Predictive modelling of flow in a two-dimensional intermediate-scale, heterogeneous porous media

USGS Publications Warehouse

Barth, Gilbert R.; Hill, M.C.; Illangasekare, T.H.; Rajaram, H.

2000-01-01

To better understand the role of sedimentary structures in flow through porous media, and to determine how small-scale laboratory-measured values of hydraulic conductivity relate to in situ values this work deterministically examines flow through simple, artificial structures constructed for a series of intermediate-scale (10 m long), two-dimensional, heterogeneous, laboratory experiments. Nonlinear regression was used to determine optimal values of in situ hydraulic conductivity, which were compared to laboratory-measured values. Despite explicit numerical representation of the heterogeneity, the optimized values were generally greater than the laboratory-measured values. Discrepancies between measured and optimal values varied depending on the sand sieve size, but their contribution to error in the predicted flow was fairly consistent for all sands. Results indicate that, even under these controlled circumstances, laboratory-measured values of hydraulic conductivity need to be applied to models cautiously.To better understand the role of sedimentary structures in flow through porous media, and to determine how small-scale laboratory-measured values of hydraulic conductivity relate to in situ values this work deterministically examines flow through simple, artificial structures constructed for a series of intermediate-scale (10 m long), two-dimensional, heterogeneous, laboratory experiments. Nonlinear regression was used to determine optimal values of in situ hydraulic conductivity, which were compared to laboratory-measured values. Despite explicit numerical representation of the heterogeneity, the optimized values were generally greater than the laboratory-measured values. Discrepancies between measured and optimal values varied depending on the sand sieve size, but their contribution to error in the predicted flow was fairly consistent for all sands. Results indicate that, even under these controlled circumstances, laboratory-measured values of hydraulic conductivity need to be applied to models cautiously.

Model-free and model-based reward prediction errors in EEG.

PubMed

Sambrook, Thomas D; Hardwick, Ben; Wills, Andy J; Goslin, Jeremy

2018-05-24

Learning theorists posit two reinforcement learning systems: model-free and model-based. Model-based learning incorporates knowledge about structure and contingencies in the world to assign candidate actions with an expected value. Model-free learning is ignorant of the world's structure; instead, actions hold a value based on prior reinforcement, with this value updated by expectancy violation in the form of a reward prediction error. Because they use such different learning mechanisms, it has been previously assumed that model-based and model-free learning are computationally dissociated in the brain. However, recent fMRI evidence suggests that the brain may compute reward prediction errors to both model-free and model-based estimates of value, signalling the possibility that these systems interact. Because of its poor temporal resolution, fMRI risks confounding reward prediction errors with other feedback-related neural activity. In the present study, EEG was used to show the presence of both model-based and model-free reward prediction errors and their place in a temporal sequence of events including state prediction errors and action value updates. This demonstration of model-based prediction errors questions a long-held assumption that model-free and model-based learning are dissociated in the brain. Copyright © 2018 Elsevier Inc. All rights reserved.

Autocorrelation structure at rest predicts value correlates of single neurons during reward-guided choice

PubMed Central

Cavanagh, Sean E; Wallis, Joni D; Kennerley, Steven W; Hunt, Laurence T

2016-01-01

Correlates of value are routinely observed in the prefrontal cortex (PFC) during reward-guided decision making. In previous work (Hunt et al., 2015), we argued that PFC correlates of chosen value are a consequence of varying rates of a dynamical evidence accumulation process. Yet within PFC, there is substantial variability in chosen value correlates across individual neurons. Here we show that this variability is explained by neurons having different temporal receptive fields of integration, indexed by examining neuronal spike rate autocorrelation structure whilst at rest. We find that neurons with protracted resting temporal receptive fields exhibit stronger chosen value correlates during choice. Within orbitofrontal cortex, these neurons also sustain coding of chosen value from choice through the delivery of reward, providing a potential neural mechanism for maintaining predictions and updating stored values during learning. These findings reveal that within PFC, variability in temporal specialisation across neurons predicts involvement in specific decision-making computations. DOI: http://dx.doi.org/10.7554/eLife.18937.001 PMID:27705742

Lossless Compression of Data into Fixed-Length Packets

NASA Technical Reports Server (NTRS)

Kiely, Aaron B.; Klimesh, Matthew A.

2009-01-01

A computer program effects lossless compression of data samples from a one-dimensional source into fixed-length data packets. The software makes use of adaptive prediction: it exploits the data structure in such a way as to increase the efficiency of compression beyond that otherwise achievable. Adaptive linear filtering is used to predict each sample value based on past sample values. The difference between predicted and actual sample values is encoded using a Golomb code.

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.

The importance of molecular structures, endpoints' values, and predictivity parameters in QSAR research: QSAR analysis of a series of estrogen receptor binders.

PubMed

Li, Jiazhong; Gramatica, Paola

2010-11-01

Quantitative structure-activity relationship (QSAR) methodology aims to explore the relationship between molecular structures and experimental endpoints, producing a model for the prediction of new data; the predictive performance of the model must be checked by external validation. Clearly, the qualities of chemical structure information and experimental endpoints, as well as the statistical parameters used to verify the external predictivity have a strong influence on QSAR model reliability. Here, we emphasize the importance of these three aspects by analyzing our models on estrogen receptor binders (Endocrine disruptor knowledge base (EDKB) database). Endocrine disrupting chemicals, which mimic or antagonize the endogenous hormones such as estrogens, are a hot topic in environmental and toxicological sciences. QSAR shows great values in predicting the estrogenic activity and exploring the interactions between the estrogen receptor and ligands. We have verified our previously published model for additional external validation on new EDKB chemicals. Having found some errors in the used 3D molecular conformations, we redevelop a new model using the same data set with corrected structures, the same method (ordinary least-square regression, OLS) and DRAGON descriptors. The new model, based on some different descriptors, is more predictive on external prediction sets. Three different formulas to calculate correlation coefficient for the external prediction set (Q2 EXT) were compared, and the results indicated that the new proposal of Consonni et al. had more reasonable results, consistent with the conclusions from regression line, Williams plot and root mean square error (RMSE) values. Finally, the importance of reliable endpoints values has been highlighted by comparing the classification assignments of EDKB with those of another estrogen receptor binders database (METI): we found that 16.1% assignments of the common compounds were opposite (20 among 124 common compounds). In order to verify the real assignments for these inconsistent compounds, we predicted these samples, as a blind external set, by our regression models and compared the results with the two databases. The results indicated that most of the predictions were consistent with METI. Furthermore, we built a kNN classification model using the 104 consistent compounds to predict those inconsistent ones, and most of the predictions were also in agreement with METI database.

RNA-SSPT: RNA Secondary Structure Prediction Tools.

PubMed

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

2013-01-01

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

RNA-SSPT: RNA Secondary Structure Prediction Tools

PubMed Central

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

2013-01-01

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

Novel, customizable scoring functions, parameterized using N-PLS, for structure-based drug discovery.

PubMed

Catana, Cornel; Stouten, Pieter F W

2007-01-01

The ability to accurately predict biological affinity on the basis of in silico docking to a protein target remains a challenging goal in the CADD arena. Typically, "standard" scoring functions have been employed that use the calculated docking result and a set of empirical parameters to calculate a predicted binding affinity. To improve on this, we are exploring novel strategies for rapidly developing and tuning "customized" scoring functions tailored to a specific need. In the present work, three such customized scoring functions were developed using a set of 129 high-resolution protein-ligand crystal structures with measured Ki values. The functions were parametrized using N-PLS (N-way partial least squares), a multivariate technique well-known in the 3D quantitative structure-activity relationship field. A modest correlation between observed and calculated pKi values using a standard scoring function (r2 = 0.5) could be improved to 0.8 when a customized scoring function was applied. To mimic a more realistic scenario, a second scoring function was developed, not based on crystal structures but exclusively on several binding poses generated with the Flo+ docking program. Finally, a validation study was conducted by generating a third scoring function with 99 randomly selected complexes from the 129 as a training set and predicting pKi values for a test set that comprised the remaining 30 complexes. Training and test set r2 values were 0.77 and 0.78, respectively. These results indicate that, even without direct structural information, predictive customized scoring functions can be developed using N-PLS, and this approach holds significant potential as a general procedure for predicting binding affinity on the basis of in silico docking.

An Integrated Theory for Predicting the Hydrothermomechanical Response of Advanced Composite Structural Components

NASA Technical Reports Server (NTRS)

Chamis, C. C.; Lark, R. F.; Sinclair, J. H.

1977-01-01

An integrated theory is developed for predicting the hydrothermomechanical (HDTM) response of fiber composite components. The integrated theory is based on a combined theoretical and experimental investigation. In addition to predicting the HDTM response of components, the theory is structured to assess the combined hydrothermal effects on the mechanical properties of unidirectional composites loaded along the material axis and off-axis, and those of angleplied laminates. The theory developed predicts values which are in good agreement with measured data at the micromechanics, macromechanics, laminate analysis and structural analysis levels.

CALCULATING PHYSICAL PROPERTIES OF ORGANIC COMPOUNDS FOR ENVIRONMENTAL MODELING FROM MOLECULAR STRUCTURE

EPA Science Inventory

Mathematical models for predicting the transport and fate of pollutants in the environment require reactivity parameter values-- that is value of the physical and chemical constants that govern reactivity. Although empirical structure activity relationships have been developed t...

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

PubMed

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

2002-04-01

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

Target and Tissue Selectivity Prediction by Integrated Mechanistic Pharmacokinetic-Target Binding and Quantitative Structure Activity Modeling.

PubMed

Vlot, Anna H C; de Witte, Wilhelmus E A; Danhof, Meindert; van der Graaf, Piet H; van Westen, Gerard J P; de Lange, Elizabeth C M

2017-12-04

Selectivity is an important attribute of effective and safe drugs, and prediction of in vivo target and tissue selectivity would likely improve drug development success rates. However, a lack of understanding of the underlying (pharmacological) mechanisms and availability of directly applicable predictive methods complicates the prediction of selectivity. We explore the value of combining physiologically based pharmacokinetic (PBPK) modeling with quantitative structure-activity relationship (QSAR) modeling to predict the influence of the target dissociation constant (K D ) and the target dissociation rate constant on target and tissue selectivity. The K D values of CB1 ligands in the ChEMBL database are predicted by QSAR random forest (RF) modeling for the CB1 receptor and known off-targets (TRPV1, mGlu5, 5-HT1a). Of these CB1 ligands, rimonabant, CP-55940, and Δ 8 -tetrahydrocanabinol, one of the active ingredients of cannabis, were selected for simulations of target occupancy for CB1, TRPV1, mGlu5, and 5-HT1a in three brain regions, to illustrate the principles of the combined PBPK-QSAR modeling. Our combined PBPK and target binding modeling demonstrated that the optimal values of the K D and k off for target and tissue selectivity were dependent on target concentration and tissue distribution kinetics. Interestingly, if the target concentration is high and the perfusion of the target site is low, the optimal K D value is often not the lowest K D value, suggesting that optimization towards high drug-target affinity can decrease the benefit-risk ratio. The presented integrative structure-pharmacokinetic-pharmacodynamic modeling provides an improved understanding of tissue and target selectivity.

Correlation of RNA secondary structure statistics with thermodynamic stability and applications to folding.

PubMed

Wu, Johnny C; Gardner, David P; Ozer, Stuart; Gutell, Robin R; Ren, Pengyu

2009-08-28

The accurate prediction of the secondary and tertiary structure of an RNA with different folding algorithms is dependent on several factors, including the energy functions. However, an RNA higher-order structure cannot be predicted accurately from its sequence based on a limited set of energy parameters. The inter- and intramolecular forces between this RNA and other small molecules and macromolecules, in addition to other factors in the cell such as pH, ionic strength, and temperature, influence the complex dynamics associated with transition of a single stranded RNA to its secondary and tertiary structure. Since all of the factors that affect the formation of an RNAs 3D structure cannot be determined experimentally, statistically derived potential energy has been used in the prediction of protein structure. In the current work, we evaluate the statistical free energy of various secondary structure motifs, including base-pair stacks, hairpin loops, and internal loops, using their statistical frequency obtained from the comparative analysis of more than 50,000 RNA sequences stored in the RNA Comparative Analysis Database (rCAD) at the Comparative RNA Web (CRW) Site. Statistical energy was computed from the structural statistics for several datasets. While the statistical energy for a base-pair stack correlates with experimentally derived free energy values, suggesting a Boltzmann-like distribution, variation is observed between different molecules and their location on the phylogenetic tree of life. Our statistical energy values calculated for several structural elements were utilized in the Mfold RNA-folding algorithm. The combined statistical energy values for base-pair stacks, hairpins and internal loop flanks result in a significant improvement in the accuracy of secondary structure prediction; the hairpin flanks contribute the most.

Integrating content and structure aspects of the self: traits, values, and self-improvement.

PubMed

Roccas, Sonia; Sagiv, Lilach; Oppenheim, Shani; Elster, Andrey; Gal, Avigail

2014-04-01

Research on the structure of the self has mostly developed separately from research on its content. Taking an integrative approach, we studied two structural aspects of the self associated with self-improvement--self-discrepancies and perceived mutability--by focusing on two content areas, traits and values. In Studies 1A-C, 337 students (61% female) reported self-discrepancies in values and traits, with the finding that self-discrepancies in values are smaller than in traits. In Study 2 (80 students, 41% female), we experimentally induced either high or low mutability and measured perceived mutability of traits and values. We found that values are perceived as less mutable than traits. In Study 3, 99 high school students (60% female) reported their values, traits, and the extent to which they wish to change them. We found that values predict the wish to change traits, whereas traits do not predict the wish to change values. In Study 4, 172 students (47.7% female) were assigned to one of four experimental conditions in which they received feedback denoting either uniqueness or similarity to others, on either their values or their traits. The results indicated that feedback that one's values (but not traits) are unique affected self-esteem. Integrating between theories of content and structure of the self can contribute to the development of both. © 2013 Wiley Periodicals, Inc.

A protein block based fold recognition method for the annotation of twilight zone sequences.

PubMed

Suresh, V; Ganesan, K; Parthasarathy, S

2013-03-01

The description of protein backbone was recently improved with a group of structural fragments called Structural Alphabets instead of the regular three states (Helix, Sheet and Coil) secondary structure description. Protein Blocks is one of the Structural Alphabets used to describe each and every region of protein backbone including the coil. According to de Brevern (2000) the Protein Blocks has 16 structural fragments and each one has 5 residues in length. Protein Blocks fragments are highly informative among the available Structural Alphabets and it has been used for many applications. Here, we present a protein fold recognition method based on Protein Blocks for the annotation of twilight zone sequences. In our method, we align the predicted Protein Blocks of a query amino acid sequence with a library of assigned Protein Blocks of 953 known folds using the local pair-wise alignment. The alignment results with z-value ≥ 2.5 and P-value ≤ 0.08 are predicted as possible folds. Our method is able to recognize the possible folds for nearly 35.5% of the twilight zone sequences with their predicted Protein Block sequence obtained by pb_prediction, which is available at Protein Block Export server.

An expert system for prediction of aquatic toxicity of contaminants

USGS Publications Warehouse

Hickey, James P.; Aldridge, Andrew J.; Passino, Dora R. May; Frank, Anthony M.; Hushon, Judith M.

1990-01-01

The National Fisheries Research Center-Great Lakes has developed an interactive computer program in muLISP that runs on an IBM-compatible microcomputer and uses a linear solvation energy relationship (LSER) to predict acute toxicity to four representative aquatic species from the detailed structure of an organic molecule. Using the SMILES formalism for a chemical structure, the expert system identifies all structural components and uses a knowledge base of rules based on an LSER to generate four structure-related parameter values. A separate module then relates these values to toxicity. The system is designed for rapid screening of potential chemical hazards before laboratory or field investigations are conducted and can be operated by users with little toxicological background. This is the first expert system based on LSER, relying on the first comprehensive compilation of rules and values for the estimation of LSER parameters.

Need for Affect and Attitudes Toward Drugs: The Mediating Role of Values.

PubMed

Lins de Holanda Coelho, Gabriel; H P Hanel, Paul; Vilar, Roosevelt; P Monteiro, Renan; Gouveia, Valdiney V; R Maio, Gregory

2018-05-04

Human values and affective traits were found to predict attitudes toward the use of different types of drugs (e.g., alcohol, marijuana, and other illegal drugs). In this study (N = 196, M age = 23.09), we aimed to gain a more comprehensive understanding of those predictors of attitudes toward drug use in a mediated structural equation model, providing a better overview of a possible motivational path that drives to such a risky behavior. Specifically, we predicted and found that the relations between need for affect and attitudes toward drug use were mediated by excitement values. Also, results showed that excitement values and need for affect positively predicted attitudes toward the use of drugs, whereas normative values predicted it negatively. The pattern of results remained the same when we investigated attitudes toward alcohol, marijuana, or illegal drugs separately. Overall, the findings indicate that emotions operate via excitement and normative values to influence risk behavior.

Towards the Application of Structure-Property Relationship Modeling in Materials Science: Predicting the Seebeck Coefficient for Ionic Liquid/Redox Couple Systems.

PubMed

Sosnowska, Anita; Barycki, Maciej; Gajewicz, Agnieszka; Bobrowski, Maciej; Freza, Sylwia; Skurski, Piotr; Uhl, Stefanie; Laux, Edith; Journot, Tony; Jeandupeux, Laure; Keppner, Herbert; Puzyn, Tomasz

2016-06-03

This work focuses on determining the influence of both ionic-liquid (IL) type and redox couple concentration on Seebeck coefficient values of such a system. The quantitative structure-property relationship (QSPR) and read-across techniques are proposed as methods to identify structural features of ILs (mixed with LiI/I2 redox couple), which have the most influence on the Seebeck coefficient (Se ) values of the system. ILs consisting of small, symmetric cations and anions with high values of vertical electron binding energy are recognized as those with the highest values of Se . In addition, the QSPR model enables the values of Se to be predicted for each IL that belongs to the applicability domain of the model. The influence of the redox-couple concentration on values of Se is also quantitatively described. Thus, it is possible to calculate how the value of Se will change with changing redox-couple concentration. The presence of the LiI/I2 redox couple in lower concentrations increases the values of Se , as expected. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A new scaling approach for the mesoscale simulation of magnetic domain structures using Monte Carlo simulations

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

Radhakrishnan, B.; Eisenbach, M.; Burress, Timothy A.

2017-01-24

A new scaling approach has been proposed for the spin exchange and the dipole–dipole interaction energy as a function of the system size. The computed scaling laws are used in atomistic Monte Carlo simulations of magnetic moment evolution to predict the transition from single domain to a vortex structure as the system size increases. The width of a 180° – domain wall extracted from the simulated structures is in close agreement with experimentally values for an F–Si alloy. In conclusion, the transition size from a single domain to a vortex structure is also in close agreement with theoretically predicted andmore » experimentally measured values for Fe.« less

Quantitative structure-activity relationships (QSARs) for the transformation of organic micropollutants during oxidative water treatment.

PubMed

Lee, Yunho; von Gunten, Urs

2012-12-01

Various oxidants such as chlorine, chlorine dioxide, ferrate(VI), ozone, and hydroxyl radicals can be applied for eliminating organic micropollutant by oxidative transformation during water treatment in systems such as drinking water, wastewater, and water reuse. Over the last decades, many second-order rate constants (k) have been determined for the reaction of these oxidants with model compounds and micropollutants. Good correlations (quantitative structure-activity relationships or QSARs) are often found between the k-values for an oxidation reaction of closely related compounds (i.e. having a common organic functional group) and substituent descriptor variables such as Hammett or Taft sigma constants. In this study, we developed QSARs for the oxidation of organic and some inorganic compounds and organic micropollutants transformation during oxidative water treatment. A number of 18 QSARs were developed based on overall 412 k-values for the reaction of chlorine, chlorine dioxide, ferrate, and ozone with organic compounds containing electron-rich moieties such as phenols, anilines, olefins, and amines. On average, 303 out of 412 (74%) k-values were predicted by these QSARs within a factor of 1/3-3 compared to the measured values. For HO(·) reactions, some principles and estimation methods of k-values (e.g. the Group Contribution Method) are discussed. The developed QSARs and the Group Contribution Method could be used to predict the k-values for various emerging organic micropollutants. As a demonstration, 39 out of 45 (87%) predicted k-values were found within a factor 1/3-3 compared to the measured values for the selected emerging micropollutants. Finally, it is discussed how the uncertainty in the predicted k-values using the QSARs affects the accuracy of prediction for micropollutant elimination during oxidative water treatment. Copyright © 2012 Elsevier Ltd. All rights reserved.

Mining the protein data bank with CReF to predict approximate 3-D structures of polypeptides.

PubMed

Dorn, Márcio; de Souza, Osmar Norberto

2010-01-01

n this paper we describe CReF, a Central Residue Fragment-based method to predict approximate 3-D structures of polypeptides by mining the Protein Data Bank (PDB). The approximate predicted structures are good enough to be used as starting conformations in refinement procedures employing state-of-the-art molecular mechanics methods such as molecular dynamics simulations. CReF is very fast and we illustrate its efficacy in three case studies of polypeptides whose sizes vary from 34 to 70 amino acids. As indicated by the RMSD values, our initial results show that the predicted structures adopt the expected fold, similar to the experimental ones.

The pKa Cooperative: A Collaborative Effort to Advance Structure-Based Calculations of pKa values and Electrostatic Effects in Proteins

PubMed Central

Nielsen, Jens E.; Gunner, M. R.; Bertrand García-Moreno, E.

2012-01-01

The pKa Cooperative http://www.pkacoop.org was organized to advance development of accurate and useful computational methods for structure-based calculation of pKa values and electrostatic energy in proteins. The Cooperative brings together laboratories with expertise and interest in theoretical, computational and experimental studies of protein electrostatics. To improve structure-based energy calculations it is necessary to better understand the physical character and molecular determinants of electrostatic effects. The Cooperative thus intends to foment experimental research into fundamental aspects of proteins that depend on electrostatic interactions. It will maintain a depository for experimental data useful for critical assessment of methods for structure-based electrostatics calculations. To help guide the development of computational methods the Cooperative will organize blind prediction exercises. As a first step, computational laboratories were invited to reproduce an unpublished set of experimental pKa values of acidic and basic residues introduced in the interior of staphylococcal nuclease by site-directed mutagenesis. The pKa values of these groups are unique and challenging to simulate owing to the large magnitude of their shifts relative to normal pKa values in water. Many computational methods were tested in this 1st Blind Prediction Challenge and critical assessment exercise. A workshop was organized in the Telluride Science Research Center to assess objectively the performance of many computational methods tested on this one extensive dataset. This volume of PROTEINS: Structure, Function, and Bioinformatics introduces the pKa Cooperative, presents reports submitted by participants in the blind prediction challenge, and highlights some of the problems in structure-based calculations identified during this exercise. PMID:22002877

Value Preferences Predicting Narcissistic Personality Traits in Young Adults

ERIC Educational Resources Information Center

Gungor, Ibrahim Halil; Eksi, Halil; Aricak, Osman Tolga

2012-01-01

This study aimed at showing how the value preferences of young adults could predict the narcissistic characteristics of young adults according to structural equation modeling. 133 female (59.6%) and 90 male (40.4%), total 223 young adults participated the study (average age: 25.66, ranging from 20 to 38). Ratio group sampling method was used while…

Quantitative structure-toxicity relationship of the aquatic toxicity for various narcotic pollutants using the norm indexes.

PubMed

Wang, Qiang; Jia, Qingzhu; Yan, Lihong; Xia, Shuqian; Ma, Peisheng

2014-08-01

The aquatic toxicity value of hazardous contaminants plays an important role in the risk assessments of aquatic ecosystems. The following study presents a stable and accurate structure-toxicity relationship model based on the norm indexes for the prediction of toxicity value (log(LC50)) for 190 diverse narcotic pollutants (96 h LC50 data for Poecilia reticulata). Research indicates that this new model is very efficient and provides satisfactory results. The suggested prediction model is evidenced by R(2) (square correlation coefficient) and ARD (average relative difference) values of 0.9376 and 10.45%, respectively, for the training set, and 0.9264 and 13.90% for the testing set. Comparison results with reference models demonstrate that this new method, based on the norm indexes proposed in this work, results in significant improvements, both in accuracy and stability for predicting aquatic toxicity values of narcotic pollutants. Copyright © 2014 Elsevier Ltd. All rights reserved.

