiles in a simple model of drift-wave turbulence. Density, vorticity, and fluctuation potential enstrophy are the fields evolved in this system. Formation of staircase structures is due to inhomogeneous mixing of generalized potential vorticity (PV), resulting in the sharpening of density and vorticity gradients in some regions, and weakening them in others. When the PV gradients steepen, the density staircase structure develops into a lattice of mesoscale "jumps," and "steps," which are, respectively, the regions of local gradient steepening and flattening. The jumps merge and migrate in radius, leading to the development of macroscale profile structures from mesoscale elements. The positive feedback process, which drives the staircase formation occurs via a Rhines scale dependent mixing length. We present extensive studies of bifurcation physics of the global state, including results on the global flux-gradient relations (flux landscapes) predicted by the model. Furthermore, we demonstrate that, depending on the sources and boundary conditions, either a region of enhanced confinement, or a region with strong turbulence can form at the edge. This suggests that the profile self-organization is a global process, though one which can be described by a local, but nonlinear model. This model is the first to demonstrate how the mesoscale condensation of staircases leads to global states of enhanced confinement.

Cell-specific prediction and application of drug-induced gene expression profiles.

PubMed

Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David; Dudley, Joel

2018-01-01

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes.

Cell-specific prediction and application of drug-induced gene expression profiles

PubMed Central

Hodos, Rachel; Zhang, Ping; Lee, Hao-Chih; Duan, Qiaonan; Wang, Zichen; Clark, Neil R.; Ma'ayan, Avi; Wang, Fei; Kidd, Brian; Hu, Jianying; Sontag, David

2017-01-01

Gene expression profiling of in vitro drug perturbations is useful for many biomedical discovery applications including drug repurposing and elucidation of drug mechanisms. However, limited data availability across cell types has hindered our capacity to leverage or explore the cell-specificity of these perturbations. While recent efforts have generated a large number of drug perturbation profiles across a variety of human cell types, many gaps remain in this combinatorial drug-cell space. Hence, we asked whether it is possible to fill these gaps by predicting cell-specific drug perturbation profiles using available expression data from related conditions--i.e. from other drugs and cell types. We developed a computational framework that first arranges existing profiles into a three-dimensional array (or tensor) indexed by drugs, genes, and cell types, and then uses either local (nearest-neighbors) or global (tensor completion) information to predict unmeasured profiles. We evaluate prediction accuracy using a variety of metrics, and find that the two methods have complementary performance, each superior in different regions in the drug-cell space. Predictions achieve correlations of 0.68 with true values, and maintain accurate differentially expressed genes (AUC 0.81). Finally, we demonstrate that the predicted profiles add value for making downstream associations with drug targets and therapeutic classes. PMID:29218867

Psychopathology profiles of acutely suicidal adolescents: Associations with post-discharge suicide attempts and rehospitalization

PubMed Central

Berona, Johnny; Horwitz, Adam G.; Czyz, Ewa K.; King, Cheryl A.

2017-01-01

Background Suicidal adolescents are heterogeneous, which can pose difficulties in predicting suicidal behavior. The Youth Self-Report (YSR) psychopathology profiles predict the future onset of psychopathology and suicide-related outcomes. The present study examined the prevalence and correlates of YSR psychopathology profiles among suicidal adolescents and prospective associations with post-discharge rates of suicide attempts and psychiatric rehospitalization. Methods Participants were acutely suicidal, psychiatrically hospitalized adolescents (N=433 at baseline; n=355 at follow-up) who were enrolled in a psychosocial intervention trial during hospitalization. Psychopathology profiles were assessed at baseline. Suicide attempts and rehospitalization were assessed for up to 12 months following discharge. Results Latent profile analysis identified four psychopathology profiles: subclinical, primarily internalizing, and moderately and severely dysregulated. At baseline, profiles differed by history of non-suicidal self-injury (NSSI) and multiple suicide attempts (MA) as well as severity of suicide ideation, hopelessness, depressive symptoms, anxiety symptoms, substance abuse, and functional impairment. The dysregulation profiles predicted suicide attempts within 3 months post-discharge. The internalizing profile predicted suicide attempts and rehospitalization at 3 and 12 months. Limitations This study’s participants were enrolled in a randomized trial and were predominantly female, which limit generalizability. Additionally, only a history of NSSI was assessed. Conclusions The dysregulation profile was overrepresented among suicidal youth and associated with impairment in several domains as well as suicide attempts shortly after discharge. Adolescents with a severe internalizing profile also reported adverse outcomes throughout the study period. Psychopathology profiles warrant further examination in terms of their potential predictive validity in relation to suicide-related outcomes. PMID:27894037

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).

Modeling and measuring the nocturnal drainage flow in a high-elevation, subalpine forest with complex terrain

USGS Publications Warehouse

Yi, C.; Monson, Russell K.; Zhai, Z.; Anderson, D.E.; Lamb, B.; Allwine, G.; Turnipseed, A.A.; Burns, Sean P.

2005-01-01

The nocturnal drainage flow of air causes significant uncertainty in ecosystem CO2, H2O, and energy budgets determined with the eddy covariance measurement approach. In this study, we examined the magnitude, nature, and dynamics of the nocturnal drainage flow in a subalpine forest ecosystem with complex terrain. We used an experimental approach involving four towers, each with vertical profiling of wind speed to measure the magnitude of drainage flows and dynamics in their occurrence. We developed an analytical drainage flow model, constrained with measurements of canopy structure and SF6 diffusion, to help us interpret the tower profile results. Model predictions were in good agreement with observed profiles of wind speed, leaf area density, and wind drag coefficient. Using theory, we showed that this one-dimensional model is reduced to the widely used exponential wind profile model under conditions where vertical leaf area density and drag coefficient are uniformly distributed. We used the model for stability analysis, which predicted the presence of a very stable layer near the height of maximum leaf area density. This stable layer acts as a flow impediment, minimizing vertical dispersion between the subcanopy air space and the atmosphere above the canopy. The prediction is consistent with the results of SF6 diffusion observations that showed minimal vertical dispersion of nighttime, subcanopy drainage flows. The stable within-canopy air layer coincided with the height of maximum wake-to-shear production ratio. We concluded that nighttime drainage flows are restricted to a relatively shallow layer of air beneath the canopy, with little vertical mixing across a relatively long horizontal fetch. Insight into the horizontal and vertical structure of the drainage flow is crucial for understanding the magnitude and dynamics of the mean advective CO2 flux that becomes significant during stable nighttime conditions and are typically missed during measurement of the turbulent CO2 flux. The model and interpretation provided in this study should lead to research strategies for the measurement of these advective fluxes and their inclusion in the overall mass balance for CO2 at this site with complex terrain. Copyright 2005 by the American Geophysical Union.

Modeling and measuring the nocturnal drainage flow in a high-elevation, subalpine forest with complex terrain

NASA Astrophysics Data System (ADS)

Yi, Chuixiang; Monson, Russell K.; Zhai, Zhiqiang; Anderson, Dean E.; Lamb, Brian; Allwine, Gene; Turnipseed, Andrew A.; Burns, Sean P.

2005-11-01

The nocturnal drainage flow of air causes significant uncertainty in ecosystem CO2, H2O, and energy budgets determined with the eddy covariance measurement approach. In this study, we examined the magnitude, nature, and dynamics of the nocturnal drainage flow in a subalpine forest ecosystem with complex terrain. We used an experimental approach involving four towers, each with vertical profiling of wind speed to measure the magnitude of drainage flows and dynamics in their occurrence. We developed an analytical drainage flow model, constrained with measurements of canopy structure and SF6 diffusion, to help us interpret the tower profile results. Model predictions were in good agreement with observed profiles of wind speed, leaf area density, and wind drag coefficient. Using theory, we showed that this one-dimensional model is reduced to the widely used exponential wind profile model under conditions where vertical leaf area density and drag coefficient are uniformly distributed. We used the model for stability analysis, which predicted the presence of a very stable layer near the height of maximum leaf area density. This stable layer acts as a flow impediment, minimizing vertical dispersion between the subcanopy air space and the atmosphere above the canopy. The prediction is consistent with the results of SF6 diffusion observations that showed minimal vertical dispersion of nighttime, subcanopy drainage flows. The stable within-canopy air layer coincided with the height of maximum wake-to-shear production ratio. We concluded that nighttime drainage flows are restricted to a relatively shallow layer of air beneath the canopy, with little vertical mixing across a relatively long horizontal fetch. Insight into the horizontal and vertical structure of the drainage flow is crucial for understanding the magnitude and dynamics of the mean advective CO2 flux that becomes significant during stable nighttime conditions and are typically missed during measurement of the turbulent CO2 flux. The model and interpretation provided in this study should lead to research strategies for the measurement of these advective fluxes and their inclusion in the overall mass balance for CO2 at this site with complex terrain.

Structure-function studies of BPP-BrachyNH2 and synthetic analogues thereof with Angiotensin I-Converting Enzyme.

PubMed

Arcanjo, Daniel D R; Vasconcelos, Andreanne G; Nascimento, Lucas A; Mafud, Ana Carolina; Plácido, Alexandra; Alves, Michel M M; Delerue-Matos, Cristina; Bemquerer, Marcelo P; Vale, Nuno; Gomes, Paula; Oliveira, Eduardo B; Lima, Francisco C A; Mascarenhas, Yvonne P; Carvalho, Fernando Aécio A; Simonsen, Ulf; Ramos, Ricardo M; Leite, José Roberto S A

2017-10-20

The vasoactive proline-rich oligopeptide termed BPP-BrachyNH 2 (H-WPPPKVSP-NH 2 ) induces in vitro inhibitory activity of angiotensin I-converting enzyme (ACE) in rat blood serum. In the present study, the removal of N-terminal tryptophan or C-terminal proline from BPP-BrachyNH 2 was investigated in order to predict which structural components are important or required for interaction with ACE. Furthermore, the toxicological profile was assessed by in silico prediction and in vitro MTT assay. Two BPP-BrachyNH 2 analogues (des-Trp 1 -BPP-BrachyNH 2 and des-Pro 8 -BPP-BrachyNH 2 ) were synthesized, and in vitro and in silico ACE inhibitory activity and toxicological profile were assessed. The des-Trp 1 -BPP-BrachyNH 2 and des-Pro 8 -BPP-BrachyNH 2 were respectively 3.2- and 29.5-fold less active than the BPP-BrachyNH 2 -induced ACE inhibitory activity. Molecular Dynamic and Molecular Mechanics Poisson-Boltzmann Surface Area simulations (MM-PBSA) demonstrated that the ACE/BBP-BrachyNH 2 complex showed lower binding and van der Wall energies than the ACE/des-Pro 8 -BPP-BrachyNH 2 complex, therefore having better stability. The removal of the N-terminal tryptophan increased the in silico predicted toxicological effects and cytotoxicity when compared with BPP-BrachyNH 2 or des-Pro 8 -BPP-BrachyNH 2 . Otherwise, des-Pro 8 -BPP-BrachyNH 2 was 190-fold less cytotoxic than BPP-BrachyNH 2 . Thus, the removal of C-terminal proline residue was able to markedly decrease both the BPP-BrachyNH 2 -induced ACE inhibitory and cytotoxic effects assessed by in vitro and in silico approaches. In conclusion, the aminoacid sequence of BPP-BrachyNH 2 is essential for its ACE inhibitory activity and associated with an acceptable toxicological profile. The perspective of the interactions of BPP-BrachyNH 2 with ACE found in the present study can be used for development of drugs with differential therapeutic profile than current ACE inhibitors. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Structure-based discovery of selective serotonin 5-HT(1B) receptor ligands.

PubMed

Rodríguez, David; Brea, José; Loza, María Isabel; Carlsson, Jens

2014-08-05

The development of safe and effective drugs relies on the discovery of selective ligands. Serotonin (5-hydroxytryptamine [5-HT]) G protein-coupled receptors are therapeutic targets for CNS disorders but are also associated with adverse drug effects. The determination of crystal structures for the 5-HT1B and 5-HT2B receptors provided an opportunity to identify subtype selective ligands using structure-based methods. From docking screens of 1.3 million compounds, 22 molecules were predicted to be selective for the 5-HT1B receptor over the 5-HT2B subtype, a requirement for safe serotonergic drugs. Nine compounds were experimentally verified as 5-HT1B-selective ligands, with up to 300-fold higher affinities for this subtype. Three of the ligands were agonists of the G protein pathway. Analysis of state-of-the-art homology models of the two 5-HT receptors revealed that the crystal structures were critical for predicting selective ligands. Our results demonstrate that structure-based screening can guide the discovery of ligands with specific selectivity profiles. Copyright © 2014 Elsevier Ltd. All rights reserved.

Modeling Coherent Structures in Canopy Flows

NASA Astrophysics Data System (ADS)

Luhar, Mitul

2017-11-01

It is well known that flows over vegetation canopies are characterized by the presence of energetic coherent structures. Since the mean profile over dense canopies exhibits an inflection point, the emergence of such structures is often attributed to a Kelvin-Helmholtz instability. However, though stability analyses provide useful mechanistic insights into canopy flows, they are limited in their ability to generate predictions for spectra and coherent structure. The present effort seeks to address this limitation by extending the resolvent formulation (McKeon and Sharma, 2010, J. Fluid Mech.) to canopy flows. Under the resolvent formulation, the turbulent velocity field is expressed as a superposition of propagating modes, identified via a gain-based (singular value) decomposition of the Navier-Stokes equations. A key advantage of this approach is that it reconciles multiple mechanisms that lead to high amplification in turbulent flows, including modal instability, transient growth, and critical-layer phenomena. Further, individual high-gain modes can be combined to generate more complete models for coherent structure and velocity spectra. Preliminary resolvent-based model predictions for canopy flows agree well with existing experiments and simulations.

almaBTE : A solver of the space-time dependent Boltzmann transport equation for phonons in structured materials

NASA Astrophysics Data System (ADS)

Carrete, Jesús; Vermeersch, Bjorn; Katre, Ankita; van Roekeghem, Ambroise; Wang, Tao; Madsen, Georg K. H.; Mingo, Natalio

2017-11-01

almaBTE is a software package that solves the space- and time-dependent Boltzmann transport equation for phonons, using only ab-initio calculated quantities as inputs. The program can predictively tackle phonon transport in bulk crystals and alloys, thin films, superlattices, and multiscale structures with size features in the nm- μm range. Among many other quantities, the program can output thermal conductances and effective thermal conductivities, space-resolved average temperature profiles, and heat-current distributions resolved in frequency and space. Its first-principles character makes almaBTE especially well suited to investigate novel materials and structures. This article gives an overview of the program structure and presents illustrative examples for some of its uses. PROGRAM SUMMARY Program Title:almaBTE Program Files doi:http://dx.doi.org/10.17632/8tfzwgtp73.1 Licensing provisions: Apache License, version 2.0 Programming language: C++ External routines/libraries: BOOST, MPI, Eigen, HDF5, spglib Nature of problem: Calculation of temperature profiles, thermal flux distributions and effective thermal conductivities in structured systems where heat is carried by phonons Solution method: Solution of linearized phonon Boltzmann transport equation, Variance-reduced Monte Carlo

A systematic review on popularity, application and characteristics of protein secondary structure prediction tools.

PubMed

Kashani-Amin, Elaheh; Tabatabaei-Malazy, Ozra; Sakhteman, Amirhossein; Larijani, Bagher; Ebrahim-Habibi, Azadeh

2018-02-27

Prediction of proteins' secondary structure is one of the major steps in the generation of homology models. These models provide structural information which is used to design suitable ligands for potential medicinal targets. However, selecting a proper tool between multiple secondary structure prediction (SSP) options is challenging. The current study is an insight onto currently favored methods and tools, within various contexts. A systematic review was performed for a comprehensive access to recent (2013-2016) studies which used or recommended protein SSP tools. Three databases, Web of Science, PubMed and Scopus were systematically searched and 99 out of 209 studies were finally found eligible to extract data. Four categories of applications for 59 retrieved SSP tools were: (I) prediction of structural features of a given sequence, (II) evaluation of a method, (III) providing input for a new SSP method and (IV) integrating a SSP tool as a component for a program. PSIPRED was found to be the most popular tool in all four categories. JPred and tools utilizing PHD (Profile network from HeiDelberg) method occupied second and third places of popularity in categories I and II. JPred was only found in the two first categories, while PHD was present in three fields. This study provides a comprehensive insight about the recent usage of SSP tools which could be helpful for selecting a proper tool's choice. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Physical modeling of river spanning rock structures: Evaluating interstitial flow, local hydraulics, downstream scour development, and structure stability

USGS Publications Warehouse

Collins, K.L.; Thornton, C.I.; Mefford, B.; Holmquist-Johnson, C. L.

2009-01-01

Rock weir and ramp structures uniquely serve a necessary role in river management: to meet water deliveries in an ecologically sound manner. Uses include functioning as low head diversion dams, permitting fish passage, creating habitat diversity, and stabilizing stream banks and profiles. Existing information on design and performance of in-stream rock structures does not provide the guidance necessary to implement repeatable and sustainable construction and retrofit techniques. As widespread use of rock structures increases, the need for reliable design methods with a broad range of applicability at individual sites grows as well. Rigorous laboratory testing programs were implemented at the U.S. Bureau of Reclamation (Reclamation) and at Colorado State University (CSU) as part of a multifaceted research project focused on expanding the current knowledge base and developing design methods to improve the success rate of river spanning rock structures in meeting project goals. Physical modeling at Reclamation is being used to measure, predict, and reduce interstitial flow through rock ramps. CSU is using physical testing to quantify and predict scour development downstream of rock weirs and its impact on the stability of rock structures. ?? 2009 ASCE.

Individual Functional ROI Optimization via Maximization of Group-wise Consistency of Structural and Functional Profiles

PubMed Central

Li, Kaiming; Guo, Lei; Zhu, Dajiang; Hu, Xintao; Han, Junwei; Liu, Tianming

2013-01-01

Studying connectivities among functional brain regions and the functional dynamics on brain networks has drawn increasing interest. A fundamental issue that affects functional connectivity and dynamics studies is how to determine the best possible functional brain regions or ROIs (regions of interest) for a group of individuals, since the connectivity measurements are heavily dependent on ROI locations. Essentially, identification of accurate, reliable and consistent corresponding ROIs is challenging due to the unclear boundaries between brain regions, variability across individuals, and nonlinearity of the ROIs. In response to these challenges, this paper presents a novel methodology to computationally optimize ROIs locations derived from task-based fMRI data for individuals so that the optimized ROIs are more consistent, reproducible and predictable across brains. Our computational strategy is to formulate the individual ROI location optimization as a group variance minimization problem, in which group-wise consistencies in functional/structural connectivity patterns and anatomic profiles are defined as optimization constraints. Our experimental results from multimodal fMRI and DTI data show that the optimized ROIs have significantly improved consistency in structural and functional profiles across individuals. These improved functional ROIs with better consistency could contribute to further study of functional interaction and dynamics in the human brain. PMID:22281931

Numerical investigation of galloping instabilities in Z-shaped profiles.

PubMed

Gomez, Ignacio; Chavez, Miguel; Alonso, Gustavo; Valero, Eusebio

2014-01-01

Aeroelastic effects are relatively common in the design of modern civil constructions such as office blocks, airport terminal buildings, and factories. Typical flexible structures exposed to the action of wind are shading devices, normally slats or louvers. A typical cross-section for such elements is a Z-shaped profile, made out of a central web and two-side wings. Galloping instabilities are often determined in practice using the Glauert-Den Hartog criterion. This criterion relies on accurate predictions of the dependence of the aerodynamic force coefficients with the angle of attack. The results of a parametric analysis based on a numerical analysis and performed on different Z-shaped louvers to determine translational galloping instability regions are presented in this paper. These numerical analysis results have been validated with a parametric analysis of Z-shaped profiles based on static wind tunnel tests. In order to perform this validation, the DLR TAU Code, which is a standard code within the European aeronautical industry, has been used. This study highlights the focus on the numerical prediction of the effect of galloping, which is shown in a visible way, through stability maps. Comparisons between numerical and experimental data are presented with respect to various meshes and turbulence models.

LRSSLMDA: Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction

PubMed Central

Huang, Li

2017-01-01

Predicting novel microRNA (miRNA)-disease associations is clinically significant due to miRNAs’ potential roles of diagnostic biomarkers and therapeutic targets for various human diseases. Previous studies have demonstrated the viability of utilizing different types of biological data to computationally infer new disease-related miRNAs. Yet researchers face the challenge of how to effectively integrate diverse datasets and make reliable predictions. In this study, we presented a computational model named Laplacian Regularized Sparse Subspace Learning for MiRNA-Disease Association prediction (LRSSLMDA), which projected miRNAs/diseases’ statistical feature profile and graph theoretical feature profile to a common subspace. It used Laplacian regularization to preserve the local structures of the training data and a L1-norm constraint to select important miRNA/disease features for prediction. The strength of dimensionality reduction enabled the model to be easily extended to much higher dimensional datasets than those exploited in this study. Experimental results showed that LRSSLMDA outperformed ten previous models: the AUC of 0.9178 in global leave-one-out cross validation (LOOCV) and the AUC of 0.8418 in local LOOCV indicated the model’s superior prediction accuracy; and the average AUC of 0.9181+/-0.0004 in 5-fold cross validation justified its accuracy and stability. In addition, three types of case studies further demonstrated its predictive power. Potential miRNAs related to Colon Neoplasms, Lymphoma, Kidney Neoplasms, Esophageal Neoplasms and Breast Neoplasms were predicted by LRSSLMDA. Respectively, 98%, 88%, 96%, 98% and 98% out of the top 50 predictions were validated by experimental evidences. Therefore, we conclude that LRSSLMDA would be a valuable computational tool for miRNA-disease association prediction. PMID:29253885

First Trimester Urine and Serum Metabolomics for Prediction of Preeclampsia and Gestational Hypertension: A Prospective Screening Study.

PubMed

Austdal, Marie; Tangerås, Line H; Skråstad, Ragnhild B; Salvesen, Kjell; Austgulen, Rigmor; Iversen, Ann-Charlotte; Bathen, Tone F

2015-09-08

Hypertensive disorders of pregnancy, including preeclampsia, are major contributors to maternal morbidity. The goal of this study was to evaluate the potential of metabolomics to predict preeclampsia and gestational hypertension from urine and serum samples in early pregnancy, and elucidate the metabolic changes related to the diseases. Metabolic profiles were obtained by nuclear magnetic resonance spectroscopy of serum and urine samples from 599 women at medium to high risk of preeclampsia (nulliparous or previous preeclampsia/gestational hypertension). Preeclampsia developed in 26 (4.3%) and gestational hypertension in 21 (3.5%) women. Multivariate analyses of the metabolic profiles were performed to establish prediction models for the hypertensive disorders individually and combined. Urinary metabolomic profiles predicted preeclampsia and gestational hypertension at 51.3% and 40% sensitivity, respectively, at 10% false positive rate, with hippurate as the most important metabolite for the prediction. Serum metabolomic profiles predicted preeclampsia and gestational hypertension at 15% and 33% sensitivity, respectively, with increased lipid levels and an atherogenic lipid profile as most important for the prediction. Combining maternal characteristics with the urinary hippurate/creatinine level improved the prediction rates of preeclampsia in a logistic regression model. The study indicates a potential future role of clinical importance for metabolomic analysis of urine in prediction of preeclampsia.

Gaseous hydrogen/oxygen injector performance characterization

NASA Technical Reports Server (NTRS)

Degroot, W. A.; Tsuei, H. H.

1994-01-01

Results are presented of spontaneous Raman scattering measurements in the combustion chamber of a 110 N thrust class gaseous hydrogen/oxygen rocket. Temperature, oxygen number density, and water number density profiles at the injector exit plane are presented. These measurements are used as input profiles to a full Navier-Stokes computational fluid dynamics (CFD) code. Predictions of this code while using the measured profiles are compared with predictions while using assumed uniform injector profiles. Axial and radial velocity profiles derived from both sets of predictions are compared with Rayleigh scattering measurements in the exit plane of a 33:1 area ratio nozzle. Temperature and number density Raman scattering measurements at the exit plane of a test rocket with a 1:1.36 area ratio nozzle are also compared with results from both sets of predictions.

Validity of a Manual Soft Tissue Profile Prediction Method Following Mandibular Setback Osteotomy

PubMed Central

Kolokitha, Olga-Elpis

2007-01-01

Objectives The aim of this study was to determine the validity of a manual cephalometric method used for predicting the post-operative soft tissue profiles of patients who underwent mandibular setback surgery and compare it to a computerized cephalometric prediction method (Dentofacial Planner). Lateral cephalograms of 18 adults with mandibular prognathism taken at the end of pre-surgical orthodontics and approximately one year after surgery were used. Methods To test the validity of the manual method the prediction tracings were compared to the actual post-operative tracings. The Dentofacial Planner software was used to develop the computerized post-surgical prediction tracings. Both manual and computerized prediction printouts were analyzed by using the cephalometric system PORDIOS. Statistical analysis was performed by means of t-test. Results Comparison between manual prediction tracings and the actual post-operative profile showed that the manual method results in more convex soft tissue profiles; the upper lip was found in a more prominent position, upper lip thickness was increased and, the mandible and lower lip were found in a less posterior position than that of the actual profiles. Comparison between computerized and manual prediction methods showed that in the manual method upper lip thickness was increased, the upper lip was found in a more anterior position and the lower anterior facial height was increased as compared to the computerized prediction method. Conclusions Cephalometric simulation of post-operative soft tissue profile following orthodontic-surgical management of mandibular prognathism imposes certain limitations related to the methods implied. However, both manual and computerized prediction methods remain a useful tool for patient communication. PMID:19212468

Validity of a manual soft tissue profile prediction method following mandibular setback osteotomy.

PubMed

Kolokitha, Olga-Elpis

2007-10-01

The aim of this study was to determine the validity of a manual cephalometric method used for predicting the post-operative soft tissue profiles of patients who underwent mandibular setback surgery and compare it to a computerized cephalometric prediction method (Dentofacial Planner). Lateral cephalograms of 18 adults with mandibular prognathism taken at the end of pre-surgical orthodontics and approximately one year after surgery were used. To test the validity of the manual method the prediction tracings were compared to the actual post-operative tracings. The Dentofacial Planner software was used to develop the computerized post-surgical prediction tracings. Both manual and computerized prediction printouts were analyzed by using the cephalometric system PORDIOS. Statistical analysis was performed by means of t-test. Comparison between manual prediction tracings and the actual post-operative profile showed that the manual method results in more convex soft tissue profiles; the upper lip was found in a more prominent position, upper lip thickness was increased and, the mandible and lower lip were found in a less posterior position than that of the actual profiles. Comparison between computerized and manual prediction methods showed that in the manual method upper lip thickness was increased, the upper lip was found in a more anterior position and the lower anterior facial height was increased as compared to the computerized prediction method. Cephalometric simulation of post-operative soft tissue profile following orthodontic-surgical management of mandibular prognathism imposes certain limitations related to the methods implied. However, both manual and computerized prediction methods remain a useful tool for patient communication.

iDBPs: a web server for the identification of DNA binding proteins.

PubMed

Nimrod, Guy; Schushan, Maya; Szilágyi, András; Leslie, Christina; Ben-Tal, Nir

2010-03-01

The iDBPs server uses the three-dimensional (3D) structure of a query protein to predict whether it binds DNA. First, the algorithm predicts the functional region of the protein based on its evolutionary profile; the assumption is that large clusters of conserved residues are good markers of functional regions. Next, various characteristics of the predicted functional region as well as global features of the protein are calculated, such as the average surface electrostatic potential, the dipole moment and cluster-based amino acid conservation patterns. Finally, a random forests classifier is used to predict whether the query protein is likely to bind DNA and to estimate the prediction confidence. We have trained and tested the classifier on various datasets and shown that it outperformed related methods. On a dataset that reflects the fraction of DNA binding proteins (DBPs) in a proteome, the area under the ROC curve was 0.90. The application of the server to an updated version of the N-Func database, which contains proteins of unknown function with solved 3D-structure, suggested new putative DBPs for experimental studies. http://idbps.tau.ac.il/

Efficient Construction of Free Energy Profiles of Breathing Metal–Organic Frameworks Using Advanced Molecular Dynamics Simulations

PubMed Central

2017-01-01

In order to reliably predict and understand the breathing behavior of highly flexible metal–organic frameworks from thermodynamic considerations, an accurate estimation of the free energy difference between their different metastable states is a prerequisite. Herein, a variety of free energy estimation methods are thoroughly tested for their ability to construct the free energy profile as a function of the unit cell volume of MIL-53(Al). The methods comprise free energy perturbation, thermodynamic integration, umbrella sampling, metadynamics, and variationally enhanced sampling. A series of molecular dynamics simulations have been performed in the frame of each of the five methods to describe structural transformations in flexible materials with the volume as the collective variable, which offers a unique opportunity to assess their computational efficiency. Subsequently, the most efficient method, umbrella sampling, is used to construct an accurate free energy profile at different temperatures for MIL-53(Al) from first principles at the PBE+D3(BJ) level of theory. This study yields insight into the importance of the different aspects such as entropy contributions and anharmonic contributions on the resulting free energy profile. As such, this thorough study provides unparalleled insight in the thermodynamics of the large structural deformations of flexible materials. PMID:29131647

Efficient Construction of Free Energy Profiles of Breathing Metal-Organic Frameworks Using Advanced Molecular Dynamics Simulations.

PubMed

Demuynck, Ruben; Rogge, Sven M J; Vanduyfhuys, Louis; Wieme, Jelle; Waroquier, Michel; Van Speybroeck, Veronique

2017-12-12

In order to reliably predict and understand the breathing behavior of highly flexible metal-organic frameworks from thermodynamic considerations, an accurate estimation of the free energy difference between their different metastable states is a prerequisite. Herein, a variety of free energy estimation methods are thoroughly tested for their ability to construct the free energy profile as a function of the unit cell volume of MIL-53(Al). The methods comprise free energy perturbation, thermodynamic integration, umbrella sampling, metadynamics, and variationally enhanced sampling. A series of molecular dynamics simulations have been performed in the frame of each of the five methods to describe structural transformations in flexible materials with the volume as the collective variable, which offers a unique opportunity to assess their computational efficiency. Subsequently, the most efficient method, umbrella sampling, is used to construct an accurate free energy profile at different temperatures for MIL-53(Al) from first principles at the PBE+D3(BJ) level of theory. This study yields insight into the importance of the different aspects such as entropy contributions and anharmonic contributions on the resulting free energy profile. As such, this thorough study provides unparalleled insight in the thermodynamics of the large structural deformations of flexible materials.

Introducing etch kernels for efficient pattern sampling and etch bias prediction

NASA Astrophysics Data System (ADS)

Weisbuch, François; Lutich, Andrey; Schatz, Jirka

2018-01-01

Successful patterning requires good control of the photolithography and etch processes. While compact litho models, mainly based on rigorous physics, can predict very well the contours printed in photoresist, pure empirical etch models are less accurate and more unstable. Compact etch models are based on geometrical kernels to compute the litho-etch biases that measure the distance between litho and etch contours. The definition of the kernels, as well as the choice of calibration patterns, is critical to get a robust etch model. This work proposes to define a set of independent and anisotropic etch kernels-"internal, external, curvature, Gaussian, z_profile"-designed to represent the finest details of the resist geometry to characterize precisely the etch bias at any point along a resist contour. By evaluating the etch kernels on various structures, it is possible to map their etch signatures in a multidimensional space and analyze them to find an optimal sampling of structures. The etch kernels evaluated on these structures were combined with experimental etch bias derived from scanning electron microscope contours to train artificial neural networks to predict etch bias. The method applied to contact and line/space layers shows an improvement in etch model prediction accuracy over standard etch model. This work emphasizes the importance of the etch kernel definition to characterize and predict complex etch effects.

A computational theory of visual receptive fields.

PubMed

Lindeberg, Tony

2013-12-01

A receptive field constitutes a region in the visual field where a visual cell or a visual operator responds to visual stimuli. This paper presents a theory for what types of receptive field profiles can be regarded as natural for an idealized vision system, given a set of structural requirements on the first stages of visual processing that reflect symmetry properties of the surrounding world. These symmetry properties include (i) covariance properties under scale changes, affine image deformations, and Galilean transformations of space-time as occur for real-world image data as well as specific requirements of (ii) temporal causality implying that the future cannot be accessed and (iii) a time-recursive updating mechanism of a limited temporal buffer of the past as is necessary for a genuine real-time system. Fundamental structural requirements are also imposed to ensure (iv) mutual consistency and a proper handling of internal representations at different spatial and temporal scales. It is shown how a set of families of idealized receptive field profiles can be derived by necessity regarding spatial, spatio-chromatic, and spatio-temporal receptive fields in terms of Gaussian kernels, Gaussian derivatives, or closely related operators. Such image filters have been successfully used as a basis for expressing a large number of visual operations in computer vision, regarding feature detection, feature classification, motion estimation, object recognition, spatio-temporal recognition, and shape estimation. Hence, the associated so-called scale-space theory constitutes a both theoretically well-founded and general framework for expressing visual operations. There are very close similarities between receptive field profiles predicted from this scale-space theory and receptive field profiles found by cell recordings in biological vision. Among the family of receptive field profiles derived by necessity from the assumptions, idealized models with very good qualitative agreement are obtained for (i) spatial on-center/off-surround and off-center/on-surround receptive fields in the fovea and the LGN, (ii) simple cells with spatial directional preference in V1, (iii) spatio-chromatic double-opponent neurons in V1, (iv) space-time separable spatio-temporal receptive fields in the LGN and V1, and (v) non-separable space-time tilted receptive fields in V1, all within the same unified theory. In addition, the paper presents a more general framework for relating and interpreting these receptive fields conceptually and possibly predicting new receptive field profiles as well as for pre-wiring covariance under scaling, affine, and Galilean transformations into the representations of visual stimuli. This paper describes the basic structure of the necessity results concerning receptive field profiles regarding the mathematical foundation of the theory and outlines how the proposed theory could be used in further studies and modelling of biological vision. It is also shown how receptive field responses can be interpreted physically, as the superposition of relative variations of surface structure and illumination variations, given a logarithmic brightness scale, and how receptive field measurements will be invariant under multiplicative illumination variations and exposure control mechanisms.

Analysis of pharmacology data and the prediction of adverse drug reactions and off-target effects from chemical structure.

PubMed

Bender, Andreas; Scheiber, Josef; Glick, Meir; Davies, John W; Azzaoui, Kamal; Hamon, Jacques; Urban, Laszlo; Whitebread, Steven; Jenkins, Jeremy L

2007-06-01

Preclinical Safety Pharmacology (PSP) attempts to anticipate adverse drug reactions (ADRs) during early phases of drug discovery by testing compounds in simple, in vitro binding assays (that is, preclinical profiling). The selection of PSP targets is based largely on circumstantial evidence of their contribution to known clinical ADRs, inferred from findings in clinical trials, animal experiments, and molecular studies going back more than forty years. In this work we explore PSP chemical space and its relevance for the prediction of adverse drug reactions. Firstly, in silico (computational) Bayesian models for 70 PSP-related targets were built, which are able to detect 93% of the ligands binding at IC(50) < or = 10 microM at an overall correct classification rate of about 94%. Secondly, employing the World Drug Index (WDI), a model for adverse drug reactions was built directly based on normalized side-effect annotations in the WDI, which does not require any underlying functional knowledge. This is, to our knowledge, the first attempt to predict adverse drug reactions across hundreds of categories from chemical structure alone. On average 90% of the adverse drug reactions observed with known, clinically used compounds were detected, an overall correct classification rate of 92%. Drugs withdrawn from the market (Rapacuronium, Suprofen) were tested in the model and their predicted ADRs align well with known ADRs. The analysis was repeated for acetylsalicylic acid and Benperidol which are still on the market. Importantly, features of the models are interpretable and back-projectable to chemical structure, raising the possibility of rationally engineering out adverse effects. By combining PSP and ADR models new hypotheses linking targets and adverse effects can be proposed and examples for the opioid mu and the muscarinic M2 receptors, as well as for cyclooxygenase-1 are presented. It is hoped that the generation of predictive models for adverse drug reactions is able to help support early SAR to accelerate drug discovery and decrease late stage attrition in drug discovery projects. In addition, models such as the ones presented here can be used for compound profiling in all development stages.

Predicting boundary shear stress and sediment transport over bed forms

USGS Publications Warehouse

McLean, S.R.; Wolfe, S.R.; Nelson, J.M.

1999-01-01

To estimate bed-load sediment transport rates in flows over bed forms such as ripples and dunes, spatially averaged velocity profiles are frequently used to predict mean boundary shear stress. However, such averaging obscures the complex, nonlinear interaction of wake decay, boundary-layer development, and topographically induced acceleration downstream of flow separation and often leads to inaccurate estimates of boundary stress, particularly skin friction, which is critically important in predicting bed-load transport rates. This paper presents an alternative methodology for predicting skin friction over 2D bed forms. The approach is based on combining the equations describing the mechanics of the internal boundary layer with semiempirical structure functions to predict the velocity at the crest of a bedform, where the flow is most similar to a uniform boundary layer. Significantly, the methodology is directed toward making specific predictions only at the bed-form crest, and as a result it avoids the difficulty and questionable validity of spatial averaging. The model provides an accurate estimate of the skin friction at the crest where transport rates are highest. Simple geometric constraints can be used to derive the mean transport rates as long as bed load is dominant.To estimate bed-load sediment transport rates in flows over bed forms such as ripples and dunes, spatially averaged velocity profiles are frequently used to predict mean boundary shear stress. However, such averaging obscures the complex, nonlinear interaction of wake decay, boundary-layer development, and topographically induced acceleration downstream of flow separation and often leads to inaccurate estimates of boundary stress, particularly skin friction, which is critically important in predicting bed-load transport rates. This paper presents an alternative methodology for predicting skin friction over 2D bed forms. The approach is based on combining the equations describing the mechanics of the internal boundary layer with semiempirical structure functions to predict the velocity at the crest of a bedform, where the flow is most similar to a uniform boundary layer. Significantly, the methodology is directed toward making specific predictions only at the bed-form crest, and as a result it avoids the difficulty and questionable validity of spatial averaging. The model provides an accurate estimate of the skin friction at the crest where transport rates are highest. Simple geometric constraints can be used to derive the mean transport rates as long as bed load is dominant.

Profile convexities in bedrock and alluvial streams

NASA Astrophysics Data System (ADS)

Phillips, Jonathan D.; Lutz, J. David

2008-12-01

Longitudinal profiles of bedrock streams in central Kentucky, and of coastal plain streams in southeast Texas, were analyzed to determine the extent to which they exhibit smoothly concave profiles and to relate profile convexities to environmental controls. None of the Kentucky streams have smoothly concave profiles. Because all observed knickpoints are associated with vertical joints, if they are migrating it either occurs rapidly between vertical joints, or migrating knickpoints become stalled at structural features. These streams have been adjusting to downcutting of the Kentucky River for at least 1.3 Ma, suggesting that the time required to produce a concave profile is long compared to the typical timescale of environmental change. A graded concave longitudinal profile is not a reasonable prediction or benchmark condition for these streams. The characteristic profile forms of the Kentucky River gorge area are contingent on a particular combination of lithology, structure, hydrologic regime, and geomorphic history, and therefore do not represent any general type of equilibrium state. Few stream profiles in SE Texas conform to the ideal of the smoothly, strongly concave profile. Major convexities are caused by inherited topography, geologic controls, recent and contemporary geomorphic processes, and anthropic effects. Both the legacy of Quaternary environmental change and ongoing changes make it unlikely that consistent boundary conditions will exist for long. Further, the few exceptions within the study area-i.e., strongly and smoothly concave longitudinal profiles-suggest that ample time has occurred for strongly concave profiles to develop and that such profiles do not necessarily represent any mutual adjustments between slope, transport capacity, and sediment supply. The simplest explanation of any tendency toward concavity is related to basic constraints on channel steepness associated with geomechanical stability and minimum slopes necessary to convey flow. This constrained gradient concept (CGC) can explain the general tendency toward concavity in channels of sufficient size, with minimal lithological constraints and with sufficient time for adjustment. Unlike grade- or equilibrium-based theories, the CGC results in interpretations of convex or low-concavity profiles or reaches in terms of local environmental constraints and geomorphic histories rather than as "disequilibrium" features.

Improvement in Protein Domain Identification Is Reached by Breaking Consensus, with the Agreement of Many Profiles and Domain Co-occurrence

PubMed Central

Bernardes, Juliana; Zaverucha, Gerson; Vaquero, Catherine; Carbone, Alessandra

2016-01-01

Traditional protein annotation methods describe known domains with probabilistic models representing consensus among homologous domain sequences. However, when relevant signals become too weak to be identified by a global consensus, attempts for annotation fail. Here we address the fundamental question of domain identification for highly divergent proteins. By using high performance computing, we demonstrate that the limits of state-of-the-art annotation methods can be bypassed. We design a new strategy based on the observation that many structural and functional protein constraints are not globally conserved through all species but might be locally conserved in separate clades. We propose a novel exploitation of the large amount of data available: 1. for each known protein domain, several probabilistic clade-centered models are constructed from a large and differentiated panel of homologous sequences, 2. a decision-making protocol combines outcomes obtained from multiple models, 3. a multi-criteria optimization algorithm finds the most likely protein architecture. The method is evaluated for domain and architecture prediction over several datasets and statistical testing hypotheses. Its performance is compared against HMMScan and HHblits, two widely used search methods based on sequence-profile and profile-profile comparison. Due to their closeness to actual protein sequences, clade-centered models are shown to be more specific and functionally predictive than the broadly used consensus models. Based on them, we improved annotation of Plasmodium falciparum protein sequences on a scale not previously possible. We successfully predict at least one domain for 72% of P. falciparum proteins against 63% achieved previously, corresponding to 30% of improvement over the total number of Pfam domain predictions on the whole genome. The method is applicable to any genome and opens new avenues to tackle evolutionary questions such as the reconstruction of ancient domain duplications, the reconstruction of the history of protein architectures, and the estimation of protein domain age. Website and software: http://www.lcqb.upmc.fr/CLADE. PMID:27472895

The importance of mRNA structure in determining the pathogenicity of synonymous and non-synonymous mutations in haemophilia

PubMed Central

Hamasaki-Katagiri, Nobuko; Lin, Brian C.; Simon, Jonathan; Hunt, Ryan C.; Schiller, Tal; Russek-Cohen, Estelle; Komar, Anton A.; Bar, Haim; Kimchi-Sarfaty, Chava

2016-01-01

Introduction Mutational analysis is commonly used to support the diagnosis and management of haemophilia. This has allowed for the generation of large mutation databases which provide unparalleled insight into genotype-phenotype relationships. Haemophilia is associated with inversions, deletions, insertions, nonsense and missense mutations. Both synonymous and non-synonymous mutations influence the base pairing of messenger RNA (mRNA), which can alter mRNA structure, cellular half-life and ribosome processivity/elongation. However, the role of mRNA structure in determining the pathogenicity of point mutations in haemophilia has not been evaluated. Aim To evaluate mRNA thermodynamic stability and associated RNA prediction software as a means to distinguish between neutral and disease-associated mutations in haemophilia. Methods Five mRNA structure prediction software programs were used to assess the thermodynamic stability of mRNA fragments carrying neutral vs. disease-associated and synonymous vs. non-synonymous point mutations in F8, F9 and a third X-linked gene, DMD (dystrophin). Results In F8 and DMD, disease-associated mutations tend to occur in more structurally stable mRNA regions, represented by lower MFE (minimum free energy) levels. In comparing multiple software packages for mRNA structure prediction, a 101–151 nucleotide fragment length appears to be a feasible range for structuring future studies. Conclusion mRNA thermodynamic stability is one predictive characteristic, which when combined with other RNA and protein features, may offer significant insight when screening sequencing data for novel disease-associated mutations. Our results also suggest potential utility in evaluating the mRNA thermodynamic stability profile of a gene when determining the viability of interchanging codons for biological and therapeutic applications. PMID:27933712

Questions of time and affect: a person’s affectivity profile, time perspective, and well-being

PubMed Central

Sailer, Uta; Nima, Ali Al; Archer, Trevor

2016-01-01

Background. A “balanced” time perspective has been suggested to have a positive influence on well-being: a sentimental and positive view of the past (high Past Positive), a less pessimistic attitude toward the past (low Past Negative), the desire of experiencing pleasure with slight concern for future consequences (high Present Hedonistic), a less fatalistic and hopeless view of the future (low Present Fatalistic), and the ability to find reward in achieving specific long-term goals (high Future). We used the affective profiles model (i.e., combinations of individuals’ experience of high/low positive/negative affectivity) to investigate differences between individuals in time perspective dimensions and to investigate if the influence of time perspective dimensions on well-being was moderated by the individual’s type of profile. Method. Participants (N = 720) answered to the Positive Affect Negative Affect Schedule, the Zimbardo Time Perspective Inventory and two measures of well-being: the Temporal Satisfaction with Life Scale and Ryff’s Scales of Psychological Well-Being-short version. A Multivariate Analysis of Variance (MANOVA) was conducted to identify differences in time perspective dimensions and well-being among individuals with distinct affective profiles. Four structural equation models (SEM) were used to investigate which time perspective dimensions predicted well-being for individuals in each profile. Results. Comparisons between individuals at the extreme of the affective profiles model suggested that individuals with a self-fulfilling profile (high positive/low negative affect) were characterized by a “balanced” time perspective and higher well-being compared to individuals with a self-destructive profile (low positive/high negative affect). However, a different pattern emerged when individuals who differed in one affect dimension but matched in the other were compared to each other. For instance, decreases in the past negative time perspective dimension lead to high positive affect when negative affect is high (i.e., self-destructive vs. high affective) but to low negative affect when positive affect was high (i.e., high affective vs. self-fulfilling). The moderation analyses showed, for example, that for individuals with a self-destructive profile, psychological well-being was significantly predicted by the past negative, present fatalistic and future time perspectives. Among individuals with a high affective or a self-fulfilling profile, psychological well-being was significantly predicted by the present fatalistic dimension. Conclusions. The interactions found here go beyond the postulation of a “balanced” time perspective being the only way to promote well-being. Instead, we present a more person-centered approach to achieve higher levels of emotional, cognitive, and psychological well-being. PMID:27019786

Questions of time and affect: a person's affectivity profile, time perspective, and well-being.