RNA secondary structure prediction with pseudoknots: Contribution of algorithm versus energy model.

PubMed

Jabbari, Hosna; Wark, Ian; Montemagno, Carlo

2018-01-01

RNA is a biopolymer with various applications inside the cell and in biotechnology. Structure of an RNA molecule mainly determines its function and is essential to guide nanostructure design. Since experimental structure determination is time-consuming and expensive, accurate computational prediction of RNA structure is of great importance. Prediction of RNA secondary structure is relatively simpler than its tertiary structure and provides information about its tertiary structure, therefore, RNA secondary structure prediction has received attention in the past decades. Numerous methods with different folding approaches have been developed for RNA secondary structure prediction. While methods for prediction of RNA pseudoknot-free structure (structures with no crossing base pairs) have greatly improved in terms of their accuracy, methods for prediction of RNA pseudoknotted secondary structure (structures with crossing base pairs) still have room for improvement. A long-standing question for improving the prediction accuracy of RNA pseudoknotted secondary structure is whether to focus on the prediction algorithm or the underlying energy model, as there is a trade-off on computational cost of the prediction algorithm versus the generality of the method. The aim of this work is to argue when comparing different methods for RNA pseudoknotted structure prediction, the combination of algorithm and energy model should be considered and a method should not be considered superior or inferior to others if they do not use the same scoring model. We demonstrate that while the folding approach is important in structure prediction, it is not the only important factor in prediction accuracy of a given method as the underlying energy model is also as of great value. Therefore we encourage researchers to pay particular attention in comparing methods with different energy models.

Image Texture Predicts Avian Density and Species Richness

PubMed Central

Wood, Eric M.; Pidgeon, Anna M.; Radeloff, Volker C.; Keuler, Nicholas S.

2013-01-01

For decades, ecologists have measured habitat attributes in the field to understand and predict patterns of animal distribution and abundance. However, the scale of inference possible from field measured data is typically limited because large-scale data collection is rarely feasible. This is problematic given that conservation and management typical require data that are fine grained yet broad in extent. Recent advances in remote sensing methodology offer alternative tools for efficiently characterizing wildlife habitat across broad areas. We explored the use of remotely sensed image texture, which is a surrogate for vegetation structure, calculated from both an air photo and from a Landsat TM satellite image, compared with field-measured vegetation structure, characterized by foliage-height diversity and horizontal vegetation structure, to predict avian density and species richness within grassland, savanna, and woodland habitats at Fort McCoy Military Installation, Wisconsin, USA. Image texture calculated from the air photo best predicted density of a grassland associated species, grasshopper sparrow (Ammodramus savannarum), within grassland habitat (R2 = 0.52, p-value <0.001), and avian species richness among habitats (R2 = 0.54, p-value <0.001). Density of field sparrow (Spizella pusilla), a savanna associated species, was not particularly well captured by either field-measured or remotely sensed vegetation structure variables, but was best predicted by air photo image texture (R2 = 0.13, p-value = 0.002). Density of ovenbird (Seiurus aurocapillus), a woodland associated species, was best predicted by pixel-level satellite data (mean NDVI, R2 = 0.54, p-value <0.001). Surprisingly and interestingly, remotely sensed vegetation structure measures (i.e., image texture) were often better predictors of avian density and species richness than field-measured vegetation structure, and thus show promise as a valuable tool for mapping habitat quality and characterizing biodiversity across broad areas. PMID:23675463

A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data

PubMed Central

Jalem, Randy; Nakayama, Masanobu; Noda, Yusuke; Le, Tam; Takeuchi, Ichiro; Tateyama, Yoshitaka; Yamazaki, Hisatsugu

2018-01-01

Abstract Increasing attention has been paid to materials informatics approaches that promise efficient and fast discovery and optimization of functional inorganic materials. Technical breakthrough is urgently requested to advance this field and efforts have been made in the development of materials descriptors to encode or represent characteristics of crystalline solids, such as chemical composition, crystal structure, electronic structure, etc. We propose a general representation scheme for crystalline solids that lifts restrictions on atom ordering, cell periodicity, and system cell size based on structural descriptors of directly binned Voronoi-tessellation real feature values and atomic/chemical descriptors based on the electronegativity of elements in the crystal. Comparison was made vs. radial distribution function (RDF) feature vector, in terms of predictive accuracy on density functional theory (DFT) material properties: cohesive energy (CE), density (d), electronic band gap (BG), and decomposition energy (Ed). It was confirmed that the proposed feature vector from Voronoi real value binning generally outperforms the RDF-based one for the prediction of aforementioned properties. Together with electronegativity-based features, Voronoi-tessellation features from a given crystal structure that are derived from second-nearest neighbor information contribute significantly towards prediction. PMID:29707064

A general representation scheme for crystalline solids based on Voronoi-tessellation real feature values and atomic property data.

PubMed

Jalem, Randy; Nakayama, Masanobu; Noda, Yusuke; Le, Tam; Takeuchi, Ichiro; Tateyama, Yoshitaka; Yamazaki, Hisatsugu

2018-01-01

Increasing attention has been paid to materials informatics approaches that promise efficient and fast discovery and optimization of functional inorganic materials. Technical breakthrough is urgently requested to advance this field and efforts have been made in the development of materials descriptors to encode or represent characteristics of crystalline solids, such as chemical composition, crystal structure, electronic structure, etc. We propose a general representation scheme for crystalline solids that lifts restrictions on atom ordering, cell periodicity, and system cell size based on structural descriptors of directly binned Voronoi-tessellation real feature values and atomic/chemical descriptors based on the electronegativity of elements in the crystal. Comparison was made vs. radial distribution function (RDF) feature vector, in terms of predictive accuracy on density functional theory (DFT) material properties: cohesive energy (CE), density ( d ), electronic band gap (BG), and decomposition energy (Ed). It was confirmed that the proposed feature vector from Voronoi real value binning generally outperforms the RDF-based one for the prediction of aforementioned properties. Together with electronegativity-based features, Voronoi-tessellation features from a given crystal structure that are derived from second-nearest neighbor information contribute significantly towards prediction.

Geometric parameter analysis to predetermine optimal radiosurgery technique for the treatment of arteriovenous malformation

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

Mestrovic, Ante; Clark, Brenda G.; Department of Medical Physics, British Columbia Cancer Agency, Vancouver, British Columbia

2005-11-01

Purpose: To develop a method of predicting the values of dose distribution parameters of different radiosurgery techniques for treatment of arteriovenous malformation (AVM) based on internal geometric parameters. Methods and Materials: For each of 18 previously treated AVM patients, four treatment plans were created: circular collimator arcs, dynamic conformal arcs, fixed conformal fields, and intensity-modulated radiosurgery. An algorithm was developed to characterize the target and critical structure shape complexity and the position of the critical structures with respect to the target. Multiple regression was employed to establish the correlation between the internal geometric parameters and the dose distribution for differentmore » treatment techniques. The results from the model were applied to predict the dosimetric outcomes of different radiosurgery techniques and select the optimal radiosurgery technique for a number of AVM patients. Results: Several internal geometric parameters showing statistically significant correlation (p < 0.05) with the treatment planning results for each technique were identified. The target volume and the average minimum distance between the target and the critical structures were the most effective predictors for normal tissue dose distribution. The structure overlap volume with the target and the mean distance between the target and the critical structure were the most effective predictors for critical structure dose distribution. The predicted values of dose distribution parameters of different radiosurgery techniques were in close agreement with the original data. Conclusions: A statistical model has been described that successfully predicts the values of dose distribution parameters of different radiosurgery techniques and may be used to predetermine the optimal technique on a patient-to-patient basis.« less

Modelling biological invasions: species traits, species interactions, and habitat heterogeneity.

PubMed

Cannas, Sergio A; Marco, Diana E; Páez, Sergio A

2003-05-01

In this paper we explore the integration of different factors to understand, predict and control ecological invasions, through a general cellular automaton model especially developed. The model includes life history traits of several species in a modular structure interacting multiple cellular automata. We performed simulations using field values corresponding to the exotic Gleditsia triacanthos and native co-dominant trees in a montane area. Presence of G. triacanthos juvenile bank was a determinant condition for invasion success. Main parameters influencing invasion velocity were mean seed dispersal distance and minimum reproductive age. Seed production had a small influence on the invasion velocity. Velocities predicted by the model agreed well with estimations from field data. Values of population density predicted matched field values closely. The modular structure of the model, the explicit interaction between the invader and the native species, and the simplicity of parameters and transition rules are novel features of the model.

Validation of Molecular Dynamics Simulations for Prediction of Three-Dimensional Structures of Small Proteins.

PubMed

Kato, Koichi; Nakayoshi, Tomoki; Fukuyoshi, Shuichi; Kurimoto, Eiji; Oda, Akifumi

2017-10-12

Although various higher-order protein structure prediction methods have been developed, almost all of them were developed based on the three-dimensional (3D) structure information of known proteins. Here we predicted the short protein structures by molecular dynamics (MD) simulations in which only Newton's equations of motion were used and 3D structural information of known proteins was not required. To evaluate the ability of MD simulationto predict protein structures, we calculated seven short test protein (10-46 residues) in the denatured state and compared their predicted and experimental structures. The predicted structure for Trp-cage (20 residues) was close to the experimental structure by 200-ns MD simulation. For proteins shorter or longer than Trp-cage, root-mean square deviation values were larger than those for Trp-cage. However, secondary structures could be reproduced by MD simulations for proteins with 10-34 residues. Simulations by replica exchange MD were performed, but the results were similar to those from normal MD simulations. These results suggest that normal MD simulations can roughly predict short protein structures and 200-ns simulations are frequently sufficient for estimating the secondary structures of protein (approximately 20 residues). Structural prediction method using only fundamental physical laws are useful for investigating non-natural proteins, such as primitive proteins and artificial proteins for peptide-based drug delivery systems.

Validation of the Unthinned Loblolly Pine Plantation Yield Model-USLYCOWG

Treesearch

V. Clark Baldwin; D.P. Feduccia

1982-01-01

Yield and stand structure predictions from an unthinned loblolly pine plantation yield prediction system (USLYCOWG computer program) were compared with observations from 80 unthinned loblolly pine plots. Overall, the predicted estimates were reasonable when compared to observed values, but predictions based on input data at or near the system's limits may be in...

Prediction of Ras-effector interactions using position energy matrices.

PubMed

Kiel, Christina; Serrano, Luis

2007-09-01

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

QSAR studies on triazole derivatives as sglt inhibitors via CoMFA and CoMSIA

NASA Astrophysics Data System (ADS)

Zhi, Hui; Zheng, Junxia; Chang, Yiqun; Li, Qingguo; Liao, Guochao; Wang, Qi; Sun, Pinghua

2015-10-01

Forty-six sodium-dependent glucose cotransporters-2 (SGLT-2) inhibitors with hypoglycemic activity were selected to develop three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models. A training set of 39 compounds were used to build up the models, which were then evaluated by a series of internal and external cross-validation techniques. A test set of 7 compounds was used for the external validation. The CoMFA model predicted a q2 value of 0.792 and an r2 value of 0.985. The best CoMSIA model predicted a q2 value of 0.633 and an r2 value of 0.895 based on a combination of steric, electrostatic, hydrophobic and hydrogen-bond acceptor effects. The predictive correlation coefficients (rpred2) of CoMFA and CoMSIA models were 0.872 and 0.839, respectively. The analysis of the contour maps from each model provided insight into the structural requirements for the development of more active sglt inhibitors, and on the basis of the models 8 new sglt inhibitors were designed and predicted.

Phase Transition and Physical Properties of InS

NASA Astrophysics Data System (ADS)

Wang, Hai-Yan; Li, Xiao-Feng; Xu, Lei; Li, Xu-Sheng; Hu, Qian-Ku

2018-02-01

Using the crystal structure prediction method based on particle swarm optimization algorithm, three phases (Pnnm, C2/m and Pm-3m) for InS are predicted. The new phase Pm-3m of InS under high pressure is firstly reported in the work. The structural features and electronic structure under high pressure of InS are fully investigated. We predicted the stable ground-state structure of InS was the Pnnm phase and phase transformation of InS from Pnnm phase to Pm-3m phase is firstly found at the pressure of about 29.5 GPa. According to the calculated enthalpies of InS with four structures in the pressure range from 20 GPa to 45 GPa, we find the C2/m phase is a metastable phase. The calculated band gap value of about 2.08 eV for InS with Pnnm structure at 0 GPa agrees well with the experimental value. Moreover, the electronic structure suggests that the C2/m and Pm-3m phase are metallic phases. Supported by the National Natural Science Foundation of China under Grant Nos. 11404099, 11304140, 11147167 and Funds of Outstanding Youth of Henan Polytechnic University, China under Grant No. J2014-05

Managing Distance Education Institutions through Value Chain Analysis: the Nigerian Experience.

ERIC Educational Resources Information Center

Aderinto, J. A.; Akintayo, M. O.

Value chain analysis can gauge, analyze, and predict organization effects to control cost in light of achieving strategic organization objectives of distance education. Value chain analysis enables organizations to accomplish their goal or mission through cost effectiveness or differentiation. The value chain activity structure in a distance…

Extended Aging Theories for Predictions of Safe Operational Life of Critical Airborne Structural Components

NASA Technical Reports Server (NTRS)

Ko, William L.; Chen, Tony

2006-01-01

The previously developed Ko closed-form aging theory has been reformulated into a more compact mathematical form for easier application. A new equivalent loading theory and empirical loading theories have also been developed and incorporated into the revised Ko aging theory for the prediction of a safe operational life of airborne failure-critical structural components. The new set of aging and loading theories were applied to predict the safe number of flights for the B-52B aircraft to carry a launch vehicle, the structural life of critical components consumed by load excursion to proof load value, and the ground-sitting life of B-52B pylon failure-critical structural components. A special life prediction method was developed for the preflight predictions of operational life of failure-critical structural components of the B-52H pylon system, for which no flight data are available.

Personal spiritual values and quality of life: evidence from Chinese college students.

PubMed

Zhang, Kaili Chen; Hui, C Harry; Lam, Jasmine; Lau, Esther Yuet Ying; Cheung, Shu-Fai; Mok, Doris Shui Ying

2014-08-01

Values are guiding principles in our life. While some studies found spiritual values to be "healthier," Sagiv and Schwartz (Eur J Soc Psychol 30:177-198, 2000) showed that people holding non-spiritual values were higher on affective well-being. We examined the predictive power of these two types of values with a longitudinal data set collected from Chinese students mainly in Hong Kong. Structural equation modeling revealed that spiritual values (as well as family income) positively predicted quality of life a year later. Non-spiritual, self-enhancement values, did not show any association. Results suggest that developing spiritual values may promote well-being through enabling individuals to find meaning and purpose in life.

Prediction Model for the Carbonation of Post-Repair Materials in Carbonated RC Structures

PubMed Central

Lee, Hyung-Min; Lee, Han-Seung; Singh, Jitendra Kumar

2017-01-01

Concrete carbonation damages the passive film that surrounds reinforcement bars, resulting in their exposure to corrosion. Studies on the prediction of concrete carbonation are thus of great significance. The repair of pre-built reinforced concrete (RC) structures by methods such as remodeling was recently introduced. While many studies have been conducted on the progress of carbonation in newly constructed buildings and RC structures fitted with new repair materials, the prediction of post-repair carbonation has not been considered. In the present study, accelerated carbonation was carried out to investigate RC structures following surface layer repair, in order to determine the carbonation depth. To validate the obtained results, a second experiment was performed under the same conditions to determine the carbonation depth by the Finite Difference Method (FDM) and Finite Element Method (FEM). For the accelerated carbonation experiment, FDM and FEM analyses, produced very similar results, thus confirming that the carbonation depth in an RC structure after surface layer repair can be predicted with accuracy. The specimen repaired using inhibiting surface coating (ISC) had the highest carbonation penetration of 19.81, while this value was the lowest for the corrosion inhibiting mortar (IM) with 13.39 mm. In addition, the carbonation depth predicted by using the carbonation prediction formula after repair indicated that that the analytical and experimental values are almost identical if the initial concentration of Ca(OH)2 is assumed to be 52%. PMID:28772852

Comparison of measured temperatures, thermal stresses and creep residues with predictions on a built-up titanium structure

NASA Technical Reports Server (NTRS)

Jenkins, Jerald M.

1987-01-01

Temperature, thermal stresses, and residual creep stresses were studied by comparing laboratory values measured on a built-up titanium structure with values calculated from finite-element models. Several such models were used to examine the relationship between computational thermal stresses and thermal stresses measured on a built-up structure. Element suitability, element density, and computational temperature discrepancies were studied to determine their impact on measured and calculated thermal stress. The optimum number of elements is established from a balance between element density and suitable safety margins, such that the answer is acceptably safe yet is economical from a computational viewpoint. It is noted that situations exist where relatively small excursions of calculated temperatures from measured values result in far more than proportional increases in thermal stress values. Measured residual stresses due to creep significantly exceeded the values computed by the piecewise linear elastic strain analogy approach. The most important element in the computation is the correct definition of the creep law. Computational methodology advances in predicting residual stresses due to creep require significantly more viscoelastic material characterization.

Correlating Structural Order with Structural Rearrangement in Dusty Plasma Liquids: Can Structural Rearrangement be Predicted by Static Structural Information?

NASA Astrophysics Data System (ADS)

Su, Yen-Shuo; Liu, Yu-Hsuan; I, Lin

2012-11-01

Whether the static microstructural order information is strongly correlated with the subsequent structural rearrangement (SR) and their predicting power for SR are investigated experimentally in the quenched dusty plasma liquid with microheterogeneities. The poor local structural order is found to be a good alarm to identify the soft spot and predict the short term SR. For the site with good structural order, the persistent time for sustaining the structural memory until SR has a large mean value but a broad distribution. The deviation of the local structural order from that averaged over nearest neighbors serves as a good second alarm to further sort out the short time SR sites. It has the similar sorting power to that using the temporal fluctuation of the local structural order over a small time interval.

Evaluating the accuracy of SHAPE-directed RNA secondary structure predictions

PubMed Central

Sükösd, Zsuzsanna; Swenson, M. Shel; Kjems, Jørgen; Heitsch, Christine E.

2013-01-01

Recent advances in RNA structure determination include using data from high-throughput probing experiments to improve thermodynamic prediction accuracy. We evaluate the extent and nature of improvements in data-directed predictions for a diverse set of 16S/18S ribosomal sequences using a stochastic model of experimental SHAPE data. The average accuracy for 1000 data-directed predictions always improves over the original minimum free energy (MFE) structure. However, the amount of improvement varies with the sequence, exhibiting a correlation with MFE accuracy. Further analysis of this correlation shows that accurate MFE base pairs are typically preserved in a data-directed prediction, whereas inaccurate ones are not. Thus, the positive predictive value of common base pairs is consistently higher than the directed prediction accuracy. Finally, we confirm sequence dependencies in the directability of thermodynamic predictions and investigate the potential for greater accuracy improvements in the worst performing test sequence. PMID:23325843

Evaluation of dual flow thrust vectored nozzles with exhaust stream impingement. MS Thesis Final Technical Report, Oct. 1990 - Jul. 1991

NASA Technical Reports Server (NTRS)

Carpenter, Thomas W.

1991-01-01

The main objective of this project was to predict the expansion wave/oblique shock wave structure in an under-expanded jet expanding from a convergent nozzle. The shock structure was predicted by combining the calculated curvature of the free pressure boundary with principles and governing equations relating to oblique shock wave and expansion wave interaction. The procedure was then continued until the shock pattern repeated itself. A mathematical model was then formulated and written in FORTRAN to calculate the oblique shock/expansion wave structure within the jet. In order to study shock waves in expanding jets, Schlieren photography, a form of flow visualization, was employed. Thirty-six Schlieren photographs of jets from both a straight and 15 degree nozzle were taken. An iterative procedure was developed to calculate the shock structure within the jet and predict the non-dimensional values of Prandtl primary wavelength (w/rn), distance to Mach Disc (Ld) and Mach Disc radius (rd). These values were then compared to measurements taken from Schlieren photographs and experimental results. The results agreed closely to measurements from Schlieren photographs and previously obtained data. This method provides excellent results for pressure ratios below that at which a Mach Disc first forms. Calculated values of non-dimensional distance to the Mach Disc (Ld) agreed closely to values measured from Schlieren photographs and published data. The calculated values of non-dimensional Mach Disc radius (rd), however, deviated from published data by as much as 25 percent at certain pressure ratios.

Structure, Process, and Outcome Quality of Surgical Site Infection Surveillance in Switzerland.

PubMed

Kuster, Stefan P; Eisenring, Marie-Christine; Sax, Hugo; Troillet, Nicolas

2017-10-01

OBJECTIVE To assess the structure and quality of surveillance activities and to validate outcome detection in the Swiss national surgical site infection (SSI) surveillance program. DESIGN Countrywide survey of SSI surveillance quality. SETTING 147 hospitals or hospital units with surgical activities in Switzerland. METHODS Site visits were conducted with on-site structured interviews and review of a random sample of 15 patient records per hospital: 10 from the entire data set and 5 from a subset of patients with originally reported infection. Process and structure were rated in 9 domains with a weighted overall validation score, and sensitivity, specificity, positive predictive value, and negative predictive value were calculated for the identification of SSI. RESULTS Of 50 possible points, the median validation score was 35.5 (range, 16.25-48.5). Public hospitals (P<.001), hospitals in the Italian-speaking region of Switzerland (P=.021), and hospitals with longer participation in the surveillance (P=.018) had higher scores than others. Domains that contributed most to lower scores were quality of chart review and quality of data extraction. Of 49 infections, 15 (30.6%) had been overlooked in a random sample of 1,110 patient records, accounting for a sensitivity of 69.4% (95% confidence interval [CI], 54.6%-81.7%), a specificity of 99.9% (95% CI, 99.5%-100%), a positive predictive value of 97.1% (95% CI, 85.1%-99.9%), and a negative predictive value of 98.6% (95% CI, 97.7%-99.2%). CONCLUSIONS Irrespective of a well-defined surveillance methodology, there is a wide variation of SSI surveillance quality. The quality of chart review and the accuracy of data collection are the main areas for improvement. Infect Control Hosp Epidemiol 2017;38:1172-1181.

Protein structure refinement using a quantum mechanics-based chemical shielding predictor.

PubMed

Bratholm, Lars A; Jensen, Jan H

2017-03-01

The accurate prediction of protein chemical shifts using a quantum mechanics (QM)-based method has been the subject of intense research for more than 20 years but so far empirical methods for chemical shift prediction have proven more accurate. In this paper we show that a QM-based predictor of a protein backbone and CB chemical shifts (ProCS15, PeerJ , 2016, 3, e1344) is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors. We present a method by which quantum chemistry based predictions of isotropic chemical shielding values (ProCS15) can be used to refine protein structures using Markov Chain Monte Carlo (MCMC) simulations, relating the chemical shielding values to the experimental chemical shifts probabilistically. Two kinds of MCMC structural refinement simulations were performed using force field geometry optimized X-ray structures as starting points: simulated annealing of the starting structure and constant temperature MCMC simulation followed by simulated annealing of a representative ensemble structure. Annealing of the CHARMM structure changes the CA-RMSD by an average of 0.4 Å but lowers the chemical shift RMSD by 1.0 and 0.7 ppm for CA and N. Conformational averaging has a relatively small effect (0.1-0.2 ppm) on the overall agreement with carbon chemical shifts but lowers the error for nitrogen chemical shifts by 0.4 ppm. If an amino acid specific offset is included the ProCS15 predicted chemical shifts have RMSD values relative to experiments that are comparable to popular empirical chemical shift predictors. The annealed representative ensemble structures differ in CA-RMSD relative to the initial structures by an average of 2.0 Å, with >2.0 Å difference for six proteins. In four of the cases, the largest structural differences arise in structurally flexible regions of the protein as determined by NMR, and in the remaining two cases, the large structural change may be due to force field deficiencies. The overall accuracy of the empirical methods are slightly improved by annealing the CHARMM structure with ProCS15, which may suggest that the minor structural changes introduced by ProCS15-based annealing improves the accuracy of the protein structures. Having established that QM-based chemical shift prediction can deliver the same accuracy as empirical shift predictors we hope this can help increase the accuracy of related approaches such as QM/MM or linear scaling approaches or interpreting protein structural dynamics from QM-derived chemical shift.

Developing hybrid approaches to predict pKa values of ionizable groups

PubMed Central

Witham, Shawn; Talley, Kemper; Wang, Lin; Zhang, Zhe; Sarkar, Subhra; Gao, Daquan; Yang, Wei

2011-01-01

Accurate predictions of pKa values of titratable groups require taking into account all relevant processes associated with the ionization/deionization. Frequently, however, the ionization does not involve significant structural changes and the dominating effects are purely electrostatic in origin allowing accurate predictions to be made based on the electrostatic energy difference between ionized and neutral forms alone using a static structure. On another hand, if the change of the charge state is accompanied by a structural reorganization of the target protein, then the relevant conformational changes have to be taken into account in the pKa calculations. Here we report a hybrid approach that first predicts the titratable groups, which ionization is expected to cause conformational changes, termed “problematic” residues, then applies a special protocol on them, while the rest of the pKa’s are predicted with rigid backbone approach as implemented in multi-conformation continuum electrostatics (MCCE) method. The backbone representative conformations for “problematic” groups are generated with either molecular dynamics simulations with charged and uncharged amino acid or with ab-initio local segment modeling. The corresponding ensembles are then used to calculate the pKa of the “problematic” residues and then the results are averaged. PMID:21744395

Pressure-induced structural phase transition in transition metal carbides TMC (TM = Ru, Rh, Pd, Os, Ir, Pt): a DFT study

NASA Astrophysics Data System (ADS)

Manikandan, M.; Rajeswarapalanichamy, R.; Iyakutti, K.

2018-03-01

First-principles calculations based on density functional theory was performed to analyse the structural stability of transition metal carbides TMC (TM = Ru, Rh, Pd, Os, Ir, Pt). It is observed that zinc-blende phase is the most stable one for these carbides. Pressure-induced structural phase transition from zinc blende to NiAs phase is predicted at the pressures of 248.5 GPa, 127 GPa and 142 GPa for OsC, IrC and PtC, respectively. The electronic structure reveals that RuC exhibits a semiconducting behaviour with an energy gap of 0.7056 eV. The high bulk modulus values of these carbides indicate that these metal carbides are super hard materials. The high B/G value predicts that the carbides are ductile in their most stable phase.

Using Lidar and Radar measurements to constrain predictions of forest ecosystem structure and function.

PubMed

Antonarakis, Alexander S; Saatchi, Sassan S; Chazdon, Robin L; Moorcroft, Paul R

2011-06-01

Insights into vegetation and aboveground biomass dynamics within terrestrial ecosystems have come almost exclusively from ground-based forest inventories that are limited in their spatial extent. Lidar and synthetic-aperture Radar are promising remote-sensing-based techniques for obtaining comprehensive measurements of forest structure at regional to global scales. In this study we investigate how Lidar-derived forest heights and Radar-derived aboveground biomass can be used to constrain the dynamics of the ED2 terrestrial biosphere model. Four-year simulations initialized with Lidar and Radar structure variables were compared against simulations initialized from forest-inventory data and output from a long-term potential-vegtation simulation. Both height and biomass initializations from Lidar and Radar measurements significantly improved the representation of forest structure within the model, eliminating the bias of too many large trees that arose in the potential-vegtation-initialized simulation. The Lidar and Radar initializations decreased the proportion of larger trees estimated by the potential vegetation by approximately 20-30%, matching the forest inventory. This resulted in improved predictions of ecosystem-scale carbon fluxes and structural dynamics compared to predictions from the potential-vegtation simulation. The Radar initialization produced biomass values that were 75% closer to the forest inventory, with Lidar initializations producing canopy height values closest to the forest inventory. Net primary production values for the Radar and Lidar initializations were around 6-8% closer to the forest inventory. Correcting the Lidar and Radar initializations for forest composition resulted in improved biomass and basal-area dynamics as well as leaf-area index. Correcting the Lidar and Radar initializations for forest composition and fine-scale structure by combining the remote-sensing measurements with ground-based inventory data further improved predictions, suggesting that further improvements of structural and carbon-flux metrics will also depend on obtaining reliable estimates of forest composition and accurate representation of the fine-scale vertical and horizontal structure of plant canopies.

Accurate Prediction of Contact Numbers for Multi-Spanning Helical Membrane Proteins

PubMed Central

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

2017-01-01

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

Tensile and compressive modulus of elasticity of pultruded fiber-reinforced polymer composite materials

NASA Astrophysics Data System (ADS)

Lee, J. H.; Kim, S. H.; Park, J. K.; Choi, W. C.; Yoon, S. J.

2018-06-01

Many researches focused on the mechanical properties of steel and concrete have been carried out for applications in the construction industry. However, in order to clarify the mechanical properties of pultruded fiber-reinforced polymer (PFRP) structural members for construction, testing is needed. Deriving the mechanical properties of PFRP structural members through testing is difficult, however, because some members cannot be tested easily due to their cross-section dimensions. This paper reports a part of studies that attempt to present conservative results in the case of members that cannot be tested reasonably. The authors obtained and compared experimental and theoretical modulus of elasticity values. If the mechanical properties of PFRP members can be predicted using reasonable and conservative values, then the structure can be designed economically and safely even in the early design stages. To this end, this paper proposes a strain energy approach as a conservative and convenient way to predict the mechanical properties of PFRP structural members. The strain energy data obtained can be used to predict the mechanical properties of PFRP members in the construction field.

Prediction of β-turns in proteins from multiple alignment using neural network

PubMed Central

Kaur, Harpreet; Raghava, Gajendra Pal Singh

2003-01-01

A neural network-based method has been developed for the prediction of β-turns in proteins by using multiple sequence alignment. Two feed-forward back-propagation networks with a single hidden layer are used where the first-sequence structure network is trained with the multiple sequence alignment in the form of PSI-BLAST–generated position-specific scoring matrices. The initial predictions from the first network and PSIPRED-predicted secondary structure are used as input to the second structure-structure network to refine the predictions obtained from the first net. A significant improvement in prediction accuracy has been achieved by using evolutionary information contained in the multiple sequence alignment. The final network yields an overall prediction accuracy of 75.5% when tested by sevenfold cross-validation on a set of 426 nonhomologous protein chains. The corresponding Qpred, Qobs, and Matthews correlation coefficient values are 49.8%, 72.3%, and 0.43, respectively, and are the best among all the previously published β-turn prediction methods. The Web server BetaTPred2 (http://www.imtech.res.in/raghava/betatpred2/) has been developed based on this approach. PMID:12592033

Building a knowledge-based statistical potential by capturing high-order inter-residue interactions and its applications in protein secondary structure assessment.

PubMed

Li, Yaohang; Liu, Hui; Rata, Ionel; Jakobsson, Eric

2013-02-25

The rapidly increasing number of protein crystal structures available in the Protein Data Bank (PDB) has naturally made statistical analyses feasible in studying complex high-order inter-residue correlations. In this paper, we report a context-based secondary structure potential (CSSP) for assessing the quality of predicted protein secondary structures generated by various prediction servers. CSSP is a sequence-position-specific knowledge-based potential generated based on the potentials of mean force approach, where high-order inter-residue interactions are taken into consideration. The CSSP potential is effective in identifying secondary structure predictions with good quality. In 56% of the targets in the CB513 benchmark, the optimal CSSP potential is able to recognize the native secondary structure or a prediction with Q3 accuracy higher than 90% as best scored in the predicted secondary structures generated by 10 popularly used secondary structure prediction servers. In more than 80% of the CB513 targets, the predicted secondary structures with the lowest CSSP potential values yield higher than 80% Q3 accuracy. Similar performance of CSSP is found on the CASP9 targets as well. Moreover, our computational results also show that the CSSP potential using triplets outperforms the CSSP potential using doublets and is currently better than the CSSP potential using quartets.

Multi-allelic haplotype model based on genetic partition for genomic prediction and variance component estimation using SNP markers.

PubMed

Da, Yang

2015-12-18

The amount of functional genomic information has been growing rapidly but remains largely unused in genomic selection. Genomic prediction and estimation using haplotypes in genome regions with functional elements such as all genes of the genome can be an approach to integrate functional and structural genomic information for genomic selection. Towards this goal, this article develops a new haplotype approach for genomic prediction and estimation. A multi-allelic haplotype model treating each haplotype as an 'allele' was developed for genomic prediction and estimation based on the partition of a multi-allelic genotypic value into additive and dominance values. Each additive value is expressed as a function of h - 1 additive effects, where h = number of alleles or haplotypes, and each dominance value is expressed as a function of h(h - 1)/2 dominance effects. For a sample of q individuals, the limit number of effects is 2q - 1 for additive effects and is the number of heterozygous genotypes for dominance effects. Additive values are factorized as a product between the additive model matrix and the h - 1 additive effects, and dominance values are factorized as a product between the dominance model matrix and the h(h - 1)/2 dominance effects. Genomic additive relationship matrix is defined as a function of the haplotype model matrix for additive effects, and genomic dominance relationship matrix is defined as a function of the haplotype model matrix for dominance effects. Based on these results, a mixed model implementation for genomic prediction and variance component estimation that jointly use haplotypes and single markers is established, including two computing strategies for genomic prediction and variance component estimation with identical results. The multi-allelic genetic partition fills a theoretical gap in genetic partition by providing general formulations for partitioning multi-allelic genotypic values and provides a haplotype method based on the quantitative genetics model towards the utilization of functional and structural genomic information for genomic prediction and estimation.

Longitudinal association of short-term, metronome-paced heart rate variability and echocardiographically assessed cardiac structure at a 4-year follow-up: results from the prospective, population-based CARLA cohort.

PubMed

Medenwald, Daniel; Swenne, Cees A; Frantz, Stefan; Nuding, Sebastian; Kors, Jan A; Pietzner, Diana; Tiller, Daniel; Greiser, Karin H; Kluttig, Alexander; Haerting, Johannes

2017-12-01

To assess the value of cardiac structure/function in predicting heart rate variability (HRV) and the possibly predictive value of HRV on cardiac parameters. Baseline and 4-year follow-up data from the population-based CARLA cohort were used (790 men, 646 women, aged 45-83 years at baseline and 50-87 years at follow-up). Echocardiographic and HRV recordings were performed at baseline and at follow-up. Linear regression models with a quadratic term were used. Crude and covariate adjusted estimates were calculated. Missing values were imputed by means of multiple imputation. Heart rate variability measures taken into account consisted of linear time and frequency domain [standard deviation of normal-to-normal intervals (SDNN), high-frequency power (HF), low-frequency power (LF), LF/HF ratio] and non-linear measures [detrended fluctuation analysis (DFA1), SD1, SD2, SD1/SD2 ratio]. Echocardiographic parameters considered were ventricular mass index, diastolic interventricular septum thickness, left ventricular diastolic dimension, left atrial dimension systolic (LADS), and ejection fraction (Teichholz). A negative quadratic relation between baseline LADS and change in SDNN and HF was observed. The maximum HF and SDNN change (an increase of roughly 0.02%) was predicted at LADS of 3.72 and 3.57 cm, respectively, while the majority of subjects experienced a decrease in HRV. There was no association between further echocardiographic parameters and change in HRV, and there was no evidence of a predictive value of HRV in the prediction of changes in cardiac structure. In the general population, LADS predicts 4-year alteration in SDNN and HF non-linearly. Because of the novelty of the result, analyses should be replicated in other populations. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2017. For permissions please email: journals.permissions@oup.com.

Estimation of ground motion parameters

USGS Publications Warehouse

Boore, David M.; Joyner, W.B.; Oliver, A.A.; Page, R.A.

1978-01-01

Strong motion data from western North America for earthquakes of magnitude greater than 5 are examined to provide the basis for estimating peak acceleration, velocity, displacement, and duration as a function of distance for three magnitude classes. A subset of the data (from the San Fernando earthquake) is used to assess the effects of structural size and of geologic site conditions on peak motions recorded at the base of structures. Small but statistically significant differences are observed in peak values of horizontal acceleration, velocity and displacement recorded on soil at the base of small structures compared with values recorded at the base of large structures. The peak acceleration tends to b3e less and the peak velocity and displacement tend to be greater on the average at the base of large structures than at the base of small structures. In the distance range used in the regression analysis (15-100 km) the values of peak horizontal acceleration recorded at soil sites in the San Fernando earthquake are not significantly different from the values recorded at rock sites, but values of peak horizontal velocity and displacement are significantly greater at soil sites than at rock sites. Some consideration is given to the prediction of ground motions at close distances where there are insufficient recorded data points. As might be expected from the lack of data, published relations for predicting peak horizontal acceleration give widely divergent estimates at close distances (three well known relations predict accelerations between 0.33 g to slightly over 1 g at a distance of 5 km from a magnitude 6.5 earthquake). After considering the physics of the faulting process, the few available data close to faults, and the modifying effects of surface topography, at the present time it would be difficult to accept estimates less than about 0.8 g, 110 cm/s, and 40 cm, respectively, for the mean values of peak acceleration, velocity, and displacement at rock sites within 5 km of fault rupture in a magnitude 6.5 earthquake. These estimates can be expected to change as more data become available.

Operational experience with VAWT blades. [structural performance

NASA Technical Reports Server (NTRS)

Sullivan, W. N.

1979-01-01

The structural performance of 17 meter diameter wind turbine rotors is discussed. Test results for typical steady and vibratory stress measurements are summarized along with predicted values of stress based on a quasi-static finite element model.

Information-theoretic indices usage for the prediction and calculation of octanol-water partition coefficient.

PubMed

Persona, Marek; Kutarov, Vladimir V; Kats, Boris M; Persona, Andrzej; Marczewska, Barbara

2007-01-01

The paper describes the new prediction method of octanol-water partition coefficient, which is based on molecular graph theory. The results obtained using the new method are well correlated with experimental values. These results were compared with the ones obtained by use of ten other structure correlated methods. The comparison shows that graph theory can be very useful in structure correlation research.

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

Coupled rotor/airframe vibration analysis

NASA Technical Reports Server (NTRS)

Sopher, R.; Studwell, R. E.; Cassarino, S.; Kottapalli, S. B. R.

1982-01-01

A coupled rotor/airframe vibration analysis developed as a design tool for predicting helicopter vibrations and a research tool to quantify the effects of structural properties, aerodynamic interactions, and vibration reduction devices on vehicle vibration levels is described. The analysis consists of a base program utilizing an impedance matching technique to represent the coupled rotor/airframe dynamics of the system supported by inputs from several external programs supplying sophisticated rotor and airframe aerodynamic and structural dynamic representation. The theoretical background, computer program capabilities and limited correlation results are presented in this report. Correlation results using scale model wind tunnel results show that the analysis can adequately predict trends of vibration variations with airspeed and higher harmonic control effects. Predictions of absolute values of vibration levels were found to be very sensitive to modal characteristics and results were not representative of measured values.

Prediction of atmospheric degradation data for POPs by gene expression programming.

PubMed

Luan, F; Si, H Z; Liu, H T; Wen, Y Y; Zhang, X Y

2008-01-01

Quantitative structure-activity relationship models for the prediction of the mean and the maximum atmospheric degradation half-life values of persistent organic pollutants were developed based on the linear heuristic method (HM) and non-linear gene expression programming (GEP). Molecular descriptors, calculated from the structures alone, were used to represent the characteristics of the compounds. HM was used both to pre-select the whole descriptor sets and to build the linear model. GEP yielded satisfactory prediction results: the square of the correlation coefficient r(2) was 0.80 and 0.81 for the mean and maximum half-life values of the test set, and the root mean square errors were 0.448 and 0.426, respectively. The results of this work indicate that the GEP is a very promising tool for non-linear approximations.

Thermodynamic characterization of tandem mismatches found in naturally occurring RNA

PubMed Central

Christiansen, Martha E.; Znosko, Brent M.

2009-01-01

Although all sequence symmetric tandem mismatches and some sequence asymmetric tandem mismatches have been thermodynamically characterized and a model has been proposed to predict the stability of previously unmeasured sequence asymmetric tandem mismatches [Christiansen,M.E. and Znosko,B.M. (2008) Biochemistry, 47, 4329–4336], experimental thermodynamic data for frequently occurring tandem mismatches is lacking. Since experimental data is preferred over a predictive model, the thermodynamic parameters for 25 frequently occurring tandem mismatches were determined. These new experimental values, on average, are 1.0 kcal/mol different from the values predicted for these mismatches using the previous model. The data for the sequence asymmetric tandem mismatches reported here were then combined with the data for 72 sequence asymmetric tandem mismatches that were published previously, and the parameters used to predict the thermodynamics of previously unmeasured sequence asymmetric tandem mismatches were updated. The average absolute difference between the measured values and the values predicted using these updated parameters is 0.5 kcal/mol. This updated model improves the prediction for tandem mismatches that were predicted rather poorly by the previous model. This new experimental data and updated predictive model allow for more accurate calculations of the free energy of RNA duplexes containing tandem mismatches, and, furthermore, should allow for improved prediction of secondary structure from sequence. PMID:19509311

Rational Design and Tuning of Functional RNA Switch to Control an Allosteric Intermolecular Interaction.

PubMed

Endoh, Tamaki; Sugimoto, Naoki

2015-08-04

Conformational transitions of biomolecules in response to specific stimuli control many biological processes. In natural functional RNA switches, often called riboswitches, a particular RNA structure that has a suppressive or facilitative effect on gene expression transitions to an alternative structure with the opposite effect upon binding of a specific metabolite to the aptamer region. Stability of RNA secondary structure (-ΔG°) can be predicted based on thermodynamic parameters and is easily tuned by changes in nucleobases. We envisioned that tuning of a functional RNA switch that causes an allosteric interaction between an RNA and a peptide would be possible based on a predicted switching energy (ΔΔG°) that corresponds to the energy difference between the RNA secondary structure before (-ΔG°before) and after (-ΔG°after) the RNA conformational transition. We first selected functional RNA switches responsive to neomycin with predicted ΔΔG° values ranging from 5.6 to 12.2 kcal mol(-1). We then demonstrated a simple strategy to rationally convert the functional RNA switch to switches responsive to natural metabolites thiamine pyrophosphate, S-adenosyl methionine, and adenine based on the predicted ΔΔG° values. The ΔΔG° values of the designed RNA switches proportionally correlated with interaction energy (ΔG°interaction) between the RNA and peptide, and we were able to tune the sensitivity of the RNA switches for the trigger molecule. The strategy demonstrated here will be generally applicable for construction of functional RNA switches and biosensors in which mechanisms are based on conformational transition of nucleic acids.

Capturing anharmonicity in a lattice thermal conductivity model for high-throughput predictions

DOE PAGES

Miller, Samuel A.; Gorai, Prashun; Ortiz, Brenden R.; ...

2017-01-06

High-throughput, low-cost, and accurate predictions of thermal properties of new materials would be beneficial in fields ranging from thermal barrier coatings and thermoelectrics to integrated circuits. To date, computational efforts for predicting lattice thermal conductivity (κ L) have been hampered by the complexity associated with computing multiple phonon interactions. In this work, we develop and validate a semiempirical model for κ L by fitting density functional theory calculations to experimental data. Experimental values for κ L come from new measurements on SrIn 2O 4, Ba 2SnO 4, Cu 2ZnSiTe 4, MoTe 2, Ba 3In 2O 6, Cu 3TaTe 4, SnO,more » and InI as well as 55 compounds from across the published literature. Here, to capture the anharmonicity in phonon interactions, we incorporate a structural parameter that allows the model to predict κ L within a factor of 1.5 of the experimental value across 4 orders of magnitude in κ L values and over a diverse chemical and structural phase space, with accuracy similar to or better than that of computationally more expensive models.« less

The habenula encodes negative motivational value associated with primary punishment in humans.

PubMed

Lawson, Rebecca P; Seymour, Ben; Loh, Eleanor; Lutti, Antoine; Dolan, Raymond J; Dayan, Peter; Weiskopf, Nikolaus; Roiser, Jonathan P

2014-08-12

Learning what to approach, and what to avoid, involves assigning value to environmental cues that predict positive and negative events. Studies in animals indicate that the lateral habenula encodes the previously learned negative motivational value of stimuli. However, involvement of the habenula in dynamic trial-by-trial aversive learning has not been assessed, and the functional role of this structure in humans remains poorly characterized, in part, due to its small size. Using high-resolution functional neuroimaging and computational modeling of reinforcement learning, we demonstrate positive habenula responses to the dynamically changing values of cues signaling painful electric shocks, which predict behavioral suppression of responses to those cues across individuals. By contrast, negative habenula responses to monetary reward cue values predict behavioral invigoration. Our findings show that the habenula plays a key role in an online aversive learning system and in generating associated motivated behavior in humans.

Expectancy-Value and Cognitive Process Outcomes in Mathematics Learning: A Structural Equation Analysis

ERIC Educational Resources Information Center

Phan, Huy P.

2014-01-01

Existing research has yielded evidence to indicate that the expectancy-value theoretical model predicts students' learning in various achievement contexts. Achievement values and self-efficacy expectations, for example, have been found to exert positive effects on cognitive process and academic achievement outcomes. We tested a conceptual model…

The value of urban tree cover: A hedonic property price model in Ramsey and Dakota Counties, Minnesota, USA

Treesearch

Heather Sander; Stephen Polasky; Robert. Haight

2010-01-01

Urban tree cover benefits communities. These benefits' economic values, however, are poorly recognized and often ignored by landowners and planners. We use hedonic property price modeling to estimate urban tree cover's value in Dakota and Ramsey Counties, MN, USA, predicting housing value as a function of structural, neighborhood, and environmental variables...

Forecasting impact injuries of unrestrained occupants in railway vehicle passenger compartments.

PubMed

Xie, Suchao; Zhou, Hui

2014-01-01

In order to predict the injury parameters of the occupants corresponding to different experimental parameters and to determine impact injury indices conveniently and efficiently, a model forecasting occupant impact injury was established in this work. The work was based on finite experimental observation values obtained by numerical simulation. First, the various factors influencing the impact injuries caused by the interaction between unrestrained occupants and the compartment's internal structures were collated and the most vulnerable regions of the occupant's body were analyzed. Then, the forecast model was set up based on a genetic algorithm-back propagation (GA-BP) hybrid algorithm, which unified the individual characteristics of the back propagation-artificial neural network (BP-ANN) model and the genetic algorithm (GA). The model was well suited to studies of occupant impact injuries and allowed multiple-parameter forecasts of the occupant impact injuries to be realized assuming values for various influencing factors. Finally, the forecast results for three types of secondary collision were analyzed using forecasting accuracy evaluation methods. All of the results showed the ideal accuracy of the forecast model. When an occupant faced a table, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 6.0 percent and the average relative error (ARE) values did not exceed 3.0 percent. When an occupant faced a seat, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 5.2 percent and the ARE values did not exceed 3.1 percent. When the occupant faced another occupant, the relative errors between the predicted and experimental values of the respective injury parameters were kept within ± 6.3 percent and the ARE values did not exceed 3.8 percent. The injury forecast model established in this article reduced repeat experiment times and improved the design efficiency of the internal compartment's structure parameters, and it provided a new way for assessing the safety performance of the interior structural parameters in existing, and newly designed, railway vehicle compartments.

Protein structure refinement using a quantum mechanics-based chemical shielding predictor† †Electronic supplementary information (ESI) available. See DOI: 10.1039/c6sc04344e Click here for additional data file.