PubMed

Garcia, Danilo; Sailer, Uta; Nima, Ali Al; Archer, Trevor

2016-01-01

Background. A "balanced" time perspective has been suggested to have a positive influence on well-being: a sentimental and positive view of the past (high Past Positive), a less pessimistic attitude toward the past (low Past Negative), the desire of experiencing pleasure with slight concern for future consequences (high Present Hedonistic), a less fatalistic and hopeless view of the future (low Present Fatalistic), and the ability to find reward in achieving specific long-term goals (high Future). We used the affective profiles model (i.e., combinations of individuals' experience of high/low positive/negative affectivity) to investigate differences between individuals in time perspective dimensions and to investigate if the influence of time perspective dimensions on well-being was moderated by the individual's type of profile. Method. Participants (N = 720) answered to the Positive Affect Negative Affect Schedule, the Zimbardo Time Perspective Inventory and two measures of well-being: the Temporal Satisfaction with Life Scale and Ryff's Scales of Psychological Well-Being-short version. A Multivariate Analysis of Variance (MANOVA) was conducted to identify differences in time perspective dimensions and well-being among individuals with distinct affective profiles. Four structural equation models (SEM) were used to investigate which time perspective dimensions predicted well-being for individuals in each profile. Results. Comparisons between individuals at the extreme of the affective profiles model suggested that individuals with a self-fulfilling profile (high positive/low negative affect) were characterized by a "balanced" time perspective and higher well-being compared to individuals with a self-destructive profile (low positive/high negative affect). However, a different pattern emerged when individuals who differed in one affect dimension but matched in the other were compared to each other. For instance, decreases in the past negative time perspective dimension lead to high positive affect when negative affect is high (i.e., self-destructive vs. high affective) but to low negative affect when positive affect was high (i.e., high affective vs. self-fulfilling). The moderation analyses showed, for example, that for individuals with a self-destructive profile, psychological well-being was significantly predicted by the past negative, present fatalistic and future time perspectives. Among individuals with a high affective or a self-fulfilling profile, psychological well-being was significantly predicted by the present fatalistic dimension. Conclusions. The interactions found here go beyond the postulation of a "balanced" time perspective being the only way to promote well-being. Instead, we present a more person-centered approach to achieve higher levels of emotional, cognitive, and psychological well-being.

Assessing the structure and meaningfulness of the dissociative subtype of PTSD.

PubMed

Ross, Jana; Baník, Gabriel; Dědová, Mária; Mikulášková, Gabriela; Armour, Cherie

2018-01-01

Studies conducted in the USA, Canada and Denmark have supported the existence of the dissociative PTSD subtype, characterized primarily by symptoms of depersonalization and derealization. The current study aimed to examine the dissociative PTSD subtype in an Eastern European, predominantly female (83.16%) sample, using an extended set of dissociative symptoms. A latent profile analysis was applied to the PTSD and dissociation data from 689 trauma-exposed university students from Slovakia. Four latent profiles of varying PTSD and dissociation symptomatology were uncovered. They were named non-symptomatic, moderate PTSD, high PTSD and dissociative PTSD. The dissociative PTSD profile showed elevations on depersonalization and derealization, but also the alternative dissociative indicators of gaps in awareness and memory, sensory misperceptions and cognitive and behavioural re-experiencing. The core PTSD symptoms of 'memory impairment' and 'reckless or self-destructive behaviour' were also significantly elevated in the dissociative PTSD profile. Moreover, anxiety and anger predicted membership in the dissociative PTSD profile. The results provide support for the proposal that the dissociative PTSD subtype can be characterized by a variety of dissociative symptoms.

Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile

PubMed Central

Stanberry, Larissa; Mias, George I.; Haynes, Winston; Higdon, Roger; Snyder, Michael; Kolker, Eugene

2013-01-01

The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test this hypothesis, we use biological pathways to integrate metabolomics and proteomics iPOP data. The approach computes the pathways’ differential expression levels at each time point, while taking into account the pathway structure and the longitudinal design. The resulting pathway levels show strong association with the disease status. Further, we identify temporal patterns in metabolite expression levels. The changes in metabolite expression levels also appear to be consistent with the disease status. The results of the integrative analysis suggest that changes in biological pathways may be used to predict and monitor the disease. The iPOP experimental design, data acquisition and analysis issues are discussed within the broader context of personal profiling. PMID:24958148

Is the technical performance of young soccer players influenced by hormonal status, sexual maturity, anthropometric profile, and physical performance?

PubMed

Moreira, Alexandre; Massa, Marcelo; Thiengo, Carlos R; Rodrigues Lopes, Rafael Alan; Lima, Marcelo R; Vaeyens, Roel; Barbosa, Wesley P; Aoki, Marcelo S

2017-12-01

The aim of this study was to examine the influence of hormonal status, anthropometric profile, sexual maturity level, and physical performance on the technical abilities of 40 young male soccer players during small-sided games (SSGs). Anthropometric profiling, saliva sampling, sexual maturity assessment (Tanner scale), and physical performance tests (Yo-Yo and vertical jumps) were conducted two weeks prior to the SSGs. Salivary testosterone was determined by the enzyme-linked immunosorbent assay method. Technical performance was determined by the frequency of actions during SSGs. Principal component analyses identified four technical actions of importance: total number of passes, effectiveness, goal attempts, and total tackles. A multivariate canonical correlation analysis was then employed to verify the prediction of a multiple dependent variables set (composed of four technical actions) from an independent set of variables, composed of testosterone concentration, stage of pubic hair and genitalia development, vertical jumps and Yo-Yo performance. A moderate-to-large relationship between the technical performance set and the independent set was observed. The canonical correlation was 0.75 with a canonical R 2 of 0.45. The highest structure coefficient in the technical performance set was observed for tackles (0.77), while testosterone presented the highest structure coefficient (0.75) for the variables of the independent set. The current data suggest that the selected independent set of variables might be useful in predicting SSG performance in young soccer players. Coaches should be aware that physical development plays a key role in technical performance to avoid decision-making mistakes during the selection of young players.

Predicting Drug Concentration‐Time Profiles in Multiple CNS Compartments Using a Comprehensive Physiologically‐Based Pharmacokinetic Model

PubMed Central

Yamamoto, Yumi; Välitalo, Pyry A.; Huntjens, Dymphy R.; Proost, Johannes H.; Vermeulen, An; Krauwinkel, Walter; Beukers, Margot W.; van den Berg, Dirk‐Jan; Hartman, Robin; Wong, Yin Cheong; Danhof, Meindert; van Hasselt, John G. C.

2017-01-01

Drug development targeting the central nervous system (CNS) is challenging due to poor predictability of drug concentrations in various CNS compartments. We developed a generic physiologically based pharmacokinetic (PBPK) model for prediction of drug concentrations in physiologically relevant CNS compartments. System‐specific and drug‐specific model parameters were derived from literature and in silico predictions. The model was validated using detailed concentration‐time profiles from 10 drugs in rat plasma, brain extracellular fluid, 2 cerebrospinal fluid sites, and total brain tissue. These drugs, all small molecules, were selected to cover a wide range of physicochemical properties. The concentration‐time profiles for these drugs were adequately predicted across the CNS compartments (symmetric mean absolute percentage error for the model prediction was <91%). In conclusion, the developed PBPK model can be used to predict temporal concentration profiles of drugs in multiple relevant CNS compartments, which we consider valuable information for efficient CNS drug development. PMID:28891201

Drug Repositioning by Kernel-Based Integration of Molecular Structure, Molecular Activity, and Phenotype Data

PubMed Central

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

2013-01-01

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

A pilot study of NMR-based sensory prediction of roasted coffee bean extracts.

PubMed

Wei, Feifei; Furihata, Kazuo; Miyakawa, Takuya; Tanokura, Masaru

2014-01-01

Nuclear magnetic resonance (NMR) spectroscopy can be considered a kind of "magnetic tongue" for the characterisation and prediction of the tastes of foods, since it provides a wealth of information in a nondestructive and nontargeted manner. In the present study, the chemical substances in roasted coffee bean extracts that could distinguish and predict the different sensations of coffee taste were identified by the combination of NMR-based metabolomics and human sensory test and the application of the multivariate projection method of orthogonal projection to latent structures (OPLS). In addition, the tastes of commercial coffee beans were successfully predicted based on their NMR metabolite profiles using our OPLS model, suggesting that NMR-based metabolomics accompanied with multiple statistical models is convenient, fast and accurate for the sensory evaluation of coffee. Copyright © 2013 Elsevier Ltd. All rights reserved.

Aerodynamic Flight-Test Results for the Adaptive Compliant Trailing Edge

NASA Technical Reports Server (NTRS)

Cumming, Stephen B.; Smith, Mark S.; Ali, Aliyah N.; Bui, Trong T.; Ellsworth, Joel C.; Garcia, Christian A.

2016-01-01

The aerodynamic effects of compliant flaps installed onto a modified Gulfstream III airplane were investigated. Analyses were performed prior to flight to predict the aerodynamic effects of the flap installation. Flight tests were conducted to gather both structural and aerodynamic data. The airplane was instrumented to collect vehicle aerodynamic data and wing pressure data. A leading-edge stagnation detection system was also installed. The data from these flights were analyzed and compared with predictions. The predictive tools compared well with flight data for small flap deflections, but differences between predictions and flight estimates were greater at larger deflections. This paper describes the methods used to examine the aerodynamics data from the flight tests and provides a discussion of the flight-test results in the areas of vehicle aerodynamics, wing sectional pressure coefficient profiles, and air data.

New insights on the voltage dependence of the KCa3.1 channel block by internal TBA.

PubMed

Banderali, Umberto; Klein, Hélène; Garneau, Line; Simoes, Manuel; Parent, Lucie; Sauvé, Rémy

2004-10-01

We present in this work a structural model of the open IKCa (KCa3.1) channel derived by homology modeling from the MthK channel structure, and used this model to compute the transmembrane potential profile along the channel pore. This analysis showed that the selectivity filter and the region extending from the channel inner cavity to the internal medium should respectively account for 81% and 16% of the transmembrane potential difference. We found however that the voltage dependence of the IKCa block by the quaternary ammonium ion TBA applied internally is compatible with an apparent electrical distance delta of 0.49 +/- 0.02 (n = 6) for negative potentials. To reconcile this observation with the electrostatic potential profile predicted for the channel pore, we modeled the IKCa block by TBA assuming that the voltage dependence of the block is governed by both the difference in potential between the channel cavity and the internal medium, and the potential profile along the selectivity filter region through an effect on the filter ion occupancy states. The resulting model predicts that delta should be voltage dependent, being larger at negative than positive potentials. The model also indicates that raising the internal K+ concentration should decrease the value of delta measured at negative potentials independently of the external K+ concentration, whereas raising the external K+ concentration should minimally affect delta for concentrations >50 mM. All these predictions are born out by our current experimental results. Finally, we found that the substitutions V275C and V275A increased the voltage sensitivity of the TBA block, suggesting that TBA could move further into the pore, thus leading to stronger interactions between TBA and the ions in the selectivity filter. Globally, these results support a model whereby the voltage dependence of the TBA block in IKCa is mainly governed by the voltage dependence of the ion occupancy states of the selectivity filter.

Topographic Controls on Landslide and Debris-Flow Mobility

NASA Astrophysics Data System (ADS)

McCoy, S. W.; Pettitt, S.

2014-12-01

Regardless of whether a granular flow initiates from failure and liquefaction of a shallow landslide or from overland flow that entrains sediment to form a debris flow, the resulting flow poses hazards to downslope communities. Understanding controls on granular-flow mobility is critical for accurate hazard prediction. The topographic form of granular-flow paths can vary significantly across different steeplands and is one of the few flow-path properties that can be readily altered by engineered control structures such as closed-type check dams. We use grain-scale numerical modeling (discrete element method simulations) of free-surface, gravity-driven granular flows to investigate how different topographic profiles with the same mean slope and total relief can produce notable differences in flow mobility due to strong nonlinearities inherent to granular-flow dynamics. We describe how varying the profile shape from planar, to convex up, to concave up, as well how varying the number, size, and location of check dams along a flow path, changes flow velocity, thickness, discharge, energy dissipation, impact force and runout distance. Our preliminary results highlight an important path dependence for this nonlinear system, show that caution should be used when predicting flow dynamics from path-averaged properties, and provide some mechanics-based guidance for engineering control structures.

Predictive Feedback Can Account for Biphasic Responses in the Lateral Geniculate Nucleus

PubMed Central

Jehee, Janneke F. M.; Ballard, Dana H.

2009-01-01

Biphasic neural response properties, where the optimal stimulus for driving a neural response changes from one stimulus pattern to the opposite stimulus pattern over short periods of time, have been described in several visual areas, including lateral geniculate nucleus (LGN), primary visual cortex (V1), and middle temporal area (MT). We describe a hierarchical model of predictive coding and simulations that capture these temporal variations in neuronal response properties. We focus on the LGN-V1 circuit and find that after training on natural images the model exhibits the brain's LGN-V1 connectivity structure, in which the structure of V1 receptive fields is linked to the spatial alignment and properties of center-surround cells in the LGN. In addition, the spatio-temporal response profile of LGN model neurons is biphasic in structure, resembling the biphasic response structure of neurons in cat LGN. Moreover, the model displays a specific pattern of influence of feedback, where LGN receptive fields that are aligned over a simple cell receptive field zone of the same polarity decrease their responses while neurons of opposite polarity increase their responses with feedback. This phase-reversed pattern of influence was recently observed in neurophysiology. These results corroborate the idea that predictive feedback is a general coding strategy in the brain. PMID:19412529

Further Evidence that Severe Scores in the Aggression/Anxiety-Depression/Attention Subscales of Child Behavior Checklist (Severe Dysregulation Profile) Can Screen for Bipolar Disorder Symptomatology: A Conditional Probability Analysis

PubMed Central

Uchida, Mai; Faraone, Stephen V; Martelon, MaryKate; Kenworthy, Tara; Woodworth, K Yvonne; Spencer, Thomas; Wozniak, Janet; Biederman, Joseph

2014-01-01

Background Previous work shows that children with high scores (2 SD, combined score ≥ 210) on the Attention Problems, Aggressive Behavior, and Anxious-Depressed (A-A-A) subscales of the Child Behavior Checklist (CBCL) are more likely than other children to meet criteria for bipolar (BP)-I disorder. However, the utility of this profile as a screening tool has remained unclear. Methods We compared 140 patients with pediatric BP-I disorder, 83 with attention deficit hyperactivity disorder (ADHD), and 114 control subjects. We defined the CBCL-Severe Dysregulation profile as an aggregate cutoff score of ≥ 210 on the A-A-A scales. Patients were assessed with structured diagnostic interviews and functional measures. Results Patients with BP-I disorder were significantly more likely than both control subjects (Odds Ratio [OR]: 173.2; 95% Confidence Interval [CI], 21.2 to 1413.8; P < 0.001) and those with ADHD (OR: 14.6; 95% CI, 6.2 to 34.3; P < 0.001) to have a positive CBCL-Severe Dysregulation profile. Receiver Operating Characteristics analyses showed that the area under the curve for this profile comparing children with BP-I disorder against control subjects and those with ADHD was 99% and 85%, respectively. The corresponding positive predictive values for this profile were 99% and 92% with false positive rates of < 0.2% and 8% for the comparisons with control subjects and patients with ADHD, respectively. Limitations Non-clinician raters administered structured diagnostic interviews, and the sample was referred and largely Caucasian. Conclusions The CBCL-Severe Dysregulation profile can be useful as a screen for BP-I disorder in children in clinical practice. PMID:24882182

Catalytic properties of thimet oligopeptidase H600A mutant

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

Machado, Mauricio F.M.; Marcondes, Marcelo F.; Rioli, Vanessa

2010-04-02

Thimet oligopeptidase (EC 3.4.24.15, TOP) is a metallo-oligopeptidase that participates in the intracellular metabolism of peptides. Predictions based on structurally analogous peptidases (Dcp and ACE-2) show that TOP can present a hinge-bend movement during substrate hydrolysis, what brings some residues closer to the substrate. One of these residues that in TOP crystallographic structure are far from the catalytic residues, but, moves toward the substrate considering this possible structural reorganization is His{sup 600}. In the present work, the role of His{sup 600} of TOP was investigated by site-directed mutagenesis. TOP H600A mutant was characterized through analysis of S{sub 1} and S{submore » 1}' specificity, pH-activity profile and inhibition by JA-2. Results showed that TOP His{sup 600} residue makes important interactions with the substrate, supporting the prediction that His{sup 600} moves toward the substrate due to a hinge movement similar to the Dcp and ACE-2. Furthermore, the mutation H600A affected both K{sub m} and k{sub cat}, showing the importance of His{sup 600} for both substrate binding and/or product release from active site. Changes in the pH-profile may indicate also the participation of His{sup 600} in TOP catalysis, transferring a proton to the newly generated NH{sub 2}-terminus or helping Tyr{sup 605} and/or Tyr{sup 612} in the intermediate oxyanion stabilization.« less

Comparisons of Crosswind Velocity Profile Estimates Used in Fast-Time Wake Vortex Prediction Models

NASA Technical Reports Server (NTRS)

Pruis, Mathew J.; Delisi, Donald P.; Ahmad, Nashat N.

2011-01-01

Five methods for estimating crosswind profiles used in fast-time wake vortex prediction models are compared in this study. Previous investigations have shown that temporal and spatial variations in the crosswind vertical profile have a large impact on the transport and time evolution of the trailing vortex pair. The most important crosswind parameters are the magnitude of the crosswind and the gradient in the crosswind shear. It is known that pulsed and continuous wave lidar measurements can provide good estimates of the wind profile in the vicinity of airports. In this study comparisons are made between estimates of the crosswind profiles from a priori information on the trajectory of the vortex pair as well as crosswind profiles derived from different sensors and a regional numerical weather prediction model.

MollDE: a homology modeling framework you can click with.

PubMed

Canutescu, Adrian A; Dunbrack, Roland L

2005-06-15

Molecular Integrated Development Environment (MolIDE) is an integrated application designed to provide homology modeling tools and protocols under a uniform, user-friendly graphical interface. Its main purpose is to combine the most frequent modeling steps in a semi-automatic, interactive way, guiding the user from the target protein sequence to the final three-dimensional protein structure. The typical basic homology modeling process is composed of building sequence profiles of the target sequence family, secondary structure prediction, sequence alignment with PDB structures, assisted alignment editing, side-chain prediction and loop building. All of these steps are available through a graphical user interface. MolIDE's user-friendly and streamlined interactive modeling protocol allows the user to focus on the important modeling questions, hiding from the user the raw data generation and conversion steps. MolIDE was designed from the ground up as an open-source, cross-platform, extensible framework. This allows developers to integrate additional third-party programs to MolIDE. http://dunbrack.fccc.edu/molide/molide.php rl_dunbrack@fccc.edu.

Revealing chemophoric sites in organophosphorus insecticides through the MIA-QSPR modeling of soil sorption data.

PubMed

Daré, Joyce K; Silva, Cristina F; Freitas, Matheus P

2017-10-01

Soil sorption of insecticides employed in agriculture is an important parameter to probe the environmental fate of organic chemicals. Therefore, methods for the prediction of soil sorption of new agrochemical candidates, as well as for the rationalization of the molecular characteristics responsible for a given sorption profile, are extremely beneficial for the environment. A quantitative structure-property relationship method based on chemical structure images as molecular descriptors provided a reliable model for the soil sorption prediction of 24 widely used organophosphorus insecticides. By means of contour maps obtained from the partial least squares regression coefficients and the variable importance in projection scores, key molecular moieties were targeted for possible structural modification, in order to obtain novel and more environmentally friendly insecticide candidates. The image-based descriptors applied encode molecular arrangement, atoms connectivity, groups size, and polarity; consequently, the findings in this work cannot be achieved by a simple relationship with hydrophobicity, usually described by the octanol-water partition coefficient. Copyright © 2017 Elsevier Inc. All rights reserved.

Structure prediction and analysis of MxaF from obligate, facultative and restricted facultative methylobacterium.

PubMed

Singh, Raghvendra Pratap; Singh, Ram Nageena; Srivastava, Manish K; Srivastava, Alok Kumar; Kumar, Sudheer; Dubey, Ramesh Chandra; Sharma, Arun Kumar

2012-01-01

Methylobacteria are ubiquitous in the biosphere which are capable of growing on C1 compounds such as formate, formaldehyde, methanol and methylamine as well as on a wide range of multi-carbon growth substrates such as C2, C3 and C4 compounds due to the methylotrophic enzymes methanol dehydrogenase (MDH). MDH is performing these functions with the help of a key protein mxaF. Unfortunately, detailed structural analysis and homology modeling of mxaF is remains undefined. Hence, the objective of this research is the characterization and three dimensional modeling of mxaF protein from three different methylotrophs by using I-TASSER server. The predicted model were further optimize and validate by Profile 3D, Errat, Verifiy3-D and PROCHECK server. Predicted and best evaluated models have been successfully deposited to PMDB database with PMDB ID PM0077505, PM0077506 and PM0077507. Active site identification revealed 11, 13 and 14 putative functional site residues in respected models. It may play a major role during protein-protein, and protein-cofactor interactions. This study can provide us an ab-initio and detail information to understand the structure, mechanism of action and regulation of mxaF protein.

Structure prediction and analysis of MxaF from obligate, facultative and restricted facultative methylobacterium

PubMed Central

Singh, Raghvendra Pratap; Singh, Ram Nageena; Srivastava, Manish K; Srivastava, Alok Kumar; Kumar, Sudheer; Dubey, Ramesh Chandra; Sharma, Arun Kumar

2012-01-01

Methylobacteria are ubiquitous in the biosphere which are capable of growing on C1 compounds such as formate, formaldehyde, methanol and methylamine as well as on a wide range of multi-carbon growth substrates such as C2, C3 and C4 compounds due to the methylotrophic enzymes methanol dehydrogenase (MDH). MDH is performing these functions with the help of a key protein mxaF. Unfortunately, detailed structural analysis and homology modeling of mxaF is remains undefined. Hence, the objective of this research is the characterization and three dimensional modeling of mxaF protein from three different methylotrophs by using I-TASSER server. The predicted model were further optimize and validate by Profile 3D, Errat, Verifiy3-D and PROCHECK server. Predicted and best evaluated models have been successfully deposited to PMDB database with PMDB ID PM0077505, PM0077506 and PM0077507. Active site identification revealed 11, 13 and 14 putative functional site residues in respected models. It may play a major role during protein-protein, and protein-cofactor interactions. This study can provide us an ab-initio and detail information to understand the structure, mechanism of action and regulation of mxaF protein. PMID:23275704

Cheminformatics analysis of assertions mined from literature that describe drug-induced liver injury in different species.

PubMed

Fourches, Denis; Barnes, Julie C; Day, Nicola C; Bradley, Paul; Reed, Jane Z; Tropsha, Alexander

2010-01-01

Drug-induced liver injury is one of the main causes of drug attrition. The ability to predict the liver effects of drug candidates from their chemical structures is critical to help guide experimental drug discovery projects toward safer medicines. In this study, we have compiled a data set of 951 compounds reported to produce a wide range of effects in the liver in different species, comprising humans, rodents, and nonrodents. The liver effects for this data set were obtained as assertional metadata, generated from MEDLINE abstracts using a unique combination of lexical and linguistic methods and ontological rules. We have analyzed this data set using conventional cheminformatics approaches and addressed several questions pertaining to cross-species concordance of liver effects, chemical determinants of liver effects in humans, and the prediction of whether a given compound is likely to cause a liver effect in humans. We found that the concordance of liver effects was relatively low (ca. 39-44%) between different species, raising the possibility that species specificity could depend on specific features of chemical structure. Compounds were clustered by their chemical similarity, and similar compounds were examined for the expected similarity of their species-dependent liver effect profiles. In most cases, similar profiles were observed for members of the same cluster, but some compounds appeared as outliers. The outliers were the subject of focused assertion regeneration from MEDLINE as well as other data sources. In some cases, additional biological assertions were identified, which were in line with expectations based on compounds' chemical similarities. The assertions were further converted to binary annotations of underlying chemicals (i.e., liver effect vs no liver effect), and binary quantitative structure-activity relationship (QSAR) models were generated to predict whether a compound would be expected to produce liver effects in humans. Despite the apparent heterogeneity of data, models have shown good predictive power assessed by external 5-fold cross-validation procedures. The external predictive power of binary QSAR models was further confirmed by their application to compounds that were retrieved or studied after the model was developed. To the best of our knowledge, this is the first study for chemical toxicity prediction that applied QSAR modeling and other cheminformatics techniques to observational data generated by the means of automated text mining with limited manual curation, opening up new opportunities for generating and modeling chemical toxicology data.

Advanced colorectal adenoma related gene expression signature may predict prognostic for colorectal cancer patients with adenoma-carcinoma sequence.

PubMed

Li, Bing; Shi, Xiao-Yu; Liao, Dai-Xiang; Cao, Bang-Rong; Luo, Cheng-Hua; Cheng, Shu-Jun

2015-01-01

There are still no absolute parameters predicting progression of adenoma into cancer. The present study aimed to characterize functional differences on the multistep carcinogenetic process from the adenoma-carcinoma sequence. All samples were collected and mRNA expression profiling was performed by using Agilent Microarray high-throughput gene-chip technology. Then, the characteristics of mRNA expression profiles of adenoma-carcinoma sequence were described with bioinformatics software, and we analyzed the relationship between gene expression profiles of adenoma-adenocarcinoma sequence and clinical prognosis of colorectal cancer. The mRNA expressions of adenoma-carcinoma sequence were significantly different between high-grade intraepithelial neoplasia group and adenocarcinoma group. The biological process of gene ontology function enrichment analysis on differentially expressed genes between high-grade intraepithelial neoplasia group and adenocarcinoma group showed that genes enriched in the extracellular structure organization, skeletal system development, biological adhesion and itself regulated growth regulation, with the P value after FDR correction of less than 0.05. In addition, IPR-related protein mainly focused on the insulin-like growth factor binding proteins. The variable trends of gene expression profiles for adenoma-carcinoma sequence were mainly concentrated in high-grade intraepithelial neoplasia and adenocarcinoma. The differentially expressed genes are significantly correlated between high-grade intraepithelial neoplasia group and adenocarcinoma group. Bioinformatics analysis is an effective way to study the gene expression profiles in the adenoma-carcinoma sequence, and may provide an effective tool to involve colorectal cancer research strategy into colorectal adenoma or advanced adenoma.

Gas chromatography/mass spectrometry based component profiling and quality prediction for Japanese sake.

PubMed

Mimura, Natsuki; Isogai, Atsuko; Iwashita, Kazuhiro; Bamba, Takeshi; Fukusaki, Eiichiro

2014-10-01

Sake is a Japanese traditional alcoholic beverage, which is produced by simultaneous saccharification and alcohol fermentation of polished and steamed rice by Aspergillus oryzae and Saccharomyces cerevisiae. About 300 compounds have been identified in sake, and the contribution of individual components to the sake flavor has been examined at the same time. However, only a few compounds could explain the characteristics alone and most of the attributes still remain unclear. The purpose of this study was to examine the relationship between the component profile and the attributes of sake. Gas chromatography coupled with mass spectrometry (GC/MS)-based non-targeted analysis was employed to obtain the low molecular weight component profile of Japanese sake including both nonvolatile and volatile compounds. Sake attributes and overall quality were assessed by analytical descriptive sensory test and the prediction model of the sensory score from the component profile was constructed by means of orthogonal projections to latent structures (OPLS) regression analysis. Our results showed that 12 sake attributes [ginjo-ka (aroma of premium ginjo sake), grassy/aldehydic odor, sweet aroma/caramel/burnt odor, sulfury odor, sour taste, umami, bitter taste, body, amakara (dryness), aftertaste, pungent/smoothness and appearance] and overall quality were accurately explained by component profiles. In addition, we were able to select statistically significant components according to variable importance on projection (VIP). Our methodology clarified the correlation between sake attribute and 200 low molecular components and presented the importance of each component thus, providing new insights to the flavor study of sake. Copyright © 2014 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

OSSE Assessment of Ocean Observing System Enhancements to Improve Coupled Tropical Cyclone Intensity Prediction

NASA Astrophysics Data System (ADS)

Halliwell, G. R., Jr.; Mehari, M. F.; Dong, J.; Kourafalou, V.; Atlas, R. M.; Kang, H.; Le Henaff, M.

2016-02-01

A new ocean OSSE system validated in the tropical/subtropical Atlantic Ocean is used to evaluate ocean observing strategies during the 2014 hurricane season with the goal of improving coupled tropical cyclone forecasts. Enhancements to the existing operational ocean observing system are evaluated prior to two storms, Edouard and Gonzalo, where ocean measurements were obtained during field experiments supported by the 2013 Disaster Relief Appropriation Act. For Gonzalo, a reference OSSE is performed to evaluate the impact of two ocean gliders deployed north and south of Puerto Rico and two Alamo profiling floats deployed in the same general region during most of the hurricane season. For Edouard, a reference OSSE is performed to evaluate impacts of the pre-storm ocean profile survey conducted by NOAA WP-3D aircraft. For both storms, additional OSSEs are then conducted to evaluate more extensive seasonal and pre-storm ocean observing strategies. These include (1) deploying a larger number of synthetic ocean gliders during the hurricane season, (2) deploying pre-storm synthetic thermistor chains or synthetic profiling floats along one or more "picket fence" lines that cross projected storm tracks, and (3) designing pre-storm airborne profiling surveys to have larger impacts than the actual pre-storm survey conducted for Edouard. Impacts are evaluated based on error reduction in ocean parameters important to SST cooling and hurricane intensity such as ocean heat content and the structure of the ocean eddy field. In all cases, ocean profiles that sample both temperature and salinity down to 1000m provide greater overall error reduction than shallower temperature profiles obtained from AXBTs and thermistor chains. Large spatial coverage with multiple instruments spanning a few degrees of longitude and latitude is necessary to sufficiently reduce ocean initialization errors over a region broad enough to significantly impact predicted surface enthalpy flux into the storm. Error reduction in hurricane intensity forecasts resulting from the additional ocean observations is then assessed by initializing the ocean component of the HYCOM-HWRF coupled prediction system with analyses produced by the OSSE system.

DSSTOX (DISTRIBUTED STRUCTURE-SEARCHABLE ...

EPA Pesticide Factsheets

Distributed Structure-Searchable Toxicity Database Network Major trends affecting public toxicity information resources have the potential to significantly alter the future of predictive toxicology. Chemical toxicity screening is undergoing shifts towards greater use of more fundamental information on gene/protein expression patterns and bioactivity and bioassay profiles, the latter generated with highthroughput screening technologies. Curated, systematically organized, and webaccessible toxicity and biological activity data in association with chemical structures, enabling the integration of diverse data information domains, will fuel the next frontier of advancement for QSAR (quantitative structure-activity relationship) and data mining technologies. The DSSTox project is supporting progress towards these goals on many fronts, promoting the use of formalized and structure-annotated toxicity data models, helping to interface these efforts with QSAR modelers, linking data from diverse sources, and creating a large, quality reviewed, central chemical structure information resource linked to various toxicity data sources

Physiologically-based pharmacokinetic model of vaginally administered dapivirine ring and film formulations.

PubMed

Kay, Katherine; Shah, Dhaval K; Rohan, Lisa; Bies, Robert

2018-05-01

A physiologically-based pharmacokinetic (PBPK) model of the vaginal space was developed with the aim of predicting concentrations in the vaginal and cervical space. These predictions can be used to optimize the probability of success of vaginally administered dapivirine (DPV) for HIV prevention. We focus on vaginal delivery using either a ring or film. A PBPK model describing the physiological structure of the vaginal tissue and fluid was defined mathematically and implemented in MATLAB. Literature reviews provided estimates for relevant physiological and physiochemical parameters. Drug concentration-time profiles were simulated in luminal fluids, vaginal tissue and plasma after administration of ring or film. Patient data were extracted from published clinical trials and used to test model predictions. The DPV ring simulations tested the two dosing regimens and predicted PK profiles and area under the curve of luminal fluids (29 079 and 33 067 mg h l -1 in groups A and B, respectively) and plasma (0.177 and 0.211 mg h l -1 ) closely matched those reported (within one standard deviation). While the DPV film study reported drug concentration at only one time point per patient, our simulated profiles pass through reported concentration range. HIV is a major public health issue and vaginal microbicides have the potential to provide a crucial, female-controlled option for protection. The PBPK model successfully simulated realistic representations of drug PK. It provides a reliable, inexpensive and accessible platform where potential effectiveness of new compounds and the robustness of treatment modalities for pre-exposure prophylaxis can be evaluated. © 2018 The Authors. British Journal of Clinical Pharmacology published by John Wiley & Sons Ltd on behalf of British Pharmacological Society.

Investigation of protein folding by coarse-grained molecular dynamics with the UNRES force field.

PubMed

Maisuradze, Gia G; Senet, Patrick; Czaplewski, Cezary; Liwo, Adam; Scheraga, Harold A

2010-04-08

Coarse-grained molecular dynamics simulations offer a dramatic extension of the time-scale of simulations compared to all-atom approaches. In this article, we describe the use of the physics-based united-residue (UNRES) force field, developed in our laboratory, in protein-structure simulations. We demonstrate that this force field offers about a 4000-times extension of the simulation time scale; this feature arises both from averaging out the fast-moving degrees of freedom and reduction of the cost of energy and force calculations compared to all-atom approaches with explicit solvent. With massively parallel computers, microsecond folding simulation times of proteins containing about 1000 residues can be obtained in days. A straightforward application of canonical UNRES/MD simulations, demonstrated with the example of the N-terminal part of the B-domain of staphylococcal protein A (PDB code: 1BDD, a three-alpha-helix bundle), discerns the folding mechanism and determines kinetic parameters by parallel simulations of several hundred or more trajectories. Use of generalized-ensemble techniques, of which the multiplexed replica exchange method proved to be the most effective, enables us to compute thermodynamics of folding and carry out fully physics-based prediction of protein structure, in which the predicted structure is determined as a mean over the most populated ensemble below the folding-transition temperature. By using principal component analysis of the UNRES folding trajectories of the formin-binding protein WW domain (PDB code: 1E0L; a three-stranded antiparallel beta-sheet) and 1BDD, we identified representative structures along the folding pathways and demonstrated that only a few (low-indexed) principal components can capture the main structural features of a protein-folding trajectory; the potentials of mean force calculated along these essential modes exhibit multiple minima, as opposed to those along the remaining modes that are unimodal. In addition, a comparison between the structures that are representative of the minima in the free-energy profile along the essential collective coordinates of protein folding (computed by principal component analysis) and the free-energy profile projected along the virtual-bond dihedral angles gamma of the backbone revealed the key residues involved in the transitions between the different basins of the folding free-energy profile, in agreement with existing experimental data for 1E0L .

Objective characterization of bruise evolution using photothermal depth profiling and Monte Carlo modeling

NASA Astrophysics Data System (ADS)

Vidovič, Luka; Milanič, Matija; Majaron, Boris

2015-01-01

Pulsed photothermal radiometry (PPTR) allows noninvasive determination of laser-induced temperature depth profiles in optically scattering layered structures. The obtained profiles provide information on spatial distribution of selected chromophores such as melanin and hemoglobin in human skin. We apply the described approach to study time evolution of incidental bruises (hematomas) in human subjects. By combining numerical simulations of laser energy deposition in bruised skin with objective fitting of the predicted and measured PPTR signals, we can quantitatively characterize the key processes involved in bruise evolution (i.e., hemoglobin mass diffusion and biochemical decomposition). Simultaneous analysis of PPTR signals obtained at various times post injury provides an insight into the variations of these parameters during the bruise healing process. The presented methodology and results advance our understanding of the bruise evolution and represent an important step toward development of an objective technique for age determination of traumatic bruises in forensic medicine.

Conference on Complex Turbulent Flows: Comparison of Computation and Experiment, Stanford University, Stanford, CA, September 14-18, 1981, Proceedings. Volume 2 - Taxonomies, reporters' summaries, evaluation, and conclusions

NASA Technical Reports Server (NTRS)

Kline, S. J. (Editor); Cantwell, B. J. (Editor); Lilley, G. M.

1982-01-01

Computational techniques for simulating turbulent flows were explored, together with the results of experimental investigations. Particular attention was devoted to the possibility of defining a universal closure model, applicable for all turbulence situations; however, conclusions were drawn that zonal models, describing localized structures, were the most promising techniques to date. The taxonomy of turbulent flows was summarized, as were algebraic, differential, integral, and partial differential methods for numerical depiction of turbulent flows. Numerous comparisons of theoretically predicted and experimentally obtained data for wall pressure distributions, velocity profiles, turbulent kinetic energy profiles, Reynolds shear stress profiles, and flows around transonic airfoils were presented. Simplifying techniques for reducing the necessary computational time for modeling complex flowfields were surveyed, together with the industrial requirements and applications of computational fluid dynamics techniques.

A theoretical and simulation study of the contact discontinuities based on a Vlasov simulation code

NASA Astrophysics Data System (ADS)

Tsai, T. C.; Lyu, L. H.; Chao, J. K.; Chen, M. Q.; Tsai, W. H.

2009-12-01

Contact discontinuity (CD) is the simplest solution that can be obtained from the magnetohydrodynamics (MHD) Rankine-Hugoniot jump conditions. Due to the limitations of the previous kinetic simulation models, the stability of the CD has become a controversial issue in the past 10 years. The stability of the CD is reexamined analytically and numerically. Our theoretical analysis shows that the electron temperature profile and the ion temperature profile must be out of phase across the CD if the CD structure is to be stable in the electron time scale and with zero electron heat flux on either side of the CD. Both a newly developed fourth-order implicit electrostatic Vlasov simulation code and an electromagnetic finite-size particle code are used to examine the stability and the electrostatic nature of the CD structure. Our theoretical prediction is verified by both simulations. Our results of Vlasov simulation also indicate that a simulation with initial electron temperature profile and ion temperature profile varying in phase across the CD will undergo very transient changes in the electron time scale but will relax into a quasi-steady CD structure within a few ion plasma oscillation periods if a real ion-electron mass ratio is used in the simulation and if the boundary conditions allow nonzero heat flux to be presented at the boundaries of the simulation box. The simulation results of this study indicate that the Vlasov simulation is a powerful tool to study nonlinear phenomena with nonperiodic boundary conditions and with nonzero heat flux at the boundaries of the simulation box.

Internal Structure and Morphology Profile in Optimizing Filter Membrane Performance

NASA Astrophysics Data System (ADS)

Sanaei, Pejman; Cummings, Linda J.

Membrane filters are in widespread use, and manufacturers have considerable interest in improving their performance, in terms of particle retention properties, and total throughput over the filter lifetime. A good question to ask is therefore: what is the optimal configuration of filter membranes, in terms of internal morphology (pore size and shape), to achieve the most efficient filtration? To answer this question, we must first propose a robust measure of filtration performance. As filtration occurs the membrane becomes blocked, or fouled, by the impurities in the feed solution, and any performance measure must take account of this. For example, one performance measure might be the total throughput (the volume of filtered feed solution) at the end of the filtration process, when the membrane is so badly blocked that it is deemed no longer functional. Here we present a simplified mathematical model, which (i) characterizes membrane internal pore structure via pore or permeability profiles in the depth of the membrane; (ii) accounts for various membrane fouling mechanisms (adsorption, blocking and cake formation); and (iv) predicts the optimum pore profile for our chosen performance measure. NSF DMS-1261596 and NSF DMS-1615719.

Predictive Modeling of Estrogen Receptor Binding Agents Using Advanced Cheminformatics Tools and Massive Public Data.

PubMed

Ribay, Kathryn; Kim, Marlene T; Wang, Wenyi; Pinolini, Daniel; Zhu, Hao

2016-03-01

Estrogen receptors (ERα) are a critical target for drug design as well as a potential source of toxicity when activated unintentionally. Thus, evaluating potential ERα binding agents is critical in both drug discovery and chemical toxicity areas. Using computational tools, e.g., Quantitative Structure-Activity Relationship (QSAR) models, can predict potential ERα binding agents before chemical synthesis. The purpose of this project was to develop enhanced predictive models of ERα binding agents by utilizing advanced cheminformatics tools that can integrate publicly available bioassay data. The initial ERα binding agent data set, consisting of 446 binders and 8307 non-binders, was obtained from the Tox21 Challenge project organized by the NIH Chemical Genomics Center (NCGC). After removing the duplicates and inorganic compounds, this data set was used to create a training set (259 binders and 259 non-binders). This training set was used to develop QSAR models using chemical descriptors. The resulting models were then used to predict the binding activity of 264 external compounds, which were available to us after the models were developed. The cross-validation results of training set [Correct Classification Rate (CCR) = 0.72] were much higher than the external predictivity of the unknown compounds (CCR = 0.59). To improve the conventional QSAR models, all compounds in the training set were used to search PubChem and generate a profile of their biological responses across thousands of bioassays. The most important bioassays were prioritized to generate a similarity index that was used to calculate the biosimilarity score between each two compounds. The nearest neighbors for each compound within the set were then identified and its ERα binding potential was predicted by its nearest neighbors in the training set. The hybrid model performance (CCR = 0.94 for cross validation; CCR = 0.68 for external prediction) showed significant improvement over the original QSAR models, particularly for the activity cliffs that induce prediction errors. The results of this study indicate that the response profile of chemicals from public data provides useful information for modeling and evaluation purposes. The public big data resources should be considered along with chemical structure information when predicting new compounds, such as unknown ERα binding agents.

Exploiting three kinds of interface propensities to identify protein binding sites.

PubMed

Liu, Bin; Wang, Xiaolong; Lin, Lei; Dong, Qiwen; Wang, Xuan

2009-08-01

Predicting the binding sites between two interacting proteins provides important clues to the function of a protein. In this study, we present a building block of proteins called order profiles to use the evolutionary information of the protein sequence frequency profiles and apply this building block to produce a class of propensities called order profile interface propensities. For comparisons, we revisit the usage of residue interface propensities and binary profile interface propensities for protein binding site prediction. Each kind of propensities combined with sequence profiles and accessible surface areas are inputted into SVM. When tested on four types of complexes (hetero-permanent complexes, hetero-transient complexes, homo-permanent complexes and homo-transient complexes), experimental results show that the order profile interface propensities are better than residue interface propensities and binary profile interface propensities. Therefore, order profile is a suitable profile-level building block of the protein sequences and can be widely used in many tasks of computational biology, such as the sequence alignment, the prediction of domain boundary, the designation of knowledge-based potentials and the protein remote homology detection.

Building Structure Feature-based Models for Predicting Isoform-specific Human Cytochrome P-450 (hCYP 3A4, 2D6 and 2C9) Inhibition Assay Results in ToxCast

EPA Science Inventory

EPA’s ToxCast project is using high-throughput screening (HTS) to profile and prioritize chemicals for further testing. ToxCast Phase I evaluated 309 unique chemicals, the majority pesticide actives, in over 500 HTS assays. These included 3 human cytochrome P450 (hCYP3A4, hCYP2...

Observation of plasmonic dipolar anti-bonding mode in silver nanoring structures.

PubMed

Ye, Jian; Van Dorpe, Pol; Lagae, Liesbet; Maes, Guido; Borghs, Gustaaf

2009-11-18

We report on a clear experimental observation of the plasmonic dipolar anti-bonding resonance in silver nanorings. The data can be explained effectively by the plasmon hybridization model, which is confirmed by the numerical calculations of the electromagnetic field and surface charge distribution profiles. The experimental demonstration of the plasmon hybridization model indicates its usefulness as a valuable tool to understand, design and predict optical properties of metallic nanostructures.