PubMed Central

2017-01-01

The accurate prediction of protein chemical shifts using a quantum mechanics (QM)-based method has been the subject of intense research for more than 20 years but so far empirical methods for chemical shift prediction have proven more accurate. In this paper we show that a QM-based predictor of a protein backbone and CB chemical shifts (ProCS15, PeerJ, 2016, 3, e1344) is of comparable accuracy to empirical chemical shift predictors after chemical shift-based structural refinement that removes small structural errors. We present a method by which quantum chemistry based predictions of isotropic chemical shielding values (ProCS15) can be used to refine protein structures using Markov Chain Monte Carlo (MCMC) simulations, relating the chemical shielding values to the experimental chemical shifts probabilistically. Two kinds of MCMC structural refinement simulations were performed using force field geometry optimized X-ray structures as starting points: simulated annealing of the starting structure and constant temperature MCMC simulation followed by simulated annealing of a representative ensemble structure. Annealing of the CHARMM structure changes the CA-RMSD by an average of 0.4 Å but lowers the chemical shift RMSD by 1.0 and 0.7 ppm for CA and N. Conformational averaging has a relatively small effect (0.1–0.2 ppm) on the overall agreement with carbon chemical shifts but lowers the error for nitrogen chemical shifts by 0.4 ppm. If an amino acid specific offset is included the ProCS15 predicted chemical shifts have RMSD values relative to experiments that are comparable to popular empirical chemical shift predictors. The annealed representative ensemble structures differ in CA-RMSD relative to the initial structures by an average of 2.0 Å, with >2.0 Å difference for six proteins. In four of the cases, the largest structural differences arise in structurally flexible regions of the protein as determined by NMR, and in the remaining two cases, the large structural change may be due to force field deficiencies. The overall accuracy of the empirical methods are slightly improved by annealing the CHARMM structure with ProCS15, which may suggest that the minor structural changes introduced by ProCS15-based annealing improves the accuracy of the protein structures. Having established that QM-based chemical shift prediction can deliver the same accuracy as empirical shift predictors we hope this can help increase the accuracy of related approaches such as QM/MM or linear scaling approaches or interpreting protein structural dynamics from QM-derived chemical shift. PMID:28451325

QSAR models for predicting octanol/water and organic carbon/water partition coefficients of polychlorinated biphenyls.

PubMed

Yu, S; Gao, S; Gan, Y; Zhang, Y; Ruan, X; Wang, Y; Yang, L; Shi, J

2016-04-01

Quantitative structure-property relationship modelling can be a valuable alternative method to replace or reduce experimental testing. In particular, some endpoints such as octanol-water (KOW) and organic carbon-water (KOC) partition coefficients of polychlorinated biphenyls (PCBs) are easier to predict and various models have been already developed. In this paper, two different methods, which are multiple linear regression based on the descriptors generated using Dragon software and hologram quantitative structure-activity relationships, were employed to predict suspended particulate matter (SPM) derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of 209 PCBs. The predictive ability of the derived models was validated using a test set. The performances of all these models were compared with EPI Suite™ software. The results indicated that the proposed models were robust and satisfactory, and could provide feasible and promising tools for the rapid assessment of the SPM derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of PCBs.

Quantitative Structure – Property Relationship Modeling of Remote Liposome Loading Of Drugs

PubMed Central

Cern, Ahuva; Golbraikh, Alexander; Sedykh, Aleck; Tropsha, Alexander; Barenholz, Yechezkel; Goldblum, Amiram

2012-01-01

Remote loading of liposomes by trans-membrane gradients is used to achieve therapeutically efficacious intra-liposome concentrations of drugs. We have developed Quantitative Structure Property Relationship (QSPR) models of remote liposome loading for a dataset including 60 drugs studied in 366 loading experiments internally or elsewhere. Both experimental conditions and computed chemical descriptors were employed as independent variables to predict the initial drug/lipid ratio (D/L) required to achieve high loading efficiency. Both binary (to distinguish high vs. low initial D/L) and continuous (to predict real D/L values) models were generated using advanced machine learning approaches and five-fold external validation. The external prediction accuracy for binary models was as high as 91–96%; for continuous models the mean coefficient R2 for regression between predicted versus observed values was 0.76–0.79. We conclude that QSPR models can be used to identify candidate drugs expected to have high remote loading capacity while simultaneously optimizing the design of formulation experiments. PMID:22154932

The evaluation of different forest structural indices to predict the stand aboveground biomass of even-aged Scotch pine (Pinus sylvestris L.) forests in Kunduz, Northern Turkey.

PubMed

Ercanli, İlker; Kahriman, Aydın

2015-03-01

We assessed the effect of stand structural diversity, including the Shannon, improved Shannon, Simpson, McIntosh, Margelef, and Berger-Parker indices, on stand aboveground biomass (AGB) and developed statistical prediction models for the stand AGB values, including stand structural diversity indices and some stand attributes. The AGB prediction model, including only stand attributes, accounted for 85 % of the total variance in AGB (R (2)) with an Akaike's information criterion (AIC) of 807.2407, Bayesian information criterion (BIC) of 809.5397, Schwarz Bayesian criterion (SBC) of 818.0426, and root mean square error (RMSE) of 38.529 Mg. After inclusion of the stand structural diversity into the model structure, considerable improvement was observed in statistical accuracy, including 97.5 % of the total variance in AGB, with an AIC of 614.1819, BIC of 617.1242, SBC of 633.0853, and RMSE of 15.8153 Mg. The predictive fitting results indicate that some indices describing the stand structural diversity can be employed as significant independent variables to predict the AGB production of the Scotch pine stand. Further, including the stand diversity indices in the AGB prediction model with the stand attributes provided important predictive contributions in estimating the total variance in AGB.

Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto-encoder deep neural network.

PubMed

Lyons, James; Dehzangi, Abdollah; Heffernan, Rhys; Sharma, Alok; Paliwal, Kuldip; Sattar, Abdul; Zhou, Yaoqi; Yang, Yuedong

2014-10-30

Because a nearly constant distance between two neighbouring Cα atoms, local backbone structure of proteins can be represented accurately by the angle between C(αi-1)-C(αi)-C(αi+1) (θ) and a dihedral angle rotated about the C(αi)-C(αi+1) bond (τ). θ and τ angles, as the representative of structural properties of three to four amino-acid residues, offer a description of backbone conformations that is complementary to φ and ψ angles (single residue) and secondary structures (>3 residues). Here, we report the first machine-learning technique for sequence-based prediction of θ and τ angles. Predicted angles based on an independent test have a mean absolute error of 9° for θ and 34° for τ with a distribution on the θ-τ plane close to that of native values. The average root-mean-square distance of 10-residue fragment structures constructed from predicted θ and τ angles is only 1.9Å from their corresponding native structures. Predicted θ and τ angles are expected to be complementary to predicted ϕ and ψ angles and secondary structures for using in model validation and template-based as well as template-free structure prediction. The deep neural network learning technique is available as an on-line server called Structural Property prediction with Integrated DEep neuRal network (SPIDER) at http://sparks-lab.org. Copyright © 2014 Wiley Periodicals, Inc.

Investigation on the Accuracy of Superposition Predictions of Film Cooling Effectiveness

NASA Astrophysics Data System (ADS)

Meng, Tong; Zhu, Hui-ren; Liu, Cun-liang; Wei, Jian-sheng

2018-05-01

Film cooling effectiveness on flat plates with double rows of holes has been studied experimentally and numerically in this paper. This configuration is widely used to simulate the multi-row film cooling on turbine vane. Film cooling effectiveness of double rows of holes and each single row was used to study the accuracy of superposition predictions. Method of stable infrared measurement technique was used to measure the surface temperature on the flat plate. This paper analyzed the factors that affect the film cooling effectiveness including hole shape, hole arrangement, row-to-row spacing and blowing ratio. Numerical simulations were performed to analyze the flow structure and film cooling mechanisms between each film cooling row. Results show that the blowing ratio within the range of 0.5 to 2 has a significant influence on the accuracy of superposition predictions. At low blowing ratios, results obtained by superposition method agree well with the experimental data. While at high blowing ratios, the accuracy of superposition prediction decreases. Another significant factor is hole arrangement. Results obtained by superposition prediction are nearly the same as experimental values of staggered arrangement structures. For in-line configurations, the superposition values of film cooling effectiveness are much higher than experimental data. For different hole shapes, the accuracy of superposition predictions on converging-expanding holes is better than cylinder holes and compound angle holes. For two different hole spacing structures in this paper, predictions show good agreement with the experiment results.

Interaction between polymer constituents and the structure of biopolymers

NASA Technical Reports Server (NTRS)

Rein, R.

1974-01-01

The paper reviews the current status of methods for calculating intermolecular interactions between biopolymer units. The nature of forces contributing to the various domains of intermolecular separations is investigated, and various approximations applicable in the respective regions are examined. The predictive value of current theory is tested by establishing a connection with macroscopic properties and comparing the theoretical predicted values with those derived from experimental data. This has led to the introduction of a statistical model describing DNA.

MetCCS predictor: a web server for predicting collision cross-section values of metabolites in ion mobility-mass spectrometry based metabolomics.

PubMed

Zhou, Zhiwei; Xiong, Xin; Zhu, Zheng-Jiang

2017-07-15

In metabolomics, rigorous structural identification of metabolites presents a challenge for bioinformatics. The use of collision cross-section (CCS) values of metabolites derived from ion mobility-mass spectrometry effectively increases the confidence of metabolite identification, but this technique suffers from the limit number of available CCS values. Currently, there is no software available for rapidly generating the metabolites' CCS values. Here, we developed the first web server, namely, MetCCS Predictor, for predicting CCS values. It can predict the CCS values of metabolites using molecular descriptors within a few seconds. Common users with limited background on bioinformatics can benefit from this software and effectively improve the metabolite identification in metabolomics. The web server is freely available at: http://www.metabolomics-shanghai.org/MetCCS/ . jiangzhu@sioc.ac.cn. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Beyond SaGMRotI: Conversion to SaArb, SaSN, and SaMaxRot

USGS Publications Warehouse

Watson-Lamprey, J. A.; Boore, D.M.

2007-01-01

In the seismic design of structures, estimates of design forces are usually provided to the engineer in the form of elastic response spectra. Predictive equations for elastic response spectra are derived from empirical recordings of ground motion. The geometric mean of the two orthogonal horizontal components of motion is often used as the response value in these predictive equations, although it is not necessarily the most relevant estimate of forces within the structure. For some applications it is desirable to estimate the response value on a randomly chosen single component of ground motion, and in other applications the maximum response in a single direction is required. We give adjustment factors that allow converting the predictions of geometric-mean ground-motion predictions into either of these other two measures of seismic ground-motion intensity. In addition, we investigate the relation of the strike-normal component of ground motion to the maximum response values. We show that the strike-normal component of ground motion seldom corresponds to the maximum horizontal-component response value (in particular, at distances greater than about 3 km from faults), and that focusing on this case in exclusion of others can result in the underestimation of the maximum component. This research provides estimates of the maximum response value of a single component for all cases, not just near-fault strike-normal components. We provide modification factors that can be used to convert predictions of ground motions in terms of the geometric mean to the maximum spectral acceleration (SaMaxRot) and the random component of spectral acceleration (SaArb). Included are modification factors for both the mean and the aleatory standard deviation of the logarithm of the motions.

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).

Study of the structure of yrast bands of neutron-rich 114-124Pd isotopes

NASA Astrophysics Data System (ADS)

Chaudhary, Ritu; Devi, Rani; Khosa, S. K.

2018-02-01

The projected shell model calculations have been carried out in the neutron-rich 114-124Pd isotopic mass chain. The results have been obtained for the deformation systematics of E(2+1) and E(4+1)/E({2}+1) values, BCS subshell occupation numbers, yrast spectra, backbending phenomena, B( E2) transition probabilities and g-factors in these nuclei. The observed systematics of E(2+1) values and R_{42} ratios in the 114-124Pd isotopic mass chain indicate that there is a decrease of collectivity as the neutron number increases from 68 to 78. The occurrence of backbending in these nuclei as well as the changes in the calculated B( E2) transition probabilities and g -factors predict that there are changes in the structure of yrast bands in these nuclei. These changes occur at the spin where there is crossing of g-band by 2-qp bands. The predicted backbendings and predicted values of B( E2)s and g-factors in some of the isotopes need to be confirmed experimentally.

Impact of Chemical Structure on Conjunctival Drug Permeability: Adopting Porcine Conjunctiva and Cassette Dosing for Construction of In Silico Model.

PubMed

Ramsay, Eva; Ruponen, Marika; Picardat, Théo; Tengvall, Unni; Tuomainen, Marjo; Auriola, Seppo; Toropainen, Elisa; Urtti, Arto; Del Amo, Eva M

2017-09-01

Conjunctiva occupies most of the ocular surface area, and conjunctival permeability affects ocular and systemic drug absorption of topical ocular medications. Therefore, the aim of this study was to obtain a computational in silico model for structure-based prediction of conjunctival drug permeability. This was done by employing cassette dosing and quantitative structure-property relationship (QSPR) approach. Permeability studies were performed ex vivo across fresh porcine conjunctiva and simultaneous dosing of a cassette mixture composed of 32 clinically relevant drug molecules with wide chemical space. The apparent permeability values were obtained using drug concentrations that were quantified with liquid chromatography tandem-mass spectrometry. The experimental data were utilized for building a QSPR model for conjunctival permeability predictions. The conjunctival permeability values presented a 17-fold range (0.63-10.74 × 10 -6 cm/s). The final QSPR had a Q 2 value of 0.62 and predicted the external test set with a mean fold error of 1.34. The polar surface area, hydrogen bond donor, and halogen ratio were the most relevant descriptors for defining conjunctival permeability. This work presents for the first time a predictive QSPR model of conjunctival drug permeability and a comprehensive description on conjunctival isolation from the porcine eye. The model can be used for developing new ocular drugs. Copyright © 2017 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

Multiple-Instance Regression with Structured Data

NASA Technical Reports Server (NTRS)

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

2008-01-01

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

Experimental and QSAR study on the surface activities of alkyl imidazoline surfactants

NASA Astrophysics Data System (ADS)

Kong, Xiangjun; Qian, Chengduo; Fan, Weiyu; Liang, Zupei

2018-03-01

15 alkyl imidazoline surfactants with different structures were synthesized and their critical micelle concentration (CMC) and surface tension under the CMC (σcmc) in aqueous solution were measured at 298 K. 54 kinds of molecular structure descriptors were selected as independent variables and the quantitative structure-activity relationship (QSAR) between surface activities of alkyl imidazoline and molecular structure were built through the genetic function approximation (GFA) method. Experimental results showed that the maximum surface excess of alkyl imidazoline molecules at the gas-liquid interface increased and the area occupied by each surfactant molecule and the free energies of micellization ΔGm decreased with increasing carbon number (NC) of the hydrophobic chain or decreasing hydrophilicity of counterions, which resulted in a CMC and σcmc decrease, while the log CMC and NC had a linear relationship and a negative correlation. The GFA-QSAR model, which was generated by a training set composed of 13 kinds of alkyl imidazoline though GFA method regression analysis, was highly correlated with predicted values and experimental values of the CMC. The correlation coefficient R was 0.9991, which means high prediction accuracy. The prediction error of 2 kinds of alkyl imidazoline CMCs in the Validation Set that quantitatively analyzed the influence of the alkyl imidazoline molecular structure on the CMC was less than 4%.

RNA structural constraints in the evolution of the influenza A virus genome NP segment

PubMed Central

Gultyaev, Alexander P; Tsyganov-Bodounov, Anton; Spronken, Monique IJ; van der Kooij, Sander; Fouchier, Ron AM; Olsthoorn, René CL

2014-01-01

Conserved RNA secondary structures were predicted in the nucleoprotein (NP) segment of the influenza A virus genome using comparative sequence and structure analysis. A number of structural elements exhibiting nucleotide covariations were identified over the whole segment length, including protein-coding regions. Calculations of mutual information values at the paired nucleotide positions demonstrate that these structures impose considerable constraints on the virus genome evolution. Functional importance of a pseudoknot structure, predicted in the NP packaging signal region, was confirmed by plaque assays of the mutant viruses with disrupted structure and those with restored folding using compensatory substitutions. Possible functions of the conserved RNA folding patterns in the influenza A virus genome are discussed. PMID:25180940

Revisiting Grodzins systematics of B(E2) values

DOE PAGES

Pritychenko, B.; Birch, M.; Singh, B.

2017-04-03

Using Grodzins formalism, we analyze systematics of our latest evaluated B(E2) data for all the even–even nuclei in Z=2–104. The analysis indicates a low predictive power of systematics for a large number of cases, and a strong correlation between B(E2) fit values and nuclear structure effects. These findings provide a strong rationale for introduction of individual or elemental (grouped by Z) fit parameters. The current estimates of quadrupole collectivities for systematics of even–even nuclei yield complementary values for comparison with experimental results and theoretical calculations. Furthermore, the lists of fit parameters and predicted B(E2) values are given and possible implicationsmore » are discussed.« less

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

Rapid experimental measurements of physicochemical properties to inform models and testing.

PubMed

Nicolas, Chantel I; Mansouri, Kamel; Phillips, Katherine A; Grulke, Christopher M; Richard, Ann M; Williams, Antony J; Rabinowitz, James; Isaacs, Kristin K; Yau, Alice; Wambaugh, John F

2018-05-02

The structures and physicochemical properties of chemicals are important for determining their potential toxicological effects, toxicokinetics, and route(s) of exposure. These data are needed to prioritize the risk for thousands of environmental chemicals, but experimental values are often lacking. In an attempt to efficiently fill data gaps in physicochemical property information, we generated new data for 200 structurally diverse compounds, which were rigorously selected from the USEPA ToxCast chemical library, and whose structures are available within the Distributed Structure-Searchable Toxicity Database (DSSTox). This pilot study evaluated rapid experimental methods to determine five physicochemical properties, including the log of the octanol:water partition coefficient (known as log(K ow ) or logP), vapor pressure, water solubility, Henry's law constant, and the acid dissociation constant (pKa). For most compounds, experiments were successful for at least one property; log(K ow ) yielded the largest return (176 values). It was determined that 77 ToxPrint structural features were enriched in chemicals with at least one measurement failure, indicating which features may have played a role in rapid method failures. To gauge consistency with traditional measurement methods, the new measurements were compared with previous measurements (where available). Since quantitative structure-activity/property relationship (QSAR/QSPR) models are used to fill gaps in physicochemical property information, 5 suites of QSPRs were evaluated for their predictive ability and chemical coverage or applicability domain of new experimental measurements. The ability to have accurate measurements of these properties will facilitate better exposure predictions in two ways: 1) direct input of these experimental measurements into exposure models; and 2) construction of QSPRs with a wider applicability domain, as their predicted physicochemical values can be used to parameterize exposure models in the absence of experimental data. Published by Elsevier B.V.

Unravelling the magnetism, high spin polarization and thermoelectric efficiency of ZrFeSi half-Heusler

NASA Astrophysics Data System (ADS)

Yousuf, Saleem; Gupta, D. C.

2018-04-01

We report the systematic investigation of structural properties, occupancy of density of states, nature of bonding and thermoelectric efficiency of half-Heusler ZrFeSi. The band structure analysis predicts the hybridization of Zr-d and Fe-d metal atoms resulting in occupation of density of states above the Fermi level (EF) while Fe-p and Si-p occupy the lower energy states below the EF. Thermoelectric transport coefficients are predicted using the Boltzmann transport theory under constant relaxation approximation, where Seebeck coefficient (S), total thermal conductivity and figure of merit are calculated. The negative value of total S as -14.02 μV/K predicts the material as n-type with thermoelectric figure of merit (zT) of 0.5 at 800 K. The lattice thermal conductivity decreases with increasing temperature with room temperature value of 4.18 W/mK and shows a significant reduction towards higher temperatures. In view of above elements, structural stability, high zT, ZrFeSi alloy have the capabilities to stimulate experimental verification as a promising materials for high temperature power generation and spintronic device fabrications.

Prediction of Interests from Values: A Longitudinal Investigation.

ERIC Educational Resources Information Center

Mason, Avonne; And Others

Interest in the attitude-behavior relationship has generated much research since the concept was introduced into research in 1934. This study examined the relationship between values and behavioral intentions, specifically in regard to interest in entering a particular medical specialization. Using structural equation techniques and a longitudinal…

Estimation of Physical Properties and Chemical Reactivity Parameters of Organic Compounds for Environmental Modeling by SPARC

EPA Science Inventory

Mathematical models for predicting the transport and fate of pollutants in the environment require reactivity parameter values that is value of the physical and chemical constants that govern reactivity. Although empirical structure activity relationships have been developed th...

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

PubMed Central

2010-01-01

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

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

PubMed

Kountouris, Petros; Hirst, Jonathan D

2010-07-31

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

Prediction of enzyme binding: human thrombin inhibition study by quantum chemical and artificial intelligence methods based on X-ray structures.

PubMed

Mlinsek, G; Novic, M; Hodoscek, M; Solmajer, T

2001-01-01

Thrombin is a serine protease which plays important roles in the human body, the key one being the control of thrombus formation. The inhibition of thrombin has become a target for new antithrombotics. The aim of our work was to (i) construct a model which would enable us to predict Ki values for the binding of an inhibitor into the active site of thrombin based on a database of known X-ray structures of inhibitor-enzyme complexes and (ii) to identify the structural and electrostatic characteristics of inhibitor molecules crucially important to their effective binding. To retain as much of the 3D structural information of the bound inhibitor as possible, we implemented the quantum mechanical/molecular mechanical (QM/MM) procedure for calculating the molecular electrostatic potential (MEP) at the van der Waals surfaces of atoms in the protein's active site. The inhibitor was treated quantum mechanically, while the rest of the complex was treated by classical means. The obtained MEP values served as inputs into the counter-propagation artificial neural network (CP-ANN), and a genetic algorithm was subsequently used to search for the combination of atoms that predominantly influences the binding. The constructed CP-ANN model yielded Ki values predictions with a correlation coefficient of 0.96, with Ki values extended over 7 orders of magnitude. Our approach also shows the relative importance of the various amino acid residues present in the active site of the enzyme for inhibitor binding. The list of residues selected by our automatic procedure is in good correlation with the current consensus regarding the importance of certain crucial residues in thrombin's active site.

Network model for thermal conductivities of unidirectional fiber-reinforced composites

NASA Astrophysics Data System (ADS)

Wang, Yang; Peng, Chaoyi; Zhang, Weihua

2014-12-01

An empirical network model has been developed to predict the in-plane thermal conductivities along arbitrary directions for unidirectional fiber-reinforced composites lamina. Measurements of thermal conductivities along different orientations were carried out. Good agreement was observed between values predicted by the network model and the experimental data; compared with the established analytical models, the newly proposed network model could give values with higher precision. Therefore, this network model is helpful to get a wider and more comprehensive understanding of heat transmission characteristics of fiber-reinforced composites and can be utilized as guidance to design and fabricate laminated composites with specific directional or specific locational thermal conductivities for structures that simultaneously perform mechanical and thermal functions, i.e. multifunctional structures (MFS).

Predictive modeling: Solubility of C60 and C70 fullerenes in diverse solvents.

PubMed

Gupta, Shikha; Basant, Nikita

2018-06-01

Solubility of fullerenes imposes a major limitation to further advanced research and technological development using these novel materials. There have been continued efforts to discover better solvents and their properties that influence the solubility of fullerenes. Here, we have developed QSPR (quantitative structure-property relationship) models based on structural features of diverse solvents and large experimental data for predicting the solubility of C 60 and C 70 fullerenes. The developed models identified most relevant features of the solvents that encode the polarizability, polarity and lipophilicity properties which largely influence the solubilizing potential of the solvent for the fullerenes. We also established Inter-moieties solubility correlations (IMSC) based quantitative property-property relationship (QPPR) models for predicting solubility of C 60 and C 70 fullerenes. The QSPR and QPPR models were internally and externally validated deriving the most stringent statistical criteria and predicted C 60 and C 70 solubility values in different solvents were in close agreement with the experimental values. In test sets, the QSPR models yielded high correlations (R 2  > 0.964) and low root mean squared error of prediction errors (RMSEP< 0.25). Results of comparison with other studies indicated that the proposed models could effectively improve the accuracy and ability for predicting solubility of C 60 and C 70 fullerenes in solvents with diverse structures and would be useful in development of more effective solvents. Copyright © 2018 Elsevier Ltd. All rights reserved.

Probabilistic and structural reliability analysis of laminated composite structures based on the IPACS code

NASA Technical Reports Server (NTRS)

Sobel, Larry; Buttitta, Claudio; Suarez, James

1993-01-01

Probabilistic predictions based on the Integrated Probabilistic Assessment of Composite Structures (IPACS) code are presented for the material and structural response of unnotched and notched, 1M6/3501-6 Gr/Ep laminates. Comparisons of predicted and measured modulus and strength distributions are given for unnotched unidirectional, cross-ply, and quasi-isotropic laminates. The predicted modulus distributions were found to correlate well with the test results for all three unnotched laminates. Correlations of strength distributions for the unnotched laminates are judged good for the unidirectional laminate and fair for the cross-ply laminate, whereas the strength correlation for the quasi-isotropic laminate is deficient because IPACS did not yet have a progressive failure capability. The paper also presents probabilistic and structural reliability analysis predictions for the strain concentration factor (SCF) for an open-hole, quasi-isotropic laminate subjected to longitudinal tension. A special procedure was developed to adapt IPACS for the structural reliability analysis. The reliability results show the importance of identifying the most significant random variables upon which the SCF depends, and of having accurate scatter values for these variables.