Observation of plasmonic dipolar anti-bonding mode in silver nanoring structures

NASA Astrophysics Data System (ADS)

Ye, Jian; Van Dorpe, Pol; Lagae, Liesbet; Maes, Guido; Borghs, Gustaaf

2009-11-01

We report on a clear experimental observation of the plasmonic dipolar anti-bonding resonance in silver nanorings. The data can be explained effectively by the plasmon hybridization model, which is confirmed by the numerical calculations of the electromagnetic field and surface charge distribution profiles. The experimental demonstration of the plasmon hybridization model indicates its usefulness as a valuable tool to understand, design and predict optical properties of metallic nanostructures.

Preliminary report on seismic-reflection studies of crustal structure in the western, central, and southern United States

USGS Publications Warehouse

Roller, J.C.; Strozier, O.P.; Jackson, W.H.; Healy, J.H.

1963-01-01

During 1963 the U.S. Geological Survey, with the assistance of United ElectroDynamics, Inc., recorded five separate reversed seismic profiles. In addition to these profiles, the U.S. Geological Survey participated in a seismic-calibration program for the DRIBBLE experiment at Tatum Dome, Mississippi, a 20,000-pound shot near Dexter, Missouri, and in a cooperative seismic experiment in the Lake Superior region. This work is a continuation of the program started in 1961; however, the emphasis has shifted from a detailed study of the earth's crust in the western United States to a study of crustal structure in various geologic environments including the Wyoming thrust belt, Colorado Plateau, Central Lowlands, the Gulf Coastal Plain, and the southern part of the Canadian Shield. The U.S. Geological Survey has now completed reversed seismic-refraction profiles in nine different geologic provinces. These data present a promising indication that it may be possible to predict the crustal structure in unexplored areas by considering the regional geologic and physiographic environment. The following Pn velocities have been determined: 8.2 km/sec in the Wyoming thrust belt, 7.9 km/sec in the Colorado Plateau, 8.1 km/sec in the Central Lowlands, and about 8.2 km/sec in the Gulf Coastal Plain. The data from the Lake Superior region have not yet been interpreted.

Effect of Roller Profile on Cylindrical Roller Bearing Life Prediction

NASA Technical Reports Server (NTRS)

Poplawski, Joseph V.; Zaretsky, Erwin V.; Peters, Steven M.

2000-01-01

Four roller profiles used in cylindrical roller bearing design and manufacture were analyzed using both a closed form solution and finite element analysis (FEA) for stress and life. The roller profiles analyzed were flat, tapered end, aerospace, and fully crowned loaded against a flat raceway. Four rolling-element bearing life models were chosen for this analysis and compared. These were those of Weibull, Lundberg and Palmgren, Ioannides and Harris, and Zaretsky. The flat roller profile without edge loading has the longest predicted life. However, edge loading can reduce life by as much as 98 percent. The end tapered profile produced the highest lives but not significantly different than the aerospace profile. The fully crowned profile produces the lowest lives. The resultant predicted life at each stress condition not only depends on the life equation used but also on the Weibull slope assumed. For Weibull slopes of 1.5 and 2, both Lundberg-Palmgren and Iaonnides-Harris equations predict lower lives than the ANSI/ABMAJISO standards. Based upon the Hertz stresses for line contact, the accepted load-life exponent of 10/3 results in a maximum Hertz stress-life exponent equal to 6.6. This value is inconsistent with that experienced in the field.

Structure-functional prediction and analysis of cancer mutation effects in protein kinases.

PubMed

Dixit, Anshuman; Verkhivker, Gennady M

2014-01-01

A central goal of cancer research is to discover and characterize the functional effects of mutated genes that contribute to tumorigenesis. In this study, we provide a detailed structural classification and analysis of functional dynamics for members of protein kinase families that are known to harbor cancer mutations. We also present a systematic computational analysis that combines sequence and structure-based prediction models to characterize the effect of cancer mutations in protein kinases. We focus on the differential effects of activating point mutations that increase protein kinase activity and kinase-inactivating mutations that decrease activity. Mapping of cancer mutations onto the conformational mobility profiles of known crystal structures demonstrated that activating mutations could reduce a steric barrier for the movement from the basal "low" activity state to the "active" state. According to our analysis, the mechanism of activating mutations reflects a combined effect of partial destabilization of the kinase in its inactive state and a concomitant stabilization of its active-like form, which is likely to drive tumorigenesis at some level. Ultimately, the analysis of the evolutionary and structural features of the major cancer-causing mutational hotspot in kinases can also aid in the correlation of kinase mutation effects with clinical outcomes.

Peak expiratory flow profiles delivered by pump systems. Limitations due to wave action.

PubMed

Miller, M R; Jones, B; Xu, Y; Pedersen, O F; Quanjer, P H

2000-06-01

Pump systems are currently used to test the performance of both spirometers and peak expiratory flow (PEF) meters, but for certain flow profiles the input signal (i.e., requested profile) and the output profile can differ. We developed a mathematical model of wave action within a pump and compared the recorded flow profiles with both the input profiles and the output predicted by the model. Three American Thoracic Society (ATS) flow profiles and four artificial flow-versus-time profiles were delivered by a pump, first to a pneumotachograph (PT) on its own, then to the PT with a 32-cm upstream extension tube (which would favor wave action), and lastly with the PT in series with and immediately downstream to a mini-Wright peak flow meter. With the PT on its own, recorded flow for the seven profiles was 2.4 +/- 1.9% (mean +/- SD) higher than the pump's input flow, and similarly was 2.3 +/- 2.3% higher than the pump's output flow as predicted by the model. With the extension tube in place, the recorded flow was 6.6 +/- 6.4% higher than the input flow (range: 0.1 to 18.4%), but was only 1.2 +/- 2.5% higher than the output flow predicted by the model (range: -0.8 to 5.2%). With the mini-Wright meter in series, the flow recorded by the PT was on average 6.1 +/- 9.1% below the input flow (range: -23.8 to 2. 5%), but was only 0.6 +/- 3.3% above the pump's output flow predicted by the model (range: -5.5 to 3.9%). The mini-Wright meter's reading (corrected for its nonlinearity) was on average 1.3 +/- 3.6% below the model's predicted output flow (range: -9.0 to 1. 5%). The mini-Wright meter would be deemed outside ATS limits for accuracy for three of the seven profiles when compared with the pump's input PEF, but this would be true for only one profile when compared with the pump's output PEF as predicted by the model. Our study shows that the output flow from pump systems can differ from the input waveform depending on the operating configuration. This effect can be predicted with reasonable accuracy using a model based on nonsteady flow analysis that takes account of pressure wave reflections within pump systems.

Electrical conductivity of old oceanic mantle in the northwestern Pacific I: 1-D profiles suggesting differences in thermal structure not predictable from a plate cooling model

NASA Astrophysics Data System (ADS)

Baba, Kiyoshi; Tada, Noriko; Matsuno, Tetsuo; Liang, Pengfei; Li, Ruibai; Zhang, Luolei; Shimizu, Hisayoshi; Abe, Natsue; Hirano, Naoto; Ichiki, Masahiro; Utada, Hisashi

2017-08-01

Seafloor magnetotelluric (MT) experiments were recently conducted in two areas of the northwestern Pacific to investigate the nature of the old oceanic upper mantle. The areas are far from any tectonic activity, and "normal" mantle structure is therefore expected. The data were carefully analyzed to reduce the effects of coastlines and seafloor topographic changes, which are significant boundaries in electrical conductivity and thus distort seafloor MT data. An isotropic, one-dimensional electrical conductivity profile was estimated for each area. The profiles were compared with those obtained from two previous study areas in the northwestern Pacific. Between the four profiles, significant differences were observed in the thickness of the resistive layer beyond expectations based on cooling of homogeneous oceanic lithosphere over time. This surprising feature is now further clarified from what was suggested in a previous study. To explain the observed spatial variation, dynamic processes must be introduced, such as influence of the plume associated with the formation of the Shatsky Rise, or spatially non-uniform, small-scale convection in the asthenosphere. There is significant room of further investigation to determine a reasonable and comprehensive interpretation of the lithosphere-asthenosphere system beneath the northwestern Pacific. The present results demonstrate that electrical conductivity provides key information for such investigation.[Figure not available: see fulltext.

A one-dimensional Fickian model to predict the Ga depth profiles in three-stage Cu(In,Ga)Se{sub 2}

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

Rodriguez-Alvarez, H., E-mail: humberto.rodriguez@helmholtz-berlin.de; Helmholtz-Zentrum Berlin, Hahn-Meitner Platz 1, 14109 Berlin; Mainz, R.

2014-05-28

We present a one-dimensional Fickian model that predicts the formation of a double Ga gradient during the fabrication of Cu(In,Ga)Se{sub 2} thin films by three-stage thermal co-evaporation. The model is based on chemical reaction equations, structural data, and effective Ga diffusivities. In the model, the Cu(In,Ga)Se{sub 2} surface is depleted from Ga during the deposition of Cu-Se in the second deposition stage, leading to an accumulation of Ga near the back contact. During the third deposition stage, where In-Ga-Se is deposited at the surface, the atomic fluxes within the growing layer are inverted. This results in the formation of amore » double Ga gradient within the Cu(In,Ga)Se{sub 2} layer and reproduces experimentally observed Ga distributions. The final shape of the Ga depth profile strongly depends on the temperatures, times and deposition rates used. The model is used to evaluate possible paths to flatten the marked Ga depth profile that is obtained when depositing at low substrate temperatures. We conclude that inserting Ga during the second deposition stage is an effective way to achieve this.« less

Bacterial endophyte communities of three agricultural important grass species differ in their response towards management regimes

NASA Astrophysics Data System (ADS)

Wemheuer, Franziska; Kaiser, Kristin; Karlovsky, Petr; Daniel, Rolf; Vidal, Stefan; Wemheuer, Bernd

2017-01-01

Endophytic bacteria are critical for plant growth and health. However, compositional and functional responses of bacterial endophyte communities towards agricultural practices are still poorly understood. Hence, we analyzed the influence of fertilizer application and mowing frequency on bacterial endophytes in three agriculturally important grass species. For this purpose, we examined bacterial endophytic communities in aerial plant parts of Dactylis glomerata L., Festuca rubra L., and Lolium perenne L. by pyrotag sequencing of bacterial 16S rRNA genes over two consecutive years. Although management regimes influenced endophyte communities, observed responses were grass species-specific. This might be attributed to several bacteria specifically associated with a single grass species. We further predicted functional profiles from obtained 16S rRNA data. These profiles revealed that predicted abundances of genes involved in plant growth promotion or nitrogen metabolism differed between grass species and between management regimes. Moreover, structural and functional community patterns showed no correlation to each other indicating that plant species-specific selection of endophytes is driven by functional rather than phylogenetic traits. The unique combination of 16S rRNA data and functional profiles provided a holistic picture of compositional and functional responses of bacterial endophytes in agricultural relevant grass species towards management practices.

A Study of the Microbial Spatial Heterogeneity of Bahamian Thrombolites Using Molecular, Biochemical, and Stable Isotope Analyses

NASA Astrophysics Data System (ADS)

Louyakis, Artemis S.; Mobberley, Jennifer M.; Vitek, Brooke E.; Visscher, Pieter T.; Hagan, Paul D.; Reid, R. Pamela; Kozdon, Reinhard; Orland, Ian J.; Valley, John W.; Planavsky, Noah J.; Casaburi, Giorgio; Foster, Jamie S.

2017-05-01

Thrombolites are buildups of carbonate that exhibit a clotted internal structure formed through the interactions of microbial mats and their environment. Despite recent advances, we are only beginning to understand the microbial and molecular processes associated with their formation. In this study, a spatial profile of the microbial and metabolic diversity of thrombolite-forming mats of Highborne Cay, The Bahamas, was generated by using 16S rRNA gene sequencing and predictive metagenomic analyses. These molecular-based approaches were complemented with microelectrode profiling and in situ stable isotope analysis to examine the dominant taxa and metabolic activities within the thrombolite-forming communities. Analyses revealed three distinctive zones within the thrombolite-forming mats that exhibited stratified populations of bacteria and archaea. Predictive metagenomics also revealed vertical profiles of metabolic capabilities, such as photosynthesis and carboxylic and fatty acid synthesis within the mats that had not been previously observed. The carbonate precipitates within the thrombolite-forming mats exhibited isotopic geochemical signatures suggesting that the precipitation within the Bahamian thrombolites is photosynthetically induced. Together, this study provides the first look at the spatial organization of the microbial populations within Bahamian thrombolites and enables the distribution of microbes to be correlated with their activities within modern thrombolite systems.

AlignMe—a membrane protein sequence alignment web server

PubMed Central

Stamm, Marcus; Staritzbichler, René; Khafizov, Kamil; Forrest, Lucy R.

2014-01-01

We present a web server for pair-wise alignment of membrane protein sequences, using the program AlignMe. The server makes available two operational modes of AlignMe: (i) sequence to sequence alignment, taking two sequences in fasta format as input, combining information about each sequence from multiple sources and producing a pair-wise alignment (PW mode); and (ii) alignment of two multiple sequence alignments to create family-averaged hydropathy profile alignments (HP mode). For the PW sequence alignment mode, four different optimized parameter sets are provided, each suited to pairs of sequences with a specific similarity level. These settings utilize different types of inputs: (position-specific) substitution matrices, secondary structure predictions and transmembrane propensities from transmembrane predictions or hydrophobicity scales. In the second (HP) mode, each input multiple sequence alignment is converted into a hydrophobicity profile averaged over the provided set of sequence homologs; the two profiles are then aligned. The HP mode enables qualitative comparison of transmembrane topologies (and therefore potentially of 3D folds) of two membrane proteins, which can be useful if the proteins have low sequence similarity. In summary, the AlignMe web server provides user-friendly access to a set of tools for analysis and comparison of membrane protein sequences. Access is available at http://www.bioinfo.mpg.de/AlignMe PMID:24753425

iDBPs: a web server for the identification of DNA binding proteins

PubMed Central

Nimrod, Guy; Schushan, Maya; Szilágyi, András; Leslie, Christina; Ben-Tal, Nir

2010-01-01

Summary: The iDBPs server uses the three-dimensional (3D) structure of a query protein to predict whether it binds DNA. First, the algorithm predicts the functional region of the protein based on its evolutionary profile; the assumption is that large clusters of conserved residues are good markers of functional regions. Next, various characteristics of the predicted functional region as well as global features of the protein are calculated, such as the average surface electrostatic potential, the dipole moment and cluster-based amino acid conservation patterns. Finally, a random forests classifier is used to predict whether the query protein is likely to bind DNA and to estimate the prediction confidence. We have trained and tested the classifier on various datasets and shown that it outperformed related methods. On a dataset that reflects the fraction of DNA binding proteins (DBPs) in a proteome, the area under the ROC curve was 0.90. The application of the server to an updated version of the N-Func database, which contains proteins of unknown function with solved 3D-structure, suggested new putative DBPs for experimental studies. Availability: http://idbps.tau.ac.il/ Contact: NirB@tauex.tau.ac.il Supplementary information: Supplementary data are available at Bioinformatics online. PMID:20089514

Time-Resolved Neutron Interferometry and the Mechanism of Electromechanical Coupling in Voltage-Gated Ion Channels.

PubMed

Blasie, J Kent

2018-01-01

The mechanism of electromechanical coupling for voltage-gated ion channels (VGICs) involved in neurological signal transmission, primarily Nav- and Kv-channels, remains unresolved. Anesthetics have been shown to directly impact this mechanism, at least for Kv-channels. Molecular dynamics computer simulations can now predict the structures of VGICs embedded within a hydrated phospholipid bilayer membrane as a function of the applied transmembrane voltage, but significant assumptions are still necessary. Nevertheless, these simulations are providing new insights into the mechanism of electromechanical coupling at the atomic level in 3-D. We show that time-resolved neutron interferometry can be used to investigate directly the profile structure of a VGIC, vectorially oriented within a single hydrated phospholipid bilayer membrane at the solid-liquid interface, as a function of the applied transmembrane voltage in the absence of any assumptions or potentially perturbing modifications of the VGIC protein and/or the host membrane. The profile structure is a projection of the membrane's 3-D structure onto the membrane normal and, in the absence of site-directed deuterium labeling, is provided at substantially lower spatial resolution than the atomic level. Nevertheless, this novel approach can be used to directly test the validity of the predictions from molecular dynamics simulations. We describe the key elements of our novel experimental approach, including why each is necessary and important to providing the essential information required for this critical comparison of "simulation" vs "experiment." In principle, the approach could be extended to higher spatial resolution and to include the effects of anesthetics on the electromechanical coupling mechanism in VGICs. © 2018 Elsevier Inc. All rights reserved.

Comparison of predicted binders in Rhipicephalus (Boophilus) microplus intestine protein variants Bm86 Campo Grande strain, Bm86 and Bm95.

PubMed

Andreotti, Renato; Pedroso, Marisela S; Caetano, Alexandre R; Martins, Natália F

2008-01-01

This paper reports the sequence analysis of Bm86 Campo Grande strain comparing it with Bm86 and Bm95 antigens from the preparations TickGardPLUS and Gavac, respectively. The PCR product was cloned into pMOSBlue and sequenced. The secondary structure prediction tool PSIPRED was used to calculate alpha helices and beta strand contents of the predicted polypeptide. The hydrophobicity profile was calculated using the algorithms from the Hopp and Woods method, in addition to identification of potential MHC class-I binding regions in the antigens. Pair-wise alignment revealed that the similarity between Bm86 Campo Grande strain and Bm86 is 0.2% higher than that between Bm86 Campo Grande strain and Bm95 antigens. The identities were 96.5% and 96.3% respectively. Major suggestive differences in hydrophobicity were predicted among the sequences in two specific regions.

Predicting Baseline for Analysis of Electricity Pricing

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

Kim, T.; Lee, D.; Choi, J.

2016-05-03

To understand the impact of new pricing structure on residential electricity demands, we need a baseline model that captures every factor other than the new price. The standard baseline is a randomized control group, however, a good control group is hard to design. This motivates us to devlop data-driven approaches. We explored many techniques and designed a strategy, named LTAP, that could predict the hourly usage years ahead. The key challenge in this process is that the daily cycle of electricity demand peaks a few hours after the temperature reaching its peak. Existing methods rely on the lagged variables ofmore » recent past usages to enforce this daily cycle. These methods have trouble making predictions years ahead. LTAP avoids this trouble by assuming the daily usage profile is determined by temperature and other factors. In a comparison against a well-designed control group, LTAP is found to produce accurate predictions.« less

On the potential use of radar-derived information in operational numerical weather prediction

NASA Technical Reports Server (NTRS)

Mcpherson, R. D.

1986-01-01

Estimates of requirements likely to be levied on a new observing system for mesoscale meteonology are given. Potential observing systems for mesoscale numerical weather prediction are discussed. Thermodynamic profiler radiometers, infrared radiometer atmospheric sounders, Doppler radar wind profilers and surveillance radar, and moisture profilers are among the instruments described.

CLASH: Weak-lensing shear-and-magnification analysis of 20 galaxy clusters

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

Umetsu, Keiichi; Czakon, Nicole; Medezinski, Elinor

2014-11-10

We present a joint shear-and-magnification weak-lensing analysis of a sample of 16 X-ray-regular and 4 high-magnification galaxy clusters at 0.19 ≲ z ≲ 0.69 selected from the Cluster Lensing And Supernova survey with Hubble (CLASH). Our analysis uses wide-field multi-color imaging, taken primarily with Suprime-Cam on the Subaru Telescope. From a stacked-shear-only analysis of the X-ray-selected subsample, we detect the ensemble-averaged lensing signal with a total signal-to-noise ratio of ≅ 25 in the radial range of 200-3500 kpc h {sup –1}, providing integrated constraints on the halo profile shape and concentration-mass relation. The stacked tangential-shear signal is well described bymore » a family of standard density profiles predicted for dark-matter-dominated halos in gravitational equilibrium, namely, the Navarro-Frenk-White (NFW), truncated variants of NFW, and Einasto models. For the NFW model, we measure a mean concentration of c{sub 200c}=4.01{sub −0.32}{sup +0.35} at an effective halo mass of M{sub 200c}=1.34{sub −0.09}{sup +0.10}×10{sup 15} M{sub ⊙}. We show that this is in excellent agreement with Λ cold dark matter (ΛCDM) predictions when the CLASH X-ray selection function and projection effects are taken into account. The best-fit Einasto shape parameter is α{sub E}=0.191{sub −0.068}{sup +0.071}, which is consistent with the NFW-equivalent Einasto parameter of ∼0.18. We reconstruct projected mass density profiles of all CLASH clusters from a joint likelihood analysis of shear-and-magnification data and measure cluster masses at several characteristic radii assuming an NFW density profile. We also derive an ensemble-averaged total projected mass profile of the X-ray-selected subsample by stacking their individual mass profiles. The stacked total mass profile, constrained by the shear+magnification data, is shown to be consistent with our shear-based halo-model predictions, including the effects of surrounding large-scale structure as a two-halo term, establishing further consistency in the context of the ΛCDM model.« less

Use of Smoothed Measured Winds to Predict and Assess Launch Environments

NASA Technical Reports Server (NTRS)

Cordova, Henry S.; Leahy, Frank; Adelfang, Stanley; Roberts, Barry; Starr, Brett; Duffin, Paul; Pueri, Daniel

2011-01-01

Since many of the larger launch vehicles are operated near their design limits during the ascent phase of flight to optimize payload to orbit, it often becomes necessary to verify that the vehicle will remain within certification limits during the ascent phase as part of the go/no-go review made prior to launch. This paper describes the approach used to predict Ares I-X launch vehicle structural air loads and controllability prior to launch which represents a distinct departure from the methodology of the Space Shuttle and Evolved Expendable Launch Vehicle (EELV) programs. Protection for uncertainty of key environment and trajectory parameters is added to the nominal assessment of launch capability to ensure that critical launch trajectory variables would be within the integrated vehicle certification envelopes. This process was applied by the launch team as a key element of the launch day go/no-go recommendation. Pre-launch assessments of vehicle launch capability for NASA's Space Shuttle and the EELV heavy lift versions require the use of a high-resolution wind profile measurements, which have relatively small sample size compared with low-resolution profile databases (which include low-resolution balloons and radar wind profilers). The approach described in this paper has the potential to allow the pre-launch assessment team to use larger samples of wind measurements from low-resolution wind profile databases that will improve the accuracy of pre-launch assessments of launch availability with no degradation of mission assurance or launch safety.

Representing high throughput expression profiles via perturbation barcodes reveals compound targets.

PubMed

Filzen, Tracey M; Kutchukian, Peter S; Hermes, Jeffrey D; Li, Jing; Tudor, Matthew

2017-02-01

High throughput mRNA expression profiling can be used to characterize the response of cell culture models to perturbations such as pharmacologic modulators and genetic perturbations. As profiling campaigns expand in scope, it is important to homogenize, summarize, and analyze the resulting data in a manner that captures significant biological signals in spite of various noise sources such as batch effects and stochastic variation. We used the L1000 platform for large-scale profiling of 978 representative genes across thousands of compound treatments. Here, a method is described that uses deep learning techniques to convert the expression changes of the landmark genes into a perturbation barcode that reveals important features of the underlying data, performing better than the raw data in revealing important biological insights. The barcode captures compound structure and target information, and predicts a compound's high throughput screening promiscuity, to a higher degree than the original data measurements, indicating that the approach uncovers underlying factors of the expression data that are otherwise entangled or masked by noise. Furthermore, we demonstrate that visualizations derived from the perturbation barcode can be used to more sensitively assign functions to unknown compounds through a guilt-by-association approach, which we use to predict and experimentally validate the activity of compounds on the MAPK pathway. The demonstrated application of deep metric learning to large-scale chemical genetics projects highlights the utility of this and related approaches to the extraction of insights and testable hypotheses from big, sometimes noisy data.

Representing high throughput expression profiles via perturbation barcodes reveals compound targets

PubMed Central

Kutchukian, Peter S.; Li, Jing; Tudor, Matthew

2017-01-01

High throughput mRNA expression profiling can be used to characterize the response of cell culture models to perturbations such as pharmacologic modulators and genetic perturbations. As profiling campaigns expand in scope, it is important to homogenize, summarize, and analyze the resulting data in a manner that captures significant biological signals in spite of various noise sources such as batch effects and stochastic variation. We used the L1000 platform for large-scale profiling of 978 representative genes across thousands of compound treatments. Here, a method is described that uses deep learning techniques to convert the expression changes of the landmark genes into a perturbation barcode that reveals important features of the underlying data, performing better than the raw data in revealing important biological insights. The barcode captures compound structure and target information, and predicts a compound’s high throughput screening promiscuity, to a higher degree than the original data measurements, indicating that the approach uncovers underlying factors of the expression data that are otherwise entangled or masked by noise. Furthermore, we demonstrate that visualizations derived from the perturbation barcode can be used to more sensitively assign functions to unknown compounds through a guilt-by-association approach, which we use to predict and experimentally validate the activity of compounds on the MAPK pathway. The demonstrated application of deep metric learning to large-scale chemical genetics projects highlights the utility of this and related approaches to the extraction of insights and testable hypotheses from big, sometimes noisy data. PMID:28182661

Genomic profiling is predictive of response to cisplatin treatment but not to PI3K inhibition in bladder cancer patient-derived xenografts

PubMed Central

Ramakrishnan, Swathi; Elbanna, May; Wang, Jianmin; Hu, Qiang; Glenn, Sean T.; Murakami, Mitsuko; Liu, Lu; Gomez, Eduardo Cortes; Sun, Yuchen; Conroy, Jacob; Miles, Kiersten Marie; Malathi, Kullappan; Ramaiah, Sudha; Anbarasu, Anand; Woloszynska-Read, Anna; Johnson, Candace S.; Conroy, Jeffrey; Liu, Song; Morrison, Carl D.; Pili, Roberto

2016-01-01

Purpose Effective systemic therapeutic options are limited for bladder cancer. In this preclinical study we tested whether bladder cancer gene alterations may be predictive of treatment response. Experimental design We performed genomic profiling of two bladder cancer patient derived tumor xenografts (PDX). We optimized the exome sequence analysis method to overcome the mouse genome interference. Results We identified a number of somatic mutations, mostly shared by the primary tumors and PDX. In particular, BLCAb001, which is less responsive to cisplatin than BLCAb002, carried non-sense mutations in several genes associated with cisplatin resistance, including MLH1, BRCA2, and CASP8. Furthermore, RNA-Seq analysis revealed the overexpression of cisplatin resistance associated genes such as SLC7A11, TLE4, and IL1A in BLCAb001. Two different PIK3CA mutations, E542K and E545K, were identified in BLCAb001 and BLCAb002, respectively. Thus, we tested whether the genomic profiling was predictive of response to a dual PI3K/mTOR targeting agent, LY3023414. Despite harboring similar PIK3CA mutations, BLCAb001 and BLCAb002 exhibited differential response, both in vitro and in vivo. Sustained target modulation was observed in the sensitive model BLCAb002 but not in BLCAb001, as well as decreased autophagy. Interestingly, computational modelling of mutant structures and affinity binding to PI3K revealed that E542K mutation was associated with weaker drug binding than E545K. Conclusions Our results suggest that the presence of activating PIK3CA mutations may not necessarily predict in vivo treatment response to PI3K targeted therapies, while specific gene alterations may be predictive for cisplatin response in bladder cancer models and, potentially, in patients as well. PMID:27823983

A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP Inhibitors

DTIC Science & Technology

2015-10-01

1 Award Number: W81XWH-10-1-0585 TITLE: A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP Inhibitors...TITLE AND SUBTITLE A Gene Expression Profile of BRCAness That Predicts for Responsiveness to Platinum and PARP Inhibitors 5a. CONTRACT NUMBER W81XWH...BRCAlike, i.e. not HR deficient and are resistant to PARPis but are sensitive to platinum . These tumors exhibit alterations in another DNA repair

An ab initio study of the structure and atomic transport in bulk liquid Ag and its liquid-vapor interface

NASA Astrophysics Data System (ADS)

del Rio, Beatriz G.; González, David J.; González, Luis E.

2016-10-01

Several static and dynamic properties of bulk liquid Ag at a thermodynamic state near its triple point have been calculated by means of ab initio molecular dynamics simulations. The calculated static structure shows a very good agreement with the available experimental data. The dynamical structure reveals propagating excitations whose dispersion at long wavelengths is compatible with the experimental sound velocity. Results are also reported for other transport coefficients. Additional simulations have also been performed so as to study the structure of the free liquid surface. The calculated longitudinal ionic density profile shows an oscillatory behaviour, whose properties are analyzed through macroscopic and microscopic methods. The intrinsic X-ray reflectivity of the surface is predicted to show a layering peak associated to the interlayer distance.

Improving prediction of heterodimeric protein complexes using combination with pairwise kernel.

PubMed

Ruan, Peiying; Hayashida, Morihiro; Akutsu, Tatsuya; Vert, Jean-Philippe

2018-02-19

Since many proteins become functional only after they interact with their partner proteins and form protein complexes, it is essential to identify the sets of proteins that form complexes. Therefore, several computational methods have been proposed to predict complexes from the topology and structure of experimental protein-protein interaction (PPI) network. These methods work well to predict complexes involving at least three proteins, but generally fail at identifying complexes involving only two different proteins, called heterodimeric complexes or heterodimers. There is however an urgent need for efficient methods to predict heterodimers, since the majority of known protein complexes are precisely heterodimers. In this paper, we use three promising kernel functions, Min kernel and two pairwise kernels, which are Metric Learning Pairwise Kernel (MLPK) and Tensor Product Pairwise Kernel (TPPK). We also consider the normalization forms of Min kernel. Then, we combine Min kernel or its normalization form and one of the pairwise kernels by plugging. We applied kernels based on PPI, domain, phylogenetic profile, and subcellular localization properties to predicting heterodimers. Then, we evaluate our method by employing C-Support Vector Classification (C-SVC), carrying out 10-fold cross-validation, and calculating the average F-measures. The results suggest that the combination of normalized-Min-kernel and MLPK leads to the best F-measure and improved the performance of our previous work, which had been the best existing method so far. We propose new methods to predict heterodimers, using a machine learning-based approach. We train a support vector machine (SVM) to discriminate interacting vs non-interacting protein pairs, based on informations extracted from PPI, domain, phylogenetic profiles and subcellular localization. We evaluate in detail new kernel functions to encode these data, and report prediction performance that outperforms the state-of-the-art.

Specificity profiling of protein-binding domains using one-bead-one-compound Peptide libraries.

PubMed

Kunys, Andrew R; Lian, Wenlong; Pei, Dehua

2012-12-01

One-bead-one-compound (OBOC) libraries consist of structurally related compounds (e.g., peptides) covalently attached to a solid support, with each resin bead carrying a unique compound. OBOC libraries of high structural diversity can be rapidly synthesized and screened without the need for any special equipment, and therefore can be employed in any chemical or biochemical laboratory. OBOC peptide libraries have been widely used to map the ligand specificity of proteins, to determine the substrate specificity of enzymes, and to develop inhibitors against macromolecular targets. They have proven particularly useful in profiling the binding specificity of protein modular domains (e.g., SH2 domains, BIR domains, and PDZ domains); subsequently, the specificity information can be used to predict the protein targets of these domains. The protocols outlined in this article describe the methodologies for synthesizing and screening OBOC peptide libraries against SH2 and PDZ domains, and the related data analysis. Curr. Protoc. Chem. Biol. 4:331-355 © 2012 by John Wiley & Sons, Inc.

Supervised extensions of chemography approaches: case studies of chemical liabilities assessment

PubMed Central

2014-01-01

Chemical liabilities, such as adverse effects and toxicity, play a significant role in modern drug discovery process. In silico assessment of chemical liabilities is an important step aimed to reduce costs and animal testing by complementing or replacing in vitro and in vivo experiments. Herein, we propose an approach combining several classification and chemography methods to be able to predict chemical liabilities and to interpret obtained results in the context of impact of structural changes of compounds on their pharmacological profile. To our knowledge for the first time, the supervised extension of Generative Topographic Mapping is proposed as an effective new chemography method. New approach for mapping new data using supervised Isomap without re-building models from the scratch has been proposed. Two approaches for estimation of model’s applicability domain are used in our study to our knowledge for the first time in chemoinformatics. The structural alerts responsible for the negative characteristics of pharmacological profile of chemical compounds has been found as a result of model interpretation. PMID:24868246

Using machine learning and quantum chemistry descriptors to predict the toxicity of ionic liquids.

PubMed

Cao, Lingdi; Zhu, Peng; Zhao, Yongsheng; Zhao, Jihong

2018-06-15

Large-scale application of ionic liquids (ILs) hinges on the advancement of designable and eco-friendly nature. Research of the potential toxicity of ILs towards different organisms and trophic levels is insufficient. Quantitative structure-activity relationships (QSAR) model is applied to evaluate the toxicity of ILs towards the leukemia rat cell line (ICP-81). The structures of 57 cations and 21 anions were optimized by quantum chemistry. The electrostatic potential surface area (S EP ) and charge distribution area (S σ-profile ) descriptors are calculated and used to predict the toxicity of ILs. The performance and predictive aptitude of extreme learning machine (ELM) model are analyzed and compared with those of multiple linear regression (MLR) and support vector machine (SVM) models. The highest R 2 and the lowest AARD% and RMSE of the training set, test set and total set for the ELM are observed, which validates the superior performance of the ELM than that of obtained by the MLR and SVM. The applicability domain of the model is assessed by the Williams plot. Copyright © 2018 Elsevier B.V. All rights reserved.

Evolution of Cross-Shore Profile Models for Sustainable Coastal Design

NASA Astrophysics Data System (ADS)

Ismail, Nabil; El-Sayed, Mohamed

2014-05-01

Selection and evaluation of coastal structures are correlated with environmental wave and current parameters as well as cross shore profiles. The coupling between the environmental conditions and cross shore profiles necessitates the ability to predict reasonably the cross shore profiles. Results obtained from the validation of a cross-shore profile evolution model, Uniform Beach Sediment Transport-Time-Averaged Cross-Shore (UNIBEST-TC), were examined and further analyzed to reveal the reasons for the discrepancy between the model predictions of the field data at the surf zone of the Duck Beach in North Carolina, USA. The UNIBEST model was developed to predict the main cross shore parameters of wave height, direction, cross shore and long shore currents. However, the results of the model predictions are generally satisfactory for wave height and direction but not satisfactory for the remaining parameters. This research is focused on exploring the discrepancy between the model predictions and the field data of the Duck site, and conducting further analyses to recommend model refinements. The discrepancy is partially attributed due to the fact that the measured values, were taken close to the seabed, while the predicted values are the depth-averaged velocity. Further examination indicated that UNIBEST-TC model runs consider the RMS of the wave height spectrum with a constant gamma-value from the offshore wave spectrum at 8.0m depth. To confirm this argument, a Wavelet Analysis was applied to the time series of wave height and longshore current velocity parameters at the Duck site. The significant wave height ranged between 0.6m and 4.0m while the frequencies ranged between 0.08 to 0.2Hz at 8.0m water depth. Four cases corresponding to events of both high water level and low water level at Duck site were considered in this study. The results show that linear and non-linear interaction between wave height and long-shore current occur over the range of frequencies embracing; the low frequency band of infragravity (0.001- 0.02Hz) waves band and short incident wave band (0.05-0.10Hz). The present results highlight the necessity of incorporating interaction terms between wave - wave and wave- current in the development of cross shore and longshore model formulations. The numerical results confirm previous field observations of nearshore processes that waves in the infragravity range, shear and edge waves, play an important role on near shore hydrodynamics and beach morphology. A prime recommendation of this research work is that the UNIBEST- TC and similar models need to take into effect the interaction between waves, cross shore and longshore currents. Furthermore the models should consider the effects of long waves within the spectrum as well as the generated edge waves. Nevertheless, modeling of this wide range of processes on real beaches needs extensive field data of high spatial and temporal resolutions. Such challenging goal remains to be pursued to enhance state of art prediction of the cross-shore evolution profiles. REFERENCES Addison, P.S. (2002). "The Illustrated Wavelet Transform Handbook, Introductory Theory and Applications in Science", 349 p., Bristol, UK, Institute of Physics Publishing. Elsayed, M.A.K. (2006). "Application of a Cross-Shore Profile Evolution Model to Barred Beaches", Journal of Coastal Research, 22(3), 645-663. Elsayed, M.A.K. (2007). "Non-linear Wave-Wave Interactions in a Mistral Event". Journal of Coastal Research, 23(5), 1318-1323. Ismail, N. M., and Wiegel, R. L. (1983). "Effect of Opposing Waves on Momentum Jets Spreading Rate", Journal of Waterway, Port, Coastal and Ocean Division, ASCE, Vol.109, No.4, 465-483. Ismail, N.M. (1984). "Wave-Current Models for the Design of Marine Structures", Journal of Waterway, Port, Coastal and Ocean Engineering, ASCE, Vol. 110, No. 4, 432-446. Ismail, N.M. (2007). "Discussion of Reynolds Stresses and Velocity Distributions in a Wave-Current Coexisting Environment", Journal of Waterway, Port, Coastal and Ocean Engineering, ASCE, Vol. 133, No. 2, 168-169. Ismail, N. and J.W. Williams. ( 2013). Sea-Level Rise Implications for Coastal Protection from Southern Mediterranean to the U.S.A. Atlantic Coast, EGU,2013-13464, European Geosciences Union, General Assembly 2013,Vienna, Austria, 07 - 12 April.

Intact Cell MALDI-TOF MS on Sperm: A Molecular Test For Male Fertility Diagnosis.

PubMed

Soler, Laura; Labas, Valérie; Thélie, Aurore; Grasseau, Isabelle; Teixeira-Gomes, Ana-Paula; Blesbois, Elisabeth

2016-06-01

Currently, evaluation of sperm quality is primarily based on in vitro measures of sperm function such as motility, viability and/or acrosome reaction. However, results are often poorly correlated with fertility, and alternative diagnostic tools are therefore needed both in veterinary and human medicine. In a recent pilot study, we demonstrated that MS profiles from intact chicken sperm using MALDI-TOF profiles could detect significant differences between fertile/subfertile spermatozoa showing that such profiles could be useful for in vitro male fertility testing. In the present study, we performed larger standardized experimental procedures designed for the development of fertility- predictive mathematical models based on sperm cell MALDI-TOF MS profiles acquired through a fast, automated method. This intact cell MALDI-TOF MS-based method showed high diagnostic accuracy in identifying fertile/subfertile males in a large male population of known fertility from two distinct genetic lineages (meat and egg laying lines). We additionally identified 40% of the m/z peaks observed in sperm MS profiles through a top-down high-resolution protein identification analysis. This revealed that the MALDI-TOF MS spectra obtained from intact sperm cells contained a large proportion of protein degradation products, many implicated in important functional pathways in sperm such as energy metabolism, structure and movement. Proteins identified by our predictive model included diverse and important functional classes providing new insights into sperm function as it relates to fertility differences in this experimental system. Thus, in addition to the chicken model system developed here, with the use of appropriate models these methods should effectively translate to other animal taxa where similar tests for fertility are warranted. © 2016 by The American Society for Biochemistry and Molecular Biology, Inc.

Personality and substance use: psychometric evaluation and validation of the Substance Use Risk Profile Scale (SURPS) in English, Irish, French, and German adolescents.

PubMed

Jurk, Sarah; Kuitunen-Paul, Sören; Kroemer, Nils B; Artiges, Eric; Banaschewski, Tobias; Bokde, Arun L W; Büchel, Christian; Conrod, Patricia; Fauth-Bühler, Mira; Flor, Herta; Frouin, Vincent; Gallinat, Jürgen; Garavan, Hugh; Heinz, Andreas; Mann, Karl F; Nees, Frauke; Paus, Tomáš; Pausova, Zdenka; Poustka, Luise; Rietschel, Marcella; Schumann, Gunter; Struve, Maren; Smolka, Michael N

2015-11-01

The aim of the present longitudinal study was the psychometric evaluation of the Substance Use Risk Profile Scale (SURPS). We analyzed data from N = 2,022 adolescents aged 13 to 15 at baseline assessment and 2 years later (mean interval 2.11 years). Missing data at follow-up were imputed (N = 522). Psychometric properties of the SURPS were analyzed using confirmatory factor analysis. We examined structural as well as convergent validity with other personality measurements and drinking motives, and predictive validity for substance use at follow-up. The hypothesized 4-factorial structure (i.e., anxiety sensitivity, hopelessness, impulsivity [IMP], and sensation seeking [SS]) based on all 23 items resulted in acceptable fit to empirical data, acceptable internal consistencies, low to moderate test-retest reliability coefficients, as well as evidence for factorial and convergent validity. The proposed factor structure was stable for both males and females and, to lesser degree, across languages. However, only the SS and the IMP subscales of the SURPS predicted substance use outcomes at 16 years of age. The SURPS is unique in its specific assessment of traits related to substance use disorders as well as the resulting shortened administration time. Test-retest reliability was low to moderate and comparable to other personality scales. However, its relation to future substance use was limited to the SS and IMP subscales, which may be due to the relatively low-risk substance use pattern in the present sample. Copyright © 2015 by the Research Society on Alcoholism.

Monte Carlo simulations of the detailed iron absorption line profiles from thermal winds in X-ray binaries

NASA Astrophysics Data System (ADS)

Tomaru, Ryota; Done, Chris; Odaka, Hirokazu; Watanabe, Shin; Takahashi, Tadayuki

2018-05-01

Blueshifted absorption lines from highly ionized iron are seen in some high inclination X-ray binary systems, indicating the presence of an equatorial disc wind. This launch mechanism is under debate, but thermal driving should be ubiquitous. X-ray irradiation from the central source heats disc surface, forming a wind from the outer disc where the local escape velocity is lower than the sound speed. The mass-loss rate from each part of the disc is determined by the luminosity and spectral shape of the central source. We use these together with an assumed density and velocity structure of the wind to predict the column density and ionization state, then combine this with a Monte Carlo radiation transfer to predict the detailed shape of the absorption (and emission) line profiles. We test this on the persistent wind seen in the bright neutron star binary GX 13+1, with luminosity L/LEdd ˜ 0.5. We approximately include the effect of radiation pressure because of high luminosity, and compute line features. We compare these to the highest resolution data, the Chandra third-order grating spectra, which we show here for the first time. This is the first physical model for the wind in this system, and it succeeds in reproducing many of the features seen in the data, showing that the wind in GX13+1 is most likely a thermal-radiation driven wind. This approach, combined with better streamline structures derived from full radiation hydrodynamic simulations, will allow future calorimeter data to explore the detail wind structure.

STRUCTURAL PARAMETERS FOR 10 HALO GLOBULAR CLUSTERS IN M33

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

Ma, Jun, E-mail: majun@nao.cas.cn

2015-05-15

In this paper, we present the properties of 10 halo globular clusters (GCs) with luminosities L ≃ 5–7 × 10{sup 5} L{sub ⊙} in the Local Group galaxy M33 using images from the Hubble Space Telescope WFPC2 in the F555W and F814W bands. We obtained the ellipticities, position angles, and surface brightness profiles for each GC. In general, the ellipticities of the M33 sample clusters are similar to those of the M31 clusters. The structural and dynamical parameters are derived by fitting the profiles to three different models combined with mass-to-light ratios (M/L values) from population-synthesis models. The structural parametersmore » include core radii, concentration, half-light radii, and central surface brightness. The dynamical parameters include the integrated cluster mass, integrated binding energy, central surface mass density, and predicted line of sight velocity dispersion at the cluster center. The velocity dispersions of the four clusters predicted here agree well with the observed dispersions by Larsen et al. The results here showed that the majority of the sample halo GCs are better fitted by both the King model and the Wilson model than the Sérsic model. In general, the properties of the clusters in M33, M31, and the Milky Way fall in the same regions of parameter spaces. The tight correlations of cluster properties indicate a “fundamental plane” for clusters, which reflects some universal physical conditions and processes operating at the epoch of cluster formation.« less

Computation of Large-Scale Structure Jet Noise Sources With Weak Nonlinear Effects Using Linear Euler

NASA Technical Reports Server (NTRS)

Dahl, Milo D.; Hixon, Ray; Mankbadi, Reda R.

2003-01-01

An approximate technique is presented for the prediction of the large-scale turbulent structure sound source in a supersonic jet. A linearized Euler equations code is used to solve for the flow disturbances within and near a jet with a given mean flow. Assuming a normal mode composition for the wave-like disturbances, the linear radial profiles are used in an integration of the Navier-Stokes equations. This results in a set of ordinary differential equations representing the weakly nonlinear self-interactions of the modes along with their interaction with the mean flow. Solutions are then used to correct the amplitude of the disturbances that represent the source of large-scale turbulent structure sound in the jet.