Crystal structure of minoxidil at low temperature and polymorph prediction.

PubMed

Martín-Islán, Africa P; Martín-Ramos, Daniel; Sainz-Díaz, C Ignacio

2008-02-01

An experimental and theoretical investigation on crystal forms of the popular and ubiquitous pharmaceutical Minoxidil is presented here. A new crystallization method is presented for Minoxidil (6-(1-piperidinyl)-2,4-pyrimidinediamide 3-oxide) in ethanol-poly(ethylene glycol), yielding crystals with good quality. The crystal structure is determined at low temperature, with a final R value of 0.035, corresponding to space group P2(1) (monoclinic) with cell dimensions a = 9.357(1) A, b = 8.231(1) A, c = 12.931(2) A, and beta = 90.353(4) degrees . Theoretical calculations of the molecular structure of Minoxidil are set forward using empirical force fields and quantum-mechanical methods. A theoretical prediction for Minoxidil crystal structure shows many possible polymorphs. The predicted crystal structures are compared with X-ray experimental data obtained in our laboratory, and the experimental crystal form is found to be one of the lowest energy polymorphs.

Predicting Flory-Huggins χ from Simulations

NASA Astrophysics Data System (ADS)

Zhang, Wenlin; Gomez, Enrique D.; Milner, Scott T.

2017-07-01

We introduce a method, based on a novel thermodynamic integration scheme, to extract the Flory-Huggins χ parameter as small as 10-3k T for polymer blends from molecular dynamics (MD) simulations. We obtain χ for the archetypical coarse-grained model of nonpolar polymer blends: flexible bead-spring chains with different Lennard-Jones interactions between A and B monomers. Using these χ values and a lattice version of self-consistent field theory (SCFT), we predict the shape of planar interfaces for phase-separated binary blends. Our SCFT results agree with MD simulations, validating both the predicted χ values and our thermodynamic integration method. Combined with atomistic simulations, our method can be applied to predict χ for new polymers from their chemical structures.

The influence of intrinsic and extrinsic job values on turnover intention among continuing care assistants in Nova Scotia.

PubMed

Dill, Donna M; Keefe, Janice M; McGrath, Daniel S

2012-01-01

This article examines the influence that intrinsic and extrinsic job values have on the turnover intention of continuing care assistants (CCAs) who work either in home care or facility-based care in Nova Scotia (n = 188). Factor analysis of job values identified three latent job values structures: "compensation and commitment," "flexibility and opportunity," and "positive work relationships." Using binary logistic regression, we examined the predictive utility of these factors on two indices of turnover intention. Regression results indicate that, in general, job values constructs did not significantly predict turnover intention when controlling for demographics and job characteristics. However, a trend was found for the "positive work relationships" factor in predicting consideration of changing employers. In addition, CCAs who work in facility-based care were significantly more likely to have considered leaving their current employer. With projected increases in the demand for these workers in both home and continuing care, more attention is needed to identify and address factors to reduce turnover intention.

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.

A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents.

PubMed

Zhu, Hao; Ye, Lin; Richard, Ann; Golbraikh, Alexander; Wright, Fred A; Rusyn, Ivan; Tropsha, Alexander

2009-08-01

Accurate prediction of in vivo toxicity from in vitro testing is a challenging problem. Large public-private consortia have been formed with the goal of improving chemical safety assessment by the means of high-throughput screening. A wealth of available biological data requires new computational approaches to link chemical structure, in vitro data, and potential adverse health effects. A database containing experimental cytotoxicity values for in vitro half-maximal inhibitory concentration (IC(50)) and in vivo rodent median lethal dose (LD(50)) for more than 300 chemicals was compiled by Zentralstelle zur Erfassung und Bewertung von Ersatz- und Ergaenzungsmethoden zum Tierversuch (ZEBET; National Center for Documentation and Evaluation of Alternative Methods to Animal Experiments). The application of conventional quantitative structure-activity relationship (QSAR) modeling approaches to predict mouse or rat acute LD(50) values from chemical descriptors of ZEBET compounds yielded no statistically significant models. The analysis of these data showed no significant correlation between IC(50) and LD(50). However, a linear IC(50) versus LD(50) correlation could be established for a fraction of compounds. To capitalize on this observation, we developed a novel two-step modeling approach as follows. First, all chemicals are partitioned into two groups based on the relationship between IC(50) and LD(50) values: One group comprises compounds with linear IC(50) versus LD(50) relationships, and another group comprises the remaining compounds. Second, we built conventional binary classification QSAR models to predict the group affiliation based on chemical descriptors only. Third, we developed k-nearest neighbor continuous QSAR models for each subclass to predict LD(50) values from chemical descriptors. All models were extensively validated using special protocols. The novelty of this modeling approach is that it uses the relationships between in vivo and in vitro data only to inform the initial construction of the hierarchical two-step QSAR models. Models resulting from this approach employ chemical descriptors only for external prediction of acute rodent toxicity.

Global QSAR modeling of logP values of phenethylamines acting as adrenergic alpha-1 receptor agonists.

PubMed

Yadav, Mukesh; Joshi, Shobha; Nayarisseri, Anuraj; Jain, Anuja; Hussain, Aabid; Dubey, Tushar

2013-06-01

Global QSAR models predict biological response of molecular structures which are generic in particular class. A global QSAR dataset admits structural features derived from larger chemical space, intricate to model but more applicable in medicinal chemistry. The present work is global in either sense of structural diversity in QSAR dataset or large number of descriptor input. Forty phenethylamine structure derivatives were selected from a large pool (904) of similar phenethylamines available in Pubchem database. LogP values of selected candidates were collected from physical properties database (PHYSPROP) determined in identical set of conditions. Attempts to model logP value have produced significant QSAR models. MLR aided linear one-variable and two-variable QSAR models with their respective R(2) (0.866, 0.937), R(2)A (0.862, 0.932), F-stat (181.936, 199.812) and Standard Error (0.365, 0.255) are statistically fit and found predictive after internal validation and external validation. The descriptors chosen after improvisation and optimization reveal mechanistic part of work in terms of Verhaar model of Fish base-line toxicity from MLOGP, i.e. (BLTF96) and 3D-MoRSE -signal 15 /unweighted molecular descriptor calculated by summing atom weights viewed by a different angular scattering function (Mor15u) are crucial in regulation of logP values of phenethylamines.

Pressure effects on band structures in dense lithium

NASA Astrophysics Data System (ADS)

Goto, Naoyuki; Nagara, Hitose

2012-07-01

We studied the change of the band structures in some structures of Li predicted at high pressures, using GGA and GW calculations. The width of the 1s band coming from the 1s electron of Li shows broadening by the pressurization, which is the normal behavior of bands at high pressure. The width of the band just below the Fermi level decreases by the pressurization, which is an opposite behavior to the normal bands. The character of this narrowing band is mostly p-like with a little s-like portion. The band gaps in some structures are really observed even by the GGA calculations. The gaps by the GW calculations increase to about 1.5 times the GGA values. Generally the one-shot GW calculation (diagonal only calculations) gives more reliable values than the GGA, but it may fail to predict band gaps for the case where band dispersion shows complex crossing near the Fermi level. There remains some structures for which GW calculations with off-diagonal elements taken into account are needed to identify the phase to be metallic or semiconducting.

First-and Second-Order Displacement Transfer Functions for Structural Shape Calculations Using Analytically Predicted Surface Strains

NASA Technical Reports Server (NTRS)

Ko, William L.; Fleischer, Van Tran

2012-01-01

New first- and second-order displacement transfer functions have been developed for deformed shape calculations of nonuniform cross-sectional beam structures such as aircraft wings. The displacement transfer functions are expressed explicitly in terms of beam geometrical parameters and surface strains (uniaxial bending strains) obtained at equally spaced strain stations along the surface of the beam structure. By inputting the measured or analytically calculated surface strains into the displacement transfer functions, one could calculate local slopes, deflections, and cross-sectional twist angles of the nonuniform beam structure for mapping the overall structural deformed shapes for visual display. The accuracy of deformed shape calculations by the first- and second-order displacement transfer functions are determined by comparing these values to the analytically predicted values obtained from finite element analyses. This comparison shows that the new displacement transfer functions could quite accurately calculate the deformed shapes of tapered cantilever tubular beams with different tapered angles. The accuracy of the present displacement transfer functions also are compared to those of the previously developed displacement transfer functions.

A general structure-property relationship to predict the enthalpy of vaporisation at ambient temperatures.

PubMed

Oberg, T

2007-01-01

The vapour pressure is the most important property of an anthropogenic organic compound in determining its partitioning between the atmosphere and the other environmental media. The enthalpy of vaporisation quantifies the temperature dependence of the vapour pressure and its value around 298 K is needed for environmental modelling. The enthalpy of vaporisation can be determined by different experimental methods, but estimation methods are needed to extend the current database and several approaches are available from the literature. However, these methods have limitations, such as a need for other experimental results as input data, a limited applicability domain, a lack of domain definition, and a lack of predictive validation. Here we have attempted to develop a quantitative structure-property relationship (QSPR) that has general applicability and is thoroughly validated. Enthalpies of vaporisation at 298 K were collected from the literature for 1835 pure compounds. The three-dimensional (3D) structures were optimised and each compound was described by a set of computationally derived descriptors. The compounds were randomly assigned into a calibration set and a prediction set. Partial least squares regression (PLSR) was used to estimate a low-dimensional QSPR model with 12 latent variables. The predictive performance of this model, within the domain of application, was estimated at n=560, q2Ext=0.968 and s=0.028 (log transformed values). The QSPR model was subsequently applied to a database of 100,000+ structures, after a similar 3D optimisation and descriptor generation. Reliable predictions can be reported for compounds within the previously defined applicability domain.

DEVELOPMENT OF QUANTITATIVE STRUCTURE ACTIVITY RELATIONSHIPS (QSARS) TO PREDICT TOXICITY FOR A VARIETY OF HUMAN AND ECOLOGICAL ENDPOINTS

EPA Science Inventory

In general, the accuracy of a predicted toxicity value increases with increase in similarity between the query chemical and the chemicals used to develop a QSAR model. A toxicity estimation methodology employing this finding has been developed. A hierarchical based clustering t...

Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals.

PubMed

Wignall, Jessica A; Muratov, Eugene; Sedykh, Alexander; Guyton, Kathryn Z; Tropsha, Alexander; Rusyn, Ivan; Chiu, Weihsueh A

2018-05-01

Human health assessments synthesize human, animal, and mechanistic data to produce toxicity values that are key inputs to risk-based decision making. Traditional assessments are data-, time-, and resource-intensive, and they cannot be developed for most environmental chemicals owing to a lack of appropriate data. As recommended by the National Research Council, we propose a solution for predicting toxicity values for data-poor chemicals through development of quantitative structure-activity relationship (QSAR) models. We used a comprehensive database of chemicals with existing regulatory toxicity values from U.S. federal and state agencies to develop quantitative QSAR models. We compared QSAR-based model predictions to those based on high-throughput screening (HTS) assays. QSAR models for noncancer threshold-based values and cancer slope factors had cross-validation-based Q 2 of 0.25-0.45, mean model errors of 0.70-1.11 log 10 units, and applicability domains covering >80% of environmental chemicals. Toxicity values predicted from QSAR models developed in this study were more accurate and precise than those based on HTS assays or mean-based predictions. A publicly accessible web interface to make predictions for any chemical of interest is available at http://toxvalue.org. An in silico tool that can predict toxicity values with an uncertainty of an order of magnitude or less can be used to quickly and quantitatively assess risks of environmental chemicals when traditional toxicity data or human health assessments are unavailable. This tool can fill a critical gap in the risk assessment and management of data-poor chemicals. https://doi.org/10.1289/EHP2998.

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

PubMed

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

2013-05-01

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

Quantitative structure-property relationship modeling of remote liposome loading of drugs.

PubMed

Cern, Ahuva; Golbraikh, Alexander; Sedykh, Aleck; Tropsha, Alexander; Barenholz, Yechezkel; Goldblum, Amiram

2012-06-10

Remote loading of liposomes by trans-membrane gradients is used to achieve therapeutically efficacious intra-liposome concentrations of drugs. We have developed Quantitative Structure Property Relationship (QSPR) models of remote liposome loading for a data set including 60 drugs studied in 366 loading experiments internally or elsewhere. Both experimental conditions and computed chemical descriptors were employed as independent variables to predict the initial drug/lipid ratio (D/L) required to achieve high loading efficiency. Both binary (to distinguish high vs. low initial D/L) and continuous (to predict real D/L values) models were generated using advanced machine learning approaches and 5-fold external validation. The external prediction accuracy for binary models was as high as 91-96%; for continuous models the mean coefficient R(2) for regression between predicted versus observed values was 0.76-0.79. We conclude that QSPR models can be used to identify candidate drugs expected to have high remote loading capacity while simultaneously optimizing the design of formulation experiments. Copyright © 2011 Elsevier B.V. All rights reserved.

Facile method for liquid-exfoliated graphene size prediction by UV-visible spectroscopy

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

Ismail, Zulhelmi, E-mail: helmie83@hotmail.com; Yusoh, Kamal, E-mail: kamal@ump.edu.my

2016-07-19

In this work, an application of UV spectroscopy for facile prediction of liquid –exfoliated graphene size is discussed. Dynamic light scattering method was used to estimate the graphene flake size ( whilst UV spectroscopy measurement was carried out for extinction coefficient value (ε) determination. It was found that the value of (ε) decreased gradually as the graphene size was further reduced after intense sonication time (7h). This observation showed the influence of sonication time on electronic structure of graphene. A mathematical equation was derived from log-log graph for correlation between () and (ε) value. Both values can be expressed inmore » a single equation as ( = (3.4 × 10{sup −2}) ε{sup 1.2}).« less

Inflight Characterization of the Cassini Spacecraft Propellant Slosh and Structural Frequencies

NASA Technical Reports Server (NTRS)

Lee, Allan Y.; Stupik, Joan

2015-01-01

While there has been extensive theoretical and analytical research regarding the characterization of spacecraft propellant slosh and structural frequencies, there have been limited studies to compare the analytical predictions with measured flight data. This paper uses flight telemetry from the Cassini spacecraft to get estimates of high-g propellant slosh frequencies and the magnetometer boom frequency characteristics, and compares these values with those predicted by theoretical works. Most Cassini attitude control data are available at a telemetry frequency of 0.5 Hz. Moreover, liquid sloshing is attenuated by propellant management device and attitude controllers. Identification of slosh and structural frequency are made on a best-effort basis. This paper reviews the analytical approaches that were used to predict the Cassini propellant slosh frequencies. The predicted frequencies are then compared with those estimated using telemetry from selected Cassini burns where propellant sloshing was observed (such as the Saturn Orbit Insertion burn). Determination of the magnetometer boom structural frequency is also discussed.

Multiobjective evolutionary algorithm with many tables for purely ab initio protein structure prediction.

PubMed

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

2013-07-30

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

Flight-determined stability analysis of multiple-input-multiple-output control systems

NASA Technical Reports Server (NTRS)

Burken, John J.

1992-01-01

Singular value analysis can give conservative stability margin results. Applying structure to the uncertainty can reduce this conservatism. This paper presents flight-determined stability margins for the X-29A lateral-directional, multiloop control system. These margins are compared with the predicted unscaled singular values and scaled structured singular values. The algorithm was further evaluated with flight data by changing the roll-rate-to-aileron command-feedback gain by +/- 20 percent. Minimum eigenvalues of the return difference matrix which bound the singular values are also presented. Extracting multiloop singular values from flight data and analyzing the feedback gain variations validates this technique as a measure of robustness. This analysis can be used for near-real-time flight monitoring and safety testing.

Flight-determined stability analysis of multiple-input-multiple-output control systems

NASA Technical Reports Server (NTRS)

Burken, John J.

1992-01-01

Singular value analysis can give conservative stability margin results. Applying structure to the uncertainty can reduce this conservatism. This paper presents flight-determined stability margins for the X-29A lateral-directional, multiloop control system. These margins are compared with the predicted unscaled singular values and scaled structured singular values. The algorithm was further evaluated with flight data by changing the roll-rate-to-aileron-command-feedback gain by +/- 20 percent. Also presented are the minimum eigenvalues of the return difference matrix which bound the singular values. Extracting multiloop singular values from flight data and analyzing the feedback gain variations validates this technique as a measure of robustness. This analysis can be used for near-real-time flight monitoring and safety testing.

A novel prediction approach for antimalarial activities of Trimethoprim, Pyrimethamine, and Cycloguanil analogues using extremely randomized trees.

PubMed

Nattee, Cholwich; Khamsemanan, Nirattaya; Lawtrakul, Luckhana; Toochinda, Pisanu; Hannongbua, Supa

2017-01-01

Malaria is still one of the most serious diseases in tropical regions. This is due in part to the high resistance against available drugs for the inhibition of parasites, Plasmodium, the cause of the disease. New potent compounds with high clinical utility are urgently needed. In this work, we created a novel model using a regression tree to study structure-activity relationships and predict the inhibition constant, K i of three different antimalarial analogues (Trimethoprim, Pyrimethamine, and Cycloguanil) based on their molecular descriptors. To the best of our knowledge, this work is the first attempt to study the structure-activity relationships of all three analogues combined. The most relevant descriptors and appropriate parameters of the regression tree are harvested using extremely randomized trees. These descriptors are water accessible surface area, Log of the aqueous solubility, total hydrophobic van der Waals surface area, and molecular refractivity. Out of all possible combinations of these selected parameters and descriptors, the tree with the strongest coefficient of determination is selected to be our prediction model. Predicted K i values from the proposed model show a strong coefficient of determination, R 2 =0.996, to experimental K i values. From the structure of the regression tree, compounds with high accessible surface area of all hydrophobic atoms (ASA_H) and low aqueous solubility of inhibitors (Log S) generally possess low K i values. Our prediction model can also be utilized as a screening test for new antimalarial drug compounds which may reduce the time and expenses for new drug development. New compounds with high predicted K i should be excluded from further drug development. It is also our inference that a threshold of ASA_H greater than 575.80 and Log S less than or equal to -4.36 is a sufficient condition for a new compound to possess a low K i . Copyright © 2016 Elsevier Inc. All rights reserved.

Predicting biomedical metadata in CEDAR: A study of Gene Expression Omnibus (GEO).

PubMed

Panahiazar, Maryam; Dumontier, Michel; Gevaert, Olivier

2017-08-01

A crucial and limiting factor in data reuse is the lack of accurate, structured, and complete descriptions of data, known as metadata. Towards improving the quantity and quality of metadata, we propose a novel metadata prediction framework to learn associations from existing metadata that can be used to predict metadata values. We evaluate our framework in the context of experimental metadata from the Gene Expression Omnibus (GEO). We applied four rule mining algorithms to the most common structured metadata elements (sample type, molecular type, platform, label type and organism) from over 1.3million GEO records. We examined the quality of well supported rules from each algorithm and visualized the dependencies among metadata elements. Finally, we evaluated the performance of the algorithms in terms of accuracy, precision, recall, and F-measure. We found that PART is the best algorithm outperforming Apriori, Predictive Apriori, and Decision Table. All algorithms perform significantly better in predicting class values than the majority vote classifier. We found that the performance of the algorithms is related to the dimensionality of the GEO elements. The average performance of all algorithm increases due of the decreasing of dimensionality of the unique values of these elements (2697 platforms, 537 organisms, 454 labels, 9 molecules, and 5 types). Our work suggests that experimental metadata such as present in GEO can be accurately predicted using rule mining algorithms. Our work has implications for both prospective and retrospective augmentation of metadata quality, which are geared towards making data easier to find and reuse. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Predicting beta-turns in proteins using support vector machines with fractional polynomials

PubMed Central

2013-01-01

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

Predicting beta-turns in proteins using support vector machines with fractional polynomials.

PubMed

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

2013-11-07

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

An improved method for predicting the evolution of the characteristic parameters of an information system

NASA Astrophysics Data System (ADS)

Dushkin, A. V.; Kasatkina, T. I.; Novoseltsev, V. I.; Ivanov, S. V.

2018-03-01

The article proposes a forecasting method that allows, based on the given values of entropy and error level of the first and second kind, to determine the allowable time for forecasting the development of the characteristic parameters of a complex information system. The main feature of the method under consideration is the determination of changes in the characteristic parameters of the development of the information system in the form of the magnitude of the increment in the ratios of its entropy. When a predetermined value of the prediction error ratio is reached, that is, the entropy of the system, the characteristic parameters of the system and the depth of the prediction in time are estimated. The resulting values of the characteristics and will be optimal, since at that moment the system possessed the best ratio of entropy as a measure of the degree of organization and orderliness of the structure of the system. To construct a method for estimating the depth of prediction, it is expedient to use the maximum principle of the value of entropy.

Inflight Characterization of the Cassini Spacecraft Propellant Slosh and Structural Frequencies

NASA Technical Reports Server (NTRS)

Lee, Allan Y.; Stupik, Joan

2015-01-01

While there has been extensive theoretical and analytical research regarding the characterization of spacecraft propellant slosh and structural frequencies, there have been limited studies to compare the analytical predictions with measured flight data. This paper uses flight telemetry from the Cassini spacecraft to get estimates of high-g propellant slosh frequencies and the magnetometer boom frequency characteristics, and compares these values with those predicted by theoretical works. Most Cassini attitude control data are available at a telemetry frequency of 0.5 Hz. Moreover, liquid sloshing is attenuated by propellant management device and attitude controllers. Identification of slosh and structural frequency are made on a best-effort basis. This paper reviews the analytical approaches that were used to predict the Cassini propellant slosh frequencies. The predicted frequencies are then compared with those estimated using telemetry from selected Cassini burns where propellant sloshing was observed (such as the Saturn Orbit Insertion burn).

Improved pose and affinity predictions using different protocols tailored on the basis of data availability

NASA Astrophysics Data System (ADS)

Prathipati, Philip; Nagao, Chioko; Ahmad, Shandar; Mizuguchi, Kenji

2016-09-01

The D3R 2015 grand drug design challenge provided a set of blinded challenges for evaluating the applicability of our protocols for pose and affinity prediction. In the present study, we report the application of two different strategies for the two D3R protein targets HSP90 and MAP4K4. HSP90 is a well-studied target system with numerous co-crystal structures and SAR data. Furthermore the D3R HSP90 test compounds showed high structural similarity to existing HSP90 inhibitors in BindingDB. Thus, we adopted an integrated docking and scoring approach involving a combination of both pharmacophoric and heavy atom similarity alignments, local minimization and quantitative structure activity relationships modeling, resulting in the reasonable prediction of pose [with the root mean square deviation (RMSD) values of 1.75 Å for mean pose 1, 1.417 Å for the mean best pose and 1.85 Å for the mean all poses] and affinity (ROC AUC = 0.702 at 7.5 pIC50 cut-off and R = 0.45 for 180 compounds). The second protein, MAP4K4, represents a novel system with limited SAR and co-crystal structure data and little structural similarity of the D3R MAP4K4 test compounds to known MAP4K4 ligands. For this system, we implemented an exhaustive pose and affinity prediction protocol involving docking and scoring using the PLANTS software which considers side chain flexibility together with protein-ligand fingerprints analysis assisting in pose prioritization. This protocol through fares poorly in pose prediction (with the RMSD values of 4.346 Å for mean pose 1, 4.69 Å for mean best pose and 4.75 Å for mean all poses) and produced reasonable affinity prediction (AUC = 0.728 at 7.5 pIC50 cut-off and R = 0.67 for 18 compounds, ranked 1st among 80 submissions).

Sensitivity study on durability variables of marine concrete structures

NASA Astrophysics Data System (ADS)

Zhou, Xin'gang; Li, Kefei

2013-06-01

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

Protein Structure Prediction Using Gas Phase Molecular Dynamics Simulation: EOTAXIN-3 Cytokine as a Case Study

NASA Astrophysics Data System (ADS)

Khairudin, Nurul Bahiyah Ahmad; Wahab, Habibah A.

In the current work, the structure of the enzyme CC chemokine eotaxin-3 (1G2S) was chosen as a case study to investigate the effects of gas phase on the predicted protein conformation using molecular dynamics simulation. Generally, simulating proteins in the gas phase tend to suffer from various drawbacks, among which excessive numbers of protein-protein hydrogen bonds. However, current results showed that the effects of gas phase simulation on 1G2S did not amplify the protein-protein hydrogen bonds. It was also found that some of the hydrogen bonds which were crucial in maintaining the secondary structural elements were disrupted. The predicted models showed high values of RMSD, 11.5 Å and 13.5 Å for both vacuum and explicit solvent simulations, respectively, indicating that the conformers were very much different from the native conformation. Even though the RMSD value for the in vacuo model was slightly lower, it somehow suffered from lower fraction of native contacts, poor hydrogen bonding networks and fewer occurrences of secondary structural elements compared to the solvated model. This finding supports the notion that water plays a dominant role in guiding the protein to fold along the correct path.

Physics-based protein-structure prediction using a hierarchical protocol based on the UNRES force field: assessment in two blind tests.

PubMed

Ołdziej, S; Czaplewski, C; Liwo, A; Chinchio, M; Nanias, M; Vila, J A; Khalili, M; Arnautova, Y A; Jagielska, A; Makowski, M; Schafroth, H D; Kaźmierkiewicz, R; Ripoll, D R; Pillardy, J; Saunders, J A; Kang, Y K; Gibson, K D; Scheraga, H A

2005-05-24

Recent improvements in the protein-structure prediction method developed in our laboratory, based on the thermodynamic hypothesis, are described. The conformational space is searched extensively at the united-residue level by using our physics-based UNRES energy function and the conformational space annealing method of global optimization. The lowest-energy coarse-grained structures are then converted to an all-atom representation and energy-minimized with the ECEPP/3 force field. The procedure was assessed in two recent blind tests of protein-structure prediction. During the first blind test, we predicted large fragments of alpha and alpha+beta proteins [60-70 residues with C(alpha) rms deviation (rmsd) <6 A]. However, for alpha+beta proteins, significant topological errors occurred despite low rmsd values. In the second exercise, we predicted whole structures of five proteins (two alpha and three alpha+beta, with sizes of 53-235 residues) with remarkably good accuracy. In particular, for the genomic target TM0487 (a 102-residue alpha+beta protein from Thermotoga maritima), we predicted the complete, topologically correct structure with 7.3-A C(alpha) rmsd. So far this protein is the largest alpha+beta protein predicted based solely on the amino acid sequence and a physics-based potential-energy function and search procedure. For target T0198, a phosphate transport system regulator PhoU from T. maritima (a 235-residue mainly alpha-helical protein), we predicted the topology of the whole six-helix bundle correctly within 8 A rmsd, except the 32 C-terminal residues, most of which form a beta-hairpin. These and other examples described in this work demonstrate significant progress in physics-based protein-structure prediction.

VARTM Process Modeling of Aerospace Composite Structures

NASA Technical Reports Server (NTRS)

Song, Xiao-Lan; Grimsley, Brian W.; Hubert, Pascal; Cano, Roberto J.; Loos, Alfred C.

2003-01-01

A three-dimensional model was developed to simulate the VARTM composite manufacturing process. The model considers the two important mechanisms that occur during the process: resin flow, and compaction and relaxation of the preform. The model was used to simulate infiltration of a carbon preform with an epoxy resin by the VARTM process. The model predicted flow patterns and preform thickness changes agreed qualitatively with the measured values. However, the predicted total infiltration times were much longer than measured most likely due to the inaccurate preform permeability values used in the simulation.

Molecular Dynamic Simulation and Inhibitor Prediction of Cysteine Synthase Structured Model as a Potential Drug Target for Trichomoniasis

PubMed Central

Singh, Satendra; Singh, Atul Kumar; Gautam, Budhayash

2013-01-01

In our presented research, we made an attempt to predict the 3D model for cysteine synthase (A2GMG5_TRIVA) using homology-modeling approaches. To investigate deeper into the predicted structure, we further performed a molecular dynamics simulation for 10 ns and calculated several supporting analysis for structural properties such as RMSF, radius of gyration, and the total energy calculation to support the predicted structured model of cysteine synthase. The present findings led us to conclude that the proposed model is stereochemically stable. The overall PROCHECK G factor for the homology-modeled structure was −0.04. On the basis of the virtual screening for cysteine synthase against the NCI subset II molecule, we present the molecule 1-N, 4-N-bis [3-(1H-benzimidazol-2-yl) phenyl] benzene-1,4-dicarboxamide (ZINC01690699) having the minimum energy score (−13.0 Kcal/Mol) and a log P value of 6 as a potential inhibitory molecule used to inhibit the growth of T. vaginalis infection. PMID:24073401

Experimental Protocol to Determine the Chloride Threshold Value for Corrosion in Samples Taken from Reinforced Concrete Structures

PubMed Central

Angst, Ueli M.; Boschmann, Carolina; Wagner, Matthias; Elsener, Bernhard

2017-01-01

The aging of reinforced concrete infrastructure in developed countries imposes an urgent need for methods to reliably assess the condition of these structures. Corrosion of the embedded reinforcing steel is the most frequent cause for degradation. While it is well known that the ability of a structure to withstand corrosion depends strongly on factors such as the materials used or the age, it is common practice to rely on threshold values stipulated in standards or textbooks. These threshold values for corrosion initiation (Ccrit) are independent of the actual properties of a certain structure, which clearly limits the accuracy of condition assessments and service life predictions. The practice of using tabulated values can be traced to the lack of reliable methods to determine Ccrit on-site and in the laboratory. Here, an experimental protocol to determine Ccrit for individual engineering structures or structural members is presented. A number of reinforced concrete samples are taken from structures and laboratory corrosion testing is performed. The main advantage of this method is that it ensures real conditions concerning parameters that are well known to greatly influence Ccrit, such as the steel-concrete interface, which cannot be representatively mimicked in laboratory-produced samples. At the same time, the accelerated corrosion test in the laboratory permits the reliable determination of Ccrit prior to corrosion initiation on the tested structure; this is a major advantage over all common condition assessment methods that only permit estimating the conditions for corrosion after initiation, i.e., when the structure is already damaged. The protocol yields the statistical distribution of Ccrit for the tested structure. This serves as a basis for probabilistic prediction models for the remaining time to corrosion, which is needed for maintenance planning. This method can potentially be used in material testing of civil infrastructures, similar to established methods used for mechanical testing. PMID:28892023

Experimental Protocol to Determine the Chloride Threshold Value for Corrosion in Samples Taken from Reinforced Concrete Structures.

PubMed

Angst, Ueli M; Boschmann, Carolina; Wagner, Matthias; Elsener, Bernhard

2017-08-31

The aging of reinforced concrete infrastructure in developed countries imposes an urgent need for methods to reliably assess the condition of these structures. Corrosion of the embedded reinforcing steel is the most frequent cause for degradation. While it is well known that the ability of a structure to withstand corrosion depends strongly on factors such as the materials used or the age, it is common practice to rely on threshold values stipulated in standards or textbooks. These threshold values for corrosion initiation (Ccrit) are independent of the actual properties of a certain structure, which clearly limits the accuracy of condition assessments and service life predictions. The practice of using tabulated values can be traced to the lack of reliable methods to determine Ccrit on-site and in the laboratory. Here, an experimental protocol to determine Ccrit for individual engineering structures or structural members is presented. A number of reinforced concrete samples are taken from structures and laboratory corrosion testing is performed. The main advantage of this method is that it ensures real conditions concerning parameters that are well known to greatly influence Ccrit, such as the steel-concrete interface, which cannot be representatively mimicked in laboratory-produced samples. At the same time, the accelerated corrosion test in the laboratory permits the reliable determination of Ccrit prior to corrosion initiation on the tested structure; this is a major advantage over all common condition assessment methods that only permit estimating the conditions for corrosion after initiation, i.e., when the structure is already damaged. The protocol yields the statistical distribution of Ccrit for the tested structure. This serves as a basis for probabilistic prediction models for the remaining time to corrosion, which is needed for maintenance planning. This method can potentially be used in material testing of civil infrastructures, similar to established methods used for mechanical testing.

[Determination and prediction for vapor pressures of organophosphate flame retardants by gas chromatography].

PubMed

Wang, Qingzhi; Zhao, Hongxia; Wang, Yan; Xie, Qing; Chen, Jingwen; Quan, Xie

2017-09-08

Organophosphate flame retardants (OPFRs) are ubiquitous in the environment. To better understand and predict their environmental transport and fate, well-defined physicochemical properties are required. Vapor pressures ( P ) of 14 OPFRs were estimated as a function of temperature ( T ) by gas chromatography (GC), while 1,1,1-trichioro-2,2-bis (4-chlorophenyl) ethane ( p,p '-DDT) was acted as a reference substance. Their log P GC values and internal energies of phase transfer (△ vap H ) ranged from -6.17 to -1.25 and 74.1 kJ/mol to 122 kJ/mol, respectively. Substitution pattern and molar volume ( V M ) were found to be capable of influencing log P GC values of the OPFRs. The halogenated alkyl-OPFRs had lower log P GC values than aryl-or alkyl-OPFRs. The bigger the molar volume was, the smaller the log P GC value was. In addition, a quantitative structure-property relationship (QSPR) model of log P GC versus different relative retention times (RRTs) was developed with a high cross-validated value ( Q 2 cum ) of 0.946, indicating a good predictive ability and stability. Therefore, the log P GC values of the OPFRs without standard substance can be predicted by using their RRTs on different GC columns.

Structure- and ligand-based structure-activity relationships for a series of inhibitors of aldolase.

PubMed

Ferreira, Leonardo G; Andricopulo, Adriano D

2012-12-01

Aldolase has emerged as a promising molecular target for the treatment of human African trypanosomiasis. Over the last years, due to the increasing number of patients infected with Trypanosoma brucei, there is an urgent need for new drugs to treat this neglected disease. In the present study, two-dimensional fragment-based quantitative-structure activity relationship (QSAR) models were generated for a series of inhibitors of aldolase. Through the application of leave-one-out and leave-many-out cross-validation procedures, significant correlation coefficients were obtained (r²=0.98 and q²=0.77) as an indication of the statistical internal and external consistency of the models. The best model was employed to predict pKi values for a series of test set compounds, and the predicted values were in good agreement with the experimental results, showing the power of the model for untested compounds. Moreover, structure-based molecular modeling studies were performed to investigate the binding mode of the inhibitors in the active site of the parasitic target enzyme. The structural and QSAR results provided useful molecular information for the design of new aldolase inhibitors within this structural class.

Further evaluation of quantitative structure--activity relationship models for the prediction of the skin sensitization potency of selected fragrance allergens.

PubMed

Patlewicz, Grace Y; Basketter, David A; Pease, Camilla K Smith; Wilson, Karen; Wright, Zoe M; Roberts, David W; Bernard, Guillaume; Arnau, Elena Giménez; Lepoittevin, Jean-Pierre

2004-02-01

Fragrance substances represent a very diverse group of chemicals; a proportion of them are associated with the ability to cause allergic reactions in the skin. Efforts to find substitute materials are hindered by the need to undertake animal testing for determining both skin sensitization hazard and potency. One strategy to avoid such testing is through an understanding of the relationships between chemical structure and skin sensitization, so-called structure-activity relationships. In recent work, we evaluated 2 groups of fragrance chemicals -- saturated aldehydes and alpha,beta-unsaturated aldehydes. Simple quantitative structure-activity relationship (QSAR) models relating the EC3 values [derived from the local lymph node assay (LLNA)] to physicochemical properties were developed for both sets of aldehydes. In the current study, we evaluated an additional group of carbonyl-containing compounds to test the predictive power of the developed QSARs and to extend their scope. The QSAR models were used to predict EC3 values of 10 newly selected compounds. Local lymph node assay data generated for these compounds demonstrated that the original QSARs were fairly accurate, but still required improvement. Development of these QSAR models has provided us with a better understanding of the potential mechanisms of action for aldehydes, and hence how to avoid or limit allergy. Knowledge generated from this work is being incorporated into new/improved rules for sensitization in the expert toxicity prediction system, deductive estimation of risk from existing knowledge (DEREK).

Bayesian model aggregation for ensemble-based estimates of protein pKa values

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

Gosink, Luke J.; Hogan, Emilie A.; Pulsipher, Trenton C.

2014-03-01

This paper investigates an ensemble-based technique called Bayesian Model Averaging (BMA) to improve the performance of protein amino acid pmore » $$K_a$$ predictions. Structure-based p$$K_a$$ calculations play an important role in the mechanistic interpretation of protein structure and are also used to determine a wide range of protein properties. A diverse set of methods currently exist for p$$K_a$$ prediction, ranging from empirical statistical models to {\\it ab initio} quantum mechanical approaches. However, each of these methods are based on a set of assumptions that have inherent bias and sensitivities that can effect a model's accuracy and generalizability for p$$K_a$$ prediction in complicated biomolecular systems. We use BMA to combine eleven diverse prediction methods that each estimate pKa values of amino acids in staphylococcal nuclease. These methods are based on work conducted for the pKa Cooperative and the pKa measurements are based on experimental work conducted by the Garc{\\'i}a-Moreno lab. Our study demonstrates that the aggregated estimate obtained from BMA outperforms all individual prediction methods in our cross-validation study with improvements from 40-70\\% over other method classes. This work illustrates a new possible mechanism for improving the accuracy of p$$K_a$$ prediction and lays the foundation for future work on aggregate models that balance computational cost with prediction accuracy.« less

Using Helical CT to Predict Stone Fragility in Shock Wave Lithotripsy (SWL)

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

Williams, James C. Jr.; Zarse, Chad A.; Jackson, Molly E.

2007-04-05

Great variability exists in the response of urinary stones to SWL, and this is true even for stones composed of the same mineral. Efforts have been made to predict stone fragility to shock waves using computed tomography (CT) patient images, but most work to date has focused on the use of stone CT number (i.e., Hounsfield units). This is an easy number to measure on a patient stone, but its value depends on a number of factors, including the relationship of the size of the stone to me resolution (i.e., the slicewidth) of the CT scan. Studies that have shownmore » a relationship between stone CT number and failure in SWL are reviewed, and all are shown to suffer from error due to stone size, which was not accounted for in the use of Hounsfield unit values. Preliminary data are then presented for a study of calcium oxalate monohydrate (COM) stones, in which stone structure-rather than simple CT number values-is shown to correlate with fragility to shock waves. COM stones that were observed to have structure by micro CT (e.g., voids, apatite regions, unusual shapes) broke to completion in about half the number of shock waves required for COM stones that were observed to be homogeneous in structure by CT. This result suggests another direction for the use of CT in predicting success of SWL: the use of CT to view stone structure, rather than simply measuring stone CT number. Viewing stone structure by CT requires the use of different viewing windows than those typically used for examining patient scans, but much research to date indicates that stone structure can be observed in the clinical setting. Future clinical studies will need to be done to verify the relationship between stone structure observed by CT and stone fragility in SWL.« less

Quantitative Structure-Antifungal Activity Relationships for cinnamate derivatives.

PubMed

Saavedra, Laura M; Ruiz, Diego; Romanelli, Gustavo P; Duchowicz, Pablo R

2015-12-01

Quantitative Structure-Activity Relationships (QSAR) are established with the aim of analyzing the fungicidal activities of a set of 27 active cinnamate derivatives. The exploration of more than a thousand of constitutional, topological, geometrical and electronic molecular descriptors, which are calculated with Dragon software, leads to predictions of the growth inhibition on Pythium sp and Corticium rolfsii fungi species, in close agreement to the experimental values extracted from the literature. A set containing 21 new structurally related cinnamate compounds is prepared. The developed QSAR models are applied to predict the unknown fungicidal activity of this set, showing that cinnamates like 38, 28 and 42 are expected to be highly active for Pythium sp, while this is also predicted for 28 and 34 in C. rolfsii. Copyright © 2015 Elsevier Inc. All rights reserved.

Model Update of a Micro Air Vehicle (MAV) Flexible Wing Frame with Uncertainty Quantification

NASA Technical Reports Server (NTRS)

Reaves, Mercedes C.; Horta, Lucas G.; Waszak, Martin R.; Morgan, Benjamin G.

2004-01-01

This paper describes a procedure to update parameters in the finite element model of a Micro Air Vehicle (MAV) to improve displacement predictions under aerodynamics loads. Because of fabrication, materials, and geometric uncertainties, a statistical approach combined with Multidisciplinary Design Optimization (MDO) is used to modify key model parameters. Static test data collected using photogrammetry are used to correlate with model predictions. Results show significant improvements in model predictions after parameters are updated; however, computed probabilities values indicate low confidence in updated values and/or model structure errors. Lessons learned in the areas of wing design, test procedures, modeling approaches with geometric nonlinearities, and uncertainties quantification are all documented.

A novel structural risk index for primary spontaneous pneumothorax: Ankara Numune Risk Index.

PubMed

Akkas, Yucel; Peri, Neslihan Gulay; Kocer, Bulent; Kaplan, Tevfik; Alhan, Aslihan

2017-07-01

In this study, we aimed to reveal a novel risk index as a structural risk marker for primary spontanoeus pneumothorax using body mass index and chest height, structural risk factors for pneumothorax development. Records of 86 cases admitted between February 2014 and January 2015 with or without primary spontaneous pneumothorax were analysed retrospectively. The patients were allocated to two groups as Group I and Group II. The patients were evaluated with regard to age, gender, pneumothorax side, duration of hospital stay, treatment type, recurrence, chest height and transverse diameter on posteroanterior chest graphy and body mass index. Body mass index ratio per cm of chest height was calculated by dividing body mass index with chest height. We named this risk index ratio which is defined first as 'Ankara Numune Risk Index'. Diagnostic value of Ankara Numune Risk Index value for prediction of primary spontaneous pneumothorax development was analysed with Receiver Operating Characteristics curver. Of 86 patients, 69 (80.2%) were male and 17 (19.8%) were female. Each group was composed of 43 (50%) patients. When Receiver Operating Characteristics curve analysis was done for optimal limit value 0.74 of Ankara Numune Risk Index determined for prediction of pneumothorax development risk, area under the curve was 0.925 (95% Cl, 0.872-0.977, p < 0.001). Ankara Numune Risk Index is one of the structural risk factors for prediction of primary spontaneous pneumothorax development however it is insufficient for determining recurrence. Copyright © 2015. Published by Elsevier Taiwan.

Evidence of Structure and Persistence in Motivational Attraction to Serial Pavlovian Cues

ERIC Educational Resources Information Center

Smedley, Elizabeth B.; Smith, Kyle S.

2018-01-01

Sign-tracking is a form of autoshaping where animals develop conditioned responding directed toward stimuli predictive of an outcome even though the outcome is not contingent on the animal's behavior. Sign-tracking behaviors are thought to arise out of the attribution of incentive salience (i.e., motivational value) to reward-predictive cues. It…

The Role of Feature Selection and Statistical Weighting in Predicting In Vivo Toxicity Using In Vitro Assay and QSAR Data (SOT)

EPA Science Inventory

Our study assesses the value of both in vitro assay and quantitative structure activity relationship (QSAR) data in predicting in vivo toxicity using numerous statistical models and approaches to process the data. Our models are built on datasets of (i) 586 chemicals for which bo...

Numerical weather prediction model tuning via ensemble prediction system

NASA Astrophysics Data System (ADS)

Jarvinen, H.; Laine, M.; Ollinaho, P.; Solonen, A.; Haario, H.

2011-12-01

This paper discusses a novel approach to tune predictive skill of numerical weather prediction (NWP) models. NWP models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. Currently, numerical values of these parameters are specified manually. In a recent dual manuscript (QJRMS, revised) we developed a new concept and method for on-line estimation of the NWP model parameters. The EPPES ("Ensemble prediction and parameter estimation system") method requires only minimal changes to the existing operational ensemble prediction infra-structure and it seems very cost-effective because practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating each member of the ensemble of predictions using different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In the presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an atmospheric general circulation model based ensemble prediction system show that the NWP model tuning capacity of EPPES scales up to realistic models and ensemble prediction systems. Finally, a global top-end NWP model tuning exercise with preliminary results is published.

Prediction of Mechanical Properties of Polymers With Various Force Fields

NASA Technical Reports Server (NTRS)

Odegard, Gregory M.; Clancy, Thomas C.; Gates, Thomas S.

2005-01-01

The effect of force field type on the predicted elastic properties of a polyimide is examined using a multiscale modeling technique. Molecular Dynamics simulations are used to predict the atomic structure and elastic properties of the polymer by subjecting a representative volume element of the material to bulk and shear finite deformations. The elastic properties of the polyimide are determined using three force fields: AMBER, OPLS-AA, and MM3. The predicted values of Young s modulus and shear modulus of the polyimide are compared with experimental values. The results indicate that the mechanical properties of the polyimide predicted with the OPLS-AA force field most closely matched those from experiment. The results also indicate that while the complexity of the force field does not have a significant effect on the accuracy of predicted properties, small differences in the force constants and the functional form of individual terms in the force fields determine the accuracy of the force field in predicting the elastic properties of the polyimide.

Topological ring currents in the "empty" ring of benzo-annelated perylenes.

PubMed

Dickens, Timothy K; Mallion, Roger B

2011-01-27

Cyclic conjugation in benzo-annelated perylenes is examined by means of the topological π-electron ring currents calculated for each of their constituent rings, in a study that is an exact analogy of a recent investigation by Gutman et al. based on energy-effect values for the corresponding rings in each of these structures. "Classical" approaches, such as Kekulé structures, Clar "sextet" formulas, and circuits of conjugation, predict that the central ring in perylene is "empty" and thus contributes negligibly to cyclic conjugation. However, conclusions from the present calculations of topological ring currents agree remarkably with those arising from the earlier study involving energy-effect values in that, contrary to what would be predicted from the classical approaches, rings annelated in an angular fashion relative to the central ring of these perylene structures materially increase the extent of that ring's involvement in cyclic conjugation. It is suggested that such close quantitative agreement between the predictions of these two superficially very different indices (energy effect and topological ring current) might be due to the fact that, ultimately, both depend, albeit in ostensibly quite different ways, only on an adjacency matrix that contains information about the carbon-carbon connectivity of the conjugated system in question.

Development of Design Analysis Methods for C/SiC Composite Structures

NASA Technical Reports Server (NTRS)

Sullivan, Roy M.; Mital, Subodh K.; Murthy, Pappu L. N.; Palko, Joseph L.; Cueno, Jacques C.; Koenig, John R.

2006-01-01

The stress-strain behavior at room temperature and at 1100 C (2000 F) was measured for two carbon-fiber-reinforced silicon carbide (C/SiC) composite materials: a two-dimensional plain-weave quasi-isotropic laminate and a three-dimensional angle-interlock woven composite. Micromechanics-based material models were developed for predicting the response properties of these two materials. The micromechanics based material models were calibrated by correlating the predicted material property values with the measured values. Four-point beam bending sub-element specimens were fabricated with these two fiber architectures and four-point bending tests were performed at room temperature and at 1100 C. Displacements and strains were measured at various locations along the beam and recorded as a function of load magnitude. The calibrated material models were used in concert with a nonlinear finite element solution to simulate the structural response of these two materials in the four-point beam bending tests. The structural response predicted by the nonlinear analysis method compares favorably with the measured response for both materials and for both test temperatures. Results show that the material models scale up fairly well from coupon to subcomponent level.

Progress in the prediction of pKa values in proteins

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

Alexov, Emil; Mehler, Ernest L.; Baker, Nathan A.

2011-12-15

The pKa-cooperative aims to provide a forum for experimental and theoretical researchers interested in protein pKa values and protein electrostatics in general. The first round of the pKa -cooperative, which challenged computational labs to carry out blind predictions against pKas experimentally determined in the laboratory of Bertrand Garcia-Moreno, was completed and results discussed at the Telluride meeting (July 6-10, 2009). This paper serves as an introduction to the reports submitted by the blind prediction participants that will be published in a special issue of PROTEINS: Structure, Function and Bioinformatics. Here we briefly outline existing approaches for pKa calculations, emphasizing methodsmore » that were used by the participants in calculating the blind pKa values in the first round of the cooperative. We then point out some of the difficulties encountered by the participating groups in making their blind predictions, and finally try to provide some insights for future developments aimed at improving the accuracy of pKa calculations.« less

Measuring the value of accurate link prediction for network seeding.