Orally active achiral N-hydroxyformamide inhibitors of ADAM-TS4 (aggrecanase-1) and ADAM-TS5 (aggrecanase-2) for the treatment of osteoarthritis.

PubMed

De Savi, Chris; Pape, Andrew; Sawyer, Yvonne; Milne, David; Davies, Chris; Cumming, John G; Ting, Attilla; Lamont, Scott; Smith, Peter D; Tart, Jonathon; Page, Ken; Moore, Peter

2011-06-01

A new achiral class of N-hydroxyformamide inhibitor of both ADAM-TS4 and ADAM-TS5, 2 has been discovered through modification of the complex P1 group present in historical inhibitors 1. This structural change improved the DMPK properties and greatly simplified the synthesis whilst maintaining excellent cross-MMP selectivity profiles. Investigation of structure-activity and structure-property relationships in the P1 group resulted in both ADAM-TS4 selective and mixed ADAM-TS4/5 inhibitors. This led to the identification of a pre-clinical candidate with excellent bioavailability across three species and predicting once daily dosing kinetics. Copyright © 2011 Elsevier Ltd. All rights reserved.

Genomics of NSCLC patients both affirm PD-L1 expression and predict their clinical responses to anti-PD-1 immunotherapy.

PubMed

Brogden, Kim A; Parashar, Deepak; Hallier, Andrea R; Braun, Terry; Qian, Fang; Rizvi, Naiyer A; Bossler, Aaron D; Milhem, Mohammed M; Chan, Timothy A; Abbasi, Taher; Vali, Shireen

2018-02-27

Programmed Death Ligand 1 (PD-L1) is a co-stimulatory and immune checkpoint protein. PD-L1 expression in non-small cell lung cancers (NSCLC) is a hallmark of adaptive resistance and its expression is often used to predict the outcome of Programmed Death 1 (PD-1) and PD-L1 immunotherapy treatments. However, clinical benefits do not occur in all patients and new approaches are needed to assist in selecting patients for PD-1 or PD-L1 immunotherapies. Here, we hypothesized that patient tumor cell genomics influenced cell signaling and expression of PD-L1, chemokines, and immunosuppressive molecules and these profiles could be used to predict patient clinical responses. We used a recent dataset from NSCLC patients treated with pembrolizumab. Deleterious gene mutational profiles in patient exomes were identified and annotated into a cancer network to create NSCLC patient-specific predictive computational simulation models. Validation checks were performed on the cancer network, simulation model predictions, and PD-1 match rates between patient-specific predicted and clinical responses. Expression profiles of these 24 chemokines and immunosuppressive molecules were used to identify patients who would or would not respond to PD-1 immunotherapy. PD-L1 expression alone was not sufficient to predict which patients would or would not respond to PD-1 immunotherapy. Adding chemokine and immunosuppressive molecule expression profiles allowed patient models to achieve a greater than 85.0% predictive correlation among predicted and reported patient clinical responses. Our results suggested that chemokine and immunosuppressive molecule expression profiles can be used to accurately predict clinical responses thus differentiating among patients who would and would not benefit from PD-1 or PD-L1 immunotherapies.

Antigenic Properties of the HIV Envelope on Virions in Solution

PubMed Central

Mengistu, Meron; Lewis, George K.; Lakowicz, Joseph R.

2014-01-01

The structural flexibility found in human immunodeficiency virus (HIV) envelope glycoproteins creates a complex relationship between antigenicity and sensitivity to antiviral antibodies. The study of this issue in the context of viral particles is particularly problematic as conventional virus capture approaches can perturb antigenicity profiles. Here, we employed a unique analytical system based on fluorescence correlation spectroscopy (FCS), which measures antibody-virion binding with all reactants continuously in solution. Panels of nine anti-envelope monoclonal antibodies (MAbs) and five virus types were used to connect antibody binding profiles with neutralizing activities. Anti-gp120 MAbs against the 2G12 or b12 epitope, which marks functional envelope structures, neutralized viruses expressing CCR5-tropic envelopes and exhibited efficient virion binding in solution. MAbs against CD4-induced (CD4i) epitopes considered hidden on functional envelope structures poorly bound these viruses and were not neutralizing. Anti-gp41 MAb 2F5 was neutralizing despite limited virion binding. Similar antigenicity patterns occurred on CXCR4-tropic viruses, except that anti-CD4i MAbs 17b and 19e were neutralizing despite little or no virion binding. Notably, anti-gp120 MAb PG9 and anti-gp41 MAb F240 bound to both CCR5-tropic and CXCR4-tropic viruses without exerting neutralizing activity. Differences in the virus production system altered the binding efficiencies of some antibodies but did not enhance antigenicity of aberrant gp120 structures. Of all viruses tested, only JRFL pseudoviruses showed a direct relationship between MAb binding efficiency and neutralizing potency. Collectively, these data indicate that the antigenic profiles of free HIV particles generally favor the exposure of functional over aberrant gp120 structures. However, the efficiency of virion-antibody interactions in solution inconsistently predicts neutralizing activity in vitro. PMID:24284318

SMURFLite: combining simplified Markov random fields with simulated evolution improves remote homology detection for beta-structural proteins into the twilight zone.

PubMed

Daniels, Noah M; Hosur, Raghavendra; Berger, Bonnie; Cowen, Lenore J

2012-05-01

One of the most successful methods to date for recognizing protein sequences that are evolutionarily related has been profile hidden Markov models (HMMs). However, these models do not capture pairwise statistical preferences of residues that are hydrogen bonded in beta sheets. These dependencies have been partially captured in the HMM setting by simulated evolution in the training phase and can be fully captured by Markov random fields (MRFs). However, the MRFs can be computationally prohibitive when beta strands are interleaved in complex topologies. We introduce SMURFLite, a method that combines both simplified MRFs and simulated evolution to substantially improve remote homology detection for beta structures. Unlike previous MRF-based methods, SMURFLite is computationally feasible on any beta-structural motif. We test SMURFLite on all propeller and barrel folds in the mainly-beta class of the SCOP hierarchy in stringent cross-validation experiments. We show a mean 26% (median 16%) improvement in area under curve (AUC) for beta-structural motif recognition as compared with HMMER (a well-known HMM method) and a mean 33% (median 19%) improvement as compared with RAPTOR (a well-known threading method) and even a mean 18% (median 10%) improvement in AUC over HHPred (a profile-profile HMM method), despite HHpred's use of extensive additional training data. We demonstrate SMURFLite's ability to scale to whole genomes by running a SMURFLite library of 207 beta-structural SCOP superfamilies against the entire genome of Thermotoga maritima, and make over a 100 new fold predictions. Availability and implementaion: A webserver that runs SMURFLite is available at: http://smurf.cs.tufts.edu/smurflite/

Evaluation of tunnel seismic prediction (TSP) result using the Japanese highway rock mass classification system for Pahang-Selangor Raw Water Transfer Tunnel

NASA Astrophysics Data System (ADS)

Von, W. C.; Ismail, M. A. M.

2017-10-01

The knowing of geological profile ahead of tunnel face is significant to minimize the risk in tunnel excavation work and cost control in preventative measure. Due to mountainous area, site investigation with vertical boring is not recommended to obtain the geological profile for Pahang-Selangor Raw Water Transfer project. Hence, tunnel seismic prediction (TSP) method is adopted to predict the geological profile ahead of tunnel face. In order to evaluate the TSP results, IBM SPSS Statistic 22 is used to run artificial neural network (ANN) analysis to back calculate the predicted Rock Grade Points (JH) from actual Rock Grade Points (JH) using Vp, Vs and Vp/Vs from TSP. The results show good correlation between predicted Rock Grade points and actual Rock Grade Points (JH). In other words, TSP can provide geological profile prediction ahead of tunnel face significantly while allowing continuously TBM excavation works. Identifying weak zones or faults ahead of tunnel face is crucial for preventative measures to be carried out in advance for a safer tunnel excavation works.

Eastern Mediterranean Sea Spatial and Temporal Variability of Thermohaline Structure and Circulation Identified from Observational (T, S) Profiles

DTIC Science & Technology

2015-12-01

effect of Etesian winds between the late May and early October. Although they are generally dry, cool and moderate; they may turn into a windstorm...very significant to provide the realization of ocean modeling and prediction. The Optimal Spectral Decomposition (OSD) method is an effective ...represents the potential density, by differentiating this equation with respect to z and multiplying with the coriolis parameter f, conservation of

Simultaneous modeling of antimycobacterial activities and ADMET profiles: a chemoinformatic approach to medicinal chemistry.

PubMed

Speck-Planche, Alejandro; Cordeiro, M N D S

2013-01-01

Mycobacteria represent a group of pathogens which cause serious diseases in mammals, including the lethal tuberculosis (Mycobacterium tuberculosis). Despite the mortality of this community-acquired and nosocomial disease mentioned above, other mycobacteria may cause similar infections, acting as dangerous opportunistic pathogens. Additionally, resistant strains belonging to Mycobacterium spp. have emerged. Thus, the design of novel antimycobacterial agents is a challenge for the scientific community. In this sense, chemoinformatics has played a vital role in drug discovery, helping to rationalize chemical synthesis, as well as the evaluation of pharmacological and ADMET (absorption, distribution, metabolism, excretion, toxicity) profiles in both medicinal and pharmaceutical chemistry. Until now, there is no in silico methodology able to assess antimycobacterial activity and ADMET properties at the same time. This work introduces the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous prediction of antimycobacterial activities and ADMET profiles of drugs/chemicals under diverse experimental conditions. The mtk-QSBER model was constructed by using a large and heterogeneous dataset of compounds (more than 34600 cases), displaying accuracies higher than 90% in both, training and prediction sets. To illustrate the utility of the present model, several molecular fragments were selected and their contributions to different biological effects were calculated and analyzed. Also, many properties of the investigational drug TMC-207 were predicted. Results confirmed that, from one side, TMC-207 can be a promising antimycobacterial drug, and on the other hand, this study demonstrates that the present mtk-QSBER model can be used for virtual screening of safer antimycobacterial agents.

In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion.

PubMed

Liu, Xian; Xu, Yuan; Li, Shanshan; Wang, Yulan; Peng, Jianlong; Luo, Cheng; Luo, Xiaomin; Zheng, Mingyue; Chen, Kaixian; Jiang, Hualiang

2014-01-01

Ligand-based in silico target fishing can be used to identify the potential interacting target of bioactive ligands, which is useful for understanding the polypharmacology and safety profile of existing drugs. The underlying principle of the approach is that known bioactive ligands can be used as reference to predict the targets for a new compound. We tested a pipeline enabling large-scale target fishing and drug repositioning, based on simple fingerprint similarity rankings with data fusion. A large library containing 533 drug relevant targets with 179,807 active ligands was compiled, where each target was defined by its ligand set. For a given query molecule, its target profile is generated by similarity searching against the ligand sets assigned to each target, for which individual searches utilizing multiple reference structures are then fused into a single ranking list representing the potential target interaction profile of the query compound. The proposed approach was validated by 10-fold cross validation and two external tests using data from DrugBank and Therapeutic Target Database (TTD). The use of the approach was further demonstrated with some examples concerning the drug repositioning and drug side-effects prediction. The promising results suggest that the proposed method is useful for not only finding promiscuous drugs for their new usages, but also predicting some important toxic liabilities. With the rapid increasing volume and diversity of data concerning drug related targets and their ligands, the simple ligand-based target fishing approach would play an important role in assisting future drug design and discovery.

Māori identity signatures: A latent profile analysis of the types of Māori identity.

PubMed

Greaves, Lara M; Houkamau, Carla; Sibley, Chris G

2015-10-01

Māori are the indigenous peoples of New Zealand. However, the term 'Māori' can refer to a wide range of people of varying ethnic compositions and cultural identity. We present a statistical model identifying 6 distinct types, or 'Māori Identity Signatures,' and estimate their proportion in the Māori population. The model is tested using a Latent Profile Analysis of a national probability sample of 686 Māori drawn from the New Zealand Attitudes and Values Study. We identify 6 distinct signatures: Traditional Essentialists (22.6%), Traditional Inclusives (16%), High Moderates (31.7%), Low Moderates (18.7%), Spiritually Orientated (4.1%), and Disassociated (6.9%). These distinct Identity Signatures predicted variation in deprivation, age, mixed-ethnic affiliation, and religion. This research presents the first formal statistical model assessing how people's identity as Māori is psychologically structured, documents the relative proportion of these different patterns of structures, and shows that these patterns reliably predict differences in core demographics. We identify a range of patterns of Māori identity far more diverse than has been previously proposed based on qualitative data, and also show that the majority of Māori fit a moderate or traditional identity pattern. The application of our model for studying Māori health and identity development is discussed. (c) 2015 APA, all rights reserved).

Observing gas in Cosmic Web filaments to constrain simulations of cosmic structure formation

NASA Astrophysics Data System (ADS)

Wakker, Bart

2016-10-01

Cosmological simulations predict that dark matter and baryons condense into multi-Mpc filamentary structures, making up the Cosmic Web. This is outlined by dark matter halos, inside which 10% of baryons are concentrated to make stars in galaxies. The other 90% of the baryons remain gaseous, with about half located outside galaxy halos. They can be traced by Lyman alpha absorbers, whose HI column density is determined by a combination of gas density and the intensity of the extragalactic ionizing background (EGB). About 1000 HST orbits have been expended to map the 50% of baryons in galaxy halos. This contrasts with 37 orbits explicitly allocated to map the other 50% (our Cycle 18 program to observe 17 AGN projected onto a single filament at cz 3500 km/s). We propose a 68-orbit program to observe 40 AGN, creating a sample of 56 sightlines covering a second filament at cz 2500 km/s. Using this dataset we will do the following: (1) measure the intensity of the EGB to within about 50%; (2) confirm that the linewidth of Lya absorbers increases near the filament axis, suggesting increasing temperature or turbulence; (3) check our earlier finding that simulations predict a transverse density HI profile (which scales with the dark-matter profile) that is much broader than is indicated by the observations.

Processing of single channel air and water gun data for imaging an impact structure at the Chesapeake Bay

USGS Publications Warehouse

Lee, Myung W.

1999-01-01

Processing of 20 seismic profiles acquired in the Chesapeake Bay area aided in analysis of the details of an impact structure and allowed more accurate mapping of the depression caused by a bolide impact. Particular emphasis was placed on enhancement of seismic reflections from the basement. Application of wavelet deconvolution after a second zero-crossing predictive deconvolution improved the resolution of shallow reflections, and application of a match filter enhanced the basement reflections. The use of deconvolution and match filtering with a two-dimensional signal enhancement technique (F-X filtering) significantly improved the interpretability of seismic sections.

Detection of stratosphere troposphere exchange in cut-off low systems

NASA Technical Reports Server (NTRS)

Price, Jeremy D.; Vaughan, Geraint

1994-01-01

The Aberystwyth MST radar has been used as part of the TOASTE program to study the structure of the tropopause in cut-off-low system with an aim to identifying regions where stratosphere-troposphere exchange are taking place. Theory predicts that the vertical gradient in reflected power is proportional to the static stability of the reflecting region, and should therefore resolve tropopause structure. Comparisons of MST power profiles with radiosonde data are presented and show good agreement, revealing regions of indefinite tropopauses, where stratosphere-troposphere exchange is thought to take place. The continuous nature of MST data allows an estimation of the size of these regions.

Geocoronal structure. 3. Optically thin, Doppler-broadened line profiles

NASA Astrophysics Data System (ADS)

Bishop, James; Chamberlain, Joseph W.

1987-11-01

Theoretical line profiles, applicable to the analysis of geocoronal Hα prifile measurements, are presented for illustrative cases. While retaining a number of simplifications (classical exobase and diffusive equilibrium plasmasphere conditions), distinctive spectral signatures of mechanisms governing the geocorona are isolated. Examining the consequences of solar radiation pressure dynamics is the main point here. In the prototype evaporative case, radiation pressure acts to form narrow profiles via the creation of an extensive quasi-satellite component. Comparison with a simple extension of the earlier analytic theory discloses the influence of an exopause in this regard. The main modifications to evaporative spectral shapes in the geocoronal application, for shadow heights greater than 2 RE, are predicted to be (1) a blueward ``shift'' or bias near line center, for look directions parallel to the antisolar axis, generated by loss mechanisms acting over the time of flight of exospheric constituents (for example, solar ionization) and (2) an enhanced redward wing at spectral displacements exceeding that defined by the shadow height escape speed, produced by plasmaspheric charge exchange collisions. Implications of these results for recent observations of geocoronal Hα line profiles are briefly discussed.

An evaluation method of the profile of plasma-induced defects based on capacitance-voltage measurement

NASA Astrophysics Data System (ADS)

Okada, Yukimasa; Ono, Kouichi; Eriguchi, Koji

2017-06-01

Aggressive shrinkage and geometrical transition to three-dimensional structures in metal-oxide-semiconductor field-effect transistors (MOSFETs) lead to potentially serious problems regarding plasma processing such as plasma-induced physical damage (PPD). For the precise control of material processing and future device designs, it is extremely important to clarify the depth and energy profiles of PPD. Conventional methods to estimate the PPD profile (e.g., wet etching) are time-consuming. In this study, we propose an advanced method using a simple capacitance-voltage (C-V) measurement. The method first assumes the depth and energy profiles of defects in Si substrates, and then optimizes the C-V curves. We applied this methodology to evaluate the defect generation in (100), (111), and (110) Si substrates. No orientation dependence was found regarding the surface-oxide layers, whereas a large number of defects was assigned in the case of (110). The damaged layer thickness and areal density were estimated. This method provides the highly sensitive PPD prediction indispensable for designing future low-damage plasma processes.

Predicting drug side-effect profiles: a chemical fragment-based approach

PubMed Central

2011-01-01

Background Drug side-effects, or adverse drug reactions, have become a major public health concern. It is one of the main causes of failure in the process of drug development, and of drug withdrawal once they have reached the market. Therefore, in silico prediction of potential side-effects early in the drug discovery process, before reaching the clinical stages, is of great interest to improve this long and expensive process and to provide new efficient and safe therapies for patients. Results In the present work, we propose a new method to predict potential side-effects of drug candidate molecules based on their chemical structures, applicable on large molecular databanks. A unique feature of the proposed method is its ability to extract correlated sets of chemical substructures (or chemical fragments) and side-effects. This is made possible using sparse canonical correlation analysis (SCCA). In the results, we show the usefulness of the proposed method by predicting 1385 side-effects in the SIDER database from the chemical structures of 888 approved drugs. These predictions are performed with simultaneous extraction of correlated ensembles formed by a set of chemical substructures shared by drugs that are likely to have a set of side-effects. We also conduct a comprehensive side-effect prediction for many uncharacterized drug molecules stored in DrugBank, and were able to confirm interesting predictions using independent source of information. Conclusions The proposed method is expected to be useful in various stages of the drug development process. PMID:21586169

PERSONAL VULNERABILITIES AND ASSORTATIVE MATE SELECTION AMONG NEWLYWED SPOUSES.

PubMed

Trombello, Joseph M; Schoebi, Dominik; Bradbury, Thomas N

2015-06-01

Assortative-mating theories propose that individuals select romantic relationship partners who are similar to them on positive and negative qualities. Furthermore, stress-generation and intergenerational transmission of divorce models argue that one's depression history or family-of-origin relationship problems predict qualities of a marital partner that predispose them to relationship distress. We analyzed data from 172 newlywed couples to examine predictors and mediators of a marital partner's risk index. First, an index of one's own and one's partner risk was created through factor analysis and was comprised of measures that indicate insecurity about oneself. This index was significantly correlated with baseline marital satisfaction and, among men, steps toward divorce at follow-up. Then, structural equation modeling tested direct and indirect pathways predicting partner's risk index, analyzing prior depression history and family-of-origin relational impairment as predictors and one's own risk index as the mediator. Results demonstrated that own risk index reliably predicted partner's risk, while own risk index also mediated the relationship between own family-of-origin relational dysfunction/depression history and partner's risk index. These results support assortative mating theories and suggest that the association between adverse family-of-origin relationships or depression history and the risk profile in one's marital partner is explained by one's own risk profile.

Brownian dynamics simulation of amphiphilic block copolymers with different tail lengths, comparison with theory and comicelles.

PubMed

Hafezi, Mohammad-Javad; Sharif, Farhad

2015-11-01

Study on the effect of amphiphilic copolymers structure on their self assembly is an interesting subject, with important applications in the area of drug delivery and biological system treatments. Brownian dynamics simulations were performed to study self-assembly of the linear amphiphilic block copolymers with the same hydrophilic head, but hydrophobic tails of different lengths. Critical micelle concentration (CMC), gyration radius distribution, micelle size distribution, density profiles of micelles, shape anisotropy, and dynamics of micellization were investigated as a function of tail length. Simulation results were compared with predictions from theory and simulation for mixed systems of block copolymers with long and short hydrophobic tail, reported in our previous work. Interestingly, the equilibrium structural and dynamic parameters of pure and mixed block copolymers were similarly dependant on the intrinsic/apparent hydrophobic block length. Log (CMC) was, however; proportional to the tail length and had a different behavior compared to the mixed system. The power law scaling relation of equilibrium structural parameters for amphiphilic block copolymers predicts the same dependence for similar hydrophobic tail lengths, but the power law prediction of CMC is different, which is due to its simplifying assumptions as discussed here. Copyright © 2015 Elsevier Inc. All rights reserved.

Signatures of Relativistic Helical Motion in the Rotation Measures of Active Galactic Nucleus Jets

NASA Astrophysics Data System (ADS)

Broderick, Avery E.; Loeb, Abraham

2009-10-01

Polarization has proven to be an invaluable tool for probing magnetic fields in relativistic jets. Maps of the intrinsic polarization vectors have provided the best evidence to date for uniform, toroidally dominated magnetic fields within jets. More recently, maps of the rotation measure (RM) in jets have for the first time probed the field geometry of the cool, moderately relativistic surrounding material. In most cases, clear signatures of the toroidal magnetic field are detected, corresponding to gradients in RM profiles transverse to the jet. However, in many objects, these profiles also display marked asymmetries that are difficult to explain in simple helical jet models. Furthermore, in some cases, the RM profiles are strongly frequency and/or time dependent. Here we show that these features may be naturally accounted for by including relativistic helical motion in the jet model. In particular, we are able to reproduce bent RM profiles observed in a variety of jets, frequency-dependent RM profile morphologies, and even the time dependence of the RM profiles of knots in 3C 273. Finally, we predict that some sources may show reversals in their RM profiles at sufficiently high frequencies, depending upon the ratio of the components of jet sheath velocity transverse and parallel to the jet. Thus, multi-frequency RM maps promise a novel way in which to probe the velocity structure of relativistic outflows.

THE ARCHES CLUSTER: EXTENDED STRUCTURE AND TIDAL RADIUS

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

Hosek, Matthew W. Jr.; Lu, Jessica R.; Anderson, Jay

At a projected distance of ∼26 pc from Sgr A*, the Arches cluster provides insight into star formation in the extreme Galactic center (GC) environment. Despite its importance, many key properties, such as the cluster’s internal structure and orbital history, are not well known. We present an astrometric and photometric study of the outer region of the Arches cluster ( R > 6.″25) using Hubble Space Telescope WFC3IR. Using proper motions, we calculate membership probabilities for stars down to F153M = 20 mag (∼2.5 M {sub ⊙}) over a 120″ × 120″ field of view, an area 144 times largermore » than previous astrometric studies of the cluster. We construct the radial profile of the Arches to a radius of 75″ (∼3 pc at 8 kpc), which can be well described by a single power law. From this profile we place a 3 σ lower limit of 2.8 pc on the observed tidal radius, which is larger than the predicted tidal radius (1–2.5 pc). Evidence of mass segregation is observed throughout the cluster, and no tidal tail structures are apparent along the orbital path. The absence of breaks in the profile suggests that the Arches has not likely experienced its closest approach to the GC between ∼0.2 and 1 Myr ago. If accurate, this constraint indicates that the cluster is on a prograde orbit and is located in front of the sky plane that intersects Sgr A*. However, further simulations of clusters in the GC potential are required to interpret the observed profile with more confidence.« less

Characterization of the Bread Made with Durum Wheat Semolina Rendered Gluten Free by Sourdough Biotechnology in Comparison with Commercial Gluten-Free Products.

PubMed

Rizzello, Carlo Giuseppe; Montemurro, Marco; Gobbetti, Marco

2016-09-01

Durum wheat semolina was fermented with sourdough lactic acid bacteria and fungal proteases aiming at a complete gluten hydrolysis. The gluten-free (GF) semolina, added with naturally GF ingredients and structuring agents, was used to produce bread (rendered GF bread; rGFB) at industrial level. An integrated approach including the characterization of the main chemical, nutritional, structural, and sensory features was used to compare rGFB to a gluten-containing bread and to 5 commercial naturally GF breads. High-performance liquid chromatography was used for free amino acids (FAAs), organic acids, and ethanol analysis. A methanolic extract was used for determining total phenols and antioxidant activity. The bread characterization also included the analysis of dietary fibers, mycotoxins, vitamins, and heavy metals. Beyond chemical analysis, nutritional profile was evaluated considering the in vitro protein digestibility and the predicted glycemic index, while the instrumental texture profile analysis was performed to investigate the structure and the physical/mechanical properties of the baked goods. Beyond the huge potential of market expansion, the main advantages of durum wheat semolina rendered GF can be resumed in the high availability of FAAs, the high protein digestibility, the low starch hydrolysis index, and the better technological properties of bread compared to the commercial GF products currently present on the market. Vitamins, minerals, and dietary fiber profiles are comparable to those of gluten-containing wheat bread. Also the sensory profile, determined by a panel test, can be considered the most similar to those of conventional baked goods, showing all the sourdough bread classic attributes. © 2016 Institute of Food Technologists®

Fatigue Behavior of AM60B Subjected to Variable Amplitude Loading

NASA Astrophysics Data System (ADS)

Kang, H.; Kari, K.; Khosrovaneh, A. K.; Nayaki, R.; Su, X.; Zhang, L.; Lee, Y.-L.

Magnesium alloys are considered as an alternative material to reduce vehicle weight due to their weight which are 33% lighter than aluminum alloys. There has been a significant expansion in the applications of magnesium alloys in automotives components in an effort to improve fuel efficiency through vehicle mass reduction. In this project, a simple front shock tower of passenger vehicle is constructed with various magnesium alloys. To predict the fatigue behavior of the structure, fatigue properties of the magnesium alloy (AM60B) were determined from strain controlled fatigue tests. Notched specimens were also tested with three different variable amplitude loading profiles obtained from the shock tower of the similar size of vehicle. The test results were compared with various fatigue prediction results. The effect of mean stress and fatigue prediction method were discussed.

SU-C-204-01: A Fast Analytical Approach for Prompt Gamma and PET Predictions in a TPS for Proton Range Verification

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

Kroniger, K; Herzog, M; Landry, G

2015-06-15

Purpose: We describe and demonstrate a fast analytical tool for prompt-gamma emission prediction based on filter functions applied on the depth dose profile. We present the implementation in a treatment planning system (TPS) of the same algorithm for positron emitter distributions. Methods: The prediction of the desired observable is based on the convolution of filter functions with the depth dose profile. For both prompt-gammas and positron emitters, the results of Monte Carlo simulations (MC) are compared with those of the analytical tool. For prompt-gamma emission from inelastic proton-induced reactions, homogeneous and inhomogeneous phantoms alongside with patient data are used asmore » irradiation targets of mono-energetic proton pencil beams. The accuracy of the tool is assessed in terms of the shape of the analytically calculated depth profiles and their absolute yields, compared to MC. For the positron emitters, the method is implemented in a research RayStation TPS and compared to MC predictions. Digital phantoms and patient data are used and positron emitter spatial density distributions are analyzed. Results: Calculated prompt-gamma profiles agree with MC within 3 % in terms of absolute yield and reproduce the correct shape. Based on an arbitrary reference material and by means of 6 filter functions (one per chemical element), profiles in any other material composed of those elements can be predicted. The TPS implemented algorithm is accurate enough to enable, via the analytically calculated positron emitters profiles, detection of range differences between the TPS and MC with errors of the order of 1–2 mm. Conclusion: The proposed analytical method predicts prompt-gamma and positron emitter profiles which generally agree with the distributions obtained by a full MC. The implementation of the tool in a TPS shows that reliable profiles can be obtained directly from the dose calculated by the TPS, without the need of full MC simulation.« less

In vitro ovine articular chondrocyte proliferation: experiments and modelling.

PubMed

Mancuso, L; Liuzzo, M I; Fadda, S; Pisu, M; Cincotti, A; Arras, M; La Nasa, G; Concas, A; Cao, G

2010-06-01

This study focuses on analysis of in vitro cultures of chondrocytes from ovine articular cartilage. Isolated cells were seeded in Petri dishes, then expanded to confluence and phenotypically characterized by flow cytometry. The sigmoidal temporal profile of total counts was obtained by classic haemocytometry and corresponding cell size distributions were measured electronically using a Coulter Counter. A mathematical model recently proposed (1) was adopted for quantitative interpretation of these experimental data. The model is based on a 1-D (that is, mass-structured), single-staged population balance approach capable of taking into account contact inhibition at confluence. The model's parameters were determined by fitting measured total cell counts and size distributions. Model reliability was verified by predicting cell proliferation counts and corresponding size distributions at culture times longer than those used when tuning the model's parameters. It was found that adoption of cell mass as the intrinsic characteristic of a growing chondrocyte population enables sigmoidal temporal profiles of total counts in the Petri dish, as well as cell size distributions at 'balanced growth', to be adequately predicted.

SFESA: a web server for pairwise alignment refinement by secondary structure shifts.

PubMed

Tong, Jing; Pei, Jimin; Grishin, Nick V

2015-09-03

Protein sequence alignment is essential for a variety of tasks such as homology modeling and active site prediction. Alignment errors remain the main cause of low-quality structure models. A bioinformatics tool to refine alignments is needed to make protein alignments more accurate. We developed the SFESA web server to refine pairwise protein sequence alignments. Compared to the previous version of SFESA, which required a set of 3D coordinates for a protein, the new server will search a sequence database for the closest homolog with an available 3D structure to be used as a template. For each alignment block defined by secondary structure elements in the template, SFESA evaluates alignment variants generated by local shifts and selects the best-scoring alignment variant. A scoring function that combines the sequence score of profile-profile comparison and the structure score of template-derived contact energy is used for evaluation of alignments. PROMALS pairwise alignments refined by SFESA are more accurate than those produced by current advanced alignment methods such as HHpred and CNFpred. In addition, SFESA also improves alignments generated by other software. SFESA is a web-based tool for alignment refinement, designed for researchers to compute, refine, and evaluate pairwise alignments with a combined sequence and structure scoring of alignment blocks. To our knowledge, the SFESA web server is the only tool that refines alignments by evaluating local shifts of secondary structure elements. The SFESA web server is available at http://prodata.swmed.edu/sfesa.

Temperament clusters associate with anxiety disorder comorbidity in depression.

PubMed

Paavonen, Vesa; Luoto, Kaisa; Lassila, Antero; Leinonen, Esa; Kampman, Olli

2018-08-15

Individual temperament is associated with psychiatric morbidity and could explain differences in psychiatric comorbidities. We investigated the association of temperament profile clusters with anxiety disorder comorbidity in patients with depression. We assessed the temperament of 204 specialized care-treated depressed patients with the Temperament and Character Inventory (TCI-R) and their diagnoses with the Mini International Neuropsychiatric Interview. Two-step cluster analysis was used for defining patients' temperament profiles and logistic regression analysis was used for predicting different anxiety disorders for various temperament profiles. Four temperament clusters were found: 1) Novelty seekers with highest Novelty Seeking scores (n = 56),2) Persistent with highest Persistence scores (n = 36), 3) Reserved with lowest Novelty Seeking scores (n = 66) and 4) Wearied with highest Harm avoidance, lowest Reward Dependence and lowest Persistence scores (n = 58). After adjusting for clinical variables, panic disorder and/or agoraphobia were predicted by Novelty seekers' temperament profile with odds ratio [OR] = 3.5 (95% confidence interval [CI] = 1.8 - 6.9, p < 0.001), social anxiety disorder was predicted by Wearied temperament profile with OR = 3.4 (95% CI = 1.6 - 7.5, p = 0.002), and generalized anxiety disorder was predicted by Reserved temperament profile with OR = 2.6 (95% CI = 1.2 - 5.3, p = 0.01). The patients' temperament profiles were assessed while displaying depressive symptoms, which may have affected results. Temperament clusters with unique dimensional profiles were specifically associated with different anxiety disorders in this study. These results suggest that TCI-R could offer a valuable dimensional method for predicting the risk of anxiety disorders in diverse depressed patients. Copyright © 2018 Elsevier B.V. All rights reserved.

Fano resonances in photonic crystal nanobeams side-coupled with nanobeam cavities

NASA Astrophysics Data System (ADS)

Meng, Zi-Ming; Liang, Anhui; Li, Zhi-Yuan

2017-05-01

Fano resonances usually arise when a narrow resonance or discrete state and a broad resonance or continuum state are coupled. In this paper, we theoretically and numerically study asymmetric Fano line shape realized in a photonic crystal nanobeam (PCN) side-coupled with a photonic crystal nanobeam cavity (PCNC). Asymmetric transmission profiles with a transmission peak and a transmission valley are obtained for a low index concentrated cavity mode. The transmission valley, associated with the destructive interference, of our PCN-PCNC structures is deeper than that of a waveguide or Fabry-Perot resonator side-coupled with a PCNC structure. Through changing the position of the photonic band gap (PBG) of the PCN, we can utilize the high or low frequency band edge modes and the Fano transmission profiles can be further controlled. The transmission spectra of our PCN-PCNC structures can be well fitted by the Fano resonance formula and agree qualitatively with the prediction made by the temporal coupled mode theory. By using the band edge modes of the PCN as the continuum state instead of a usual broad resonance, we have demonstrated a new way to generate a prominent Fano resonance. Our PCN-PCNC structures are compact and feasible to achieve large-scale high-performance integrated photonic devices, such as optical modulators or switches.

Phylogenetic profiles reveal structural/functional determinants of TRPC3 signal-sensing antennae

PubMed Central

Ko, Kyung Dae; Bhardwaj, Gaurav; Hong, Yoojin; Chang, Gue Su; Kiselyov, Kirill

2009-01-01

Biochemical assessment of channel structure/function is incredibly challenging. Developing computational tools that provide these data would enable translational research, accelerating mechanistic experimentation for the bench scientist studying ion channels. Starting with the premise that protein sequence encodes information about structure, function and evolution (SF&E), we developed a unified framework for inferring SF&E from sequence information using a knowledge-based approach. The Gestalt Domain Detection Algorithm-Basic Local Alignment Tool (GDDA-BLAST) provides phylogenetic profiles that can model, ab initio, SF&E relationships of biological sequences at the whole protein, single domain and single-amino acid level.1,2 In our recent paper,4 we have applied GDDA-BLAST analysis to study canonical TRP (TRPC) channels1 and empirically validated predicted lipid-binding and trafficking activities contained within the TRPC3 TRP_2 domain of unknown function. Overall, our in silico, in vitro, and in vivo experiments support a model in which TRPC3 has signal-sensing antennae which are adorned with lipid-binding, trafficking and calmodulin regulatory domains. In this Addendum, we correlate our functional domain analysis with the cryo-EM structure of TRPC3.3 In addition, we synthesize recent studies with our new findings to provide a refined model on the mechanism(s) of TRPC3 activation/deactivation. PMID:19704910

Distributions of Gas and Galaxies from Galaxy Clusters to Larger Scales

NASA Astrophysics Data System (ADS)

Patej, Anna

2017-01-01

We address the distributions of gas and galaxies on three scales: the outskirts of galaxy clusters, the clustering of galaxies on large scales, and the extremes of the galaxy distribution. In the outskirts of galaxy clusters, long-standing analytical models of structure formation and recent simulations predict the existence of density jumps in the gas and dark matter profiles. We use these features to derive models for the gas density profile, obtaining a simple fiducial model that is in agreement with both observations of cluster interiors and simulations of the outskirts. We next consider the galaxy density profiles of clusters; under the assumption that the galaxies in cluster outskirts follow similar collisionless dynamics as the dark matter, their distribution should show a steep jump as well. We examine the profiles of a low-redshift sample of clusters and groups, finding evidence for the jump in some of these clusters. Moving to larger scales where massive galaxies of different types are expected to trace the same large-scale structure, we present a test of this prediction by measuring the clustering of red and blue galaxies at z 0.6, finding low stochasticity between the two populations. These results address a key source of systematic uncertainty - understanding how target populations of galaxies trace large-scale structure - in galaxy redshift surveys. Such surveys use baryon acoustic oscillations (BAO) as a cosmological probe, but are limited by the expense of obtaining sufficiently dense spectroscopy. With the intention of leveraging upcoming deep imaging data, we develop a new method of detecting the BAO in sparse spectroscopic samples via cross-correlation with a dense photometric catalog. This method will permit the extension of BAO measurements to higher redshifts than possible with the existing spectroscopy alone. Lastly, we connect galaxies near and far: the Local Group dwarfs and the high redshift galaxies observed by Hubble and Spitzer. We examine how the local dwarfs may have appeared in the past and compare their properties to the detection limits of the upcoming James Webb Space Telescope (JWST), finding that JWST should be able to detect galaxies similar to the progenitors of a few of the brightest of the local galaxies, revealing a hitherto unobserved population of galaxies at high redshifts.

A comparison of predicted and measured inlet distortion flows in a subsonic axial inlet flow compressor rotor

NASA Technical Reports Server (NTRS)

Owen, Albert K.

1992-01-01

Detailed flow measurements were taken inside an isolated axial compressor rotor operating subsonically near peak efficiency. These Laser Anemometer measurements were made with two inlet velocity profiles. One profile consisted of an unmodified baseline flow, and the second profile was distorted by placing axisymmetric screens on the hub and shroud well upstream of the rotor. A detailed comparison in the rotor relative reference frame between a Navier-Stokes solver and the measured experimental results showed good agreement between the predicted and measured flows. A primary flow is defined in the rotor and deviations and the computed predictions is made to assess the development of a passage vortex due to the distortion of the inlet flow. Computer predictions indicate that a distorted inlet profile has a minimal effect on the development of the flow in the rotor passage and the resulting passage vortex.

Control of Flow Structure in Square Cross-Sectioned U Bend using Numerical Modeling

NASA Astrophysics Data System (ADS)

Yavuz, Mehmet Metin; Guden, Yigitcan

2014-11-01

Due to the curvature in U-bends, the flow development involves complex flow structures including Dean vortices and high levels of turbulence that are quite critical in considering noise problems and structural failure of the ducts. Computational fluid dynamic (CFD) models are developed using ANSYS Fluent to analyze and to control the flow structure in a square cross-sectioned U-bend with a radius of curvature Rc/D = 0.65. The predictions of velocity profiles on different angular positions of the U-bend are compared against the experimental results available in the literature and the previous numerical studies. The performances of different turbulence models are evaluated to propose the best numerical approach that has high accuracy with reduced computation time. The numerical results of the present study indicate improvements with respect to the previous numerical predictions and very good agreement with the available experimental results. In addition, a flow control technique is utilized to regulate the flow inside the bend. The elimination of Dean vortices along with significant reduction in turbulence levels in different cross flow planes are successfully achieved when the flow control technique is applied. The project is supported by Meteksan Defense Industries, Inc.

New Toxico-Cheminformatics & Computational Toxicology ...

EPA Pesticide Factsheets

EPA’s National Center for Computational Toxicology is building capabilities to support a new paradigm for toxicity screening and prediction. The DSSTox project is improving public access to quality structure-annotated chemical toxicity information in less summarized forms than traditionally employed in SAR modeling, and in ways that facilitate data-mining, and data read-across. The DSSTox Structure-Browser provides structure searchability across all published DSSTox toxicity-related inventory, and is enabling linkages between previously isolated toxicity data resources. As of early March 2008, the public DSSTox inventory has been integrated into PubChem, allowing a user to take full advantage of PubChem structure-activity and bioassay clustering features. The most recent DSSTox version of the Carcinogenic Potency Database file (CPDBAS) illustrates ways in which various summary definitions of carcinogenic activity can be employed in modeling and data mining. Phase I of the ToxCastTM project is generating high-throughput screening data from several hundred biochemical and cell-based assays for a set of 320 chemicals, mostly pesticide actives, with rich toxicology profiles. Incorporating and expanding traditional SAR concepts into this new high-throughput and data-rich world pose conceptual and practical challenges, but also holds great promise for improving predictive capabilities.

Effects of historical and predictive information on ability of transport pilot to predict an alert

NASA Technical Reports Server (NTRS)

Trujillo, Anna C.

1994-01-01

In the aviation community, the early detection of the development of a possible subsystem problem during a flight is potentially useful for increasing the safety of the flight. Commercial airlines are currently using twin-engine aircraft for extended transport operations over water, and the early detection of a possible problem might increase the flight crew's options for safely landing the aircraft. One method for decreasing the severity of a developing problem is to predict the behavior of the problem so that appropriate corrective actions can be taken. To investigate the pilots' ability to predict long-term events, a computer workstation experiment was conducted in which 18 airline pilots predicted the alert time (the time to an alert) using 3 different dial displays and 3 different parameter behavior complexity levels. The three dial displays were as follows: standard (resembling current aircraft round dial presentations); history (indicating the current value plus the value of the parameter 5 sec in the past); and predictive (indicating the current value plus the value of the parameter 5 sec into the future). The time profiles describing the behavior of the parameter consisted of constant rate-of-change profiles, decelerating profiles, and accelerating-then-decelerating profiles. Although the pilots indicated that they preferred the near term predictive dial, the objective data did not support its use. The objective data did show that the time profiles had the most significant effect on performance in estimating the time to an alert.

Predicting taxonomic and functional structure of microbial communities in acid mine drainage

PubMed Central

Kuang, Jialiang; Huang, Linan; He, Zhili; Chen, Linxing; Hua, Zhengshuang; Jia, Pu; Li, Shengjin; Liu, Jun; Li, Jintian; Zhou, Jizhong; Shu, Wensheng

2016-01-01

Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray–Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities. PMID:26943622

Predicting taxonomic and functional structure of microbial communities in acid mine drainage.

PubMed

Kuang, Jialiang; Huang, Linan; He, Zhili; Chen, Linxing; Hua, Zhengshuang; Jia, Pu; Li, Shengjin; Liu, Jun; Li, Jintian; Zhou, Jizhong; Shu, Wensheng

2016-06-01

Predicting the dynamics of community composition and functional attributes responding to environmental changes is an essential goal in community ecology but remains a major challenge, particularly in microbial ecology. Here, by targeting a model system with low species richness, we explore the spatial distribution of taxonomic and functional structure of 40 acid mine drainage (AMD) microbial communities across Southeast China profiled by 16S ribosomal RNA pyrosequencing and a comprehensive microarray (GeoChip). Similar environmentally dependent patterns of dominant microbial lineages and key functional genes were observed regardless of the large-scale geographical isolation. Functional and phylogenetic β-diversities were significantly correlated, whereas functional metabolic potentials were strongly influenced by environmental conditions and community taxonomic structure. Using advanced modeling approaches based on artificial neural networks, we successfully predicted the taxonomic and functional dynamics with significantly higher prediction accuracies of metabolic potentials (average Bray-Curtis similarity 87.8) as compared with relative microbial abundances (similarity 66.8), implying that natural AMD microbial assemblages may be better predicted at the functional genes level rather than at taxonomic level. Furthermore, relative metabolic potentials of genes involved in many key ecological functions (for example, nitrogen and phosphate utilization, metals resistance and stress response) were extrapolated to increase under more acidic and metal-rich conditions, indicating a critical strategy of stress adaptation in these extraordinary communities. Collectively, our findings indicate that natural selection rather than geographic distance has a more crucial role in shaping the taxonomic and functional patterns of AMD microbial community that readily predicted by modeling methods and suggest that the model-based approach is essential to better understand natural acidophilic microbial communities.

Geometrical theory to predict eccentric photorefraction intensity profiles in the human eye

NASA Astrophysics Data System (ADS)

Roorda, Austin; Campbell, Melanie C. W.; Bobier, W. R.

1995-08-01

In eccentric photorefraction, light returning from the retina of the eye is photographed by a camera focused on the eye's pupil. We use a geometrical model of eccentric photorefraction to generate intensity profiles across the pupil image. The intensity profiles for three different monochromatic aberration functions induced in a single eye are predicted and show good agreement with the measured eccentric photorefraction intensity profiles. A directional reflection from the retina is incorporated into the calculation. Intensity profiles for symmetric and asymmetric aberrations are generated and measured. The latter profile shows a dependency on the source position and the meridian. The magnitude of the effect of thresholding on measured pattern extents is predicted. Monochromatic aberrations in human eyes will cause deviations in the eccentric photorefraction measurements from traditional crescents caused by defocus and may cause misdiagnoses of ametropia or anisometropia. Our results suggest that measuring refraction along the vertical meridian is preferred for screening studies with the eccentric photorefractor.