PubMed

Wei, Yijin; Spencer, Gwen

2017-01-01

The influence-maximization literature seeks small sets of individuals whose structural placement in the social network can drive large cascades of behavior. Optimization efforts to find the best seed set often assume perfect knowledge of the network topology. Unfortunately, social network links are rarely known in an exact way. When do seeding strategies based on less-than-accurate link prediction provide valuable insight? We introduce optimized-against-a-sample ([Formula: see text]) performance to measure the value of optimizing seeding based on a noisy observation of a network. Our computational study investigates [Formula: see text] under several threshold-spread models in synthetic and real-world networks. Our focus is on measuring the value of imprecise link information. The level of investment in link prediction that is strategic appears to depend closely on spread model: in some parameter ranges investments in improving link prediction can pay substantial premiums in cascade size. For other ranges, such investments would be wasted. Several trends were remarkably consistent across topologies.

Value of serum N-terminal B-type natriuretic peptide in asymptomatic structural heart disease in Taiwanese population: Comparisons with current ESC Guidelines.

PubMed

Hung, Ta-Chuan; Wang, Kuang-Te; Yun, Chun-Ho; Kuo, Jen-Yuan; Hou, Charles Jia-Yin; Liu, Chia-Yuan; Wu, Tung-Hsin; Bezerra, Hiram G; Cheng, Hsiao-Yang; Hung, Chung-Lieh; Yeh, Hung-I

2017-03-15

The relationship between N-terminal pro-brain natriuretic peptide (NT-proBNP) and cardiac structural or functional anomalies in pre-clinical, asymptomatic Asian populations has not been well identified. From October 2005 to March 2008, we enrolled consecutive asymptomatic adults with preserved global left ventricular (LV) function (ejection fraction>50%) who underwent annual cardiovascular health survey. Circulating NT-proBNP was used to identify echo-defined cardiac structural/functional anomalies and compared to current recommended cut-off from the European Society of Heart Failure. Among 976 eligible subjects, 371 (38%) had structural heart diseases. Echocardiography-based left atrial diameter (Coef: 71.2), diastolic dysfunction (Coef: 35.4), and presence of pulmonary hypertension (Coef: 83.1) or valvular heart disease (Coef: 56.1, all p<0.05) of any form independently predicted circulating NT-ProBNP. NT-ProBNP cut-off values of 32.8 and 115.4pg/ml for subjects aged ≤ and >75years, respectively, demonstrated areas under the receiver operating characteristic curve of 0.76 (95% CI: 0.73-0.80) and 0.70 (95% CI: 0.52-0.88) for predicting structural or functional anomaly. We examined the feasibility of NT-ProBNP for identifying cardiac structural and functional anomaly in an asymptomatic ethnic Taiwanese population with a relatively lower cut-off value, indicating its potential role for pre-clinical screening of Asian patients. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Determination of an ensemble of structures representing the intermediate state of the bacterial immunity protein Im7.

PubMed

Gsponer, Joerg; Hopearuoho, Harri; Whittaker, Sara B-M; Spence, Graham R; Moore, Geoffrey R; Paci, Emanuele; Radford, Sheena E; Vendruscolo, Michele

2006-01-03

We present a detailed structural characterization of the intermediate state populated during the folding and unfolding of the bacterial immunity protein Im7. We achieve this result by incorporating a variety of experimental data available for this species in molecular dynamics simulations. First, we define the structure of the exchange-competent intermediate state of Im7 by using equilibrium hydrogen-exchange protection factors. Second, we use this ensemble to predict Phi-values and compare the results with the experimentally determined Phi-values of the kinetic refolding intermediate. Third, we predict chemical-shift measurements and compare them with the measured chemical shifts of a mutational variant of Im7 for which the kinetic folding intermediate is the most stable state populated at equilibrium. Remarkably, we found that the properties of the latter two species are predicted with high accuracy from the exchange-competent intermediate that we determined, suggesting that these three states are characterized by a similar architecture in which helices I, II, and IV are aligned in a native-like, but reorganized, manner. Furthermore, the structural ensemble that we obtained enabled us to rationalize the results of tryptophan fluorescence experiments in the WT protein and a series of mutational variants. The results show that the integration of diverse sets of experimental data at relatively low structural resolution is a powerful approach that can provide insights into the structural organization of this conformationally heterogeneous three-helix intermediate with unprecedented detail and highlight the importance of both native and non-native interactions in stabilizing its structure.

A simple extension to the CMASA method for the prediction of catalytic residues in the presence of single point mutations.

PubMed

Flores, David I; Sotelo-Mundo, Rogerio R; Brizuela, Carlos A

2014-01-01

The automatic identification of catalytic residues still remains an important challenge in structural bioinformatics. Sequence-based methods are good alternatives when the query shares a high percentage of identity with a well-annotated enzyme. However, when the homology is not apparent, which occurs with many structures from the structural genome initiative, structural information should be exploited. A local structural comparison is preferred to a global structural comparison when predicting functional residues. CMASA is a recently proposed method for predicting catalytic residues based on a local structure comparison. The method achieves high accuracy and a high value for the Matthews correlation coefficient. However, point substitutions or a lack of relevant data strongly affect the performance of the method. In the present study, we propose a simple extension to the CMASA method to overcome this difficulty. Extensive computational experiments are shown as proof of concept instances, as well as for a few real cases. The results show that the extension performs well when the catalytic site contains mutated residues or when some residues are missing. The proposed modification could correctly predict the catalytic residues of a mutant thymidylate synthase, 1EVF. It also successfully predicted the catalytic residues for 3HRC despite the lack of information for a relevant side chain atom in the PDB file.

Calculating the dermal flux of chemicals with OELs based on their molecular structure: An attempt to assign the skin notation.

PubMed

Kupczewska-Dobecka, Małgorzata; Jakubowski, Marek; Czerczak, Sławomir

2010-09-01

Our objectives included calculating the permeability coefficient and dermal penetration rates (flux value) for 112 chemicals with occupational exposure limits (OELs) according to the LFER (linear free-energy relationship) model developed using published methods. We also attempted to assign skin notations based on each chemical's molecular structure. There are many studies available where formulae for coefficients of permeability from saturated aqueous solutions (K(p)) have been related to physicochemical characteristics of chemicals. The LFER model is based on the solvation equation, which contains five main descriptors predicted from chemical structure: solute excess molar refractivity, dipolarity/polarisability, summation hydrogen bond acidity and basicity, and the McGowan characteristic volume. Descriptor values, available for about 5000 compounds in the Pharma Algorithms Database were used to calculate permeability coefficients. Dermal penetration rate was estimated as a ratio of permeability coefficient and concentration of chemical in saturated aqueous solution. Finally, estimated dermal penetration rates were used to assign the skin notation to chemicals. Defined critical fluxes defined from the literature were recommended as reference values for skin notation. The application of Abraham descriptors predicted from chemical structure and LFER analysis in calculation of permeability coefficients and flux values for chemicals with OELs was successful. Comparison of calculated K(p) values with data obtained earlier from other models showed that LFER predictions were comparable to those obtained by some previously published models, but the differences were much more significant for others. It seems reasonable to conclude that skin should not be characterised as a simple lipophilic barrier alone. Both lipophilic and polar pathways of permeation exist across the stratum corneum. It is feasible to predict skin notation on the basis of the LFER and other published models; from among 112 chemicals 94 (84%) should have the skin notation in the OEL list based on the LFER calculations. The skin notation had been estimated by other published models for almost 94% of the chemicals. Twenty-nine (25.8%) chemicals were identified to have significant absorption and 65 (58%) the potential for dermal toxicity. We found major differences between alternative published analytical models and their ability to determine whether particular chemicals were potentially dermotoxic. Copyright © 2010 Elsevier B.V. All rights reserved.

Comparative values of medical school assessments in the prediction of internship performance.

PubMed

Lee, Ming; Vermillion, Michelle

2018-02-01

Multiple undergraduate achievements have been used for graduate admission consideration. Their relative values in the prediction of residency performance are not clear. This study compared the contributions of major undergraduate assessments to the prediction of internship performance. Internship performance ratings of the graduates of a medical school were collected from 2012 to 2015. Hierarchical multiple regression analyses were used to examine the predictive values of undergraduate measures assessing basic and clinical sciences knowledge and clinical performances, after controlling for differences in the Medical College Admission Test (MCAT). Four hundred eighty (75%) graduates' archived data were used in the study. Analyses revealed that clinical competencies, assessed by the USMLE Step 2 CK, NBME medicine exam, and an eight-station objective structured clinical examination (OSCE), were strong predictors of internship performance. Neither the USMLE Step 1 nor the inpatient internal medicine clerkship evaluation predicted internship performance. The undergraduate assessments as a whole showed a significant collective relationship with internship performance (ΔR 2  = 0.12, p < 0.001). The study supports the use of clinical competency assessments, instead of pre-clinical measures, in graduate admission consideration. It also provides validity evidence for OSCE scores in the prediction of workplace performance.

DFT calculations on spectroscopic and structural properties of a NLO chromophore

NASA Astrophysics Data System (ADS)

Altürk, Sümeyye; Avci, Davut; Tamer, Ömer; Atalay, Yusuf

2016-03-01

The molecular geometry optimization, vibrational frequencies and gauge including atomic orbital (GIAO) 1H and 13C NMR chemical shift values of 2-(1'-(4'''-Methoxyphenyl)-5'-(thien-2″-yl)pyrrol-2'-yl)-1,3-benzothiazole as potential nonlinear optical (NLO) material were calculated using density functional theory (DFT) HSEh1PBE method with 6-311G(d,p) basis set. The best of our knowledge, this study have not been reported to date. Additionally, a detailed vibrational study was performed on the basis of potential energy distribution (PED) using VEDA program. It is noteworthy that NMR chemical shifts are quite useful for understanding the relationship between the molecular structure and electronic properties of molecules. The computed IR and NMR spectra were used to determine the types of the experimental bands observed. Predicted values of structural and spectroscopic parameters of the chromophore were compared with each other so as to display the effects of the different substituents on the spectroscopic and structural properties. Obtained data showed that there is an agreement between the predicted and experimental data.

A shielding theory for upward lightning

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

Shindo, Takatoshi; Aihara, Yoshinori

1993-01-01

A new shielding theory is proposed based on the assumption that the occurrence of lightning strokes on the Japan Sea coast in winter is due to the inception of upward leaders from tall structures. Ratios of the numbers of lightning strokes to high structures observed there in winter show reasonable agreement with values calculated by this theory. Shielding characteristics of a high structure in various conditions are predicted.

Belt-hierarchic structure of th ring, satellite and planet systems: prediction S/2001 U1 and others objects in Solar system

NASA Astrophysics Data System (ADS)

Barkin, Yu. V.

2003-04-01

BELT-HIERARCHIC STRUCTURE OF THE RING, SATELLITE AND PLANET SYSTEMS: PREDICTION S/2001 U1 AND OTHERS OBJECTS IN SOLAR SYSTEM Yu.V.Barkin Sternberg Astronomical Institute, Moscow, Russia, barkin@sai.msu.ru Structure regularities of the planet and satellite systems have been studied. Statistic analysis of the distribution of the major semi-axes of the orbits of the planets, comets and centaurs of the Solar system, satellite and ring systems of Jupiter, Saturn, Neptune and Uran, exoplanet systems of the pulsars PSR 1257+12, PSR 1828-11 and of the main consequence star Ups And was fulfilled. The following empirical regularities were described [1]: 1) the bodies of systems are combined into hierarchic groups and main from them combine 5 companions; 2) differences of the major semi-axes of the neighboring orbits for bodies of every group are constant; 4) for main neighboring hierarchic group these distances are distinguished in 6 times increasing to external grope; 5) the filling of the gropes and some present changes in their structure are caused by the past catastrophes in corresponding systems. The special method of reconstruction of the catastrophes which had place in the life of the Solar system (SS) was developed. Suggested method has let us to explain uniformly observed values of the major semi-axes and average values of eccentricities of the planets. In particular the Pancul’s hypothesis about Jupiter formation from two giant protoplanets (Jupiter I and Jupiter II) was confirmed. The new empirical law of the filling of the orbits of the regular groups of the planets or satellites (or rings structures) of the hierarchic ordered systems of celestial bodies was established. It was shown that sum number of bodies is proportional to the value of catastrophic value of the eccentricities which are same for first, second ,.... and fifth orbits of all gropes. The theoretical numbers of bodies for pointed orbits practically coincide with their observed numbers in main gropes of the all considered systems of celestial bodies (in Solar system and also in exoplanets systems of the pulsars PSR 1257+12, PSR 1828-11 and Ups And). Established regularities of the orbit structures let us to predict some new objects in the Solar system and in exoplanet systems. Some from them have been predicted in last years. So the new satellite of Uran (S/2001 U 1) is characterized by major semi-axis in 8 570 000 km (Minor Planet Electronic Circular, Issued 2002 Sept. 30). This satellite was predicted earlier as satellite E1 (8 640 000 km) [1]. [1] Yu.V.Barkin (2001) Electronic journal «Studied in Russia», 161, pp.1821-1830. http: // zhurnal. ape. relarn.ru/articles/2001/161.pdf.

Incorporating Psychological Predictors of Treatment Response into Health Economic Simulation Models: A Case Study in Type 1 Diabetes.

PubMed

Kruger, Jen; Pollard, Daniel; Basarir, Hasan; Thokala, Praveen; Cooke, Debbie; Clark, Marie; Bond, Rod; Heller, Simon; Brennan, Alan

2015-10-01

. Health economic modeling has paid limited attention to the effects that patients' psychological characteristics have on the effectiveness of treatments. This case study tests 1) the feasibility of incorporating psychological prediction models of treatment response within an economic model of type 1 diabetes, 2) the potential value of providing treatment to a subgroup of patients, and 3) the cost-effectiveness of providing treatment to a subgroup of responders defined using 5 different algorithms. . Multiple linear regressions were used to investigate relationships between patients' psychological characteristics and treatment effectiveness. Two psychological prediction models were integrated with a patient-level simulation model of type 1 diabetes. Expected value of individualized care analysis was undertaken. Five different algorithms were used to provide treatment to a subgroup of predicted responders. A cost-effectiveness analysis compared using the algorithms to providing treatment to all patients. . The psychological prediction models had low predictive power for treatment effectiveness. Expected value of individualized care results suggested that targeting education at responders could be of value. The cost-effectiveness analysis suggested, for all 5 algorithms, that providing structured education to a subgroup of predicted responders would not be cost-effective. . The psychological prediction models tested did not have sufficient predictive power to make targeting treatment cost-effective. The psychological prediction models are simple linear models of psychological behavior. Collection of data on additional covariates could potentially increase statistical power. . By collecting data on psychological variables before an intervention, we can construct predictive models of treatment response to interventions. These predictive models can be incorporated into health economic models to investigate more complex service delivery and reimbursement strategies. © The Author(s) 2015.

ChemStable: a web server for rule-embedded naïve Bayesian learning approach to predict compound stability.

PubMed

Liu, Zhihong; Zheng, Minghao; Yan, Xin; Gu, Qiong; Gasteiger, Johann; Tijhuis, Johan; Maas, Peter; Li, Jiabo; Xu, Jun

2014-09-01

Predicting compound chemical stability is important because unstable compounds can lead to either false positive or to false negative conclusions in bioassays. Experimental data (COMDECOM) measured from DMSO/H2O solutions stored at 50 °C for 105 days were used to predicted stability by applying rule-embedded naïve Bayesian learning, based upon atom center fragment (ACF) features. To build the naïve Bayesian classifier, we derived ACF features from 9,746 compounds in the COMDECOM dataset. By recursively applying naïve Bayesian learning from the data set, each ACF is assigned with an expected stable probability (p(s)) and an unstable probability (p(uns)). 13,340 ACFs, together with their p(s) and p(uns) data, were stored in a knowledge base for use by the Bayesian classifier. For a given compound, its ACFs were derived from its structure connection table with the same protocol used to drive ACFs from the training data. Then, the Bayesian classifier assigned p(s) and p(uns) values to the compound ACFs by a structural pattern recognition algorithm, which was implemented in-house. Compound instability is calculated, with Bayes' theorem, based upon the p(s) and p(uns) values of the compound ACFs. We were able to achieve performance with an AUC value of 84% and a tenfold cross validation accuracy of 76.5%. To reduce false negatives, a rule-based approach has been embedded in the classifier. The rule-based module allows the program to improve its predictivity by expanding its compound instability knowledge base, thus further reducing the possibility of false negatives. To our knowledge, this is the first in silico prediction service for the prediction of the stabilities of organic compounds.

Longer-Term Investigation of the Value of 18F-FDG-PET and Magnetic Resonance Imaging for Predicting the Conversion of Mild Cognitive Impairment to Alzheimer's Disease: A Multicenter Study.

PubMed

Inui, Yoshitaka; Ito, Kengo; Kato, Takashi

2017-01-01

The value of fluorine-18-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) and magnetic resonance imaging (MRI) for predicting conversion of mild cognitive impairment (MCI) to Alzheimer's disease (AD) in longer-term is unclear. To evaluate longer-term prediction of MCI to AD conversion using 18F-FDG-PET and MRI in a multicenter study. One-hundred and fourteen patients with MCI were followed for 5 years. They underwent clinical and neuropsychological examinations, 18F-FDG-PET, and MRI at baseline. PET images were visually classified into predefined dementia patterns. PET scores were calculated as a semi quantitative index. For structural MRI, z-scores in medial temporal area were calculated by automated volume-based morphometry (VBM). Overall, 72% patients with amnestic MCI progressed to AD during the 5-year follow-up. The diagnostic accuracy of PET scores over 5 years was 60% with 53% sensitivity and 84% specificity. Visual interpretation of PET images predicted conversion to AD with an overall 82% diagnostic accuracy, 94% sensitivity, and 53% specificity. The accuracy of VBM analysis presented little fluctuation through 5 years and it was highest (73%) at the 5-year follow-up, with 79% sensitivity and 63% specificity. The best performance (87.9% diagnostic accuracy, 89.8% sensitivity, and 82.4% specificity) was with a combination identified using multivariate logistic regression analysis that included PET visual interpretation, educational level, and neuropsychological tests as predictors. 18F-FDG-PET visual assessment showed high performance for predicting conversion to AD from MCI, particularly in combination with neuropsychological tests. PET scores showed high diagnostic specificity. Structural MRI focused on the medial temporal area showed stable predictive value throughout the 5-year course.

Rapid Structural Design Change Evaluation with AN Experiment Based FEM

NASA Astrophysics Data System (ADS)

Chu, C.-H.; Trethewey, M. W.

1998-04-01

The work in this paper proposes a dynamic structural design model that can be developed in a rapid fashion. The approach endeavours to produce a simplified FEM developed in conjunction with an experimental modal database. The FEM is formulated directly from the geometry and connectivity used in an experimental modal test using beam/frame elements. The model sacrifices fine detail for a rapid development time. The FEM is updated at the element level so the dynamic response replicates the experimental results closely. The physical attributes of the model are retained, making it well suited to evaluate the effect of potential design changes. The capabilities are evaluated in a series of computational and laboratory tests. First, a study is performed with a simulated cantilever beam with a variable mass and stiffness distribution. The modal characteristics serve as the updating target with random noise added to simulate experimental uncertainty. A uniformly distributed FEM is developed and updated. The results show excellent results, all natural frequencies are within 0·001% with MAC values above 0·99. Next, the method is applied to predict the dynamic changes of a hardware portal frame structure for a radical design change. Natural frequency predictions from the original FEM differ by as much as almost 18% with reasonable MAC values. The results predicted from the updated model produce excellent results when compared to the actual hardware changes, the first five modal natural frequency difference is around 5% and the corresponding mode shapes producing MAC values above 0·98.

Modeling the dynamic equilibrium between oligomers of (AlOCH3)n in methylaluminoxane (MAO). A theoretical study based on a combined quantum mechanical and statistical mechanical approach.

PubMed

Zurek, E; Woo, T K; Firman, T K; Ziegler, T

2001-01-15

Density functional theory (DFT) has been used to calculate the energies of 36 different methylaluminoxane (MAO) cage structures with the general formula (MeAlO)n, where n ranges from 4 to 16. A least-squares fit has been used to devise a formula which predicts the total energies of the MAO with different n's giving an rms deviation of 4.70 kcal/mol. These energies in conjunction with frequency calculations based on molecular mechanics have been used to estimate the finite temperature enthalpies, entropies, and free energies for these MAO structures. Furthermore, formulas have been devised which predict finite temperature enthalpies and entropies for MAO structures of any n for a temperature range of 198.15-598.15 K. Using these formulas, the free energies at different temperatures have been predicted for MAO structures where n ranges from 17 to 30. The free energy values were then used to predict the percentage of each n found at a given temperature. Our calculations give an average n value of 18.41, 17.23, 16.89, and 15.72 at 198.15, 298.15, 398.15, and 598.15 K, respectively. Topological arguments have also been used to show that the MAO cage structure contains a limited amount of square faces as compared to octagonal and hexagonal ones. It is also suggested that the limited number of square faces with their strained Al-O bonds explain the high molar Al:catalyst ratio required for activation. Moreover, in this study we outline a general methodology which may be used to calculate the percent abundance of an equilibrium mixture of oligomers with the general formula (X)n.

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.

EPRB Gedankenexperiment and Entanglement with Classical Light Waves

NASA Astrophysics Data System (ADS)

Rashkovskiy, Sergey A.

2018-06-01

In this article we show that results similar to those of the Einstein-Podolsky-Rosen-Bohm (EPRB) Gedankenexperiment and entanglement of photons can be obtained using weak classical light waves if we take into account the discrete (atomic) structure of the detectors and a specific nature of the light-atom interaction. We show that the CHSH (Clauser, Horne, Shimony, and Holt) criterion in the EPRB Gedankenexperiment with classical light waves can exceed not only the maximum value SHV=2 that is predicted by the local hidden-variable theories but also the maximum value S_{QM} = 2√2 predicted by quantum mechanics.

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

NASA Astrophysics Data System (ADS)

Men, Ke-Pei; Zhao, Kai

2014-12-01

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

Variability amongst radiographers in the categorization of clinical acceptability for digital trauma radiography

NASA Astrophysics Data System (ADS)

Decoster, Robin; Toomey, Rachel; Smits, Dirk; Mol, Harrie; Verhelle, Filip; Butler, Marie-Louise

2016-03-01

Introduction: Radiographers evaluate anatomical structures to judge clinical acceptability of a radiograph. Whether a radiograph is deemed acceptable for diagnosis or not depends on the individual decision of the radiographer. Individual decisions cause variation in the accepted image quality. To minimise these variations definitions of acceptability, such as in RadLex, were developed. On which criteria radiographers attribute a RadLex categories to radiographs is unknown. Insight into these criteria helps to further optimise definitions and reduce variability in acceptance between radiographers. Therefore, this work aims the evaluation of the correlation between the RadLex classification and the evaluation of anatomical structures, using a Visual Grading Analysis (VGA) Methods: Four radiographers evaluated the visibility of five anatomical structures of 25 lateral cervical spine radiographs on a secondary class display with a VGA. They judged clinical acceptability of each radiograph using RadLex. Relations between VGAS and RadLex category were analysed with Kendall's Tau correlation and Nagelkerke pseudo-R². Results: The overall VGA score (VGAS) and the RadLex score correlate (rτ= 0.62, p<0.01, R2=0.72) strongly. The observers' evaluation of contrast between bone, air (trachea) and soft tissue has low value in predicting (rτ=0.55, p<0.01, R2=0.03) the RadLex score. The reproduction of spinous processes (rτ=0.67, p<0.01, R2=0.31) and the evaluation of the exposure (rτ=0.65, p<0.01, R2=0.56) have a strong correlation with high predictive value for the RadLex score. Conclusion: RadLex scores and VGAS correlate positively, strongly and significantly. The predictive value of bony structures may support the use of these in the judgement of clinical acceptability. Considerable inter-observer variations in the VGAS within a certain RadLex category, suggest that observers use of observer specific cut-off values.

Type I and II β-turns prediction using NMR chemical shifts.