Simultaneous virtual prediction of anti-Escherichia coli activities and ADMET profiles: A chemoinformatic complementary approach for high-throughput screening.

PubMed

Speck-Planche, Alejandro; Cordeiro, M N D S

2014-02-10

Escherichia coli remains one of the principal pathogens that cause nosocomial infections, medical conditions that are increasingly common in healthcare facilities. E. coli is intrinsically resistant to many antibiotics, and multidrug-resistant strains have emerged recently. Chemoinformatics has been a great ally of experimental methodologies such as high-throughput screening, playing an important role in the discovery of effective antibacterial agents. However, there is no approach that can design safer anti-E. coli agents, because of the multifactorial nature and complexity of bacterial diseases and the lack of desirable ADMET (absorption, distribution, metabolism, elimination, and toxicity) profiles as a major cause of disapproval of drugs. In this work, we introduce the first multitasking model based on quantitative-structure biological effect relationships (mtk-QSBER) for simultaneous virtual prediction of anti-E. coli activities and ADMET properties of drugs and/or chemicals under many experimental conditions. The mtk-QSBER model was developed from a large and heterogeneous data set of more than 37800 cases, exhibiting overall accuracies of >95% in both training and prediction (validation) sets. The utility of our mtk-QSBER model was demonstrated by performing virtual prediction of properties for the investigational drug avarofloxacin (AVX) under 260 different experimental conditions. Results converged with the experimental evidence, confirming the remarkable anti-E. coli activities and safety of AVX. Predictions also showed that our mtk-QSBER model can be a promising computational tool for virtual screening of desirable anti-E. coli agents, and this chemoinformatic approach could be extended to the search for safer drugs with defined pharmacological activities.

Direct glycan structure determination of intact N-linked glycopeptides by low-energy collision-induced dissociation tandem mass spectrometry and predicted spectral library searching.

PubMed

Pai, Pei-Jing; Hu, Yingwei; Lam, Henry

2016-08-31

Intact glycopeptide MS analysis to reveal site-specific protein glycosylation is an important frontier of proteomics. However, computational tools for analyzing MS/MS spectra of intact glycopeptides are still limited and not well-integrated into existing workflows. In this work, a new computational tool which combines the spectral library building/searching tool, SpectraST (Lam et al. Nat. Methods2008, 5, 873-875), and the glycopeptide fragmentation prediction tool, MassAnalyzer (Zhang et al. Anal. Chem.2010, 82, 10194-10202) for intact glycopeptide analysis has been developed. Specifically, this tool enables the determination of the glycan structure directly from low-energy collision-induced dissociation (CID) spectra of intact glycopeptides. Given a list of possible glycopeptide sequences as input, a sample-specific spectral library of MassAnalyzer-predicted spectra is built using SpectraST. Glycan identification from CID spectra is achieved by spectral library searching against this library, in which both m/z and intensity information of the possible fragmentation ions are taken into consideration for improved accuracy. We validated our method using a standard glycoprotein, human transferrin, and evaluated its potential to be used in site-specific glycosylation profiling of glycoprotein datasets from LC-MS/MS. In addition, we further applied our method to reveal, for the first time, the site-specific N-glycosylation profile of recombinant human acetylcholinesterase expressed in HEK293 cells. For maximum usability, SpectraST is developed as part of the Trans-Proteomic Pipeline (TPP), a freely available and open-source software suite for MS data analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

Numerical prediction of mechanical properties of Pb-Sn solder alloys containing antimony, bismuth and or silver ternary trace elements

NASA Astrophysics Data System (ADS)

Gadag, Shiva P.; Patra, Susant

2000-12-01

Solder joint interconnects are mechanical means of structural support for bridging the various electronic components and providing electrical contacts and a thermal path for heat dissipation. The functionality of the electronic device often relies on the structural integrity of the solder. The dimensional stability of solder joints is numerically predicted based on their mechanical properties. Algorithms to model the kinetics of dissolution and subsequent growth of intermetallic from the complete knowledge of a single history of time-temperature-reflow profile, by considering equivalent isothermal time intervals, have been developed. The information for dissolution is derived during the heating cycle of reflow and for the growth process from cooling curve of reflow profile. A simple and quick analysis tool to derive tensile stress-strain maps as a function of the reflow temperature of solder and strain rate has been developed by numerical program. The tensile properties are used in modeling thermal strain, thermal fatigue and to predict the overall fatigue life of solder joints. The numerical analysis of the tensile properties as affected by their composition and rate of testing, has been compiled in this paper. A numerical model using constitutive equation has been developed to evaluate the interfacial fatigue crack growth rate. The model can assess the effect of cooling rate, which depends on the level of strain energy release rate. Increasing cooling rate from normalizing to water-quenching, enhanced the fatigue resistance to interfacial crack growth by up to 50% at low strain energy release rate. The increased cooling rates enhanced the fatigue crack growth resistance by surface roughening at the interface of solder joint. This paper highlights salient features of process modeling. Interfacial intermetallic microstructure is affected by cooling rate and thereby affects the mechanical properties.

Experimental study on interfacial area transport in downward two-phase flow

NASA Astrophysics Data System (ADS)

Wang, Guanyi

In view of the importance of two group interfacial area transport equations and lack of corresponding accurate downward flow database that can reveal two group interfacial area transport, a systematic database for adiabatic, air-water, vertically downward two-phase flow in a round pipe with inner diameter of 25.4 mm was collected to gain an insight of interfacial structure and provide benchmarking data for two-group interfacial area transport models. A four-sensor conductivity probe was used to measure the local two phase flow parameters and data was collected with data sampling frequency much higher than conventional data sampling frequency to ensure the accuracy. Axial development of local flow parameter profiles including void fraction, interfacial area concentration, and Sauter mean diameter were presented. Drastic inter-group transfer of void fraction and interfacial area was observed at bubbly to slug transition flow. And the wall peaked interfacial area concentration profiles were observed in churn-turbulent flow. The importance of local data about these phenomenon on flow structure prediction and interfacial area transport equation benchmark was analyzed. Bedsides, in order to investigate the effect of inlet conditions, all experiments were repeated after installing the flow straightening facility, and the results were briefly analyzed. In order to check the accuracy of current data, the experiment results were cross-checked with rotameter measurement as well as drift-flux model prediction, the averaged error is less than 15%. Current models for two-group interfacial area transport equation were evaluated using these data. The results show that two-group interfacial area transport equations with current models can predict most flow conditions with error less than 20%, except some bubbly to slug transition flow conditions and some churn-turbulent flow conditions. The disagreement between models and experiments could result from underestimate of inter-group void transfer.

MoRFPred-plus: Computational Identification of MoRFs in Protein Sequences using Physicochemical Properties and HMM profiles.

PubMed

Sharma, Ronesh; Bayarjargal, Maitsetseg; Tsunoda, Tatsuhiko; Patil, Ashwini; Sharma, Alok

2018-01-21

Intrinsically Disordered Proteins (IDPs) lack stable tertiary structure and they actively participate in performing various biological functions. These IDPs expose short binding regions called Molecular Recognition Features (MoRFs) that permit interaction with structured protein regions. Upon interaction they undergo a disorder-to-order transition as a result of which their functionality arises. Predicting these MoRFs in disordered protein sequences is a challenging task. In this study, we present MoRFpred-plus, an improved predictor over our previous proposed predictor to identify MoRFs in disordered protein sequences. Two separate independent propensity scores are computed via incorporating physicochemical properties and HMM profiles, these scores are combined to predict final MoRF propensity score for a given residue. The first score reflects the characteristics of a query residue to be part of MoRF region based on the composition and similarity of assumed MoRF and flank regions. The second score reflects the characteristics of a query residue to be part of MoRF region based on the properties of flanks associated around the given residue in the query protein sequence. The propensity scores are processed and common averaging is applied to generate the final prediction score of MoRFpred-plus. Performance of the proposed predictor is compared with available MoRF predictors, MoRFchibi, MoRFpred, and ANCHOR. Using previously collected training and test sets used to evaluate the mentioned predictors, the proposed predictor outperforms these predictors and generates lower false positive rate. In addition, MoRFpred-plus is a downloadable predictor, which makes it useful as it can be used as input to other computational tools. https://github.com/roneshsharma/MoRFpred-plus/wiki/MoRFpred-plus:-Download. Copyright © 2017 Elsevier Ltd. All rights reserved.

Towards cheminformatics-based estimation of drug therapeutic index: Predicting the protective index of anticonvulsants using a new quantitative structure-index relationship approach.

PubMed

Chen, Shangying; Zhang, Peng; Liu, Xin; Qin, Chu; Tao, Lin; Zhang, Cheng; Yang, Sheng Yong; Chen, Yu Zong; Chui, Wai Keung

2016-06-01

The overall efficacy and safety profile of a new drug is partially evaluated by the therapeutic index in clinical studies and by the protective index (PI) in preclinical studies. In-silico predictive methods may facilitate the assessment of these indicators. Although QSAR and QSTR models can be used for predicting PI, their predictive capability has not been evaluated. To test this capability, we developed QSAR and QSTR models for predicting the activity and toxicity of anticonvulsants at accuracy levels above the literature-reported threshold (LT) of good QSAR models as tested by both the internal 5-fold cross validation and external validation method. These models showed significantly compromised PI predictive capability due to the cumulative errors of the QSAR and QSTR models. Therefore, in this investigation a new quantitative structure-index relationship (QSIR) model was devised and it showed improved PI predictive capability that superseded the LT of good QSAR models. The QSAR, QSTR and QSIR models were developed using support vector regression (SVR) method with the parameters optimized by using the greedy search method. The molecular descriptors relevant to the prediction of anticonvulsant activities, toxicities and PIs were analyzed by a recursive feature elimination method. The selected molecular descriptors are primarily associated with the drug-like, pharmacological and toxicological features and those used in the published anticonvulsant QSAR and QSTR models. This study suggested that QSIR is useful for estimating the therapeutic index of drug candidates. Copyright © 2016. Published by Elsevier Inc.

LYRA, a webserver for lymphocyte receptor structural modeling.

PubMed

Klausen, Michael Schantz; Anderson, Mads Valdemar; Jespersen, Martin Closter; Nielsen, Morten; Marcatili, Paolo

2015-07-01

The accurate structural modeling of B- and T-cell receptors is fundamental to gain a detailed insight in the mechanisms underlying immunity and in developing new drugs and therapies. The LYRA (LYmphocyte Receptor Automated modeling) web server (http://www.cbs.dtu.dk/services/LYRA/) implements a complete and automated method for building of B- and T-cell receptor structural models starting from their amino acid sequence alone. The webserver is freely available and easy to use for non-specialists. Upon submission, LYRA automatically generates alignments using ad hoc profiles, predicts the structural class of each hypervariable loop, selects the best templates in an automatic fashion, and provides within minutes a complete 3D model that can be downloaded or inspected online. Experienced users can manually select or exclude template structures according to case specific information. LYRA is based on the canonical structure method, that in the last 30 years has been successfully used to generate antibody models of high accuracy, and in our benchmarks this approach proves to achieve similarly good results on TCR modeling, with a benchmarked average RMSD accuracy of 1.29 and 1.48 Å for B- and T-cell receptors, respectively. To the best of our knowledge, LYRA is the first automated server for the prediction of TCR structure. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Genomic and metabolic characterisation of alkaloid biosynthesis by asexual Epichloë fungal endophytes of tall fescue pasture grasses.

PubMed

Ekanayake, Piyumi N; Kaur, Jatinder; Tian, Pei; Rochfort, Simone J; Guthridge, Kathryn M; Sawbridge, Timothy I; Spangenberg, German C; Forster, John W

2017-06-01

Symbiotic associations between tall fescue grasses and asexual Epichloë fungal endophytes exhibit biosynthesis of alkaloid compounds causing both beneficial and detrimental effects. Candidate novel endophytes with favourable chemotypic profiles have been identified in germplasm collections by screening for genetic diversity, followed by metabolite profile analysis in endogenous genetic backgrounds. A subset of candidates was subjected to genome survey sequencing to detect the presence or absence and structural status of known genes for biosynthesis of the major alkaloid classes. The capacity to produce specific metabolites was directly predictable from metabolic data. In addition, study of duplicated gene structure in heteroploid genomic constitutions provided further evidence for the origin of such endophytes. Selected strains were inoculated into meristem-derived callus cultures from specific tall fescue genotypes to perform isogenic comparisons of alkaloid profile in different host backgrounds, revealing evidence for host-specific quantitative control of metabolite production, consistent with previous studies. Certain strains were capable of both inoculation and formation of longer-term associations with a nonhost species, perennial ryegrass (Lolium perenne L.). Discovery and primary characterisation of novel endophytes by DNA analysis, followed by confirmatory metabolic studies, offers improvements of speed and efficiency and hence accelerated deployment in pasture grass improvement programs.

Characterization of peeling modes in a low aspect ratio tokamak

NASA Astrophysics Data System (ADS)

Bongard, M. W.; Thome, K. E.; Barr, J. L.; Burke, M. G.; Fonck, R. J.; Hinson, E. T.; Redd, A. J.; Schlossberg, D. J.

2014-11-01

Peeling modes are observed at the plasma edge in the Pegasus Toroidal Experiment under conditions of high edge current density (Jedge ˜ 0.1 MA m-2) and low magnetic field (B ˜ 0.1 T) present at near-unity aspect ratio. Their macroscopic properties are measured using external Mirnov coil arrays, Langmuir probes and high-speed visible imaging. The modest edge parameters and short pulse lengths of Pegasus discharges permit direct measurement of the internal magnetic field structure with an insertable array of Hall-effect sensors, providing the current profile and its temporal evolution. Peeling modes generate coherent, edge-localized electromagnetic activity with low toroidal mode numbers n ⩽ 3 and high poloidal mode numbers, in agreement with theoretical expectations of a low-n external kink structure. Coherent MHD fluctuation amplitudes are found to be strongly dependent on the experimentally measured Jedge/B peeling instability drive, consistent with theory. Peeling modes nonlinearly generate ELM-like, field-aligned filamentary structures that detach from the edge and propagate radially outward. The KFIT equilibrium code is extended with an Akima spline profile parameterization and an improved model for induced toroidal wall current estimation to obtain a reconstruction during peeling activity with its current profile constrained by internal Hall measurements. It is used to test the analytic peeling stability criterion and numerically evaluate ideal MHD stability. Both approaches predict instability, in agreement with experiment, with the latter identifying an unstable external kink.

Correlating molecular spectroscopy and molecular chemometrics to explore carbohydrate functional groups and utilization of coproducts from biofuel and biobrewing processing.

PubMed

Chen, Limei; Zhang, Xuewei; Yu, Peiqiang

2014-06-04

Dried distillers grains with solubles (DDGS) was coproducts from bioethanol and biobrewing industry. It was an excellent resource of protein and energy feedstuff in China. Conventional studies often focus on traditional nutritional profiles. To data, there is little research on molecular structure-nutrition interaction of carbohydrate in coproducts. In this study, five kinds of corn-grain based DDGS and two kinds of barley-grain based DDGS were collected from different manufactures in the north of China. They were coded as "1, 2, 3, 4, 5, 6, and 7", respectively. The primary purposes of this project were to investigate the molecular structure-nutrition interaction of carbohydrate in coproducts, in terms of (1) carbohydrate-related chemical composition and nutrient profiles, (2) predicted values for energy in coproducts for animal, and (3) in situ digestion of dry matter. The result showed that acid detergent fiber content in corn DDGS and barley DDGS had negative correlation with structural carbohydrate peak area, cellulose compounds, and carbohydrate component peaks (first, second, and total peak area), which were measured with molecular spectroscopy. The correlation between carbohydrate peak area (second and total) and digestible fiber (tdNDF) were negative. There were no correlation between carbohydrate spectral intensities and energy values, carbohydrate subfractions partitioned by CNCPS system, and in situ rumen degradation. The results indicate that carbohydrate spectral profiles (functional groups) are associated with the carbohydrate nutritive values in coproducts from biofuel and biobrewing processing.

An Evolution-Based Approach to De Novo Protein Design and Case Study on Mycobacterium tuberculosis

PubMed Central

Brender, Jeffrey R.; Czajka, Jeff; Marsh, David; Gray, Felicia; Cierpicki, Tomasz; Zhang, Yang

2013-01-01

Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria Mycobacterium tuberculosis, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality. PMID:24204234

Predicting Ga and Cu Profiles in Co-Evaporated Cu(In,Ga)Se 2 Using Modified Diffusion Equations and a Spreadsheet

DOE PAGES

Repins, Ingrid L.; Harvey, Steve; Bowers, Karen; ...

2017-05-15

Cu(In,Ga)Se 2(CIGS) photovoltaic absorbers frequently develop Ga gradients during growth. These gradients vary as a function of growth recipe, and are important to device performance. Prediction of Ga profiles using classic diffusion equations is not possible because In and Ga atoms occupy the same lattice sites and thus diffuse interdependently, and there is not yet a detailed experimental knowledge of the chemical potential as a function of composition that describes this interaction. Here, we show how diffusion equations can be modified to account for site sharing between In and Ga atoms. The analysis has been implemented in an Excel spreadsheet,more » and outputs predicted Cu, In, and Ga profiles for entered deposition recipes. A single set of diffusion coefficients and activation energies are chosen, such that simulated elemental profiles track with published data and those from this study. Extent and limits of agreement between elemental profiles predicted from the growth recipes and the spreadsheet tool are demonstrated.« less

Predicting the spatial distribution of soil profile in Adapazari/Turkey by artificial neural networks using CPT data

NASA Astrophysics Data System (ADS)

Arel, Ersin

2012-06-01

The infamous soils of Adapazari, Turkey, that failed extensively during the 46-s long magnitude 7.4 earthquake in 1999 have since been the subject of a research program. Boreholes, piezocone soundings and voluminous laboratory testing have enabled researchers to apply sophisticated methods to determine the soil profiles in the city using the existing database. This paper describes the use of the artificial neural network (ANN) model to predict the complex soil profiles of Adapazari, based on cone penetration test (CPT) results. More than 3236 field CPT readings have been collected from 117 soundings spread over an area of 26 km2. An attempt has been made to develop the ANN model using multilayer perceptrons trained with a feed-forward back-propagation algorithm. The results show that the ANN model is fairly accurate in predicting complex soil profiles. Soil identification using CPT test results has principally been based on the Robertson charts. Applying neural network systems using the chart offers a powerful and rapid route to reliable prediction of the soil profiles.

Predicting Ga and Cu Profiles in Co-Evaporated Cu(In,Ga)Se 2 Using Modified Diffusion Equations and a Spreadsheet

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

Repins, Ingrid L.; Harvey, Steve; Bowers, Karen

Cu(In,Ga)Se 2(CIGS) photovoltaic absorbers frequently develop Ga gradients during growth. These gradients vary as a function of growth recipe, and are important to device performance. Prediction of Ga profiles using classic diffusion equations is not possible because In and Ga atoms occupy the same lattice sites and thus diffuse interdependently, and there is not yet a detailed experimental knowledge of the chemical potential as a function of composition that describes this interaction. Here, we show how diffusion equations can be modified to account for site sharing between In and Ga atoms. The analysis has been implemented in an Excel spreadsheet,more » and outputs predicted Cu, In, and Ga profiles for entered deposition recipes. A single set of diffusion coefficients and activation energies are chosen, such that simulated elemental profiles track with published data and those from this study. Extent and limits of agreement between elemental profiles predicted from the growth recipes and the spreadsheet tool are demonstrated.« less

A Prognostic Gene Expression Profile That Predicts Circulating Tumor Cell Presence in Breast Cancer Patients

PubMed Central

Molloy, Timothy J.; Roepman, Paul; Naume, Bjørn; van't Veer, Laura J.

2012-01-01

The detection of circulating tumor cells (CTCs) in the peripheral blood and microarray gene expression profiling of the primary tumor are two promising new technologies able to provide valuable prognostic data for patients with breast cancer. Meta-analyses of several established prognostic breast cancer gene expression profiles in large patient cohorts have demonstrated that despite sharing few genes, their delineation of patients into “good prognosis” or “poor prognosis” are frequently very highly correlated, and combining prognostic profiles does not increase prognostic power. In the current study, we aimed to develop a novel profile which provided independent prognostic data by building a signature predictive of CTC status rather than outcome. Microarray gene expression data from an initial training cohort of 72 breast cancer patients for which CTC status had been determined in a previous study using a multimarker QPCR-based assay was used to develop a CTC-predictive profile. The generated profile was validated in two independent datasets of 49 and 123 patients and confirmed to be both predictive of CTC status, and independently prognostic. Importantly, the “CTC profile” also provided prognostic information independent of the well-established and powerful ‘70-gene’ prognostic breast cancer signature. This profile therefore has the potential to not only add prognostic information to currently-available microarray tests but in some circumstances even replace blood-based prognostic CTC tests at time of diagnosis for those patients already undergoing testing by multigene assays. PMID:22384245

Predicting the oral pharmacokinetic profiles of multiple-unit (pellet) dosage forms using a modeling and simulation approach coupled with biorelevant dissolution testing: case example diclofenac sodium.

PubMed

Kambayashi, Atsushi; Blume, Henning; Dressman, Jennifer B

2014-07-01

The objective of this research was to characterize the dissolution profile of a poorly soluble drug, diclofenac, from a commercially available multiple-unit enteric coated dosage form, Diclo-Puren® capsules, and to develop a predictive model for its oral pharmacokinetic profile. The paddle method was used to obtain the dissolution profiles of this dosage form in biorelevant media, with the exposure to simulated gastric conditions being varied in order to simulate the gastric emptying behavior of pellets. A modified Noyes-Whitney theory was subsequently fitted to the dissolution data. A physiologically-based pharmacokinetic (PBPK) model for multiple-unit dosage forms was designed using STELLA® software and coupled with the biorelevant dissolution profiles in order to simulate the plasma concentration profiles of diclofenac from Diclo-Puren® capsule in both the fasted and fed state in humans. Gastric emptying kinetics relevant to multiple-units pellets were incorporated into the PBPK model by setting up a virtual patient population to account for physiological variations in emptying kinetics. Using in vitro biorelevant dissolution coupled with in silico PBPK modeling and simulation it was possible to predict the plasma profile of this multiple-unit formulation of diclofenac after oral administration in both the fasted and fed state. This approach might be useful to predict variability in the plasma profiles for other drugs housed in multiple-unit dosage forms. Copyright © 2014 Elsevier B.V. All rights reserved.

Beyond the continuum: how molecular solvent structure affects electrostatics and hydrodynamics at solid-electrolyte interfaces.

PubMed

Bonthuis, Douwe Jan; Netz, Roland R

2013-10-03

Standard continuum theory fails to predict several key experimental results of electrostatic and electrokinetic measurements at aqueous electrolyte interfaces. In order to extend the continuum theory to include the effects of molecular solvent structure, we generalize the equations for electrokinetic transport to incorporate a space dependent dielectric profile, viscosity profile, and non-electrostatic interaction potential. All necessary profiles are extracted from atomistic molecular dynamics (MD) simulations. We show that the MD results for the ion-specific distribution of counterions at charged hydrophilic and hydrophobic interfaces are accurately reproduced using the dielectric profile of pure water and a non-electrostatic repulsion in an extended Poisson-Boltzmann equation. The distributions of Na(+) at both surface types and Cl(-) at hydrophilic surfaces can be modeled using linear dielectric response theory, whereas for Cl(-) at hydrophobic surfaces it is necessary to apply nonlinear response theory. The extended Poisson-Boltzmann equation reproduces the experimental values of the double-layer capacitance for many different carbon-based surfaces. In conjunction with a generalized hydrodynamic theory that accounts for a space dependent viscosity, the model captures the experimentally observed saturation of the electrokinetic mobility as a function of the bare surface charge density and the so-called anomalous double-layer conductivity. The two-scale approach employed here-MD simulations and continuum theory-constitutes a successful modeling scheme, providing basic insight into the molecular origins of the static and kinetic properties of charged surfaces, and allowing quantitative modeling at low computational cost.

Structure of Saturn's rings: Optical and dynamical considerations

NASA Technical Reports Server (NTRS)

Franklin, F. A.

1974-01-01

The photometric phase curves of Saturn's rings are considered, as well as a conflict between dynamical and photometric models of the rings. The dependence of ring brightness on angular separation of the earth and sun as viewed from Saturn is discussed. The nonlinear brightness surge is interpreted. Some quantitative calculations were carried out for bodies in and near the asteroidal belt. Predicted density profiles of the ring obtained with Mimas in an eccentric orbit and in a circular orbit are also included.

Confinement-induced liquid ordering investigated by x-ray phase retrieval

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

Bunk, Oliver; Diaz, Ana; Pfeiffer, Franz

2007-02-15

Using synchrotron x-ray diffraction, we have determined the ensemble-averaged density profile of colloidal fluids within confining channels of different widths. We observe an oscillatory ordering-disordering behavior of the colloidal particles as a function of the channel width, while the colloidal solution remains in the liquid state. This phenomenon has been suggested by surface force studies of hard-sphere fluids and also theoretically predicted, but here we see it by direct measurements of the structure for comparable systems.

A molecular dynamics study of polymer/graphene interfacial systems

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

Rissanou, Anastassia N.; Harmandaris, Vagelis

2014-05-15

Graphene based polymer nanocomposites are hybrid materials with a very broad range of technological applications. In this work, we study three hybrid polymer/graphene interfacial systems (polystyrene/graphene, poly(methyl methacrylate)/graphene and polyethylene/graphene) through detailed atomistic molecular dynamics (MD) simulations. Density profiles, structural characteristics and mobility aspects are being examined at the molecular level for all model systems. In addition, we compare the properties of the hybrid systems to the properties of the corresponding bulk ones, as well as to theoretical predictions.

The extension of a uniform canopy reflectance model to include row effects

NASA Technical Reports Server (NTRS)

Suits, G. H. (Principal Investigator)

1981-01-01

The effect of row structure is assumed to be caused by the variation in density of vegetation across rows rather than to a profile in canopy height. The calculation of crop reflectance using vegetation density modulation across rows follows a parallel procedure to that for a uniform canopy. Predictions using the row model for wheat show that the effect of changes in sun to row azimuth are greatest in Landsat Band 5 (red band) and can result in underestimation of crop vigor.

Structure-Functional Prediction and Analysis of Cancer Mutation Effects in Protein Kinases

PubMed Central

Dixit, Anshuman; Verkhivker, Gennady M.

2014-01-01

A central goal of cancer research is to discover and characterize the functional effects of mutated genes that contribute to tumorigenesis. In this study, we provide a detailed structural classification and analysis of functional dynamics for members of protein kinase families that are known to harbor cancer mutations. We also present a systematic computational analysis that combines sequence and structure-based prediction models to characterize the effect of cancer mutations in protein kinases. We focus on the differential effects of activating point mutations that increase protein kinase activity and kinase-inactivating mutations that decrease activity. Mapping of cancer mutations onto the conformational mobility profiles of known crystal structures demonstrated that activating mutations could reduce a steric barrier for the movement from the basal “low” activity state to the “active” state. According to our analysis, the mechanism of activating mutations reflects a combined effect of partial destabilization of the kinase in its inactive state and a concomitant stabilization of its active-like form, which is likely to drive tumorigenesis at some level. Ultimately, the analysis of the evolutionary and structural features of the major cancer-causing mutational hotspot in kinases can also aid in the correlation of kinase mutation effects with clinical outcomes. PMID:24817905

Demographic, Physiologic, and Psychosocial Correlates of Physical Activity in Structured Exercise and Sports Among Low-Income, Overweight Children.

PubMed

Hatfield, Daniel P; Chomitz, Virginia R; Chui, Kenneth K H; Sacheck, Jennifer M; Economos, Christina D

2015-01-01

To describe correlates of physical activity (PA) in structured exercise and structured sports sessions among low-income, overweight children participating in a community-based PA program. A total of 93 children (55% male; 91% Hispanic) aged 8-14 years were included. Participants wore pedometers in a sample of 10 of 59 total sessions offered; mean steps per minute were calculated for structured exercise and sports sessions. Separate multivariable regression models tested associations between steps per minute in exercise and sports sessions and 5 potential correlates: baseline body mass index z-score, aerobic fitness (Progressive Aerobic Cardiorespiratory Endurance Run laps), perceived athletic competence (Harter self-perception profile), sex, and age. Only age (ß = -2.9; P = .02) significantly predicted steps per minute in exercise sessions. Age (ß = -4.3; P = .007), fitness (ß = 0.45; P = .03), and male sex (ß = 8.7; P = .02) significantly predicted steps per minute in sports. In structured exercise and sports, perceived competence may not influence overweight and obese children's PA. However, girls and older or less fit children may engage less actively, especially in sports. Copyright © 2015 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

Quantitative 1D diffraction signatures during dual detector scatter VOI breast CBCT

NASA Astrophysics Data System (ADS)

LeClair, Robert J.

2017-03-01

Dual detector VOI scatter CBCT is similar to dual detector VOI CBCT except that during the high resolution scan, the low resolution flat panel detector is also used to capture the scattered photons. Simulations show a potential use of scatter to diagnose suspicious VOIs. Energy integrated signals due to scatter (EISs) were computed for a specific imaging task involving a malignant lesion and was labelled as a hypothetical experiment (expt) result. The signal was compared to predictions (pred) using benign and malignant lesions. The ΔEISs=EISs|expt - EISs|pred displayed eye catching diffraction structure when the prediction calculation used a benign lesion. The structure occurred even when the phantom compositions were different for prediction and experiment calculations. Since the diffraction structure has a circularly symmetric behaviour because the tissues are amorphous in nature, the 2D ΔEISs patterns were transformed to 1D signals. The 1D signals were obtained by calculating the mean ΔEISs signals in rings. The mean pixel values were a function of the momentum transfer argument q = 4π sin(θ/2)/λ which ranged from 12 to 46 nm-1. The 1D signals correlated well with the 2D profiles. Of particular interest were scatter signatures between q = 20 and 30 nm-1 where malignant tissue is predicted to scatter more than benign fibroglandular tissue. The 1D diffraction signatures could allow a better method to diagnose a suspicious lesion during dual detector scatter VOI CBCT.

Advanced seismic imaging of overdeepened alpine valleys

NASA Astrophysics Data System (ADS)

Burschil, Thomas; Buness, Hermann; Tanner, David; Gabriel, Gerald; Krawczyk, Charlotte M.

2017-04-01

Major European alpine valleys and basins are densely populated areas with infrastructure of international importance. To protect the environment by, e.g., geohazard assessment or groundwater estimation, understanding of the geological structure of these valleys is essential. The shape and deposits of a valley can clarify its genesis and allows a prediction of behaviour in future glaciations. The term "overdeepened" refers to valleys and basins, in which pressurized melt-water under the glacier erodes the valley below the fluvial level. Most overdeepened valleys or basins were thus refilled during the ice melt or remain in the form of lakes. The ICDP-project Drilling Overdeepened Alpine Valleys (DOVE) intends to correlate the sedimentary succession from boreholes between valleys in the entire alpine range. Hereby, seismic exploration is essential to predict the most promising well path and drilling site. In a first step, this DFG-funded project investigates the benefit of multi-component techniques for seismic imaging. At two test sites, the Tannwald Basin and the Lienz Basin, the Leibniz Institute for Applied Geophysics acquired P-wave reflection profiles to gain structural and facies information. Built on the P-wave information, several S-wave reflection profiles were acquired in the pure SH-wave domain as well as 6-C reflection profiles using a horizontal S-wave source in inline and crossline excitation and 3-C receivers. Five P-wave sections reveal the structure of the Tannwald Basin, which is a distal branch basin of the Rhine Glacier. Strong reflections mark the base of the basin, which has a maximum depth of 240 metres. Internal structures and facies vary strongly and spatially, but allow a seismic facies characterization. We distinguish lacustrine, glacio-fluvial, and deltaic deposits, which make up the fill of the Tannwald Basin. Elements of the SH-wave and 6-C seismic imaging correlate with major structures in the P-wave image, but vary in detail. Based on the interpretation, two possible drilling sites are suggested for DOVE that will also prove the seismic interpretation and explain differences in P- and S-wave imaging. First results for the intermountain Lienz Basin are available from four parallel P-wave sections which show the asymmetric basin shape. The sedimentary base is well imaged down to ca. 0.6 km depth, and internal reflectors point to a diverse fill. Here, S-wave imaging produces less distinct sections and requires more sophisticated processing. In summary, P-wave imaging is suitable to map overdeepened structures in the Alps while S-wave imaging can contribute additional information.

Prediction of Process-Induced Distortions in L-Shaped Composite Profiles Using Path-Dependent Constitutive Law

NASA Astrophysics Data System (ADS)

Ding, Anxin; Li, Shuxin; Wang, Jihui; Ni, Aiqing; Sun, Liangliang; Chang, Lei

2016-10-01

In this paper, the corner spring-in angles of AS4/8552 L-shaped composite profiles with different thicknesses are predicted using path-dependent constitutive law with the consideration of material properties variation due to phase change during curing. The prediction accuracy mainly depends on the properties in the rubbery and glassy states obtained by homogenization method rather than experimental measurements. Both analytical and finite element (FE) homogenization methods are applied to predict the overall properties of AS4/8552 composite. The effect of fiber volume fraction on the properties is investigated for both rubbery and glassy states using both methods. And the predicted results are compared with experimental measurements for the glassy state. Good agreement is achieved between the predicted results and available experimental data, showing the reliability of the homogenization method. Furthermore, the corner spring-in angles of L-shaped composite profiles are measured experimentally and the reliability of path-dependent constitutive law is validated as well as the properties prediction by FE homogenization method.

Identification of tissular origin of particles based on autofluorescence multispectral image analysis at the macroscopic scale

NASA Astrophysics Data System (ADS)

Corcel, Mathias; Devaux, Marie-Françoise; Guillon, Fabienne; Barron, Cécile

2017-06-01

Powders produced from plant materials are heterogeneous in relation to native plant heterogeneity, and during grinding, dissociation often occurred at the tissue scale. The tissue composition of powdery samples could be modified through dry fractionation diagrams and impact their end-uses properties. If tissue identification is often made on native plant structure, this characterization is not straightforward in destructured samples such powders. Taking advantage of the autofluorescence properties of cell wall components, multispectral image acquisition is envisioned to identify the tissular origin of particles. Images were acquired on maize stem sections and ground tissues isolated from the same stem by hand dissection. The variability in fluorescence intensity profiles was analysed using principal component analysis. The correspondence between fluorescence profiles and the different tissues observed in maize sections was assessed based on histology or known compositional heterogeneity. Similar variability was encountered in fluorescence profiles extracted from powder leading to the potential ability to predict tissular origin based on this autofluorescence multispectral signal.

Transcriptome profile of a bovine respiratory disease pathogen: Mannheimia haemolytica PHL213

PubMed Central

2012-01-01

Background Computational methods for structural gene annotation have propelled gene discovery but face certain drawbacks with regards to prokaryotic genome annotation. Identification of transcriptional start sites, demarcating overlapping gene boundaries, and identifying regulatory elements such as small RNA are not accurate using these approaches. In this study, we re-visit the structural annotation of Mannheimia haemolytica PHL213, a bovine respiratory disease pathogen. M. haemolytica is one of the causative agents of bovine respiratory disease that results in about $3 billion annual losses to the cattle industry. We used RNA-Seq and analyzed the data using freely-available computational methods and resources. The aim was to identify previously unannotated regions of the genome using RNA-Seq based expression profile to complement the existing annotation of this pathogen. Results Using the Illumina Genome Analyzer, we generated 9,055,826 reads (average length ~76 bp) and aligned them to the reference genome using Bowtie. The transcribed regions were analyzed using SAMTOOLS and custom Perl scripts in conjunction with BLAST searches and available gene annotation information. The single nucleotide resolution map enabled the identification of 14 novel protein coding regions as well as 44 potential novel sRNA. The basal transcription profile revealed that 2,506 of the 2,837 annotated regions were expressed in vitro, at 95.25% coverage, representing all broad functional gene categories in the genome. The expression profile also helped identify 518 potential operon structures involving 1,086 co-expressed pairs. We also identified 11 proteins with mutated/alternate start codons. Conclusions The application of RNA-Seq based transcriptome profiling to structural gene annotation helped correct existing annotation errors and identify potential novel protein coding regions and sRNA. We used computational tools to predict regulatory elements such as promoters and terminators associated with the novel expressed regions for further characterization of these novel functional elements. Our study complements the existing structural annotation of Mannheimia haemolytica PHL213 based on experimental evidence. Given the role of sRNA in virulence gene regulation and stress response, potential novel sRNA described in this study can form the framework for future studies to determine the role of sRNA, if any, in M. haemolytica pathogenesis. PMID:23046475

Predicted thermal response of a cryogenic fuel tank exposed to simulated aerodynamic heating profiles with different cryogens and fill levels

NASA Technical Reports Server (NTRS)

Hanna, Gregory J.; Stephens, Craig A.

1991-01-01

A two dimensional finite difference thermal model was developed to predict the effects of heating profile, fill level, and cryogen type prior to experimental testing the Generic Research Cryogenic Tank (GRCT). These numerical predictions will assist in defining test scenarios, sensor locations, and venting requirements for the GRCT experimental tests. Boiloff rates, tank-wall and fluid temperatures, and wall heat fluxes were determined for 20 computational test cases. The test cases spanned three discrete fill levels and three heating profiles for hydrogen and nitrogen.

Evaluation of Variable-Depth Liner Configurations for Increased Broadband Noise Reduction

NASA Technical Reports Server (NTRS)

Jones, M. G.; Watson, W. R.; Nark, D. M.; Howerton, B. M.

2015-01-01

This paper explores the effects of variable-depth geometry on the amount of noise reduction that can be achieved with acoustic liners. Results for two variable-depth liners tested in the NASA Langley Grazing Flow Impedance Tube demonstrate significant broadband noise reduction. An impedance prediction model is combined with two propagation codes to predict corresponding sound pressure level profiles over the length of the Grazing Flow Impedance Tube. The comparison of measured and predicted sound pressure level profiles is sufficiently favorable to support use of these tools for investigation of a number of proposed variable-depth liner configurations. Predicted sound pressure level profiles for these proposed configurations reveal a number of interesting features. Liner orientation clearly affects the sound pressure level profile over the length of the liner, but the effect on the total attenuation is less pronounced. The axial extent of attenuation at an individual frequency continues well beyond the location where the liner depth is optimally tuned to the quarter-wavelength of that frequency. The sound pressure level profile is significantly affected by the way in which variable-depth segments are distributed over the length of the liner. Given the broadband noise reduction capability for these liner configurations, further development of impedance prediction models and propagation codes specifically tuned for this application is warranted.

Optimized Structures for Low-Profile Phase Change Thermal Spreaders

NASA Astrophysics Data System (ADS)

Sharratt, Stephen Andrew

Thin, low-profile phase change thermal spreaders can provide cooling solutions for some of today's most pressing heat flux dissipation issues. These thermal issues are only expected to increase as future electronic circuitry requirements lead to denser and potentially 3D chip packaging. Phase change based heat spreaders, such as heat pipes or vapor chambers, can provide a practical solution for effectively dissipating large heat fluxes. This thesis reports a comprehensive study of state-of-the-art capillary pumped wick structures using computational modeling, micro wick fabrication, and experimental analysis. Modeling efforts focus on predicting the shape of the liquid meniscus inside a complicated 3D wick structure. It is shown that this liquid shape can drastically affect the wick's thermal resistance. In addition, knowledge of the liquid meniscus shape allows for the computation of key parameters such as permeability and capillary pressure which are necessary for predicting the maximum heat flux. After the model is validated by comparison to experimental results, the wick structure is optimized so as to decrease overall wick thermal resistance and increase the maximum capillary limited heat flux before dryout. The optimized structures are then fabricated out of both silicon and copper using both traditional and novel micro-fabrication techniques. The wicks are made super-hydrophilic using chemical and thermal oxidation schemes. A sintered monolayer of Cu particles is fabricated and analyzed as well. The fabricated wick structures are experimentally tested for their heat transfer performance inside a well controlled copper vacuum chamber. Heat fluxes as high as 170 W/cm2 are realized for Cu wicks with structure heights of 100 μm. The structures optimized for both minimized thermal resistance and high liquid supply ability perform much better than their non-optimized counterparts. The super-hydrophilic oxidation scheme is found to drastically increase the maximum heat flux and decrease thermal resistance. This research provides key insights as to how to optimize heat pipe structures to minimize thermal resistance and increase maximum heat flux. These thin wick structures can also be combined with a thicker liquid supply layer so that thin, low-resistance evaporator layers can be constructed and higher heat fluxes realized. The work presented in this thesis can be used to aid in the development of high-performance phase change thermal spreaders, allowing for temperature control of a variety of powerful electronic components.

Protein Molecular Structures, Protein SubFractions, and Protein Availability Affected by Heat Processing: A Review

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

Yu,P.

2007-01-01

The utilization and availability of protein depended on the types of protein and their specific susceptibility to enzymatic hydrolysis (inhibitory activities) in the gastrointestine and was highly associated with protein molecular structures. Studying internal protein structure and protein subfraction profiles leaded to an understanding of the components that make up a whole protein. An understanding of the molecular structure of the whole protein was often vital to understanding its digestive behavior and nutritive value in animals. In this review, recently obtained information on protein molecular structural effects of heat processing was reviewed, in relation to protein characteristics affecting digestive behaviormore » and nutrient utilization and availability. The emphasis of this review was on (1) using the newly advanced synchrotron technology (S-FTIR) as a novel approach to reveal protein molecular chemistry affected by heat processing within intact plant tissues; (2) revealing the effects of heat processing on the profile changes of protein subfractions associated with digestive behaviors and kinetics manipulated by heat processing; (3) prediction of the changes of protein availability and supply after heat processing, using the advanced DVE/OEB and NRC-2001 models, and (4) obtaining information on optimal processing conditions of protein as intestinal protein source to achieve target values for potential high net absorbable protein in the small intestine. The information described in this article may give better insight in the mechanisms involved and the intrinsic protein molecular structural changes occurring upon processing.« less

CABS-flex: server for fast simulation of protein structure fluctuations

PubMed Central

Jamroz, Michal; Kolinski, Andrzej; Kmiecik, Sebastian

2013-01-01

The CABS-flex server (http://biocomp.chem.uw.edu.pl/CABSflex) implements CABS-model–based protocol for the fast simulations of near-native dynamics of globular proteins. In this application, the CABS model was shown to be a computationally efficient alternative to all-atom molecular dynamics—a classical simulation approach. The simulation method has been validated on a large set of molecular dynamics simulation data. Using a single input (user-provided file in PDB format), the CABS-flex server outputs an ensemble of protein models (in all-atom PDB format) reflecting the flexibility of the input structure, together with the accompanying analysis (residue mean-square-fluctuation profile and others). The ensemble of predicted models can be used in structure-based studies of protein functions and interactions. PMID:23658222

CABS-flex: Server for fast simulation of protein structure fluctuations.

PubMed

Jamroz, Michal; Kolinski, Andrzej; Kmiecik, Sebastian

2013-07-01

The CABS-flex server (http://biocomp.chem.uw.edu.pl/CABSflex) implements CABS-model-based protocol for the fast simulations of near-native dynamics of globular proteins. In this application, the CABS model was shown to be a computationally efficient alternative to all-atom molecular dynamics--a classical simulation approach. The simulation method has been validated on a large set of molecular dynamics simulation data. Using a single input (user-provided file in PDB format), the CABS-flex server outputs an ensemble of protein models (in all-atom PDB format) reflecting the flexibility of the input structure, together with the accompanying analysis (residue mean-square-fluctuation profile and others). The ensemble of predicted models can be used in structure-based studies of protein functions and interactions.

Around the macrolide - Impact of 3D structure of macrocycles on lipophilicity and cellular accumulation.

PubMed

Koštrun, Sanja; Munic Kos, Vesna; Matanović Škugor, Maja; Palej Jakopović, Ivana; Malnar, Ivica; Dragojević, Snježana; Ralić, Jovica; Alihodžić, Sulejman

2017-06-16

The aim of this study was to investigate lipophilicity and cellular accumulation of rationally designed azithromycin and clarithromycin derivatives at the molecular level. The effect of substitution site and substituent properties on a global physico-chemical profile and cellular accumulation of investigated compounds was studied using calculated structural parameters as well as experimentally determined lipophilicity. In silico models based on the 3D structure of molecules were generated to investigate conformational effect on studied properties and to enable prediction of lipophilicity and cellular accumulation for this class of molecules based on non-empirical parameters. The applicability of developed models was explored on a validation and test sets and compared with previously developed empirical models. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

Global proteomics profiling improves drug sensitivity prediction: results from a multi-omics, pan-cancer modeling approach.

PubMed

Ali, Mehreen; Khan, Suleiman A; Wennerberg, Krister; Aittokallio, Tero

2018-04-15

Proteomics profiling is increasingly being used for molecular stratification of cancer patients and cell-line panels. However, systematic assessment of the predictive power of large-scale proteomic technologies across various drug classes and cancer types is currently lacking. To that end, we carried out the first pan-cancer, multi-omics comparative analysis of the relative performance of two proteomic technologies, targeted reverse phase protein array (RPPA) and global mass spectrometry (MS), in terms of their accuracy for predicting the sensitivity of cancer cells to both cytotoxic chemotherapeutics and molecularly targeted anticancer compounds. Our results in two cell-line panels demonstrate how MS profiling improves drug response predictions beyond that of the RPPA or the other omics profiles when used alone. However, frequent missing MS data values complicate its use in predictive modeling and required additional filtering, such as focusing on completely measured or known oncoproteins, to obtain maximal predictive performance. Rather strikingly, the two proteomics profiles provided complementary predictive signal both for the cytotoxic and targeted compounds. Further, information about the cellular-abundance of primary target proteins was found critical for predicting the response of targeted compounds, although the non-target features also contributed significantly to the predictive power. The clinical relevance of the selected protein markers was confirmed in cancer patient data. These results provide novel insights into the relative performance and optimal use of the widely applied proteomic technologies, MS and RPPA, which should prove useful in translational applications, such as defining the best combination of omics technologies and marker panels for understanding and predicting drug sensitivities in cancer patients. Processed datasets, R as well as Matlab implementations of the methods are available at https://github.com/mehr-een/bemkl-rbps. mehreen.ali@helsinki.fi or tero.aittokallio@fimm.fi. Supplementary data are available at Bioinformatics online.

Characterising the latent structure and organisation of self-reported thoughts, feelings and behaviours in adolescents and young adults

PubMed Central

Neufeld, Sharon; Jones, Peter B.; Fonagy, Peter; Bullmore, Edward T.; Dolan, Raymond J.; Moutoussis, Michael; Toseeb, Umar; Goodyer, Ian M.

2017-01-01

Little is known about the underlying relationships between self-reported mental health items measuring both positive and negative emotional and behavioural symptoms at the population level in young people. Improved measurement of the full range of mental well-being and mental illness may aid in understanding the aetiological substrates underlying the development of both mental wellness as well as specific psychiatric diagnoses. A general population sample aged 14 to 24 years completed self-report questionnaires on anxiety, depression, psychotic-like symptoms, obsessionality and well-being. Exploratory and confirmatory factor models for categorical data and latent profile analyses were used to evaluate the structure of both mental wellness and illness items. First order, second order and bifactor structures were evaluated on 118 self-reported items obtained from 2228 participants. A bifactor solution was the best fitting latent variable model with one general latent factor termed ‘distress’ and five ‘distress independent’ specific factors defined as self-confidence, antisocial behaviour, worry, aberrant thinking, and mood. Next, six distinct subgroups were derived from a person-centred latent profile analysis of the factor scores. Finally, concurrent validity was assessed using information on hazardous behaviours (alcohol use, substance misuse, self-harm) and treatment for mental ill health: both discriminated between the latent traits and latent profile subgroups. The findings suggest a complex, multidimensional mental health structure in the youth population rather than the previously assumed first or second order factor structure. Additionally, the analysis revealed a low hazardous behaviour/low mental illness risk subgroup not previously described. Population sub-groups show greater validity over single variable factors in revealing mental illness risks. In conclusion, our findings indicate that the structure of self reported mental health is multidimensional in nature and uniquely finds improved prediction to mental illness risk within person-centred subgroups derived from the multidimensional latent traits. PMID:28403164

Characterising the latent structure and organisation of self-reported thoughts, feelings and behaviours in adolescents and young adults.

PubMed

St Clair, Michelle C; Neufeld, Sharon; Jones, Peter B; Fonagy, Peter; Bullmore, Edward T; Dolan, Raymond J; Moutoussis, Michael; Toseeb, Umar; Goodyer, Ian M

2017-01-01

Little is known about the underlying relationships between self-reported mental health items measuring both positive and negative emotional and behavioural symptoms at the population level in young people. Improved measurement of the full range of mental well-being and mental illness may aid in understanding the aetiological substrates underlying the development of both mental wellness as well as specific psychiatric diagnoses. A general population sample aged 14 to 24 years completed self-report questionnaires on anxiety, depression, psychotic-like symptoms, obsessionality and well-being. Exploratory and confirmatory factor models for categorical data and latent profile analyses were used to evaluate the structure of both mental wellness and illness items. First order, second order and bifactor structures were evaluated on 118 self-reported items obtained from 2228 participants. A bifactor solution was the best fitting latent variable model with one general latent factor termed 'distress' and five 'distress independent' specific factors defined as self-confidence, antisocial behaviour, worry, aberrant thinking, and mood. Next, six distinct subgroups were derived from a person-centred latent profile analysis of the factor scores. Finally, concurrent validity was assessed using information on hazardous behaviours (alcohol use, substance misuse, self-harm) and treatment for mental ill health: both discriminated between the latent traits and latent profile subgroups. The findings suggest a complex, multidimensional mental health structure in the youth population rather than the previously assumed first or second order factor structure. Additionally, the analysis revealed a low hazardous behaviour/low mental illness risk subgroup not previously described. Population sub-groups show greater validity over single variable factors in revealing mental illness risks. In conclusion, our findings indicate that the structure of self reported mental health is multidimensional in nature and uniquely finds improved prediction to mental illness risk within person-centred subgroups derived from the multidimensional latent traits.

Prediction of phenotypes of missense mutations in human proteins from biological assemblies.

PubMed

Wei, Qiong; Xu, Qifang; Dunbrack, Roland L

2013-02-01

Single nucleotide polymorphisms (SNPs) are the most frequent variation in the human genome. Nonsynonymous SNPs that lead to missense mutations can be neutral or deleterious, and several computational methods have been presented that predict the phenotype of human missense mutations. These methods use sequence-based and structure-based features in various combinations, relying on different statistical distributions of these features for deleterious and neutral mutations. One structure-based feature that has not been studied significantly is the accessible surface area within biologically relevant oligomeric assemblies. These assemblies are different from the crystallographic asymmetric unit for more than half of X-ray crystal structures. We find that mutations in the core of proteins or in the interfaces in biological assemblies are significantly more likely to be disease-associated than those on the surface of the biological assemblies. For structures with more than one protein in the biological assembly (whether the same sequence or different), we find the accessible surface area from biological assemblies provides a statistically significant improvement in prediction over the accessible surface area of monomers from protein crystal structures (P = 6e-5). When adding this information to sequence-based features such as the difference between wildtype and mutant position-specific profile scores, the improvement from biological assemblies is statistically significant but much smaller (P = 0.018). Combining this information with sequence-based features in a support vector machine leads to 82% accuracy on a balanced dataset of 50% disease-associated mutations from SwissVar and 50% neutral mutations from human/primate sequence differences in orthologous proteins. Copyright © 2012 Wiley Periodicals, Inc.

Dimerization of a flocculent protein from Moringa oleifera: experimental evidence and in silico interpretation.

PubMed

Pavankumar, Asalapuram R; Kayathri, Rajarathinam; Murugan, Natarajan A; Zhang, Qiong; Srivastava, Vaibhav; Okoli, Chuka; Bulone, Vincent; Rajarao, Gunaratna K; Ågren, Hans

2014-01-01

Many proteins exist in dimeric and other oligomeric forms to gain stability and functional advantages. In this study, the dimerization property of a coagulant protein (MO2.1) from Moringa oleifera seeds was addressed through laboratory experiments, protein-protein docking studies and binding free energy calculations. The structure of MO2.1 was predicted by homology modelling, while binding free energy and residues-distance profile analyses provided insight into the energetics and structural factors for dimer formation. Since the coagulation activities of the monomeric and dimeric forms of MO2.1 were comparable, it was concluded that oligomerization does not affect the biological activity of the protein.

Effects of DTM resolution on slope steepness and soil loss prediction on hillslope profiles

Treesearch

Eder Paulo Moreira; William J. Elliot; Andrew T. Hudak

2011-01-01

Topographic attributes play a critical role in predicting erosion in models such as the Water Erosion Prediction Project (WEPP). The effects of four different high resolution hillslope profiles were studied using four different DTM resolutions: 1-m, 3-m, 5-m and 10-m. The WEPP model used a common scenario encountered in the forest environment and the selected hillslope...

Systematic drug safety evaluation based on public genomic expression (Connectivity Map) data: Myocardial and infectious adverse reactions as application cases

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

Wang, Kejian, E-mail: kejian.wang.bio@gmail.com; Weng, Zuquan; Sun, Liya

Adverse drug reaction (ADR) is of great importance to both regulatory agencies and the pharmaceutical industry. Various techniques, such as quantitative structure–activity relationship (QSAR) and animal toxicology, are widely used to identify potential risks during the preclinical stage of drug development. Despite these efforts, drugs with safety liabilities can still pass through safety checkpoints and enter the market. This situation raises the concern that conventional chemical structure analysis and phenotypic screening are not sufficient to avoid all clinical adverse events. Genomic expression data following in vitro drug treatments characterize drug actions and thus have become widely used in drug repositioning. Inmore » the present study, we explored prediction of ADRs based on the drug-induced gene-expression profiles from cultured human cells in the Connectivity Map (CMap) database. The results showed that drugs inducing comparable ADRs generally lead to similar CMap expression profiles. Based on such ADR-gene expression association, we established prediction models for various ADRs, including severe myocardial and infectious events. Drugs with FDA boxed warnings of safety liability were effectively identified. We therefore suggest that drug-induced gene expression change, in combination with effective computational methods, may provide a new dimension of information to facilitate systematic drug safety evaluation. - Highlights: • Drugs causing common toxicity lead to similar in vitro gene expression changes. • We built a model to predict drug toxicity with drug-specific expression profiles. • Drugs with FDA black box warnings were effectively identified by our model. • In vitro assay can detect severe toxicity in the early stage of drug development.« less

Higher schizotypy predicts better metabolic profile in unaffected siblings of patients with schizophrenia.

PubMed

Atbasoglu, E Cem; Gumus-Akay, Guvem; Guloksuz, Sinan; Saka, Meram Can; Ucok, Alp; Alptekin, Koksal; Gullu, Sevim; van Os, Jim

2018-04-01

Type 2 diabetes (T2D) is more frequent in schizophrenia (Sz) than in the general population. This association is partly accounted for by shared susceptibility genetic variants. We tested the hypotheses that a genetic predisposition to Sz would be associated with higher likelihood of insulin resistance (IR), and that IR would be predicted by subthreshold psychosis phenotypes. Unaffected siblings of Sz patients (n = 101) were compared with a nonclinical sample (n = 305) in terms of IR, schizotypy (SzTy), and a behavioural experiment of "jumping to conclusions". The measures, respectively, were the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), Structured Interview for Schizotypy-Revised (SIS-R), and the Beads Task (BT). The likelihood of IR was examined in multiple regression models that included sociodemographic, metabolic, and cognitive parameters alongside group status, SIS-R scores, and BT performance. Insulin resistance was less frequent in siblings (31.7%) compared to controls (43.3%) (p < 0.05), and negatively associated with SzTy, as compared among the tertile groups for the latter (p < 0.001). The regression model that examined all relevant parameters included the tSzTy tertiles, TG and HDL-C levels, and BMI, as significant predictors of IR. Lack of IR was predicted by the highest as compared to the lowest SzTy tertile [OR (95%CI): 0.43 (0.21-0.85), p = 0.015]. Higher dopaminergic activity may contribute to both schizotypal features and a favourable metabolic profile in the same individual. This is compatible with dopamine's regulatory role in glucose metabolism via indirect central actions and a direct action on pancreatic insulin secretion. The relationship between dopaminergic activity and metabolic profile in Sz must be examined in longitudinal studies with younger unaffected siblings.

Developing Enhanced Blood–Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling

PubMed Central

Wang, Wenyi; Kim, Marlene T.; Sedykh, Alexander

2015-01-01

Purpose Experimental Blood–Brain Barrier (BBB) permeability models for drug molecules are expensive and time-consuming. As alternative methods, several traditional Quantitative Structure-Activity Relationship (QSAR) models have been developed previously. In this study, we aimed to improve the predictivity of traditional QSAR BBB permeability models by employing relevant public bio-assay data in the modeling process. Methods We compiled a BBB permeability database consisting of 439 unique compounds from various resources. The database was split into a modeling set of 341 compounds and a validation set of 98 compounds. Consensus QSAR modeling workflow was employed on the modeling set to develop various QSAR models. A five-fold cross-validation approach was used to validate the developed models, and the resulting models were used to predict the external validation set compounds. Furthermore, we used previously published membrane transporter models to generate relevant transporter profiles for target compounds. The transporter profiles were used as additional biological descriptors to develop hybrid QSAR BBB models. Results The consensus QSAR models have R2=0.638 for fivefold cross-validation and R2=0.504 for external validation. The consensus model developed by pooling chemical and transporter descriptors showed better predictivity (R2=0.646 for five-fold cross-validation and R2=0.526 for external validation). Moreover, several external bio-assays that correlate with BBB permeability were identified using our automatic profiling tool. Conclusions The BBB permeability models developed in this study can be useful for early evaluation of new compounds (e.g., new drug candidates). The combination of chemical and biological descriptors shows a promising direction to improve the current traditional QSAR models. PMID:25862462

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action.

PubMed

Papamokos, George; Silins, Ilona

2016-01-01

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens.

Combining QSAR Modeling and Text-Mining Techniques to Link Chemical Structures and Carcinogenic Modes of Action

PubMed Central

Papamokos, George; Silins, Ilona

2016-01-01

There is an increasing need for new reliable non-animal based methods to predict and test toxicity of chemicals. Quantitative structure-activity relationship (QSAR), a computer-based method linking chemical structures with biological activities, is used in predictive toxicology. In this study, we tested the approach to combine QSAR data with literature profiles of carcinogenic modes of action automatically generated by a text-mining tool. The aim was to generate data patterns to identify associations between chemical structures and biological mechanisms related to carcinogenesis. Using these two methods, individually and combined, we evaluated 96 rat carcinogens of the hematopoietic system, liver, lung, and skin. We found that skin and lung rat carcinogens were mainly mutagenic, while the group of carcinogens affecting the hematopoietic system and the liver also included a large proportion of non-mutagens. The automatic literature analysis showed that mutagenicity was a frequently reported endpoint in the literature of these carcinogens, however, less common endpoints such as immunosuppression and hormonal receptor-mediated effects were also found in connection with some of the carcinogens, results of potential importance for certain target organs. The combined approach, using QSAR and text-mining techniques, could be useful for identifying more detailed information on biological mechanisms and the relation with chemical structures. The method can be particularly useful in increasing the understanding of structure and activity relationships for non-mutagens. PMID:27625608

Comparison of two LES codes for wind turbine wake studies

NASA Astrophysics Data System (ADS)

Sarlak, H.; Pierella, F.; Mikkelsen, R.; Sørensen, J. N.

2014-06-01

For the third time a blind test comparison in Norway 2013, was conducted comparing numerical simulations for the rotor Cp and Ct and wake profiles with the experimental results. As the only large eddy simulation study among participants, results of the Technical University of Denmark (DTU) using their in-house CFD solver, EllipSys3D, proved to be more reliable among the other models for capturing the wake profiles and the turbulence intensities downstream the turbine. It was therefore remarked in the workshop to investigate other LES codes to compare their performance with EllipSys3D. The aim of this paper is to investigate on two CFD solvers, the DTU's in-house code, EllipSys3D and the open-sourse toolbox, OpenFoam, for a set of actuator line based LES computations. Two types of simulations are performed: the wake behind a signle rotor and the wake behind a cluster of three inline rotors. Results are compared in terms of velocity deficit, turbulence kinetic energy and eddy viscosity. It is seen that both codes predict similar near-wake flow structures with the exception of OpenFoam's simulations without the subgrid-scale model. The differences begin to increase with increasing the distance from the upstream rotor. From the single rotor simulations, EllipSys3D is found to predict a slower wake recovery in the case of uniform laminar flow. From the 3-rotor computations, it is seen that the difference between the codes is smaller as the disturbance created by the downstream rotors causes break down of the wake structures and more homogenuous flow structures. It is finally observed that OpenFoam computations are more sensitive to the SGS models.

Diagnosis of an intense atmospheric river impacting the pacific northwest: Storm summary and offshore vertical structure observed with COSMIC satellite retrievals

USGS Publications Warehouse

Neiman, P.J.; Ralph, F.M.; Wick, G.A.; Kuo, Y.-H.; Wee, T.-K.; Ma, Z.; Taylor, G.H.; Dettinger, M.D.

2008-01-01

This study uses the new satellite-based Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) mission to retrieve tropospheric profiles of temperature and moisture over the data-sparse eastern Pacific Ocean. The COSMIC retrievals, which employ a global positioning system radio occultation technique combined with "first-guess" information from numerical weather prediction model analyses, are evaluated through the diagnosis of an intense atmospheric river (AR; i.e., a narrow plume of strong water vapor flux) that devastated the Pacific Northwest with flooding rains in early November 2006. A detailed analysis of this AR is presented first using conventional datasets and highlights the fact that ARs are critical contributors to West Coast extreme precipitation and flooding events. Then, the COSMIC evaluation is provided. Offshore composite COSMIC soundings north of, within, and south of this AR exhibited vertical structures that are meteorologically consistent with satellite imagery and global reanalysis fields of this case and with earlier composite dropsonde results from other landfalling ARs. Also, a curtain of 12 offshore COSMIC soundings through the AR yielded cross-sectional thermodynamic and moisture structures that were similarly consistent, including details comparable to earlier aircraft-based dropsonde analyses. The results show that the new COSMIC retrievals, which are global (currently yielding ???2000 soundings per day), provide high-resolution vertical-profile information beyond that found in the numerical model first-guess fields and can help monitor key lower-tropospheric mesoscale phenomena in data-sparse regions. Hence, COSMIC will likely support a wide array of applications, from physical process studies to data assimilation, numerical weather prediction, and climate research. ?? 2008 American Meteorological Society.

Simulation of Orientation in Injection Molding of High Aspect Ratio Particle Thermoplastic Composites

NASA Astrophysics Data System (ADS)

Vélez-García, Gregorio M.; Ortman, Kevin C.; Eberle, Aaron P. R.; Wapperom, Peter; Baird, Donald G.

2008-07-01

A 2D coupled Hele-Shaw flow approximation for predicting the flow-induced orientation of high aspect ratio particles in injection molded composite parts is presented. For a highly concentrated short glass fiber PBT suspension, the impact of inter-particle interactions and the orientation at the gate is investigated for a center-gated disk using material parameters determined from rheometry. Experimental orientation is determined from confocal laser micrographs using the methods of ellipses. The constitutive equations are discretized using discontinuous Galerkin Finite Elements. Model predictions are significantly improved by using a localized orientation measured experimentally at the gate region instead of random or averaged gapwise measured orientation assumed in previous studies. The predicted profile in different radial positions can be related to the layered structure along the gapwise direction. Model modifications including interactions have lower impact than the initial conditions.

Williams syndrome-specific neuroanatomical profile and its associations with behavioral features.

PubMed

Fan, Chun Chieh; Brown, Timothy T; Bartsch, Hauke; Kuperman, Joshua M; Hagler, Donald J; Schork, Andrew; Searcy, Yvonne; Bellugi, Ursula; Halgren, Eric; Dale, Anders M

2017-01-01

Williams Syndrome (WS) is a rare genetic disorder with unique behavioral features. Yet the rareness of WS has limited the number and type of studies that can be conducted in which inferences are made about how neuroanatomical abnormalities mediate behaviors. In this study, we extracted a WS-specific neuroanatomical profile from structural magnetic resonance imaging (MRI) measurements and tested its association with behavioral features of WS. Using a WS adult cohort (22 WS, 16 healthy controls), we modeled a sparse representation of a WS-specific neuroanatomical profile. The predictive performances are robust within the training cohort (10-fold cross-validation, AUC = 1.0) and accurately identify all WS individuals in an independent child WS cohort (seven WS, 59 children with diverse developmental status, AUC = 1.0). The WS-specific neuroanatomical profile includes measurements in the orbitofrontal cortex, superior parietal cortex, Sylvian fissures, and basal ganglia, and variability within these areas related to the underlying size of hemizygous deletion in patients with partial deletions. The profile intensity mediated the overall cognitive impairment as well as personality features related to hypersociability. Our results imply that the unique behaviors in WS were mediated through the constellation of abnormalities in cortical-subcortical circuitry consistent in child WS and adult WS. The robustness of the derived WS-specific neuroanatomical profile also demonstrates the potential utility of our approach in both clinical and research applications.

Whole Tumor Histogram-profiling of Diffusion-Weighted Magnetic Resonance Images Reflects Tumorbiological Features of Primary Central Nervous System Lymphoma.

PubMed

Schob, Stefan; Münch, Benno; Dieckow, Julia; Quäschling, Ulf; Hoffmann, Karl-Titus; Richter, Cindy; Garnov, Nikita; Frydrychowicz, Clara; Krause, Matthias; Meyer, Hans-Jonas; Surov, Alexey

2018-04-01

Diffusion weighted imaging (DWI) quantifies motion of hydrogen nuclei in biological tissues and hereby has been used to assess the underlying tissue microarchitecture. Histogram-profiling of DWI provides more detailed information on diffusion characteristics of a lesion than the standardly calculated values of the apparent diffusion coefficient (ADC)-minimum, mean and maximum. Hence, the aim of our study was to investigate, which parameters of histogram-profiling of DWI in primary central nervous system lymphoma can be used to specifically predict features like cellular density, chromatin content and proliferative activity. Pre-treatment ADC maps of 21 PCNSL patients (8 female, 13 male, 28-89 years) from a 1.5T system were used for Matlab-based histogram profiling. Results of histopathology (H&E staining) and immunohistochemistry (Ki-67 expression) were quantified. Correlations between histogram-profiling parameters and neuropathologic examination were calculated using SPSS 23.0. The lower percentiles (p10 and p25) showed significant correlations with structural parameters of the neuropathologic examination (cellular density, chromatin content). The highest percentile, p90, correlated significantly with Ki-67 expression, resembling proliferative activity. Kurtosis of the ADC histogram correlated significantly with cellular density. Histogram-profiling of DWI in PCNSL provides a comprehensible set of parameters, which reflect distinct tumor-architectural and tumor-biological features, and hence, are promising biomarkers for treatment response and prognosis. Copyright © 2018. Published by Elsevier Inc.

Metabolite profiling of soy sauce using gas chromatography with time-of-flight mass spectrometry and analysis of correlation with quantitative descriptive analysis.

PubMed

Yamamoto, Shinya; Bamba, Takeshi; Sano, Atsushi; Kodama, Yukako; Imamura, Miho; Obata, Akio; Fukusaki, Eiichiro

2012-08-01

Soy sauces, produced from different ingredients and brewing processes, have variations in components and quality. Therefore, it is extremely important to comprehend the relationship between components and the sensory attributes of soy sauces. The current study sought to perform metabolite profiling in order to devise a method of assessing the attributes of soy sauces. Quantitative descriptive analysis (QDA) data for 24 soy sauce samples were obtained from well selected sensory panelists. Metabolite profiles primarily concerning low-molecular-weight hydrophilic components were based on gas chromatography with time-of-flightmass spectrometry (GC/TOFMS). QDA data for soy sauces were accurately predicted by projection to latent structure (PLS), with metabolite profiles serving as explanatory variables and QDA data set serving as a response variable. Moreover, analysis of correlation between matrices of metabolite profiles and QDA data indicated contributing compounds that were highly correlated with QDA data. Especially, it was indicated that sugars are important components of the tastes of soy sauces. This new approach which combines metabolite profiling with QDA is applicable to analysis of sensory attributes of food as a result of the complex interaction between its components. This approach is effective to search important compounds that contribute to the attributes. Copyright © 2012 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

cisMEP: an integrated repository of genomic epigenetic profiles and cis-regulatory modules in Drosophila

PubMed Central

2014-01-01

Background Cis-regulatory modules (CRMs), or the DNA sequences required for regulating gene expression, play the central role in biological researches on transcriptional regulation in metazoan species. Nowadays, the systematic understanding of CRMs still mainly resorts to computational methods due to the time-consuming and small-scale nature of experimental methods. But the accuracy and reliability of different CRM prediction tools are still unclear. Without comparative cross-analysis of the results and combinatorial consideration with extra experimental information, there is no easy way to assess the confidence of the predicted CRMs. This limits the genome-wide understanding of CRMs. Description It is known that transcription factor binding and epigenetic profiles tend to determine functions of CRMs in gene transcriptional regulation. Thus integration of the genome-wide epigenetic profiles with systematically predicted CRMs can greatly help researchers evaluate and decipher the prediction confidence and possible transcriptional regulatory functions of these potential CRMs. However, these data are still fragmentary in the literatures. Here we performed the computational genome-wide screening for potential CRMs using different prediction tools and constructed the pioneer database, cisMEP (cis-regulatory module epigenetic profile database), to integrate these computationally identified CRMs with genomic epigenetic profile data. cisMEP collects the literature-curated TFBS location data and nine genres of epigenetic data for assessing the confidence of these potential CRMs and deciphering the possible CRM functionality. Conclusions cisMEP aims to provide a user-friendly interface for researchers to assess the confidence of different potential CRMs and to understand the functions of CRMs through experimentally-identified epigenetic profiles. The deposited potential CRMs and experimental epigenetic profiles for confidence assessment provide experimentally testable hypotheses for the molecular mechanisms of metazoan gene regulation. We believe that the information deposited in cisMEP will greatly facilitate the comparative usage of different CRM prediction tools and will help biologists to study the modular regulatory mechanisms between different TFs and their target genes. PMID:25521507

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.

Simultaneous prediction of binding free energy and specificity for PDZ domain-peptide interactions

NASA Astrophysics Data System (ADS)

Crivelli, Joseph J.; Lemmon, Gordon; Kaufmann, Kristian W.; Meiler, Jens

2013-12-01

Interactions between protein domains and linear peptides underlie many biological processes. Among these interactions, the recognition of C-terminal peptides by PDZ domains is one of the most ubiquitous. In this work, we present a mathematical model for PDZ domain-peptide interactions capable of predicting both affinity and specificity of binding based on X-ray crystal structures and comparative modeling with R osetta. We developed our mathematical model using a large phage display dataset describing binding specificity for a wild type PDZ domain and 91 single mutants, as well as binding affinity data for a wild type PDZ domain binding to 28 different peptides. Structural refinement was carried out through several R osetta protocols, the most accurate of which included flexible peptide docking and several iterations of side chain repacking and backbone minimization. Our findings emphasize the importance of backbone flexibility and the energetic contributions of side chain-side chain hydrogen bonds in accurately predicting interactions. We also determined that predicting PDZ domain-peptide interactions became increasingly challenging as the length of the peptide increased in the N-terminal direction. In the training dataset, predicted binding energies correlated with those derived through calorimetry and specificity switches introduced through single mutations at interface positions were recapitulated. In independent tests, our best performing protocol was capable of predicting dissociation constants well within one order of magnitude of the experimental values and specificity profiles at the level of accuracy of previous studies. To our knowledge, this approach represents the first integrated protocol for predicting both affinity and specificity for PDZ domain-peptide interactions.

RCK: accurate and efficient inference of sequence- and structure-based protein-RNA binding models from RNAcompete data.

PubMed

Orenstein, Yaron; Wang, Yuhao; Berger, Bonnie

2016-06-15

Protein-RNA interactions, which play vital roles in many processes, are mediated through both RNA sequence and structure. CLIP-based methods, which measure protein-RNA binding in vivo, suffer from experimental noise and systematic biases, whereas in vitro experiments capture a clearer signal of protein RNA-binding. Among them, RNAcompete provides binding affinities of a specific protein to more than 240 000 unstructured RNA probes in one experiment. The computational challenge is to infer RNA structure- and sequence-based binding models from these data. The state-of-the-art in sequence models, Deepbind, does not model structural preferences. RNAcontext models both sequence and structure preferences, but is outperformed by GraphProt. Unfortunately, GraphProt cannot detect structural preferences from RNAcompete data due to the unstructured nature of the data, as noted by its developers, nor can it be tractably run on the full RNACompete dataset. We develop RCK, an efficient, scalable algorithm that infers both sequence and structure preferences based on a new k-mer based model. Remarkably, even though RNAcompete data is designed to be unstructured, RCK can still learn structural preferences from it. RCK significantly outperforms both RNAcontext and Deepbind in in vitro binding prediction for 244 RNAcompete experiments. Moreover, RCK is also faster and uses less memory, which enables scalability. While currently on par with existing methods in in vivo binding prediction on a small scale test, we demonstrate that RCK will increasingly benefit from experimentally measured RNA structure profiles as compared to computationally predicted ones. By running RCK on the entire RNAcompete dataset, we generate and provide as a resource a set of protein-RNA structure-based models on an unprecedented scale. Software and models are freely available at http://rck.csail.mit.edu/ bab@mit.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Structure of 311 service requests as a signature of urban location

PubMed Central

Wang, Lingjing; Qian, Cheng; Kats, Philipp; Kontokosta, Constantine; Sobolevsky, Stanislav

2017-01-01

While urban systems demonstrate high spatial heterogeneity, many urban planning, economic and political decisions heavily rely on a deep understanding of local neighborhood contexts. We show that the structure of 311 Service Requests enables one possible way of building a unique signature of the local urban context, thus being able to serve as a low-cost decision support tool for urban stakeholders. Considering examples of New York City, Boston and Chicago, we demonstrate how 311 Service Requests recorded and categorized by type in each neighborhood can be utilized to generate a meaningful classification of locations across the city, based on distinctive socioeconomic profiles. Moreover, the 311-based classification of urban neighborhoods can present sufficient information to model various socioeconomic features. Finally, we show that these characteristics are capable of predicting future trends in comparative local real estate prices. We demonstrate 311 Service Requests data can be used to monitor and predict socioeconomic performance of urban neighborhoods, allowing urban stakeholders to quantify the impacts of their interventions. PMID:29040314

Structure of 311 service requests as a signature of urban location.

PubMed

Wang, Lingjing; Qian, Cheng; Kats, Philipp; Kontokosta, Constantine; Sobolevsky, Stanislav

2017-01-01

While urban systems demonstrate high spatial heterogeneity, many urban planning, economic and political decisions heavily rely on a deep understanding of local neighborhood contexts. We show that the structure of 311 Service Requests enables one possible way of building a unique signature of the local urban context, thus being able to serve as a low-cost decision support tool for urban stakeholders. Considering examples of New York City, Boston and Chicago, we demonstrate how 311 Service Requests recorded and categorized by type in each neighborhood can be utilized to generate a meaningful classification of locations across the city, based on distinctive socioeconomic profiles. Moreover, the 311-based classification of urban neighborhoods can present sufficient information to model various socioeconomic features. Finally, we show that these characteristics are capable of predicting future trends in comparative local real estate prices. We demonstrate 311 Service Requests data can be used to monitor and predict socioeconomic performance of urban neighborhoods, allowing urban stakeholders to quantify the impacts of their interventions.

Comparison of two bioinformatics tools used to characterize the microbial diversity and predictive functional attributes of microbial mats from Lake Obersee, Antarctica.

PubMed

Koo, Hyunmin; Hakim, Joseph A; Morrow, Casey D; Eipers, Peter G; Davila, Alfonso; Andersen, Dale T; Bej, Asim K

2017-09-01

In this study, using NextGen sequencing of the collective 16S rRNA genes obtained from two sets of samples collected from Lake Obersee, Antarctica, we compared and contrasted two bioinformatics tools, PICRUSt and Tax4Fun. We then developed an R script to assess the taxonomic and predictive functional profiles of the microbial communities within the samples. Taxa such as Pseudoxanthomonas, Planctomycetaceae, Cyanobacteria Subsection III, Nitrosomonadaceae, Leptothrix, and Rhodobacter were exclusively identified by Tax4Fun that uses SILVA database; whereas PICRUSt that uses Greengenes database uniquely identified Pirellulaceae, Gemmatimonadetes A1-B1, Pseudanabaena, Salinibacterium and Sinobacteraceae. Predictive functional profiling of the microbial communities using Tax4Fun and PICRUSt separately revealed common metabolic capabilities, while also showing specific functional IDs not shared between the two approaches. Combining these functional predictions using a customized R script revealed a more inclusive metabolic profile, such as hydrolases, oxidoreductases, transferases; enzymes involved in carbohydrate and amino acid metabolisms; and membrane transport proteins known for nutrient uptake from the surrounding environment. Our results present the first molecular-phylogenetic characterization and predictive functional profiles of the microbial mat communities in Lake Obersee, while demonstrating the efficacy of combining both the taxonomic assignment information and functional IDs using the R script created in this study for a more streamlined evaluation of predictive functional profiles of microbial communities. Copyright © 2017 Elsevier B.V. All rights reserved.

Implementation of atomic layer etching of silicon: Scaling parameters, feasibility, and profile control

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

Ranjan, Alok, E-mail: alok.ranjan@us.tel.com; Wang, Mingmei; Sherpa, Sonam D.

2016-05-15

Atomic or layer by layer etching of silicon exploits temporally segregated self-limiting adsorption and material removal steps to mitigate the problems associated with continuous or quasicontinuous (pulsed) plasma processes: selectivity loss, damage, and profile control. Successful implementation of atomic layer etching requires careful choice of the plasma parameters for adsorption and desorption steps. This paper illustrates how process parameters can be arrived at through basic scaling exercises, modeling and simulation, and fundamental experimental tests of their predictions. Using chlorine and argon plasma in a radial line slot antenna plasma source as a platform, the authors illustrate how cycle time, ionmore » energy, and radical to ion ratio can be manipulated to manage the deviation from ideality when cycle times are shortened or purges are incomplete. Cell based Monte Carlo feature scale modeling is used to illustrate profile outcomes. Experimental results of atomic layer etching processes are illustrated on silicon line and space structures such that iso-dense bias and aspect ratio dependent free profiles are produced. Experimental results also illustrate the profile control margin as processes move from atomic layer to multilayer by layer etching. The consequence of not controlling contamination (e.g., oxygen) is shown to result in deposition and roughness generation.« less

An integrated structure- and system-based framework to identify new targets of metabolites and known drugs

PubMed Central

Naveed, Hammad; Hameed, Umar S.; Harrus, Deborah; Bourguet, William; Arold, Stefan T.; Gao, Xin

2015-01-01

Motivation: The inherent promiscuity of small molecules towards protein targets impedes our understanding of healthy versus diseased metabolism. This promiscuity also poses a challenge for the pharmaceutical industry as identifying all protein targets is important to assess (side) effects and repositioning opportunities for a drug. Results: Here, we present a novel integrated structure- and system-based approach of drug-target prediction (iDTP) to enable the large-scale discovery of new targets for small molecules, such as pharmaceutical drugs, co-factors and metabolites (collectively called ‘drugs’). For a given drug, our method uses sequence order–independent structure alignment, hierarchical clustering and probabilistic sequence similarity to construct a probabilistic pocket ensemble (PPE) that captures promiscuous structural features of different binding sites on known targets. A drug’s PPE is combined with an approximation of its delivery profile to reduce false positives. In our cross-validation study, we use iDTP to predict the known targets of 11 drugs, with 63% sensitivity and 81% specificity. We then predicted novel targets for these drugs—two that are of high pharmacological interest, the peroxisome proliferator-activated receptor gamma and the oncogene B-cell lymphoma 2, were successfully validated through in vitro binding experiments. Our method is broadly applicable for the prediction of protein-small molecule interactions with several novel applications to biological research and drug development. Availability and implementation: The program, datasets and results are freely available to academic users at http://sfb.kaust.edu.sa/Pages/Software.aspx. Contact: xin.gao@kaust.edu.sa and stefan.arold@kaust.edu.sa Supplementary information: Supplementary data are available at Bioinformatics online. PMID:26286808

In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features.

PubMed

Ding, Yiliang; Tang, Yin; Kwok, Chun Kit; Zhang, Yu; Bevilacqua, Philip C; Assmann, Sarah M

2014-01-30

RNA structure has critical roles in processes ranging from ligand sensing to the regulation of translation, polyadenylation and splicing. However, a lack of genome-wide in vivo RNA structural data has limited our understanding of how RNA structure regulates gene expression in living cells. Here we present a high-throughput, genome-wide in vivo RNA structure probing method, structure-seq, in which dimethyl sulphate methylation of unprotected adenines and cytosines is identified by next-generation sequencing. Application of this method to Arabidopsis thaliana seedlings yielded the first in vivo genome-wide RNA structure map at nucleotide resolution for any organism, with quantitative structural information across more than 10,000 transcripts. Our analysis reveals a three-nucleotide periodic repeat pattern in the structure of coding regions, as well as a less-structured region immediately upstream of the start codon, and shows that these features are strongly correlated with translation efficiency. We also find patterns of strong and weak secondary structure at sites of alternative polyadenylation, as well as strong secondary structure at 5' splice sites that correlates with unspliced events. Notably, in vivo structures of messenger RNAs annotated for stress responses are poorly predicted in silico, whereas mRNA structures of genes related to cell function maintenance are well predicted. Global comparison of several structural features between these two categories shows that the mRNAs associated with stress responses tend to have more single-strandedness, longer maximal loop length and higher free energy per nucleotide, features that may allow these RNAs to undergo conformational changes in response to environmental conditions. Structure-seq allows the RNA structurome and its biological roles to be interrogated on a genome-wide scale and should be applicable to any organism.

Altered structural network architecture is predictive of the presence of psychotic symptoms in patients with 22q11.2 deletion syndrome.

PubMed

Padula, Maria C; Scariati, Elisa; Schaer, Marie; Sandini, Corrado; Ottet, Marie Christine; Schneider, Maude; Van De Ville, Dimitri; Eliez, Stephan

2017-01-01

22q11.2 deletion syndrome (22q11DS) represents a homogeneous model of schizophrenia particularly suitable for the search of neural biomarkers of psychosis. Impairments in structural connectivity related to the presence of psychotic symptoms have been reported in patients with 22q11DS. However, the relationships between connectivity changes in patients with different symptomatic profiles are still largely unknown and warrant further investigations. In this study, we used structural connectivity to discriminate patients with 22q11DS with ( N  = 31) and without ( N  = 31) attenuated positive psychotic symptoms. Different structural connectivity measures were used, including the number of streamlines connecting pairs of brain regions, graph theoretical measures, and diffusion measures. We used univariate group comparisons as well as predictive multivariate approaches. The univariate comparison of connectivity measures between patients with or without attenuated positive psychotic symptoms did not give significant results. However, the multivariate prediction revealed that altered structural network architecture discriminates patient subtypes (accuracy = 67.7%). Among the regions contributing to the classification we found the anterior cingulate cortex, which is known to be associated to the presence of psychotic symptoms in patients with 22q11DS. Furthermore, a significant discrimination (accuracy = 64%) was obtained with fractional anisotropy and radial diffusivity in the left inferior longitudinal fasciculus and the right cingulate gyrus. Our results point to alterations in structural network architecture and white matter microstructure in patients with 22q11DS with attenuated positive symptoms, mainly involving connections of the limbic system. These alterations may therefore represent a potential biomarker for an increased risk of psychosis that should be further tested in longitudinal studies.

Road structural elements temperature trends diagnostics using sensory system of own design

NASA Astrophysics Data System (ADS)

Dudak, Juraj; Gaspar, Gabriel; Sedivy, Stefan; Pepucha, Lubomir; Florkova, Zuzana

2017-09-01

A considerable funds is spent for the roads maintenance in large areas during the winter. The road maintenance is significantly affected by the temperature change of the road structure. In remote locations may occur a situation, when it is not clear whether the sanding is actually needed because the lack of information on road conditions. In these cases, the actual road conditions are investigated by a personal inspection or by sending out a gritting vehicle. Here, however, is a risk of unnecessary trip the sanding vehicle. This situation is economically and environmentally unfavorable. The proposed system solves the problem of measuring the temperature profile of the road and the utilization of the predictive model to determine the future development trend of temperature. The system was technically designed as a set of sensors to monitor environmental values such as the temperature of the road, ambient temperature, relative air humidity, solar radiation and atmospheric pressure at the measuring point. An important part of the proposal is prediction model which based on the inputs from sensors and historical measurements can, with some probability, predict temperature trends at the measuring point. The proposed system addresses the economic and environmental aspects of winter road maintenance.

An EQT-based cDFT approach for thermodynamic properties of confined fluid mixtures

NASA Astrophysics Data System (ADS)

Motevaselian, M. H.; Aluru, N. R.

2017-04-01

We present an empirical potential-based quasi-continuum theory (EQT) to predict the structure and thermodynamic properties of confined fluid mixtures. The central idea in the EQT is to construct potential energies that integrate important atomistic details into a continuum-based model such as the Nernst-Planck equation. The EQT potentials can be also used to construct the excess free energy functional, which is required for the grand potential in the classical density functional theory (cDFT). In this work, we use the EQT-based grand potential to predict various thermodynamic properties of a confined binary mixture of hydrogen and methane molecules inside graphene slit channels of different widths. We show that the EQT-cDFT predictions for the structure, surface tension, solvation force, and local pressure tensor profiles are in good agreement with the molecular dynamics simulations. Moreover, we study the effect of different bulk compositions and channel widths on the thermodynamic properties. Our results reveal that the composition of methane in the mixture can significantly affect the ordering of molecules and thermodynamic properties under confinement. In addition, we find that graphene is selective to methane molecules.

The segmented non-uniform dielectric module design for uniformity control of plasma profile in a capacitively coupled plasma chamber

NASA Astrophysics Data System (ADS)

Xia, Huanxiong; Xiang, Dong; Yang, Wang; Mou, Peng

2014-12-01

Low-temperature plasma technique is one of the critical techniques in IC manufacturing process, such as etching and thin-film deposition, and the uniformity greatly impacts the process quality, so the design for the plasma uniformity control is very important but difficult. It is hard to finely and flexibly regulate the spatial distribution of the plasma in the chamber via controlling the discharge parameters or modifying the structure in zero-dimensional space, and it just can adjust the overall level of the process factors. In the view of this problem, a segmented non-uniform dielectric module design solution is proposed for the regulation of the plasma profile in a CCP chamber. The solution achieves refined and flexible regulation of the plasma profile in the radial direction via configuring the relative permittivity and the width of each segment. In order to solve this design problem, a novel simulation-based auto-design approach is proposed, which can automatically design the positional sequence with multi independent variables to make the output target profile in the parameterized simulation model approximate the one that users preset. This approach employs an idea of quasi-closed-loop control system, and works in an iterative mode. It starts from initial values of the design variable sequences, and predicts better sequences via the feedback of the profile error between the output target profile and the expected one. It never stops until the profile error is narrowed in the preset tolerance.

COSIM: A Finite-Difference Computer Model to Predict Ternary Concentration Profiles Associated With Oxidation and Interdiffusion of Overlay-Coated Substrates

NASA Technical Reports Server (NTRS)

Nesbitt, James A.

2001-01-01

A finite-difference computer program (COSIM) has been written which models the one-dimensional, diffusional transport associated with high-temperature oxidation and interdiffusion of overlay-coated substrates. The program predicts concentration profiles for up to three elements in the coating and substrate after various oxidation exposures. Surface recession due to solute loss is also predicted. Ternary cross terms and concentration-dependent diffusion coefficients are taken into account. The program also incorporates a previously-developed oxide growth and spalling model to simulate either isothermal or cyclic oxidation exposures. In addition to predicting concentration profiles after various oxidation exposures, the program can also be used to predict coating life based on a concentration dependent failure criterion (e.g., surface solute content drops to 2%). The computer code is written in FORTRAN and employs numerous subroutines to make the program flexible and easily modifiable to other coating oxidation problems.

Active region flows

NASA Technical Reports Server (NTRS)

Foukal, Peter

1987-01-01

A wide range of observations has shown that active region phenomena in the photospheric, chromospheric and coronal temperature regimes are dynamical in nature. At the photosphere, recent observations of full line profiles place an upper limit of about + or - 20/msec on any downflows at supergranule cell edges. Observations of the full Stokes 5 profiles in the network show no evidence for downflows in magnetic flux tubes. In the area of chromospheric dynamics, several models were put forward recently to reproduce the observed behavior of spicules. However, it is pointed out that these adiabatic models do not include the powerful radiative dissipation which tend to damp out the large amplitude disturbances that produce the spicular acceleration in the models. In the corona, loop flows along field lines clearly transport mass and energy at rates important for the dynamics of these structures. However, advances in understanding the heating and mass balance of the loop structures seem to require new kinds of observations. Some results are presented using a remote sensing diagnostic of the intensity and orientation of macroscopic plasma electric fields predicted by models of reconnective heating and also wave heating.

Observations of pockmark flow structure in Belfast Bay, Maine, Part 2: evidence for cavity flow

USGS Publications Warehouse

Fandel, Christina L.; Lippmann, Thomas C.; Foster, Diane L.; Brothers, Laura L.

2017-01-01

Pockmark flow circulation patterns were investigated through current measurements along the rim and center of two pockmarks in Belfast Bay, Maine. Observed time-varying current profiles have a complex vertical and directional structure that rotates significantly with depth and is strongly dependent on the phase of the tide. Observations of the vertical profiles of horizontal velocities in relation to relative geometric parameters of the pockmark are consistent with circulation patterns described qualitatively by cavity flow models (Ashcroft and Zhang 2005). The time-mean behavior of the shear layer is typically used to characterize cavity flow, and was estimated using vorticity thickness to quantify the growth rate of the shear layer horizontally across the pockmark. Estimated positive vorticity thickness spreading rates are consistent with cavity flow predictions, and occur at largely different rates between the two pockmarks. Previously modeled flow (Brothers et al. 2011) and laboratory measurements (Pau et al. 2014) over pockmarks of similar geometry to those examined herein are also qualitatively consistent with cavity flow circulation, suggesting that cavity flow may be a good first-order flow model for pockmarks in general.

The statistical geometry of transcriptome divergence in cell-type evolution and cancer.

PubMed

Liang, Cong; Forrest, Alistair R R; Wagner, Günter P

2015-01-14

In evolution, body plan complexity increases due to an increase in the number of individualized cell types. Yet, there is very little understanding of the mechanisms that produce this form of organismal complexity. One model for the origin of novel cell types is the sister cell-type model. According to this model, each cell type arises together with a sister cell type through specialization from an ancestral cell type. A key prediction of the sister cell-type model is that gene expression profiles of cell types exhibit tree structure. Here we present a statistical model for detecting tree structure in transcriptomic data and apply it to transcriptomes from ENCODE and FANTOM5. We show that transcriptomes of normal cells harbour substantial amounts of hierarchical structure. In contrast, cancer cell lines have less tree structure, suggesting that the emergence of cancer cells follows different principles from that of evolutionary cell-type origination.

Psychological Assessment of Adolescents and Adults with Autism.

ERIC Educational Resources Information Center

Perez, Juan; del Sol Fortea Sevilla, Maria

1993-01-01

This study compared scores of 17 children with autism and mental retardation on the Psychoeducational Profile with scores on the Adolescent and Adult Psychoeducational Profile 5 years later. Eye-hand coordination predicted scores in vocational skills, independent functioning, and vocational behavior; imitation predicted interpersonal behavior;…

Chemically Aware Model Builder (camb): an R package for property and bioactivity modelling of small molecules.

PubMed

Murrell, Daniel S; Cortes-Ciriano, Isidro; van Westen, Gerard J P; Stott, Ian P; Bender, Andreas; Malliavin, Thérèse E; Glen, Robert C

2015-01-01

In silico predictive models have proved to be valuable for the optimisation of compound potency, selectivity and safety profiles in the drug discovery process. camb is an R package that provides an environment for the rapid generation of quantitative Structure-Property and Structure-Activity models for small molecules (including QSAR, QSPR, QSAM, PCM) and is aimed at both advanced and beginner R users. camb's capabilities include the standardisation of chemical structure representation, computation of 905 one-dimensional and 14 fingerprint type descriptors for small molecules, 8 types of amino acid descriptors, 13 whole protein sequence descriptors, filtering methods for feature selection, generation of predictive models (using an interface to the R package caret), as well as techniques to create model ensembles using techniques from the R package caretEnsemble). Results can be visualised through high-quality, customisable plots (R package ggplot2). Overall, camb constitutes an open-source framework to perform the following steps: (1) compound standardisation, (2) molecular and protein descriptor calculation, (3) descriptor pre-processing and model training, visualisation and validation, and (4) bioactivity/property prediction for new molecules. camb aims to speed model generation, in order to provide reproducibility and tests of robustness. QSPR and proteochemometric case studies are included which demonstrate camb's application.Graphical abstractFrom compounds and data to models: a complete model building workflow in one package.

Multiple biomarkers in molecular oncology. II. Molecular diagnostics applications in breast cancer management.

PubMed

Malinowski, Douglas P

2007-05-01

In recent years, the application of genomic and proteomic technologies to the problem of breast cancer prognosis and the prediction of therapy response have begun to yield encouraging results. Independent studies employing transcriptional profiling of primary breast cancer specimens using DNA microarrays have identified gene expression profiles that correlate with clinical outcome in primary breast biopsy specimens. Recent advances in microarray technology have demonstrated reproducibility, making clinical applications more achievable. In this regard, one such DNA microarray device based upon a 70-gene expression signature was recently cleared by the US FDA for application to breast cancer prognosis. These DNA microarrays often employ at least 70 gene targets for transcriptional profiling and prognostic assessment in breast cancer. The use of PCR-based methods utilizing a small subset of genes has recently demonstrated the ability to predict the clinical outcome in early-stage breast cancer. Furthermore, protein-based immunohistochemistry methods have progressed from using gene clusters and gene expression profiling to smaller subsets of expressed proteins to predict prognosis in early-stage breast cancer. Beyond prognostic applications, DNA microarray-based transcriptional profiling has demonstrated the ability to predict response to chemotherapy in early-stage breast cancer patients. In this review, recent advances in the use of multiple markers for prognosis of disease recurrence in early-stage breast cancer and the prediction of therapy response will be discussed.

Follow-on Low Noise Fan Aerodynamic Study

NASA Technical Reports Server (NTRS)

Heidegger, Nathan J.; Hall, Edward J.; Delaney, Robert A.

1999-01-01

The focus of the project was to investigate the effects of turbulence models on the prediction of rotor wake structures. The Advanced Ducted Propfan Analysis (ADPAC) code was modified through the incorporation of the Spalart-Allmaras one-equation turbulence model. Suitable test cases were solved numerically using ADPAC employing the Spalart-Allmaras turbulence model and another prediction code for comparison. A near-wall spacing study was also completed to determine the adequate spacing of the first computational cell off the wall. Solutions were also collected using two versions of the algebraic Baldwin-Lomax turbulence model in ADPAC. The effects of the turbulence model on the rotor wake definition was examined by obtaining ADPAC solutions for the Low Noise Fan rotor-only steady-flow case using the standard algebraic Baldwin-Lomax turbulence model, a modified version of the Baldwin-Lomax turbulence model and the one-equation Spalart-Allmaras turbulence model. The results from the three different turbulence modeling techniques were compared with each other and the available experimental data. These results include overall rotor performance, spanwise exit profiles, and contours of axial velocity taken along constant axial locations and along blade-to-blade surfaces. Wake characterizations were also performed on the experimental and ADPAC predicted results including the definition of a wake correlation function. Correlations were evaluated for wake width and wake depth. Similarity profiles of the wake shape were also compared between all numerical solutions and experimental data.

Enabling Efficient and Confident Annotation of LC−MS Metabolomics Data through MS1 Spectrum and Time Prediction

DOE PAGES

Broeckling, Corey D.; Ganna, Andrea; Layer, Mark; ...

2016-09-08

Liquid chromatography coupled to electrospray ionization-mass spectrometry (LC–ESI-MS) is a versatile and robust platform for metabolomic analysis. However, while ESI is a soft ionization technique, in-source phenomena including multimerization, nonproton cation adduction, and in-source fragmentation complicate interpretation of MS data. Here, we report chromatographic and mass spectrometric behavior of 904 authentic standards collected under conditions identical to a typical nontargeted profiling experiment. The data illustrate that the often high level of complexity in MS spectra is likely to result in misinterpretation during the annotation phase of the experiment and a large overestimation of the number of compounds detected. However, ourmore » analysis of this MS spectral library data indicates that in-source phenomena are not random but depend at least in part on chemical structure. These nonrandom patterns enabled predictions to be made as to which in-source signals are likely to be observed for a given compound. Using the authentic standard spectra as a training set, we modeled the in-source phenomena for all compounds in the Human Metabolome Database to generate a theoretical in-source spectrum and retention time library. A novel spectral similarity matching platform was developed to facilitate efficient spectral searching for nontargeted profiling applications. Taken together, this collection of experimental spectral data, predictive modeling, and informatic tools enables more efficient, reliable, and transparent metabolite annotation.« less

Enabling Efficient and Confident Annotation of LC-MS Metabolomics Data through MS1 Spectrum and Time Prediction

DOE PAGES

Broeckling, Corey D.; Ganna, Andrea; Layer, Mark; ...

2016-08-25

Liquid chromatography coupled to electrospray ionization-mass spectrometry (LC–ESI-MS) is a versatile and robust platform for metabolomic analysis. However, while ESI is a soft ionization technique, in-source phenomena including multimerization, nonproton cation adduction, and in-source fragmentation complicate interpretation of MS data. Here, we report chromatographic and mass spectrometric behavior of 904 authentic standards collected under conditions identical to a typical nontargeted profiling experiment. The data illustrate that the often high level of complexity in MS spectra is likely to result in misinterpretation during the annotation phase of the experiment and a large overestimation of the number of compounds detected. However, ourmore » analysis of this MS spectral library data indicates that in-source phenomena are not random but depend at least in part on chemical structure. These nonrandom patterns enabled predictions to be made as to which in-source signals are likely to be observed for a given compound. Using the authentic standard spectra as a training set, we modeled the in-source phenomena for all compounds in the Human Metabolome Database to generate a theoretical in-source spectrum and retention time library. A novel spectral similarity matching platform was developed to facilitate efficient spectral searching for nontargeted profiling applications. Taken together, this collection of experimental spectral data, predictive modeling, and informatic tools enables more efficient, reliable, and transparent metabolite annotation.« less

Unsupervised user similarity mining in GSM sensor networks.

PubMed

Shad, Shafqat Ali; Chen, Enhong

2013-01-01

Mobility data has attracted the researchers for the past few years because of its rich context and spatiotemporal nature, where this information can be used for potential applications like early warning system, route prediction, traffic management, advertisement, social networking, and community finding. All the mentioned applications are based on mobility profile building and user trend analysis, where mobility profile building is done through significant places extraction, user's actual movement prediction, and context awareness. However, significant places extraction and user's actual movement prediction for mobility profile building are a trivial task. In this paper, we present the user similarity mining-based methodology through user mobility profile building by using the semantic tagging information provided by user and basic GSM network architecture properties based on unsupervised clustering approach. As the mobility information is in low-level raw form, our proposed methodology successfully converts it to a high-level meaningful information by using the cell-Id location information rather than previously used location capturing methods like GPS, Infrared, and Wifi for profile mining and user similarity mining.

Two-dimensional streamflow simulations of the Jordan River, Midvale and West Jordan, Utah

USGS Publications Warehouse

Kenney, Terry A.; Freeman, Michael L.

2011-01-01

The Jordan River in Midvale and West Jordan, Utah, flows adjacent to two U.S. Environmental Protection Agency Superfund sites: Midvale Slag and Sharon Steel. At both sites, geotechnical caps extend to the east bank of the river. The final remediation tasks for these sites included the replacement of a historic sheet-pile dam and the stabilization of the river banks adjacent to the Superfund sites. To assist with these tasks, two hydraulic modeling codes contained in the U.S. Geological Survey (USGS) Multi-Dimensional Surface-Water Modeling System (MD_SWMS), System for Transport and River Modeling (SToRM) and Flow and Sediment Transport and Morphological Evolution of Channels (FaSTMECH), were used to provide predicted water-surface elevations, velocities, and boundary shear-stress values throughout the study reach of the Jordan River. A SToRM model of a 0.7 mile subreach containing the sheet-pile dam was used to compare water-surface elevations and velocities associated with the sheet-pile dam and a proposed replacement structure. Maps showing water-surface elevation and velocity differences computed from simulations of the historic sheet-pile dam and the proposed replacement structure topographies for streamflows of 500 and 1,000 cubic feet per second (ft3/s) were created. These difference maps indicated that the velocities associated with the proposed replacement structure topographies were less than or equal to those associated with the historic sheet-pile dam. Similarly, water-surface elevations associated with the proposed replacement structure topographies were all either greater than or equal to water-surface elevations associated with the sheet-pile dam. A FaSTMECH model was developed for the 2.5-mile study reach to aid engineers in bank stabilization designs. Predicted water-surface elevations, velocities and shear-stress values were mapped on an aerial photograph of the study reach to place these parameters in a spatial context. Profile plots of predicted cross-stream average water-surface elevations and cross-stream maximum and average velocities showed how these parameters change along the study reach for two simulated discharges of 1,040 ft3/s and 2,790 ft3/s. The profile plots for the simulated streamflow of 1,040 ft3/s show that the highest velocities are associated with the constructed sheet-pile replacement structure. Results for the simulated streamflow of 2,790 ft3/s indicate that the geometry of the 7800 South Bridge causes more backwater and higher velocities than the constructed sheet-pile replacement structure.

The role of tidal current characteristics on the development of intertidal morphology in tropical and subtropical mangrove forests

NASA Astrophysics Data System (ADS)

Bryan, K. R.; Nardin, W.; Fagherazzi, S.; Mullarney, J. C.; Norris, B. K.; Henderson, S. M.

2016-12-01

Mangroves are a common intertidal species in tropical and sub-tropical environments, with growth forms that vary substantially between species such as the pencil roots in Avicennia, the prop or stilt roots of Rhizophora and the knee roots in Bruguiera. Here we investigate the role root and tree structures may play on the longterm development of intertidal morphology in mangrove-dominated environments. We use a one-dimensional Delft3D numerical simulation in conjunction with a simple model to determine that the dominant controls on the tidally-driven momentum balance are the frictional characteristics of the forest, which delay the propagation of the tide into the forest. Details of the vegetation at the seaward fringe along with sediment grain size determine the shape of the ensuing profile, with sparser vegetation and coarser grainsizes creating more linear profiles whereas denser vegetation and finer grainsizes generating convex intertidal profiles. Examples showing these different profile developments are provided from the Mekong Delta in Vietnam, which tends to a linear profile, and the Firth of Thames in New Zealand, which has a distinctive convex profile. Preliminary validation using current meter measurements from the Mekong Delta show that the currents diminish quickly between the mudflat seaward of the forest and the fringe, then remain fairly constant several hundred meters into the forest indicating that this linear profile has probably developed into an equilibrium shape. Understanding the forces that shape the development of the intertidal profile shape is critical to predicting the resilience of these sensitive intertidal areas to changes in inundation caused by sea level rise.

Longitudinal prediction and concurrent functioning of adolescent girls demonstrating various profiles of dating violence and victimization.

PubMed

Chiodo, Debbie; Crooks, Claire V; Wolfe, David A; McIsaac, Caroline; Hughes, Ray; Jaffe, Peter G

2012-08-01

Adolescent girls are involved in physical dating violence as both perpetrators and victims, and there are negative consequences associated with each of these behaviors. This article used a prospective design with 519 girls dating in grade 9 to predict profiles of dating violence in grade 11 based on relationships with families of origin (child maltreatment experiences, harsh parenting), and peers (harassment, delinquency, relational aggression). In addition, dating violence profiles were compared on numerous indices of adjustment (school connectedness, grades, self-efficacy and community connectedness) and maladjustment (suicide attempts, distress, delinquency, sexual behavior) for descriptive purposes. The most common profile was no dating violence (n = 367) followed by mutual violence (n = 81). Smaller numbers of girls reported victimization or perpetration only (ns = 39 and 32, respectively). Predicting grade 11 dating violence profile membership from grade 9 relationships was limited, although delinquency, parental rejection, and sexual harassment perpetration predicted membership to the mutually violent group, and delinquency predicted the perpetrator-only group. Compared to the non-violent group, the mutually violent girls in grade 11 had lower grades, poorer self-efficacy, and lower school connectedness and community involvement. Furthermore, they had higher rates of peer aggression and delinquency, were less likely to use condoms and were much more likely to have considered suicide. There were fewer differences among the profiles for girls involved with dating violence. In addition, the victims-only group reported higher rates of sexual intercourse, comparable to the mutually violent group and those involved in nonviolent relationships. Implications for prevention and intervention are highlighted.

MetabolitePredict: A de novo human metabolomics prediction system and its applications in rheumatoid arthritis.

PubMed

Wang, QuanQiu; Xu, Rong

2017-07-01

Human metabolomics has great potential in disease mechanism understanding, early diagnosis, and therapy. Existing metabolomics studies are often based on profiling patient biofluids and tissue samples and are difficult owing to the challenges of sample collection and data processing. Here, we report an alternative approach and developed a computation-based prediction system, MetabolitePredict, for disease metabolomics biomarker prediction. We applied MetabolitePredict to identify metabolite biomarkers and metabolite targeting therapies for rheumatoid arthritis (RA), a last-lasting complex disease with multiple genetic and environmental factors involved. MetabolitePredict is a de novo prediction system. It first constructs a disease-specific genetic profile using genes and pathways data associated with an input disease. It then constructs genetic profiles for a total of 259,170 chemicals/metabolites using known chemical genetics and human metabolomic data. MetabolitePredict prioritizes metabolites for a given disease based on the genetic profile similarities between disease and metabolites. We evaluated MetabolitePredict using 63 known RA-associated metabolites. MetabolitePredict found 24 of the 63 metabolites (recall: 0.38) and ranked them highly (mean ranking: top 4.13%, median ranking: top 1.10%, P-value: 5.08E-19). MetabolitePredict performed better than an existing metabolite prediction system, PROFANCY, in predicting RA-associated metabolites (PROFANCY: recall: 0.31, mean ranking: 20.91%, median ranking: 16.47%, P-value: 3.78E-7). Short-chain fatty acids (SCFAs), the abundant metabolites of gut microbiota in the fermentation of fiber, ranked highly (butyrate, 0.03%; acetate, 0.05%; propionate, 0.38%). Finally, we established MetabolitePredict's potential in novel metabolite targeting for disease treatment: MetabolitePredict ranked highly three known metabolite inhibitors for RA treatments (methotrexate:0.25%; leflunomide: 0.56%; sulfasalazine: 0.92%). MetabolitePredict is a generalizable disease metabolite prediction system. The only required input to the system is a disease name or a set of disease-associated genes. The web-based MetabolitePredict is available at:http://xulab. edu/MetabolitePredict. Copyright © 2017 Elsevier Inc. All rights reserved.

Analysis of temporal transcription expression profiles reveal links between protein function and developmental stages of Drosophila melanogaster.

PubMed

Wan, Cen; Lees, Jonathan G; Minneci, Federico; Orengo, Christine A; Jones, David T

2017-10-01

Accurate gene or protein function prediction is a key challenge in the post-genome era. Most current methods perform well on molecular function prediction, but struggle to provide useful annotations relating to biological process functions due to the limited power of sequence-based features in that functional domain. In this work, we systematically evaluate the predictive power of temporal transcription expression profiles for protein function prediction in Drosophila melanogaster. Our results show significantly better performance on predicting protein function when transcription expression profile-based features are integrated with sequence-derived features, compared with the sequence-derived features alone. We also observe that the combination of expression-based and sequence-based features leads to further improvement of accuracy on predicting all three domains of gene function. Based on the optimal feature combinations, we then propose a novel multi-classifier-based function prediction method for Drosophila melanogaster proteins, FFPred-fly+. Interpreting our machine learning models also allows us to identify some of the underlying links between biological processes and developmental stages of Drosophila melanogaster.

Moving Toward Integrating Gene Expression Profiling Into High-Throughput Testing: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in a Microarray Compendium

PubMed Central

Ryan, Natalia; Chorley, Brian; Tice, Raymond R.; Judson, Richard; Corton, J. Christopher

2016-01-01

Microarray profiling of chemical-induced effects is being increasingly used in medium- and high-throughput formats. Computational methods are described here to identify molecular targets from whole-genome microarray data using as an example the estrogen receptor α (ERα), often modulated by potential endocrine disrupting chemicals. ERα biomarker genes were identified by their consistent expression after exposure to 7 structurally diverse ERα agonists and 3 ERα antagonists in ERα-positive MCF-7 cells. Most of the biomarker genes were shown to be directly regulated by ERα as determined by ESR1 gene knockdown using siRNA as well as through chromatin immunoprecipitation coupled with DNA sequencing analysis of ERα-DNA interactions. The biomarker was evaluated as a predictive tool using the fold-change rank-based Running Fisher algorithm by comparison to annotated gene expression datasets from experiments using MCF-7 cells, including those evaluating the transcriptional effects of hormones and chemicals. Using 141 comparisons from chemical- and hormone-treated cells, the biomarker gave a balanced accuracy for prediction of ERα activation or suppression of 94% and 93%, respectively. The biomarker was able to correctly classify 18 out of 21 (86%) ER reference chemicals including “very weak” agonists. Importantly, the biomarker predictions accurately replicated predictions based on 18 in vitro high-throughput screening assays that queried different steps in ERα signaling. For 114 chemicals, the balanced accuracies were 95% and 98% for activation or suppression, respectively. These results demonstrate that the ERα gene expression biomarker can accurately identify ERα modulators in large collections of microarray data derived from MCF-7 cells. PMID:26865669

Hydrodynamic simulations of moonlet induced propellers and the size of Blériot

NASA Astrophysics Data System (ADS)

Seiss, Martin; Albers, Nicole; Sremcevic, Miodrag; Schmidt, Jürgen; Salo, Heikki; Hoffmann, Holger; Spahn, Frank

2016-10-01

Small moons embedded in Saturn's rings can cause S-shaped density structures in their close vicinity called propellers. These structures have been predicted on base of a combined model involving gravitational scattering of test particles (creating the structure) and diffusion (smearing out the structure, see Spahn and Sremčević (2000, Astron. Astrophys.) and Sremčević et al. (2002,MNRAS)). The propeller model was confirmed later by N-body simulations, which additionally show the appearance of moonlet wakes adjacent to the S-shaped gaps (Seiß et al., 2005, GRL; Lewis and Stewart, 2009, Astron. J.). It was a great success of the Cassini mission when propellers were detected in the data of the ISS (Tiscareno et al., 2006, Nature; Sremčević et al., 2007, Nature; Tiscareno et al., 2008, Astron. J. and 2010, ApJL) and UVIS (Baillié et al., 2013) instruments.Here we present isothermal hydrodynamic simulations of propellers as a further development of the original model (Spahn and Sremčević, 2000, Astron. Astrophys.) where gravitational scattering and diffusion had to be treated separately. We confirm the correctness of the predicted scaling laws for the radial and azimuthal extent of propellers and show that the analytical solution by Sremčević et al. (2002, MNRAS) can be fitted to the azimuthal profile. Furthermore, we show that this new approach is in a good agreement with N-body simulations performed with parameters suitable for the A-ring. Finally, we present simulation results of the giant propeller Blériot and fit them to optical depth profiles gathered by the UVIS experiment aboard of the spacecraft Cassini. The fits are consistent using 600 m for the Hill radius of the moonlet and 350 cm2/s for the kinematic shear viscosity.

Prediction of Human Pharmacokinetic Profile After Transdermal Drug Application Using Excised Human Skin.

PubMed

Yamamoto, Syunsuke; Karashima, Masatoshi; Arai, Yuta; Tohyama, Kimio; Amano, Nobuyuki

2017-09-01

Although several mathematical models have been reported for the estimation of human plasma concentration profiles of drug substances after dermal application, the successful cases that can predict human pharmacokinetic profiles are limited. Therefore, the aim of this study is to investigate the prediction of human plasma concentrations after dermal application using in vitro permeation parameters obtained from excised human skin. The in vitro skin permeability of 7 marketed drug products was evaluated. The plasma concentration-time profiles of the drug substances in humans after their dermal application were simulated using compartment models and the clinical pharmacokinetic parameters. The transdermal process was simulated using the in vitro skin permeation rate and lag time assuming a zero-order absorption. These simulated plasma concentration profiles were compared with the clinical data. The result revealed that the steady-state plasma concentration of diclofenac and the maximum concentrations of nicotine, bisoprolol, rivastigmine, and lidocaine after topical application were within 2-fold of the clinical data. Furthermore, the simulated concentration profiles of bisoprolol, nicotine, and rivastigmine reproduced the decrease in absorption due to drug depletion from the formulation. In conclusion, this simple compartment model using in vitro human skin permeation parameters as zero-order absorption predicted the human plasma concentrations accurately. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Impacts of fragmented accretion streams onto classical T Tauri stars: UV and X-ray emission lines

NASA Astrophysics Data System (ADS)

Colombo, S.; Orlando, S.; Peres, G.; Argiroffi, C.; Reale, F.

2016-10-01

Context. The accretion process in classical T Tauri stars (CTTSs) can be studied through the analysis of some UV and X-ray emission lines which trace hot gas flows and act as diagnostics of the post-shock downfalling plasma. In the UV-band, where higher spectral resolution is available, these lines are characterized by rather complex profiles whose origin is still not clear. Aims: We investigate the origin of UV and X-ray emission at impact regions of density structured (fragmented) accretion streams. We study if and how the stream fragmentation and the resulting structure of the post-shock region determine the observed profiles of UV and X-ray emission lines. Methods: We modeled the impact of an accretion stream consisting of a series of dense blobs onto the chromosphere of a CTTS through two-dimensional (2D) magnetohydrodynamic (MHD) simulations. We explored different levels of stream fragmentation and accretion rates. From the model results, we synthesize C IV (1550 Å) and O VIII (18.97 Å) line profiles. Results: The impacts of accreting blobs onto the stellar chromosphere produce reverse shocks propagating through the blobs and shocked upflows. These upflows, in turn, hit and shock the subsequent downfalling fragments. As a result, several plasma components differing for the downfalling velocity, density, and temperature are present altoghether. The profiles of C IV doublet are characterized by two main components: one narrow and redshifted to speed ≈ 50 km s-1 and the other broader and consisting of subcomponents with redshift to speed in the range 200-400 km s-1. The profiles of O VIII lines appear more symmetric than C IV and are redshifted to speed ≈ 150 km s-1. Conclusions: Our model predicts profiles of C IV line remarkably similar to those observed and explains their origin in a natural way as due to stream fragmentation. Movies are available at http://www.aanda.org

Species-Specific Predictive Signatures of Developmental Toxicity Using the ToxCast Chemical Library

EPA Science Inventory

EPA’s ToxCastTM project is profiling the in vitro bioactivity of chemicals to generate predictive signatures that correlate with observed in vivo toxicity. In vitro profiling methods from ToxCast data consist of over 600 high-throughput screening (HTS) and high-content screening ...

Models of Mars' atmosphere (1974)

NASA Technical Reports Server (NTRS)

1974-01-01

Atmospheric models for support of design and mission planning of space vehicles that are to orbit the planet Mars, enter its atmosphere, or land on the surface are presented. Quantitative data for the Martian atmosphere were obtained from Earth-base observations and from spacecraft that have orbited Mars or passed within several planetary radii. These data were used in conjunction with existing theories of planetary atmospheres to predict other characteristics of the Martian atmosphere. Earth-based observations provided information on the composition, temperature, and optical properties of Mars with rather coarse spatial resolution, whereas spacecraft measurements yielded data on composition, temperature, pressure, density, and atmospheric structure with moderately good spatial resolution. The models provide the temperature, pressure, and density profiles required to perform basic aerodynamic analyses. The profiles are supplemented by computed values of viscosity, specific heat, and speed of sound.

Continuum Reverberation Mapping of AGN Accretion Disks

NASA Astrophysics Data System (ADS)

Fausnaugh, Michael M.; Peterson, Bradley M.; Starkey, David A.; Horne, Keith; AGN Storm Collaboration

2017-12-01

We show recent detections of inter-band continuum lags in three AGN (NGC 5548, NGC 2617, and MCG+08-11-011), which provide new constraints on the temperature profiles and absolute sizes of the accretion disks. We find lags larger than would be predicted for standard geometrically thin, optically thick accretion disks by factors of 2.3 to 3.3. For NGC 5548, the data span UV through optical/near-IR wavelengths, and we are able to discern a steeper temperature profile than the T˜ R^{-3/4} expected for a standard thin disk . Using a physical model, we are also able to estimate the inclinations of the disks for two objects. These results are similar to those found from gravitational microlensing of strongly lensed quasars, and provide a complementary approach for investigating the accretion disk structure in local, low luminsoity AGN.

Electric Double-Layer Structure in Primitive Model Electrolytes. Comparing Molecular Dynamics with Local-Density Approximations

DOE PAGES

Giera, Brian; Lawrence Livermore National Lab.; Henson, Neil; ...

2015-02-27

We evaluate the accuracy of local-density approximations (LDAs) using explicit molecular dynamics simulations of binary electrolytes comprised of equisized ions in an implicit solvent. The Bikerman LDA, which considers ions to occupy a lattice, poorly captures excluded volume interactions between primitive model ions. Instead, LDAs based on the Carnahan–Starling (CS) hard-sphere equation of state capture simulated values of ideal and excess chemical potential profiles extremely well, as is the relationship between surface charge density and electrostatic potential. Excellent agreement between the EDL capacitances predicted by CS-LDAs and computed in molecular simulations is found even in systems where ion correlations drivemore » strong density and free charge oscillations within the EDL, despite the inability of LDAs to capture the oscillations in the detailed EDL profiles.« less

Prediction of the retention of s-triazines in reversed-phase high-performance liquid chromatography under linear gradient-elution conditions.

PubMed

D'Archivio, Angelo Antonio; Maggi, Maria Anna; Ruggieri, Fabrizio

2014-08-01

In this paper, a multilayer artificial neural network is used to model simultaneously the effect of solute structure and eluent concentration profile on the retention of s-triazines in reversed-phase high-performance liquid chromatography under linear gradient elution. The retention data of 24 triazines, including common herbicides and their metabolites, are collected under 13 different elution modes, covering the following experimental domain: starting acetonitrile volume fraction ranging between 40 and 60% and gradient slope ranging between 0 and 1% acetonitrile/min. The gradient parameters together with five selected molecular descriptors, identified by quantitative structure-retention relationship modelling applied to individual separation conditions, are the network inputs. Predictive performance of this model is evaluated on six external triazines and four unseen separation conditions. For comparison, retention of triazines is modelled by both quantitative structure-retention relationships and response surface methodology, which describe separately the effect of molecular structure and gradient parameters on the retention. Although applied to a wider variable domain, the network provides a performance comparable to that of the above "local" models and retention times of triazines are modelled with accuracy generally better than 7%. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

EPA Project Updates: DSSTox and ToxCast Generating New ...

EPA Pesticide Factsheets

EPAs National Center for Computational Toxicology is building capabilities to support a new paradigm for toxicity screening and prediction. The DSSTox project is improving public access to quality structure-annotated chemical toxicity information in less summarized forms than traditionally employed in SAR modeling, and in ways that facilitate data-mining, and data read-across. The DSSTox Structure-Browser, launched in September 2007, provides structure searchability across all published DSSTox toxicity-related inventory, and is enabling linkages between previously isolated toxicity data resources. As of early March 2008, the public DSSTox inventory as been integrated into PubChem, allowing a user to take full advantage of PubChem structure-activity and bioassay clustering features. The most recent DSSTox version of Carcinogenic Potency Database file (CPDBAS) illustrates ways in which various summary definitions of carcinogenic activity can be employed in modeling and data mining. Phase I of the ToxCast project is generating high-throughput screening data from several hundred biochemical and cell-based assays for a set of 320 chemicals, mostly pesticide actives, with rich toxicology profiles. Incorporating and expanding traditional SAR Concepts into this new high-throughput and data-rich would pose conceptual and practical challenges, but also holds great promise for improving predictive capabilities. EPA's National Center for Computational Toxicology is bu

Charge Structure and Counterion Distribution in Hexagonal DNA Liquid Crystal

PubMed Central

Dai, Liang; Mu, Yuguang; Nordenskiöld, Lars; Lapp, Alain; van der Maarel, Johan R. C.

2007-01-01

A hexagonal liquid crystal of DNA fragments (double-stranded, 150 basepairs) with tetramethylammonium (TMA) counterions was investigated with small angle neutron scattering (SANS). We obtained the structure factors pertaining to the DNA and counterion density correlations with contrast matching in the water. Molecular dynamics (MD) computer simulation of a hexagonal assembly of nine DNA molecules showed that the inter-DNA distance fluctuates with a correlation time around 2 ns and a standard deviation of 8.5% of the interaxial spacing. The MD simulation also showed a minimal effect of the fluctuations in inter-DNA distance on the radial counterion density profile and significant penetration of the grooves by TMA. The radial density profile of the counterions was also obtained from a Monte Carlo (MC) computer simulation of a hexagonal array of charged rods with fixed interaxial spacing. Strong ordering of the counterions between the DNA molecules and the absence of charge fluctuations at longer wavelengths was shown by the SANS number and charge structure factors. The DNA-counterion and counterion structure factors are interpreted with the correlation functions derived from the Poisson-Boltzmann equation, MD, and MC simulation. Best agreement is observed between the experimental structure factors and the prediction based on the Poisson-Boltzmann equation and/or MC simulation. The SANS results show that TMA is too large to penetrate the grooves to a significant extent, in contrast to what is shown by MD simulation. PMID:17098791

Analysis of energy dispersive x-ray diffraction profiles for material identification, imaging and system control

NASA Astrophysics Data System (ADS)

Cook, Emily Jane

2008-12-01

This thesis presents the analysis of low angle X-ray scatter measurements taken with an energy dispersive system for substance identification, imaging and system control. Diffraction measurements were made on illicit drugs, which have pseudo- crystalline structures and thus produce diffraction patterns comprising a se ries of sharp peaks. Though the diffraction profiles of each drug are visually characteristic, automated detection systems require a substance identification algorithm, and multivariate analysis was selected as suitable. The software was trained with measured diffraction data from 60 samples covering 7 illicit drugs and 5 common cutting agents, collected with a range of statistical qual ities and used to predict the content of 7 unknown samples. In all cases the constituents were identified correctly and the contents predicted to within 15%. Soft tissues exhibit broad peaks in their diffraction patterns. Diffraction data were collected from formalin fixed breast tissue samples and used to gen erate images. Maximum contrast between healthy and suspicious regions was achieved using momentum transfer windows 1.04-1.10 and 1.84-1.90 nm_1. The resulting images had an average contrast of 24.6% and 38.9% compared to the corresponding transmission X-ray images (18.3%). The data was used to simulate the feedback for an adaptive imaging system and the ratio of the aforementioned momentum transfer regions found to be an excellent pa rameter. Investigation into the effects of formalin fixation on human breast tissue and animal tissue equivalents indicated that fixation in standard 10% buffered formalin does not alter the diffraction profiles of tissue in the mo mentum transfer regions examined, though 100% unbuffered formalin affects the profile of porcine muscle tissue (a substitute for glandular and tumourous tissue), though fat is unaffected.

The structure of clusters of galaxies

NASA Astrophysics Data System (ADS)

Fox, David Charles

When infalling gas is accreted onto a cluster of galaxies, its kinetic energy is converted to thermal energy in a shock, heating the ions. Using a self-similar spherical model, we calculate the collisional heating of the electrons by the ions, and predict the electron and ion temperature profiles. While there are significant differences between the two, they occur at radii larger than currently observable, and too large to explain observed X-ray temperature declines in clusters. Numerical simulations by Navarro, Frenk, & White (1996) predict a universal dark matter density profile. We calculate the expected number of multiply-imaged background galaxies in the Hubble Deep Field due to foreground groups and clusters with this profile. Such groups are up to 1000 times less efficient at lensing than the standard singular isothermal spheres. However, with either profile, the expected number of galaxies lensed by groups in the Hubble Deep Field is at most one, consistent with the lack of clearly identified group lenses. X-ray and Sunyaev-Zel'dovich (SZ) effect observations can be combined to determine the distance to clusters of galaxies, provided the clusters are spherical. When applied to an aspherical cluster, this method gives an incorrect distance. We demonstrate a method for inferring the three-dimensional shape of a cluster and its correct distance from X-ray, SZ effect, and weak gravitational lensing observations, under the assumption of hydrostatic equilibrium. We apply this method to simple, analytic models of clusters, and to a numerically simulated cluster. Using artificial observations based on current X-ray and SZ effect instruments, we recover the true distance without detectable bias and with uncertainties of 4 percent.

Multi-Dimensional, Non-Pyrolyzing Ablation Test Problems

NASA Technical Reports Server (NTRS)

Risch, Tim; Kostyk, Chris

2016-01-01

Non-pyrolyzingcarbonaceous materials represent a class of candidate material for hypersonic vehicle components providing both structural and thermal protection system capabilities. Two problems relevant to this technology are presented. The first considers the one-dimensional ablation of a carbon material subject to convective heating. The second considers two-dimensional conduction in a rectangular block subject to radiative heating. Surface thermochemistry for both problems includes finite-rate surface kinetics at low temperatures, diffusion limited ablation at intermediate temperatures, and vaporization at high temperatures. The first problem requires the solution of both the steady-state thermal profile with respect to the ablating surface and the transient thermal history for a one-dimensional ablating planar slab with temperature-dependent material properties. The slab front face is convectively heated and also reradiates to a room temperature environment. The back face is adiabatic. The steady-state temperature profile and steady-state mass loss rate should be predicted. Time-dependent front and back face temperature, surface recession and recession rate along with the final temperature profile should be predicted for the time-dependent solution. The second problem requires the solution for the transient temperature history for an ablating, two-dimensional rectangular solid with anisotropic, temperature-dependent thermal properties. The front face is radiatively heated, convectively cooled, and also reradiates to a room temperature environment. The back face and sidewalls are adiabatic. The solution should include the following 9 items: final surface recession profile, time-dependent temperature history of both the front face and back face at both the centerline and sidewall, as well as the time-dependent surface recession and recession rate on the front face at both the centerline and sidewall. The results of the problems from all submitters will be collected, summarized, and presented at a later conference.

Soil-covered strategy for ecological restoration alters the bacterial community structure and predictive energy metabolic functions in mine tailings profiles.

PubMed

Li, Yang; Sun, Qingye; Zhan, Jing; Yang, Yang; Wang, Dan

2017-03-01

Native soil amendment has been widely used to stabilize mine tailings and speed up the development of soil biogeochemical functions before revegetation; however, it remains poorly understood about the response of microbial communities to ecological restoration of mine tailings with soil-covered strategy. In this study, microbial communities along a 60-cm profile were investigated in mine tailings during ecological restoration of two revegetation strategies (directly revegetation and native soil covered) with different plant species. The mine tailings were covered by native soils as thick as 40 cm for more than 10 years, and the total nitrogen, total organic carbon, water content, and heavy metal (Fe, Cu, and Zn) contents in the 0-40 cm intervals of profiles were changed. In addition, increased microbial diversity and changed microbial community structure were also found in the 10-40 cm intervals of profiles in soil-covered area. Soil-covered strategy rather than plant species and soil depth was the main factor influencing the bacterial community, which explained the largest portion (29.96%) of the observed variation. Compared directly to revegetation, soil-covered strategy exhibited the higher relative abundance of Acidobacteria and Deltaproteobacteria and the lower relative abundance of Bacteroidetes, Gemmatimonadetes, Betaproteobacteria, and Gammaproteobacteria. PICRUSt analysis further demonstrated that soil-covered caused energy metabolic functional changes in carbon, nitrogen, and sulfur metabolism. Given all these, the soil-covered strategy may be used to fast-track the establishment of native microbial communities and is conducive to the rehabilitation of biogeochemical processes for establishing native plant species.

Comparison of realistic and idealized breathing patterns in computational models of airflow and vapor dosimetry in the rodent upper respiratory tract

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

Colby, Sean M.; Kabilan, Senthil; Jacob, Richard E.

Abstract Context: Computational fluid dynamics (CFD) simulations of airflows coupled with physiologically based pharmacokinetic (PBPK) modeling of respiratory tissue doses of airborne materials have traditionally used either steady-state inhalation or a sinusoidal approximation of the breathing cycle for airflow simulations despite their differences from normal breathing patterns. Objective: Evaluate the impact of realistic breathing patterns, including sniffing, on predicted nasal tissue concentrations of a reactive vapor that targets the nose in rats as a case study. Materials and methods: Whole-body plethysmography measurements from a free-breathing rat were used to produce profiles of normal breathing, sniffing and combinations of both asmore » flow inputs to CFD/PBPK simulations of acetaldehyde exposure. Results: For the normal measured ventilation profile, modest reductions in time- and tissue depth-dependent areas under the curve (AUC) acetaldehyde concentrations were predicted in the wet squamous, respiratory and transitional epithelium along the main airflow path, while corresponding increases were predicted in the olfactory epithelium, especially the most distal regions of the ethmoid turbinates, versus the idealized profile. The higher amplitude/frequency sniffing profile produced greater AUC increases over the idealized profile in the olfactory epithelium, especially in the posterior region. Conclusions: The differences in tissue AUCs at known lesion-forming regions for acetaldehyde between normal and idealized profiles were minimal, suggesting that sinusoidal profiles may be used for this chemical and exposure concentration. However, depending upon the chemical, exposure system and concentration and the time spent sniffing, the use of realistic breathing profiles, including sniffing, could become an important modulator for local tissue dose predictions.« less

Prediction of incidence and stability of alcohol use disorders by latent internalizing psychopathology risk profiles in adolescence and young adulthood.

PubMed

Behrendt, Silke; Bühringer, Gerhard; Höfler, Michael; Lieb, Roselind; Beesdo-Baum, Katja

2017-10-01

Comorbid internalizing mental disorders in alcohol use disorders (AUD) can be understood as putative independent risk factors for AUD or as expressions of underlying shared psychopathology vulnerabilities. However, it remains unclear whether: 1) specific latent internalizing psychopathology risk-profiles predict AUD-incidence and 2) specific latent internalizing comorbidity-profiles in AUD predict AUD-stability. To investigate baseline latent internalizing psychopathology risk profiles as predictors of subsequent AUD-incidence and -stability in adolescents and young adults. Data from the prospective-longitudinal EDSP study (baseline age 14-24 years) were used. The study-design included up to three follow-up assessments in up to ten years. DSM-IV mental disorders were assessed with the DIA-X/M-CIDI. To investigate risk-profiles and their associations with AUD-outcomes, latent class analysis with auxiliary outcome variables was applied. AUD-incidence: a 4-class model (N=1683) was identified (classes: normative-male [45.9%], normative-female [44.2%], internalizing [5.3%], nicotine dependence [4.5%]). Compared to the normative-female class, all other classes were associated with a higher risk of subsequent incident alcohol dependence (p<0.05). AUD-stability: a 3-class model (N=1940) was identified with only one class (11.6%) with high probabilities for baseline AUD. This class was further characterized by elevated substance use disorder (SUD) probabilities and predicted any subsequent AUD (OR 8.5, 95% CI 5.4-13.3). An internalizing vulnerability may constitute a pathway to AUD incidence in adolescence and young adulthood. In contrast, no indication for a role of internalizing comorbidity profiles in AUD-stability was found, which may indicate a limited importance of such profiles - in contrast to SUD-related profiles - in AUD stability. Copyright © 2017 Elsevier B.V. All rights reserved.

Modeling Structure and Dynamics of Protein Complexes with SAXS Profiles

PubMed Central

Schneidman-Duhovny, Dina; Hammel, Michal

2018-01-01

Small-angle X-ray scattering (SAXS) is an increasingly common and useful technique for structural characterization of molecules in solution. A SAXS experiment determines the scattering intensity of a molecule as a function of spatial frequency, termed SAXS profile. SAXS profiles can be utilized in a variety of molecular modeling applications, such as comparing solution and crystal structures, structural characterization of flexible proteins, assembly of multi-protein complexes, and modeling of missing regions in the high-resolution structure. Here, we describe protocols for modeling atomic structures based on SAXS profiles. The first protocol is for comparing solution and crystal structures including modeling of missing regions and determination of the oligomeric state. The second protocol performs multi-state modeling by finding a set of conformations and their weights that fit the SAXS profile starting from a single-input structure. The third protocol is for protein-protein docking based on the SAXS profile of the complex. We describe the underlying software, followed by demonstrating their application on interleukin 33 (IL33) with its primary receptor ST2 and DNA ligase IV-XRCC4 complex. PMID:29605933

NanoESI-MS-based lipidomics to discriminate between cultivars, cultivation ages, and parts of Panax ginseng.

PubMed

Kim, So-Hyun; Shin, Yoo-Soo; Choi, Hyung-Kyoon

2016-03-01

Korean ginseng (Panax ginseng C.A. Meyer) is one of the most popular medicinal herbs used in Asia, including Korea and China. In the present study lipid profiling of two officially registered cultivars (P. ginseng 'Chunpoong' and P. ginseng 'Yunpoong') was performed at different cultivation ages (5 and 6 years) and on different parts (tap roots, lateral roots, and rhizomes) using nano-electrospray ionization-mass spectrometry (nanoESI-MS). In total, 30 compounds including galactolipids, phospholipids, triacylglycerols, and ginsenosides were identified. Among them, triacylglycerol 54:6 (18:2/18:2/18:2), phosphatidylglycerol 34:3 (16:0/18:3), monogalactosyldiacylglycerol 36:4 (18:2/18:2), phosphatidic acid species 36:4 (18:2/18:2), and 34:1 (16:0/18:1) were selected as biomarkers to discriminate cultivars, cultivation ages, and parts. In addition, an unknown P. ginseng sample was successfully predicted by applying validated partial least squares projection to latent structures regression models. This is the first study regarding the identification of intact lipid species from P. ginseng and to predict cultivars, cultivation ages, and parts of P. ginseng using nanoESI-MS-based lipidomic profiling with a multivariate statistical analysis.

ARMOUR - A Rice miRNA: mRNA Interaction Resource.

PubMed

Sanan-Mishra, Neeti; Tripathi, Anita; Goswami, Kavita; Shukla, Rohit N; Vasudevan, Madavan; Goswami, Hitesh

2018-01-01

ARMOUR was developed as A Rice miRNA:mRNA interaction resource. This informative and interactive database includes the experimentally validated expression profiles of miRNAs under different developmental and abiotic stress conditions across seven Indian rice cultivars. This comprehensive database covers 689 known and 1664 predicted novel miRNAs and their expression profiles in more than 38 different tissues or conditions along with their predicted/known target transcripts. The understanding of miRNA:mRNA interactome in regulation of functional cellular machinery is supported by the sequence information of the mature and hairpin structures. ARMOUR provides flexibility to users in querying the database using multiple ways like known gene identifiers, gene ontology identifiers, KEGG identifiers and also allows on the fly fold change analysis and sequence search query with inbuilt BLAST algorithm. ARMOUR database provides a cohesive platform for novel and mature miRNAs and their expression in different experimental conditions and allows searching for their interacting mRNA targets, GO annotation and their involvement in various biological pathways. The ARMOUR database includes a provision for adding more experimental data from users, with an aim to develop it as a platform for sharing and comparing experimental data contributed by research groups working on rice.

Numerical Study for a Large-Volume Droplet on the Dual-Rough Surface: Apparent Contact Angle, Contact Angle Hysteresis, and Transition Barrier.

PubMed

Dong, Jian; Jin, Yanli; Dong, He; Liu, Jiawei; Ye, Senbin

2018-06-26

The profile, apparent contact angle (ACA), contact angle hysteresis (CAH), and wetting state transmission energy barrier (WSTEB) are important static and dynamic properties of a large-volume droplet on the hierarchical surface. Understanding them can provide us with important insights into functional surfaces and promote the application in corresponding areas. In this paper, we establish three theoretical models (models 1-3) and the corresponding numerical methods, which were obtained by the free energy minimization and the nonlinear optimization algorithm, to predict the profile, ACA, CAH, and WSTEB of a large-volume droplet on the horizontal regular dual-rough surface. In consideration of the gravity, the energy barrier on the contact circle, the dual heterogeneous structures and their roughness on the surface, the models are more universal and accurate than the previous models. It showed that the predictions of the models were in good agreement with the results from the experiment or literature. The models are promising to become novel design approaches of functional surfaces, which are frequently applied in microfluidic chips, water self-catchment system, and dropwise condensation heat transfer system.

Quantitative radiomic profiling of glioblastoma represents transcriptomic expression.

PubMed

Kong, Doo-Sik; Kim, Junhyung; Ryu, Gyuha; You, Hye-Jin; Sung, Joon Kyung; Han, Yong Hee; Shin, Hye-Mi; Lee, In-Hee; Kim, Sung-Tae; Park, Chul-Kee; Choi, Seung Hong; Choi, Jeong Won; Seol, Ho Jun; Lee, Jung-Il; Nam, Do-Hyun

2018-01-19

Quantitative imaging biomarkers have increasingly emerged in the field of research utilizing available imaging modalities. We aimed to identify good surrogate radiomic features that can represent genetic changes of tumors, thereby establishing noninvasive means for predicting treatment outcome. From May 2012 to June 2014, we retrospectively identified 65 patients with treatment-naïve glioblastoma with available clinical information from the Samsung Medical Center data registry. Preoperative MR imaging data were obtained for all 65 patients with primary glioblastoma. A total of 82 imaging features including first-order statistics, volume, and size features, were semi-automatically extracted from structural and physiologic images such as apparent diffusion coefficient and perfusion images. Using commercially available software, NordicICE, we performed quantitative imaging analysis and collected the dataset composed of radiophenotypic parameters. Unsupervised clustering methods revealed that the radiophenotypic dataset was composed of three clusters. Each cluster represented a distinct molecular classification of glioblastoma; classical type, proneural and neural types, and mesenchymal type. These clusters also reflected differential clinical outcomes. We found that extracted imaging signatures does not represent copy number variation and somatic mutation. Quantitative radiomic features provide a potential evidence to predict molecular phenotype and treatment outcome. Radiomic profiles represents transcriptomic phenotypes more well.

Pre-clinical cognitive phenotypes for Alzheimer disease: a latent profile approach.

PubMed

Hayden, Kathleen M; Kuchibhatla, Maragatha; Romero, Heather R; Plassman, Brenda L; Burke, James R; Browndyke, Jeffrey N; Welsh-Bohmer, Kathleen A

2014-11-01

Cognitive profiles for pre-clinical Alzheimer disease (AD) can be used to identify groups of individuals at risk for disease and better characterize pre-clinical disease. Profiles or patterns of performance as pre-clinical phenotypes may be more useful than individual test scores or measures of global decline. To evaluate patterns of cognitive performance in cognitively normal individuals to derive latent profiles associated with later onset of disease using a combination of factor analysis and latent profile analysis. The National Alzheimer Coordinating Centers collect data, including a battery of neuropsychological tests, from participants at 29 National Institute on Aging-funded Alzheimer Disease Centers across the United States. Prior factor analyses of this battery demonstrated a four-factor structure comprising memory, attention, language, and executive function. Factor scores from these analyses were used in a latent profile approach to characterize cognition among a group of cognitively normal participants (N = 3,911). Associations between latent profiles and disease outcomes an average of 3 years later were evaluated with multinomial regression models. Similar analyses were used to determine predictors of profile membership. Four groups were identified; each with distinct characteristics and significantly associated with later disease outcomes. Two groups were significantly associated with development of cognitive impairment. In post hoc analyses, both the Trail Making Test Part B, and a contrast score (Delayed Recall - Trails B), significantly predicted group membership and later cognitive impairment. Latent profile analysis is a useful method to evaluate patterns of cognition in large samples for the identification of preclinical AD phenotypes; comparable results, however, can be achieved with very sensitive tests and contrast scores. Copyright © 2014 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

The effects of plant extracts on microbial community structure in a rumen-simulating continuous-culture system as revealed by molecular profiling.

PubMed

Ferme, D; Banjac, M; Calsamiglia, S; Busquet, M; Kamel, C; Avgustin, G

2004-01-01

An in vitro study in dual-flow continuous-culture fermentors was conducted with two different concentrations of monensin, cinnamaldehyde or garlic extract added to 1:1 forage-to-concentrate diet in order to determine their effects on selected rumen bacterial populations. Samples were subjected to total DNA extraction, restriction analysis of PCR amplified parts of 16S rRNA genes (ARDRA) and subsequent analysis of the restriction profiles by lab-on-chip technology with the Agilent's Bioanalyser 2100. Eub338-BacPre primer pair was used to select for the bacteria from the genera Bacteroides, Porphyromonas and Prevotella, especially the latter representing the dominant Gram-negative bacterial population in the rumen. Preliminary results of HaeIII restriction analysis show that the effects of monensin, cinnamaldehyde and garlic extract on the BacPre targeted ruminal bacteria are somewhat different in regard to targeted populations and to the nature of the effect. Garlic extract was found to trigger the most intensive changes in the structure of the BacPre targeted population. Comparison of the in silico restriction analysis of BacPre sequences deposited in different DNA databanks and of the results of performed amplified ribosomal DNA restriction analysis showed differences between the predicted and obtained HaeIII restriction profiles, and suggested the presence of novel, still unknown Prevotella populations in studied samples.

Modeling and measurements of XRD spectra of extended solids under high pressure

NASA Astrophysics Data System (ADS)

Batyrev, I. G.; Coleman, S. P.; Stavrou, E.; Zaug, J. M.; Ciezak-Jenkins, J. A.

2017-06-01

We present results of evolutionary simulations based on density functional calculations of various extended solids: N-Si and N-H using variable and fixed concentration methods of USPEX. Predicted from the evolutionary simulations structures were analyzed in terms of thermo-dynamical stability and agreement with experimental X-ray diffraction spectra. Stability of the predicted system was estimated from convex-hull plots. X-ray diffraction spectra were calculated using a virtual diffraction algorithm which computes kinematic diffraction intensity in three-dimensional reciprocal space before being reduced to a two-theta line profile. Calculations of thousands of XRD spectra were used to search for a structure of extended solids at certain pressures with best fits to experimental data according to experimental XRD peak position, peak intensity and theoretically calculated enthalpy. Comparison of Raman and IR spectra calculated for best fitted structures with available experimental data shows reasonable agreement for certain vibration modes. Part of this work was performed by LLNL, Contract DE-AC52-07NA27344. We thank the Joint DoD / DOE Munitions Technology Development Program, the HE C-II research program at LLNL and Advanced Light Source, supported by BES DOE, Contract No. DE-AC02-05CH112.

Modeling adaptive kernels from probabilistic phylogenetic trees.

PubMed

Nicotra, Luca; Micheli, Alessio

2009-01-01

Modeling phylogenetic interactions is an open issue in many computational biology problems. In the context of gene function prediction we introduce a class of kernels for structured data leveraging on a hierarchical probabilistic modeling of phylogeny among species. We derive three kernels belonging to this setting: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel. The new kernels are used in the context of support vector machine learning. The kernels adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution using as observed data phylogenetic profiles encoding the presence or absence of specific genes in a set of fully sequenced genomes. We report results obtained in the prediction of the functional class of the proteins of the budding yeast Saccharomyces cerevisae which favorably compare to a standard vector based kernel and to a non-adaptive tree kernel function. A further comparative analysis is performed in order to assess the impact of the different components of the proposed approach. We show that the key features of the proposed kernels are the adaptivity to the input domain and the ability to deal with structured data interpreted through a graphical model representation.

p.Arg82Leu von Hippel-Lindau (VHL) Gene Mutation among Three Members of a Family with Familial Bilateral Pheochromocytoma in India: Molecular Analysis and In Silico Characterization

PubMed Central

John, Anulekha Mary; C, George Priya Doss; Ebenazer, Andrew; Seshadri, Mandalam Subramaniam; Nair, Aravindan; Rajaratnam, Simon; Pai, Rekha

2013-01-01

Various missense mutations in the VHL gene have been reported among patients with familial bilateral pheochromocytoma. However, the p.Arg82Leu mutation in the VHL gene described here among patients with familial bilateral pheochromocytoma, has never been reported previously in a germline configuration. Interestingly, long-term follow-up of these patients indicated that the mutation might have had little impact on the normal function of the VHL gene, since all of them have remained asymptomatic. We further attempted to correlate this information with the results obtained by in silico analysis of this mutation using SIFT, PhD-SNP SVM profile, MutPred, PolyPhen2, and SNPs&GO prediction tools. To gain, new mechanistic insight into the structural effect, we mapped the mutation on to 3D structure (PDB ID 1LM8). Further, we analyzed the structural level changes in time scale level with respect to native and mutant protein complexes by using 12 ns molecular dynamics simulation method. Though these methods predict the mutation to have a pathogenic potential, it remains to be seen if these patients will eventually develop symptomatic disease. PMID:23626751

Seismic behavior of breakwaters on complex ground by numerical tests: Liquefaction and post liquefaction ground settlements

NASA Astrophysics Data System (ADS)

Gu, Linlin; Zhang, Feng; Bao, Xiaohua; Shi, Zhenming; Ye, Guanlin; Ling, Xianzhang

2018-04-01

A large number of breakwaters have been constructed along coasts to protect humans and infrastructures from tsunamis. There is a risk that foundation soils of these structures may liquefy, or partially liquefy during the earthquake preceding a tsunami, which would greatly reduce the structures' capacity to resist the tsunami. It is necessary to consider not only the soil's liquefaction behavior due to earthquake motions but also its post-liquefaction behavior because this behavior will affect the breakwater's capacity to resist an incoming tsunami. In this study, numerical tests based on a sophisticated constitutive model and a soil-water coupled finite element method are used to predict the mechanical behavior of breakwaters and the surrounding soils. Two real breakwaters subjected to two different seismic excitations are examined through numerical simulation. The simulation results show that, earthquakes affect not only the immediate behavior of breakwaters and the surrounding soils but also their long-term settlements due to post-earthquake consolidation. A soil profile with thick clayey layers beneath liquefied soil is more vulnerable to tsunami than a soil profile with only sandy layers. Therefore, quantitatively evaluating the seismic behavior of breakwaters and surrounding soils is important for the design of breakwater structures to resist tsunamis.

Structure-Activity Relationships Based on 3D-QSAR CoMFA/CoMSIA and Design of Aryloxypropanol-Amine Agonists with Selectivity for the Human β3-Adrenergic Receptor and Anti-Obesity and Anti-Diabetic Profiles.

PubMed

Lorca, Marcos; Morales-Verdejo, Cesar; Vásquez-Velásquez, David; Andrades-Lagos, Juan; Campanini-Salinas, Javier; Soto-Delgado, Jorge; Recabarren-Gajardo, Gonzalo; Mella, Jaime

2018-05-16

The wide tissue distribution of the adrenergic β3 receptor makes it a potential target for the treatment of multiple pathologies such as diabetes, obesity, depression, overactive bladder (OAB), and cancer. Currently, there is only one drug on the market, mirabegron, approved for the treatment of OAB. In the present study, we have carried out an extensive structure-activity relationship analysis of a series of 41 aryloxypropanolamine compounds based on three-dimensional quantitative structure-activity relationship (3D-QSAR) techniques. This is the first combined comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) study in a series of selective aryloxypropanolamines displaying anti-diabetes and anti-obesity pharmacological profiles. The best CoMFA and CoMSIA models presented values of r ² ncv = 0.993 and 0.984 and values of r ² test = 0.865 and 0.918, respectively. The results obtained were subjected to extensive external validation ( q ², r ², r ² m , etc.) and a final series of compounds was designed and their biological activity was predicted (best pEC 50 = 8.561).

Cosmology and astrophysics from relaxed galaxy clusters - V. Consistency with cold dark matter structure formation

NASA Astrophysics Data System (ADS)

Mantz, A. B.; Allen, S. W.; Morris, R. G.

2016-10-01

This is the fifth in a series of papers studying the astrophysics and cosmology of massive, dynamically relaxed galaxy clusters. Our sample comprises 40 clusters identified as being dynamically relaxed and hot in Papers I and II of this series. Here we use constraints on cluster mass profiles from X-ray data to test some of the basic predictions of cosmological structure formation in the cold dark matter (CDM) paradigm. We present constraints on the concentration-mass relation for massive clusters, finding a power-law mass dependence with a slope of κm = -0.16 ± 0.07, in agreement with CDM predictions. For this relaxed sample, the relation is consistent with a constant as a function of redshift (power-law slope with 1 + z of κζ = -0.17 ± 0.26), with an intrinsic scatter of σln c = 0.16 ± 0.03. We investigate the shape of cluster mass profiles over the radial range probed by the data (typically ˜50 kpc-1 Mpc), and test for departures from the simple Navarro-Frenk-White (NFW) form, for which the logarithmic slope of the density profile tends to -1 at small radii. Specifically, we consider as alternatives the generalized NFW (GNFW) and Einasto parametrizations. For the GNFW model, we find an average value of (minus) the logarithmic inner slope of β = 1.02 ± 0.08, with an intrinsic scatter of σβ = 0.22 ± 0.07, while in the Einasto case we constrain the average shape parameter to be α = 0.29 ± 0.04 with an intrinsic scatter of σα = 0.12 ± 0.04. Our results are thus consistent with the simple NFW model on average, but we clearly detect the presence of intrinsic, cluster-to-cluster scatter about the average.

Cosmology and astrophysics from relaxed galaxy clusters – V. Consistency with cold dark matter structure formation

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

Mantz, A. B.; Allen, S. W.; Morris, R. G.

This is the fifth in a series of papers studying the astrophysics and cosmology of massive, dynamically relaxed galaxy clusters. Our sample comprises 40 clusters identified as being dynamically relaxed and hot in Papers I and II of this series. Here we use constraints on cluster mass profiles from X-ray data to test some of the basic predictions of cosmological structure formation in the cold dark matter (CDM) paradigm. In addition, we present constraints on the concentration–mass relation for massive clusters, finding a power-law mass dependence with a slope of κ m = –0.16 ± 0.07, in agreement with CDMmore » predictions. For this relaxed sample, the relation is consistent with a constant as a function of redshift (power-law slope with 1 + z of κ ζ = –0.17 ± 0.26), with an intrinsic scatter of σln c = 0.16 ± 0.03. We investigate the shape of cluster mass profiles over the radial range probed by the data (typically ~50 kpc–1 Mpc), and test for departures from the simple Navarro–Frenk–White (NFW) form, for which the logarithmic slope of the density profile tends to –1 at small radii. Specifically, we consider as alternatives the generalized NFW (GNFW) and Einasto parametrizations. For the GNFW model, we find an average value of (minus) the logarithmic inner slope of β = 1.02 ± 0.08, with an intrinsic scatter of σ β = 0.22 ± 0.07, while in the Einasto case we constrain the average shape parameter to be α = 0.29 ± 0.04 with an intrinsic scatter of σ α = 0.12 ± 0.04. Our results are thus consistent with the simple NFW model on average, but we clearly detect the presence of intrinsic, cluster-to-cluster scatter about the average.« less

Cosmology and astrophysics from relaxed galaxy clusters – V. Consistency with cold dark matter structure formation

DOE PAGES

Mantz, A. B.; Allen, S. W.; Morris, R. G.

2016-07-15

This is the fifth in a series of papers studying the astrophysics and cosmology of massive, dynamically relaxed galaxy clusters. Our sample comprises 40 clusters identified as being dynamically relaxed and hot in Papers I and II of this series. Here we use constraints on cluster mass profiles from X-ray data to test some of the basic predictions of cosmological structure formation in the cold dark matter (CDM) paradigm. In addition, we present constraints on the concentration–mass relation for massive clusters, finding a power-law mass dependence with a slope of κ m = –0.16 ± 0.07, in agreement with CDMmore » predictions. For this relaxed sample, the relation is consistent with a constant as a function of redshift (power-law slope with 1 + z of κ ζ = –0.17 ± 0.26), with an intrinsic scatter of σln c = 0.16 ± 0.03. We investigate the shape of cluster mass profiles over the radial range probed by the data (typically ~50 kpc–1 Mpc), and test for departures from the simple Navarro–Frenk–White (NFW) form, for which the logarithmic slope of the density profile tends to –1 at small radii. Specifically, we consider as alternatives the generalized NFW (GNFW) and Einasto parametrizations. For the GNFW model, we find an average value of (minus) the logarithmic inner slope of β = 1.02 ± 0.08, with an intrinsic scatter of σ β = 0.22 ± 0.07, while in the Einasto case we constrain the average shape parameter to be α = 0.29 ± 0.04 with an intrinsic scatter of σ α = 0.12 ± 0.04. Our results are thus consistent with the simple NFW model on average, but we clearly detect the presence of intrinsic, cluster-to-cluster scatter about the average.« less

Multiple-labelling immunoEM using different sizes of colloidal gold: alternative approaches to test for differential distribution and colocalization in subcellular structures.

PubMed

Mayhew, Terry M; Lucocq, John M

2011-03-01

Various methods for quantifying cellular immunogold labelling on transmission electron microscope thin sections are currently available. All rely on sound random sampling principles and are applicable to single immunolabelling across compartments within a given cell type or between different experimental groups of cells. Although methods are also available to test for colocalization in double/triple immunogold labelling studies, so far, these have relied on making multiple measurements of gold particle densities in defined areas or of inter-particle nearest neighbour distances. Here, we present alternative two-step approaches to codistribution and colocalization assessment that merely require raw counts of gold particles in distinct cellular compartments. For assessing codistribution over aggregate compartments, initial statistical evaluation involves combining contingency table and chi-squared analyses to provide predicted gold particle distributions. The observed and predicted distributions allow testing of the appropriate null hypothesis, namely, that there is no difference in the distribution patterns of proteins labelled by different sizes of gold particle. In short, the null hypothesis is that of colocalization. The approach for assessing colabelling recognises that, on thin sections, a compartment is made up of a set of sectional images (profiles) of cognate structures. The approach involves identifying two groups of compartmental profiles that are unlabelled and labelled for one gold marker size. The proportions in each group that are also labelled for the second gold marker size are then compared. Statistical analysis now uses a 2 × 2 contingency table combined with the Fisher exact probability test. Having identified double labelling, the profiles can be analysed further in order to identify characteristic features that might account for the double labelling. In each case, the approach is illustrated using synthetic and/or experimental datasets and can be refined to correct observed labelling patterns to specific labelling patterns. These simple and efficient approaches should be of more immediate utility to those interested in codistribution and colocalization in multiple immunogold labelling investigations.

Comparison of particle tracking algorithms in commercial CFD packages: sedimentation and diffusion.

PubMed

Robinson, Risa J; Snyder, Pam; Oldham, Michael J

2007-05-01

Computational fluid dynamic modeling software has enabled microdosimetry patterns of inhaled toxins and toxicants to be predicted and visualized, and is being used in inhalation toxicology and risk assessment. These predicted microdosimetry patterns in airway structures are derived from predicted airflow patterns within these airways and particle tracking algorithms used in computational fluid dynamics (CFD) software packages. Although these commercial CFD codes have been tested for accuracy under various conditions, they have not been well tested for respiratory flows in general. Nor has their particle tracking algorithm accuracy been well studied. In this study, three software packages, Fluent Discrete Phase Model (DPM), Fluent Fine Particle Model (FPM), and ANSYS CFX, were evaluated. Sedimentation and diffusion were each isolated in a straight tube geometry and tested for accuracy. A range of flow rates corresponding to adult low activity (minute ventilation = 10 L/min) and to heavy exertion (minute ventilation = 60 L/min) were tested by varying the range of dimensionless diffusion and sedimentation parameters found using the Weibel symmetric 23 generation lung morphology. Numerical results for fully developed parabolic and uniform (slip) profiles were compared respectively, to Pich (1972) and Yu (1977) analytical sedimentation solutions. Schum and Yeh (1980) equations for sedimentation were also compared. Numerical results for diffusional deposition were compared to analytical solutions of Ingham (1975) for parabolic and uniform profiles. Significant differences were found among the various CFD software packages and between numerical and analytical solutions. Therefore, it is prudent to validate CFD predictions against analytical solutions in idealized geometry before tackling the complex geometries of the respiratory tract.

Convective Detrainment and Control of the Tropical Water Vapor Distribution

NASA Astrophysics Data System (ADS)

Kursinski, E. R.; Rind, D.

2006-12-01

Sherwood et al. (2006) developed a simple power law model describing the relative humidity distribution in the tropical free troposphere where the power law exponent is the ratio of a drying time scale (tied to subsidence rates) and a moistening time which is the average time between convective moistening events whose temporal distribution is described as a Poisson distribution. Sherwood et al. showed that the relative humidity distribution observed by GPS occultations and MLS is indeed close to a power law, approximately consistent with the simple model's prediction. Here we modify this simple model to be in terms of vertical length scales rather than time scales in a manner that we think more correctly matches the model predictions to the observations. The subsidence is now in terms of the vertical distance the air mass has descended since it last detrained from a convective plume. The moisture source term becomes a profile of convective detrainment flux versus altitude. The vertical profile of the convective detrainment flux is deduced from the observed distribution of the specific humidity at each altitude combined with sinking rates estimated from radiative cooling. The resulting free tropospheric detrainment profile increases with altitude above 3 km somewhat like an exponential profile which explains the approximate power law behavior observed by Sherwood et al. The observations also reveal a seasonal variation in the detrainment profile reflecting changes in the convective behavior expected by some based on observed seasonal changes in the vertical structure of convective regions. The simple model results will be compared with the moisture control mechanisms in a GCM with many additional mechanisms, the GISS climate model, as described in Rind (2006). References Rind. D., 2006: Water-vapor feedback. In Frontiers of Climate Modeling, J. T. Kiehl and V. Ramanathan (eds), Cambridge University Press [ISBN-13 978-0-521- 79132-8], 251-284. Sherwood, S., E. R. Kursinski and W. Read, A distribution law for free-tropospheric relative humidity, J. Clim. In press. 2006

Profiles of observed infant anger predict preschool behavior problems: Moderation by life stress

PubMed Central

Brooker, Rebecca J.; Buss, Kristin A.; Lemery-Chalfant, Kathryn; Aksan, Nazan; Davidson, Richard J.; Goldsmith, H. Hill

2014-01-01

Using both traditional composites and novel profiles of anger, we examined associations between infant anger and preschool behavior problems in a large, longitudinal data set (N = 966). We also tested the role of life stress as a moderator of the link between early anger and the development of behavior problems. Although traditional measures of anger were largely unrelated to later behavior problems, profiles of anger that dissociated typical from atypical development predicted behavior problems during preschool. Moreover, the relation between infant anger profiles and preschool behavior problems was moderated such that, when early life stress was low, infants with atypical profiles of early anger showed more preschool behavior problems than did infants with normative anger profiles. However, when early life stress was high, infants with atypical and normative profiles of infant anger did not differ in preschool behavior problems. We conclude that a discrete emotions approach including latent profile analysis is useful for elucidating biological and environmental developmental pathways to early problem behaviors. PMID:25151247

Demonstrating the Operational Value of Atmospheric Infrared Sounder (AIRS) Profiles in the Pre-Convective Environment

NASA Technical Reports Server (NTRS)

Kozlowski, Danielle; Zavodsky, Bradley; Stano, Geoffrey; Jedlovec, Gary

2011-01-01

The Short-term Prediction Research and Transition (SPoRT) is a project to transition those NASA observations and research capabilities to the weather forecasting community to improve the short-term regional forecasts. This poster reviews the work to demonstrate the value to these forecasts of profiles from the Atmospheric Infrared Sounder (AIRS) instrument on board the Aqua satellite with particular assistance in predicting thunderstorm forecasts by the profiles of the pre-convective environment.

Investigation of blown boundary layers with an improved wall jet system

NASA Technical Reports Server (NTRS)

Saripalli, K. R.; Simpson, R. L.

1980-01-01

Measurements were made in a two dimensional incompressible wall jet submerged under a thick upstream boundary layer with a zero pressure gradient and an adverse pressure gradient. The measurements included mean velocity and Reynolds stresses profiles, skin friction, and turbulence spectra. The measurements were confined to practical ratios (less than 2) of the jet velocity to the free stream velocity. The wall jet used in the experiments had an asymmetric velocity profile with a relatively higher concentration of momentum away from the wall. An asymmetric jet velocity profile has distinct advantages over a uniform jet velocity profile, especially in the control of separation. Predictions were made using Irwin's (1974) method for blown boundary layers. The predictions clearly show the difference in flow development between an asymmetric jet velocity profile and a uniform jet velocity profile.

Molecular structure, chemical and nutrient profiles, and metabolic characteristics of the proteins and energy in new cool-season corn varieties harvested as fresh forage for dairy cattle.

PubMed

Abeysekara, Saman; Christensen, David A; Niu, Zhiyuan; Theodoridou, Katerina; Yu, Peiqiang

2013-10-01

To our knowledge, no previous research exists concerning the molecular structure and metabolic characteristics of the proteins and energy that new cool-season corn varieties provide for dairy cattle. The objectives of this study were to identify the differences in the molecular structures of proteins among several new cool-season corn varieties [Pioneer P7443R, Pioneer P7213R, Pioneer P7535R (Pioneer Hi-Bred International Inc., Johnston, IA), Hyland Baxxos RR, Hyland SR22, and Hyland SR06 (Hyland Seeds, Blenheim, ON, Canada)] using Fourier transform infrared attenuated total reflectance (FT/IR-ATR) molecular spectroscopy, and to determine the nutrient profile and supply that each variety provided for dairy cattle. The protein molecular structure studies showed that the amide I to amide II ratio ranged from 1.09 to 1.66 and that the α-helix to β-sheet ratio ranged from 0.95 to 1.01 among the new cool-season corn varieties. Energy content was significantly different among the new varieties. We found significant differences in the protein and carbohydrate subfractions and in the ruminal degradation kinetics of the organic matter, crude protein, starch, and neutral detergent fiber of the new varieties. The new varieties had similar estimated intestinal digestibilities for rumen undegraded crude protein. However, the new varieties had significant differences in predicted total truly absorbable protein, ranging from 39 to 57 g/kg of dry matter, indicating that these newly developed varieties are satisfactory sources of truly absorbed protein for dairy cattle. Further study on the molecular structure profiles of cool-season corn in relation to its nutrient utilization and availability in dairy cattle is necessary. Copyright © 2013 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

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

Motevaselian, M. H.; Mashayak, S. Y.; Aluru, N. R., E-mail: aluru@illinois.edu

Empirical potential-based quasi-continuum theory (EQT) provides a route to incorporate atomistic detail into continuum framework such as the Nernst-Planck equation. EQT can also be used to construct a grand potential functional for classical density functional theory (cDFT). The combination of EQT and cDFT provides a simple and fast approach to predict the inhomogeneous density, potential profiles, and thermodynamic properties of confined fluids. We extend the EQT-cDFT approach to confined fluid mixtures and demonstrate it by simulating a mixture of methane and hydrogen inside slit-like channels of graphene. We show that the EQT-cDFT predictions for the structure of the confined fluidmore » mixture compare well with the molecular dynamics simulation results. In addition, our results show that graphene slit nanopores exhibit a selective adsorption of methane over hydrogen.« less

Energy profiles of four American states

NASA Astrophysics Data System (ADS)

Song, Jiamei

2018-06-01

Energy production and usage are the major portion of any economy. With the constant consumption of the polluting energy and the deteriorating environment, people are paying more and more attention to clean, renewable energy. Based on autoregressive model and TOPSIS, though analyzing the past data, this paper establishes the energy profiles of four American states from 1960 to 2009, predict the energy profiles for 2025 and 2050 and obtain the ideal criteria for future clean, renewable energy usage at last. This study finds that by analyzing and predicting the energy profile, human beings can better understand and grasp the trend of energy development and take appropriate measures to deal with future energy trends.

Species-specific predictive models of developmental toxicity using the ToxCast chemical library

EPA Science Inventory

EPA’s ToxCastTM project is profiling the in vitro bioactivity of chemicals to generate predictive models that correlate with observed in vivo toxicity. In vitro profiling methods are based on ToxCast data, consisting of over 600 high-throughput screening (HTS) and high-content sc...

3D gut-liver chip with a PK model for prediction of first-pass metabolism.

PubMed

Lee, Dong Wook; Ha, Sang Keun; Choi, Inwook; Sung, Jong Hwan

2017-11-07

Accurate prediction of first-pass metabolism is essential for improving the time and cost efficiency of drug development process. Here, we have developed a microfluidic gut-liver co-culture chip that aims to reproduce the first-pass metabolism of oral drugs. This chip consists of two separate layers for gut (Caco-2) and liver (HepG2) cell lines, where cells can be co-cultured in both 2D and 3D forms. Both cell lines were maintained well in the chip, verified by confocal microscopy and measurement of hepatic enzyme activity. We investigated the PK profile of paracetamol in the chip, and corresponding PK model was constructed, which was used to predict PK profiles for different chip design parameters. Simulation results implied that a larger absorption surface area and a higher metabolic capacity are required to reproduce the in vivo PK profile of paracetamol more accurately. Our study suggests the possibility of reproducing the human PK profile on a chip, contributing to accurate prediction of pharmacological effect of drugs.

Effects of electrode surface structure on the mechanoelectrical transduction of IPMC sensors

NASA Astrophysics Data System (ADS)

Palmre, Viljar; Pugal, David; Kim, Kwang

2014-03-01

This study investigates the effects of electrode surface structure on the mechanoelectrical transduction of IPMC sensors. A physics-based mechanoelectrical transduction model was developed that takes into account the electrode surface profile (shape) by describing the polymer-electrode interface as a Koch fractal structure. Based on the model, the electrode surface effects were experimentally investigated in case of IPMCs with Pd-Pt electrodes. IPMCs with different electrode surface structures were fabricated through electroless plating process by appropriately controlling the synthesis parameters and conditions. The changes in the electrode surface morphology and the corresponding effects on the IPMC mechanoelectrical transduction were examined. Our experimental results indicate that increasing the dispersion of Pd particles near the membrane surface, and thus the polymer-electrode interfacial area, leads to a higher peak mechanoelectrically induced voltage of IPMC. However, the overall effect of the electrode surface structure is relatively low compared to the electromechanical transduction, which is in good agreement with theoretical prediction.

Functional Annotation of Ion Channel Structures by Molecular Simulation.

PubMed

Trick, Jemma L; Chelvaniththilan, Sivapalan; Klesse, Gianni; Aryal, Prafulla; Wallace, E Jayne; Tucker, Stephen J; Sansom, Mark S P

2016-12-06

Ion channels play key roles in cell membranes, and recent advances are yielding an increasing number of structures. However, their functional relevance is often unclear and better tools are required for their functional annotation. In sub-nanometer pores such as ion channels, hydrophobic gating has been shown to promote dewetting to produce a functionally closed (i.e., non-conductive) state. Using the serotonin receptor (5-HT 3 R) structure as an example, we demonstrate the use of molecular dynamics to aid the functional annotation of channel structures via simulation of the behavior of water within the pore. Three increasingly complex simulation analyses are described: water equilibrium densities; single-ion free-energy profiles; and computational electrophysiology. All three approaches correctly predict the 5-HT 3 R crystal structure to represent a functionally closed (i.e., non-conductive) state. We also illustrate the application of water equilibrium density simulations to annotate different conformational states of a glycine receptor. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

Protein structure database search and evolutionary classification.

PubMed

Yang, Jinn-Moon; Tung, Chi-Hua

2006-01-01

As more protein structures become available and structural genomics efforts provide structural models in a genome-wide strategy, there is a growing need for fast and accurate methods for discovering homologous proteins and evolutionary classifications of newly determined structures. We have developed 3D-BLAST, in part, to address these issues. 3D-BLAST is as fast as BLAST and calculates the statistical significance (E-value) of an alignment to indicate the reliability of the prediction. Using this method, we first identified 23 states of the structural alphabet that represent pattern profiles of the backbone fragments and then used them to represent protein structure databases as structural alphabet sequence databases (SADB). Our method enhanced BLAST as a search method, using a new structural alphabet substitution matrix (SASM) to find the longest common substructures with high-scoring structured segment pairs from an SADB database. Using personal computers with Intel Pentium4 (2.8 GHz) processors, our method searched more than 10 000 protein structures in 1.3 s and achieved a good agreement with search results from detailed structure alignment methods. [3D-BLAST is available at http://3d-blast.life.nctu.edu.tw].

The Scintillation Prediction Observations Research Task (SPORT) Mission

NASA Astrophysics Data System (ADS)

Spann, J. F.; Swenson, C.; Durão, O.; Loures, L.; Heelis, R. A.; Bishop, R. L.; Le, G.; Abdu, M. A.; Habash Krause, L.; De Nardin, C. M.; Fonseca, E.

2015-12-01

Structure in the charged particle number density in the equatorial ionosphere can have a profound impact on the fidelity of HF, VHF and UHF radio signals that are used for ground-to-ground and space-to-ground communication and navigation. The degree to which such systems can be compromised depends in large part on the spatial distribution of the structured regions in the ionosphere and the background plasma density in which they are embedded. In order to address these challenges it is necessary to accurately distinguish the background ionospheric conditions that favor the generation of irregularities from those that do not. Additionally we must relate the evolution of those conditions to the subsequent evolution of the irregular plasma regions themselves. The background ionospheric conditions are conveniently described by latitudinal profiles of the plasma density at nearly constant altitude, which describe the effects of ExB drifts and neutral winds, while the appearance and growth of plasma structure requires committed observations from the ground from at least one fixed longitude. This talk will present an international collaborative CubeSat mission called SPORT that stands for Scintillation Prediction Observations Research Task. This mission that will advance our understanding of the nature and evolution of ionospheric structures around sunset to improve predictions of disturbances that affect radio propagation and telecommunication signals. The science goals will be accomplished by a unique combination of satellite observations from a nearly circular middle inclination orbit and the extensive operation of ground based observations from South America near the magnetic equator. This approach promises Explorer class science at a CubeSat price.

The Scintillation Prediction Observations Research Task (SPORT) Mission

NASA Astrophysics Data System (ADS)

Spann, James; Le, Guan; Swenson, Charles; Denardini, Clezio Marcos; Bishop, Rebecca L.; Abdu, Mangalathayil A.; Cupertino Durao, Otavio S.; Heelis, Roderick; Loures, Luis; Krause, Linda; Fonseca, Eloi

2016-07-01

Structure in the charged particle number density in the equatorial ionosphere can have a profound impact on the fidelity of HF, VHF and UHF radio signals that are used for ground-to-ground and space-to-ground communication and navigation. The degree to which such systems can be compromised depends in large part on the spatial distribution of the structured regions in the ionosphere and the background plasma density in which they are embedded. In order to address these challenges it is necessary to accurately distinguish the background ionospheric conditions that favor the generation of irregularities from those that do not. Additionally we must relate the evolution of those conditions to the subsequent evolution of the irregular plasma regions themselves. The background ionospheric conditions are conveniently described by latitudinal profiles of the plasma density at nearly constant altitude, which describe the effects of ExB drifts and neutral winds, while the appearance and growth of plasma structure requires committed observations from the ground from at least one fixed longitude. This talk will present an international collaborative CubeSat mission called SPORT that stands for the Scintillation Prediction Observations Research Task. This mission will advance our understanding of the nature and evolution of ionospheric structures around sunset to improve predictions of disturbances that affect radio propagation and telecommunication signals. The science goals will be accomplished by a unique combination of satellite observations from a nearly circular middle inclination orbit and the extensive operation of ground based observations from South America near the magnetic equator. This approach promises Explorer class science at a CubeSat price.

The Scintillation Prediction Observations Research Task (SPORT) Mission

NASA Astrophysics Data System (ADS)

Spann, James; Swenson, Charles; Durão, Otavio; Loures, Luis; Heelis, Rod; Bishop, Rebecca; Le, Guan; Abdu, Mangalathayil; Krause, Linda; Nardin, Clezio; Fonseca, Eloi

2016-04-01

Structure in the charged particle number density in the equatorial ionosphere can have a profound impact on the fidelity of HF, VHF and UHF radio signals that are used for ground-to-ground and space-to-ground communication and navigation. The degree to which such systems can be compromised depends in large part on the spatial distribution of the structured regions in the ionosphere and the background plasma density in which they are embedded. In order to address these challenges it is necessary to accurately distinguish the background ionospheric conditions that favor the generation of irregularities from those that do not. Additionally we must relate the evolution of those conditions to the subsequent evolution of the irregular plasma regions themselves. The background ionospheric conditions are conveniently described by latitudinal profiles of the plasma density at nearly constant altitude, which describe the effects of ExB drifts and neutral winds, while the appearance and growth of plasma structure requires committed observations from the ground from at least one fixed longitude. This talk will present an international collaborative CubeSat mission called SPORT that stands for the Scintillation Prediction Observations Research Task. This mission will advance our understanding of the nature and evolution of ionospheric structures around sunset to improve predictions of disturbances that affect radio propagation and telecommunication signals. The science goals will be accomplished by a unique combination of satellite observations from a nearly circular middle inclination orbit and the extensive operation of ground based observations from South America near the magnetic equator. This approach promises Explorer class science at a CubeSat price.

Drug-induced death signaling strategy rapidly predicts cancer response to chemotherapy.

PubMed

Montero, Joan; Sarosiek, Kristopher A; DeAngelo, Joseph D; Maertens, Ophélia; Ryan, Jeremy; Ercan, Dalia; Piao, Huiying; Horowitz, Neil S; Berkowitz, Ross S; Matulonis, Ursula; Jänne, Pasi A; Amrein, Philip C; Cichowski, Karen; Drapkin, Ronny; Letai, Anthony

2015-02-26

There is a lack of effective predictive biomarkers to precisely assign optimal therapy to cancer patients. While most efforts are directed at inferring drug response phenotype based on genotype, there is very focused and useful phenotypic information to be gained from directly perturbing the patient's living cancer cell with the drug(s) in question. To satisfy this unmet need, we developed the Dynamic BH3 Profiling technique to measure early changes in net pro-apoptotic signaling at the mitochondrion ("priming") induced by chemotherapeutic agents in cancer cells, not requiring prolonged ex vivo culture. We find in cell line and clinical experiments that early drug-induced death signaling measured by Dynamic BH3 Profiling predicts chemotherapy response across many cancer types and many agents, including combinations of chemotherapies. We propose that Dynamic BH3 Profiling can be used as a broadly applicable predictive biomarker to predict cytotoxic response of cancers to chemotherapeutics in vivo. Copyright © 2015 Elsevier Inc. All rights reserved.

Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells

DOE PAGES

Yurkovich, James T.; Yang, Laurence; Palsson, Bernhard O.; ...

2017-03-06

Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites ( p < 0.05) in RBC metabolism using only measurements of these five biomarkers.more » The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. Furthermore, the ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.« less

Biomarkers are used to predict quantitative metabolite concentration profiles in human red blood cells

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

Yurkovich, James T.; Yang, Laurence; Palsson, Bernhard O.

Deep-coverage metabolomic profiling has revealed a well-defined development of metabolic decay in human red blood cells (RBCs) under cold storage conditions. A set of extracellular biomarkers has been recently identified that reliably defines the qualitative state of the metabolic network throughout this metabolic decay process. Here, we extend the utility of these biomarkers by using them to quantitatively predict the concentrations of other metabolites in the red blood cell. We are able to accurately predict the concentration profile of 84 of the 91 (92%) measured metabolites ( p < 0.05) in RBC metabolism using only measurements of these five biomarkers.more » The median of prediction errors (symmetric mean absolute percent error) across all metabolites was 13%. Furthermore, the ability to predict numerous metabolite concentrations from a simple set of biomarkers offers the potential for the development of a powerful workflow that could be used to evaluate the metabolic state of a biological system using a minimal set of measurements.« less

COSIM: A Finite-Difference Computer Model to Predict Ternary Concentration Profiles Associated with Oxidation and Interdiffusion of Overlay-Coated Substrates

NASA Technical Reports Server (NTRS)

Nesbitt, James A.

2000-01-01

A finite-difference computer program (COSIM) has been written which models the one-dimensional, diffusional transport associated with high-temperature oxidation and interdiffusion of overlay-coated substrates. The program predicts concentration profiles for up to three elements in the coating and substrate after various oxidation exposures. Surface recession due to solute loss is also predicted. Ternary cross terms and concentration-dependent diffusion coefficients are taken into account. The program also incorporates a previously-developed oxide growth and spalling model to simulate either isothermal or cyclic oxidation exposures. In addition to predicting concentration profiles after various oxidation exposures, the program can also be used to predict coating fife based on a concentration dependent failure criterion (e.g., surface solute content drops to two percent). The computer code, written in an extension of FORTRAN 77, employs numerous subroutines to make the program flexible and easily modifiable to other coating oxidation problems.

HIPPI: highly accurate protein family classification with ensembles of HMMs.

PubMed

Nguyen, Nam-Phuong; Nute, Michael; Mirarab, Siavash; Warnow, Tandy

2016-11-11

Given a new biological sequence, detecting membership in a known family is a basic step in many bioinformatics analyses, with applications to protein structure and function prediction and metagenomic taxon identification and abundance profiling, among others. Yet family identification of sequences that are distantly related to sequences in public databases or that are fragmentary remains one of the more difficult analytical problems in bioinformatics. We present a new technique for family identification called HIPPI (Hierarchical Profile Hidden Markov Models for Protein family Identification). HIPPI uses a novel technique to represent a multiple sequence alignment for a given protein family or superfamily by an ensemble of profile hidden Markov models computed using HMMER. An evaluation of HIPPI on the Pfam database shows that HIPPI has better overall precision and recall than blastp, HMMER, and pipelines based on HHsearch, and maintains good accuracy even for fragmentary query sequences and for protein families with low average pairwise sequence identity, both conditions where other methods degrade in accuracy. HIPPI provides accurate protein family identification and is robust to difficult model conditions. Our results, combined with observations from previous studies, show that ensembles of profile Hidden Markov models can better represent multiple sequence alignments than a single profile Hidden Markov model, and thus can improve downstream analyses for various bioinformatic tasks. Further research is needed to determine the best practices for building the ensemble of profile Hidden Markov models. HIPPI is available on GitHub at https://github.com/smirarab/sepp .