PubMed

Wang, Ching-Cheng; Lai, Wen-Chung; Chuang, Woei-Jer

2014-07-01

A method for predicting type I and II β-turns using nuclear magnetic resonance (NMR) chemical shifts is proposed. Isolated β-turn chemical-shift data were collected from 1,798 protein chains. One-dimensional statistical analyses on chemical-shift data of three classes β-turn (type I, II, and VIII) showed different distributions at four positions, (i) to (i + 3). Considering the central two residues of type I β-turns, the mean values of Cο, Cα, H(N), and N(H) chemical shifts were generally (i + 1) > (i + 2). The mean values of Cβ and Hα chemical shifts were (i + 1) < (i + 2). The distributions of the central two residues in type II and VIII β-turns were also distinguishable by trends of chemical shift values. Two-dimensional cluster analyses on chemical-shift data show positional distributions more clearly. Based on these propensities of chemical shift classified as a function of position, rules were derived using scoring matrices for four consecutive residues to predict type I and II β-turns. The proposed method achieves an overall prediction accuracy of 83.2 and 84.2% with the Matthews correlation coefficient values of 0.317 and 0.632 for type I and II β-turns, indicating that its higher accuracy for type II turn prediction. The results show that it is feasible to use NMR chemical shifts to predict the β-turn types in proteins. The proposed method can be incorporated into other chemical-shift based protein secondary structure prediction methods.

A novel model to predict gas-phase hydroxyl radical oxidation kinetics of polychlorinated compounds.

PubMed

Luo, Shuang; Wei, Zongsu; Spinney, Richard; Yang, Zhihui; Chai, Liyuan; Xiao, Ruiyang

2017-04-01

In this study, a novel model based on aromatic meta-substituent grouping was presented to predict the second-order rate constants (k) for OH oxidation of PCBs in gas-phase. Since the oxidation kinetics are dependent on the chlorination degree and position, we hypothesized that it may be more accurate for k value prediction if we group PCB congeners based on substitution positions (i.e., ortho (o), meta (m), and para (p)). To test this hypothesis, we examined the correlation of polarizability (α), a quantum chemical based descriptor for k values, with an empirical Hammett constant (σ + ) on each substitution position. Our result shows that α is highly linearly correlated to ∑σ o,m,p + based on aromatic meta-substituents leading to the grouping based predictive model. With the new model, the calculated k values exhibited an excellent agreement with experimental measurements, and greater predictive power than the quantum chemical based quantitative structure activity relationship (QSAR) model. Further, the relationship of α and ∑σ o,m,p + for PCDDs congeners, together with highest occupied molecular orbital (HOMO) distribution, were used to validate the aromatic meta-substituent grouping method. This newly developed model features a combination of good predictability of quantum chemical based QSAR model and simplicity of Hammett relationship, showing a great potential for fast and computational tractable prediction of k values for gas-phase OH oxidation of polychlorinated compounds. Copyright © 2017 Elsevier Ltd. All rights reserved.

Improved prediction of antibody VL–VH orientation

PubMed Central

Marze, Nicholas A.; Lyskov, Sergey; Gray, Jeffrey J.

2016-01-01

Antibodies are important immune molecules with high commercial value and therapeutic interest because of their ability to bind diverse antigens. Computational prediction of antibody structure can quickly reveal valuable information about the nature of these antigen-binding interactions, but only if the models are of sufficient quality. To achieve high model quality during complementarity-determining region (CDR) structural prediction, one must account for the VL–VH orientation. We developed a novel four-metric VL–VH orientation coordinate frame. Additionally, we extended the CDR grafting protocol in RosettaAntibody with a new method that diversifies VL–VH orientation by using 10 VL–VH orientation templates rather than a single one. We tested the multiple-template grafting protocol on two datasets of known antibody crystal structures. During the template-grafting phase, the new protocol improved the fraction of accurate VL–VH orientation predictions from only 26% (12/46) to 72% (33/46) of targets. After the full RosettaAntibody protocol, including CDR H3 remodeling and VL–VH re-orientation, the new protocol produced more candidate structures with accurate VL–VH orientation than the standard protocol in 43/46 targets (93%). The improved ability to predict VL–VH orientation will bolster predictions of other parts of the paratope, including the conformation of CDR H3, a grand challenge of antibody homology modeling. PMID:27276984

Large Dataset of Acute Oral Toxicity Data Created for Testing ...

EPA Pesticide Factsheets

Acute toxicity data is a common requirement for substance registration in the US. Currently only data derived from animal tests are accepted by regulatory agencies, and the standard in vivo tests use lethality as the endpoint. Non-animal alternatives such as in silico models are being developed due to animal welfare and resource considerations. We compiled a large dataset of oral rat LD50 values to assess the predictive performance currently available in silico models. Our dataset combines LD50 values from five different sources: literature data provided by The Dow Chemical Company, REACH data from eChemportal, HSDB (Hazardous Substances Data Bank), RTECS data from Leadscope, and the training set underpinning TEST (Toxicity Estimation Software Tool). Combined these data sources yield 33848 chemical-LD50 pairs (data points), with 23475 unique data points covering 16439 compounds. The entire dataset was loaded into a chemical properties database. All of the compounds were registered in DSSTox and 59.5% have publically available structures. Compounds without a structure in DSSTox are currently having their structures registered. The structural data will be used to evaluate the predictive performance and applicable chemical domains of three QSAR models (TIMES, PROTOX, and TEST). Future work will combine the dataset with information from ToxCast assays, and using random forest modeling, assess whether ToxCast assays are useful in predicting acute oral toxicity. Pre

Quantitative structure-permeability relationships at various pH values for acidic and basic drugs and drug-like compounds.

PubMed

Oja, M; Maran, U

2015-01-01

Absorption in gastrointestinal tract compartments varies and is largely influenced by pH. Therefore, considering pH in studies and analyses of membrane permeability provides an opportunity to gain a better understanding of the behaviour of compounds and to obtain good permeability estimates for prediction purposes. This study concentrates on relationships between the chemical structure and membrane permeability of acidic and basic drugs and drug-like compounds. The membrane permeability of 36 acidic and 61 basic compounds was measured using the parallel artificial membrane permeability assay (PAMPA) at pH 3, 5, 7.4 and 9. Descriptive and/or predictive single-parameter quantitative structure-permeability relationships were derived for all pH values. For acidic compounds, membrane permeability is mainly influenced by hydrogen bond donor properties, as revealed by models with r(2) > 0.8 for pH 3 and pH 5. For basic compounds, the best (r(2) > 0.7) structure-permeability relationships are obtained with the octanol-water distribution coefficient for pH 7.4 and pH 9, indicating the importance of partition properties. In addition to the validation set, the prediction quality of the developed models was tested with folic acid and astemizole, showing good matches between experimental and calculated membrane permeabilities at key pHs. Selected QSAR models are available at the QsarDB repository ( http://dx.doi.org/10.15152/QDB.166 ).

Modeling the binding affinity of structurally diverse industrial chemicals to carbon using the artificial intelligence approaches.

PubMed

Gupta, Shikha; Basant, Nikita; Rai, Premanjali; Singh, Kunwar P

2015-11-01

Binding affinity of chemical to carbon is an important characteristic as it finds vast industrial applications. Experimental determination of the adsorption capacity of diverse chemicals onto carbon is both time and resource intensive, and development of computational approaches has widely been advocated. In this study, artificial intelligence (AI)-based ten different qualitative and quantitative structure-property relationship (QSPR) models (MLPN, RBFN, PNN/GRNN, CCN, SVM, GEP, GMDH, SDT, DTF, DTB) were established for the prediction of the adsorption capacity of structurally diverse chemicals to activated carbon following the OECD guidelines. Structural diversity of the chemicals and nonlinear dependence in the data were evaluated using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. The generalization and prediction abilities of the constructed models were established through rigorous internal and external validation procedures performed employing a wide series of statistical checks. In complete dataset, the qualitative models rendered classification accuracies between 97.04 and 99.93%, while the quantitative models yielded correlation (R(2)) values of 0.877-0.977 between the measured and the predicted endpoint values. The quantitative prediction accuracies for the higher molecular weight (MW) compounds (class 4) were relatively better than those for the low MW compounds. Both in the qualitative and quantitative models, the Polarizability was the most influential descriptor. Structural alerts responsible for the extreme adsorption behavior of the compounds were identified. Higher number of carbon and presence of higher halogens in a molecule rendered higher binding affinity. Proposed QSPR models performed well and outperformed the previous reports. A relatively better performance of the ensemble learning models (DTF, DTB) may be attributed to the strengths of the bagging and boosting algorithms which enhance the predictive accuracies. The proposed AI models can be useful tools in screening the chemicals for their binding affinities toward carbon for their safe management.

Structured Set Intra Prediction With Discriminative Learning in a Max-Margin Markov Network for High Efficiency Video Coding

PubMed Central

Dai, Wenrui; Xiong, Hongkai; Jiang, Xiaoqian; Chen, Chang Wen

2014-01-01

This paper proposes a novel model on intra coding for High Efficiency Video Coding (HEVC), which simultaneously predicts blocks of pixels with optimal rate distortion. It utilizes the spatial statistical correlation for the optimal prediction based on 2-D contexts, in addition to formulating the data-driven structural interdependences to make the prediction error coherent with the probability distribution, which is desirable for successful transform and coding. The structured set prediction model incorporates a max-margin Markov network (M3N) to regulate and optimize multiple block predictions. The model parameters are learned by discriminating the actual pixel value from other possible estimates to maximize the margin (i.e., decision boundary bandwidth). Compared to existing methods that focus on minimizing prediction error, the M3N-based model adaptively maintains the coherence for a set of predictions. Specifically, the proposed model concurrently optimizes a set of predictions by associating the loss for individual blocks to the joint distribution of succeeding discrete cosine transform coefficients. When the sample size grows, the prediction error is asymptotically upper bounded by the training error under the decomposable loss function. As an internal step, we optimize the underlying Markov network structure to find states that achieve the maximal energy using expectation propagation. For validation, we integrate the proposed model into HEVC for optimal mode selection on rate-distortion optimization. The proposed prediction model obtains up to 2.85% bit rate reduction and achieves better visual quality in comparison to the HEVC intra coding. PMID:25505829

3D FSE Cube and VIPR-aTR 3.0 Tesla magnetic resonance imaging predicts canine cranial cruciate ligament structural properties.

PubMed

Racette, Molly; Al saleh, Habib; Waller, Kenneth R; Bleedorn, Jason A; McCabe, Ronald P; Vanderby, Ray; Markel, Mark D; Brounts, Sabrina H; Block, Walter F; Muir, Peter

2016-03-01

Estimation of cranial cruciate ligament (CrCL) structural properties in client-owned dogs with incipient cruciate rupture would be advantageous. The objective of this study was to determine whether magnetic resonance imaging (MRI) measurement of normal CrCL volume in an ex-vivo canine model predicts structural properties. Stifles from eight dogs underwent 3.0 Tesla 3D MRI. CrCL volume and normalized median grayscale values were determined using 3D Fast Spin Echo (FSE) Cube and Vastly under-sampled Isotropic PRojection (VIPR)-alternative repetition time (aTR) sequences. Stifles were then mechanically tested. After joint laxity testing, CrCL structural properties were determined, including displacement at yield, yield load, load to failure, and stiffness. Yield load and load to failure (R(2)=0.56, P <0.01) were correlated with CrCL volume determined by VIPR-aTR. Yield load was also correlated with CrCL volume determined by 3D FSE Cube (R(2)=0.32, P <0.05). Structural properties were not related to median grayscale values. Joint laxity and CrCL stiffness were not related to MRI parameters, but displacement at yield load was related to CrCL volume for both sequences during testing (R(2)>0.57, P <0.005). In conclusion, 3D MRI offers a predictive method for estimating canine CrCL structural properties. 3D MRI may be useful for monitoring CrCL properties in clinical trials. Copyright © 2016 Elsevier Ltd. All rights reserved.

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

Nelson, Stacy; English, Shawn; Briggs, Timothy

Fiber-reinforced composite materials offer light-weight solutions to many structural challenges. In the development of high-performance composite structures, a thorough understanding is required of the composite materials themselves as well as methods for the analysis and failure prediction of the relevant composite structures. However, the mechanical properties required for the complete constitutive definition of a composite material can be difficult to determine through experimentation. Therefore, efficient methods are necessary that can be used to determine which properties are relevant to the analysis of a specific structure and to establish a structure's response to a material parameter that can only be definedmore » through estimation. The objectives of this paper deal with demonstrating the potential value of sensitivity and uncertainty quantification techniques during the failure analysis of loaded composite structures; and the proposed methods are applied to the simulation of the four-point flexural characterization of a carbon fiber composite material. Utilizing a recently implemented, phenomenological orthotropic material model that is capable of predicting progressive composite damage and failure, a sensitivity analysis is completed to establish which material parameters are truly relevant to a simulation's outcome. Then, a parameter study is completed to determine the effect of the relevant material properties' expected variations on the simulated four-point flexural behavior as well as to determine the value of an unknown material property. This process demonstrates the ability to formulate accurate predictions in the absence of a rigorous material characterization effort. Finally, the presented results indicate that a sensitivity analysis and parameter study can be used to streamline the material definition process as the described flexural characterization was used for model validation.« less

Prediction of pKa Values for Neutral and Basic Drugs based on Hybrid Artificial Intelligence Methods.

PubMed

Li, Mengshan; Zhang, Huaijing; Chen, Bingsheng; Wu, Yan; Guan, Lixin

2018-03-05

The pKa value of drugs is an important parameter in drug design and pharmacology. In this paper, an improved particle swarm optimization (PSO) algorithm was proposed based on the population entropy diversity. In the improved algorithm, when the population entropy was higher than the set maximum threshold, the convergence strategy was adopted; when the population entropy was lower than the set minimum threshold the divergence strategy was adopted; when the population entropy was between the maximum and minimum threshold, the self-adaptive adjustment strategy was maintained. The improved PSO algorithm was applied in the training of radial basis function artificial neural network (RBF ANN) model and the selection of molecular descriptors. A quantitative structure-activity relationship model based on RBF ANN trained by the improved PSO algorithm was proposed to predict the pKa values of 74 kinds of neutral and basic drugs and then validated by another database containing 20 molecules. The validation results showed that the model had a good prediction performance. The absolute average relative error, root mean square error, and squared correlation coefficient were 0.3105, 0.0411, and 0.9685, respectively. The model can be used as a reference for exploring other quantitative structure-activity relationships.

Nomograms for two-dimensional echocardiography derived valvular and arterial dimensions in Caucasian children.

PubMed

Cantinotti, Massimiliano; Giordano, Raffaele; Scalese, Marco; Murzi, Bruno; Assanta, Nadia; Spadoni, Isabella; Maura, Crocetti; Marco, Marotta; Molinaro, Sabrina; Kutty, Shelby; Iervasi, Giorgio

2017-01-01

Despite recent advances, current pediatric echocardiographic nomograms for valvular and arterial dimensions remain limited. We prospectively studied healthy Caucasian Italian children by two-dimensional (2D) echocardiography. Echocardiographic measurements for 18 valvular and arterial dimensions were performed and models were generated testing for linear, logarithmic, exponential, and square root relationships. Heteroscedasticity was accounted for by White or Breusch-Pagan test. Age, weight, height, heart rate, and body surface area (BSA) were used as independent variables in different analyses to predict the mean values of each measurement. Structured Z-scores were then computed. In all, 1151 subjects (age 0 days to 17 years; 45% females; BSA 0.12-2.12m 2 ) were studied. The Haycock formula was used when presenting data as predicted values (mean±2 SDs) for a given BSA and within equations relating echocardiographic measurements to BSA. The predicted values and Z-score boundaries for all measurements are presented. We report echocardiographic nomograms for valvular and arterial dimensions derived from a large population of children. Integration of these data with those of previous reports would allow for a comprehensive coverage of pediatric 2D echocardiographic nomograms for measurement of 2D cardiac structures. Copyright © 2016 Japanese College of Cardiology. Published by Elsevier Ltd. All rights reserved.

Cooperativeness and competitiveness as two distinct constructs: validating the Cooperative and Competitive Personality Scale in a social dilemma context.

PubMed

Lu, Su; Au, Wing-Tung; Jiang, Feng; Xie, Xiaofei; Yam, Paton

2013-01-01

The present research validated the construct and criterion validities of the Cooperative and Competitive Personality Scale (CCPS) in a social dilemma context. The results from three studies supported the notion that cooperativeness and competitiveness are two independent dimensions, challenging the traditional view that they are two ends of a single continuum. First, confirmatory factor analyses revealed that a two-factor structure fit the data significantly better than a one-factor structure. Moreover, cooperativeness and competitiveness were either not significantly correlated (Studies 1 and 3) or only moderately positively correlated (Study 2). Second, cooperativeness and competitiveness were differentially associated with Schwartz's Personal Values. These results further supported the idea that cooperativeness and competitiveness are two distinct constructs. Specifically, the individuals who were highly cooperative emphasized self-transcendent values (i.e., universalism and benevolence) more, whereas the individuals who were highly competitive emphasized self-enhancement values (i.e., power and achievement) more. Finally, the CCPS, which adheres to the trait perspective of personality, was found to be a useful supplement to more prevalent social motive measures (i.e., social value orientation) in predicting cooperative behaviors. Specifically, in Study 2, when social value orientation was controlled for, the CCPS significantly predicted cooperative behaviors in a public goods dilemma (individuals who score higher on cooperativeness scale contributed more to the public goods). In Study 3, when social value orientation was controlled for, the CCPS significantly predicted cooperative behaviors in commons dilemmas (individuals who score higher on cooperativeness scale requested fewer resources from the common resource pool). The practical implications of the CCPS in conflict resolution, as well as in recruitment and selection settings, are discussed.

QSPR for predicting chloroform formation in drinking water disinfection.

PubMed

Luilo, G B; Cabaniss, S E

2011-01-01

Chlorination is the most widely used technique for water disinfection, but may lead to the formation of chloroform (trichloromethane; TCM) and other by-products. This article reports the first quantitative structure-property relationship (QSPR) for predicting the formation of TCM in chlorinated drinking water. Model compounds (n = 117) drawn from 10 literature sources were divided into training data (n = 90, analysed by five-way leave-many-out internal cross-validation) and external validation data (n = 27). QSPR internal cross-validation had Q² = 0.94 and root mean square error (RMSE) of 0.09 moles TCM per mole compound, consistent with external validation Q2 of 0.94 and RMSE of 0.08 moles TCM per mole compound, and met criteria for high predictive power and robustness. In contrast, log TCM QSPR performed poorly and did not meet the criteria for predictive power. The QSPR predictions were consistent with experimental values for TCM formation from tannic acid and for model fulvic acid structures. The descriptors used are consistent with a relatively small number of important TCM precursor structures based upon 1,3-dicarbonyls or 1,3-diphenols.

A Predictive Model of Intein Insertion Site for Use in the Engineering of Molecular Switches

PubMed Central

Apgar, James; Ross, Mary; Zuo, Xiao; Dohle, Sarah; Sturtevant, Derek; Shen, Binzhang; de la Vega, Humberto; Lessard, Philip; Lazar, Gabor; Raab, R. Michael

2012-01-01

Inteins are intervening protein domains with self-splicing ability that can be used as molecular switches to control activity of their host protein. Successfully engineering an intein into a host protein requires identifying an insertion site that permits intein insertion and splicing while allowing for proper folding of the mature protein post-splicing. By analyzing sequence and structure based properties of native intein insertion sites we have identified four features that showed significant correlation with the location of the intein insertion sites, and therefore may be useful in predicting insertion sites in other proteins that provide native-like intein function. Three of these properties, the distance to the active site and dimer interface site, the SVM score of the splice site cassette, and the sequence conservation of the site showed statistically significant correlation and strong predictive power, with area under the curve (AUC) values of 0.79, 0.76, and 0.73 respectively, while the distance to secondary structure/loop junction showed significance but with less predictive power (AUC of 0.54). In a case study of 20 insertion sites in the XynB xylanase, two features of native insertion sites showed correlation with the splice sites and demonstrated predictive value in selecting non-native splice sites. Structural modeling of intein insertions at two sites highlighted the role that the insertion site location could play on the ability of the intein to modulate activity of the host protein. These findings can be used to enrich the selection of insertion sites capable of supporting intein splicing and hosting an intein switch. PMID:22649521

Prediction of biodegradability of aromatics in water using QSAR modeling.

PubMed

Cvetnic, Matija; Juretic Perisic, Daria; Kovacic, Marin; Kusic, Hrvoje; Dermadi, Jasna; Horvat, Sanja; Bolanca, Tomislav; Marin, Vedrana; Karamanis, Panaghiotis; Loncaric Bozic, Ana

2017-05-01

The study was aimed at developing models for predicting the biodegradability of aromatic water pollutants. For that purpose, 36 single-benzene ring compounds, with different type, number and position of substituents, were used. The biodegradability was estimated according to the ratio of the biochemical (BOD 5 ) and chemical (COD) oxygen demand values determined for parent compounds ((BOD 5 /COD) 0 ), as well as for their reaction mixtures in half-life achieved by UV-C/H 2 O 2 process ((BOD 5 /COD) t1/2 ). The models correlating biodegradability and molecular structure characteristics of studied pollutants were derived using quantitative structure-activity relationship (QSAR) principles and tools. Upon derivation of the models and calibration on the training and subsequent testing on the test set, 3- and 5-variable models were selected as the most predictive for (BOD 5 /COD) 0 and (BOD 5 /COD) t1/2 , respectively, according to the values of statistical parameters R 2 and Q 2 . Hence, 3-variable model predicting (BOD 5 /COD) 0 possessed R 2 =0.863 and Q 2 =0.799 for training set, and R 2 =0.710 for test set, while 5-variable model predicting (BOD 5 /COD) 1/2 possessed R 2 =0.886 and Q 2 =0.788 for training set, and R 2 =0.564 for test set. The selected models are interpretable and transparent, reflecting key structural features that influence targeted biodegradability and can be correlated with the degradation mechanisms of studied compounds by UV-C/H 2 O 2 . Copyright © 2017 Elsevier Inc. All rights reserved.

In vivo study of the effectiveness of quantitative percussion diagnostics as an indicator of the level of structural pathology of teeth after restoration.

PubMed

Sheets, Cherilyn G; Wu, Jean C; Rashad, Samer; Phelan, Michael; Earthman, James C

2017-02-01

Conventional diagnostic aids based upon imagery and patient symptoms do not indicate whether restorative treatments have eliminated structural pathology. The purpose of this clinical study was to evaluate quantitative percussion diagnostics (QPD), a mechanics-based methodology that tests the structural integrity of teeth noninvasively. The study hypothesis was that QPD would provide knowledge of the structural instability of teeth after restorative work. Eight participants with 60 sites needing restoration were enrolled in an IRB-approved clinical study. Each participant was examined comprehensively, including QPD testing. Each site was disassembled and microscopically video documented, and the results were recorded on a defect assessment sheet. A predictive model was developed for the pathology rating based on normalized fit error (NFE) values using data from the before treatment phase of the study published previously. Each restored site was then tested using QPD. The mean change in NFE values after restoration was evaluated by the pathology rating before treatment. The model was then used to predictively classify the rating after restoration based on the NFE values after treatment. The diagnostic potential of the rating was explored as a marker for risk of pathology after restoration. After restoration, 51 of the 60 sites fell below an NFE of 0.04, representing a greatly stabilized tooth site sample group. Several sites remained in the high-risk category and some increased in pathologic micromovement. Two models were used to determine severity with indicative cutoff points to group sites with similar values. The data support the hypothesis that QPD can indicate a revised level of structural instability of teeth after restoration. Copyright © 2016 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.

Structural and functional predictors of regional peak pressures under the foot during walking.

PubMed

Morag, E; Cavanagh, P R

1999-04-01

The objective of this study was to identify structural and functional factors which are predictors of peak pressure underneath the human foot during walking. Peak plantar pressure during walking and eight data sets of structural and functional measures were collected on 55 asymptomatic subjects between 20 and 70 yr. A best subset regression approach was used to establish models which predicted peak regional pressure under the foot. Potential predictor variables were chosen from physical characteristics, anthropometric data, passive range of motion (PROM), measurements from standardized weight bearing foot radiographs, mechanical properties of the plantar soft tissue, stride parameters, foot motion in 3D, and EMG during walking. Peak pressure values under the rearfoot, midfoot, MTH1, and hallux were measured. Heel pressure was a function of linear kinematics, longitudinal arch structure, thickness of plantar soft tissue, and age. Midfoot pressure prediction was dominated by arch structure, while MTH1 pressure was a function of radiographic measurements, talo-crural joint motion, and gastrocnemius activity. Hallux pressure was a function of structural measures and MTP1 joint motion. Foot structure and function predicted only approximately 50% of the variance in peak pressure, although the relative contributions in different anatomical regions varied dramatically. Structure was dominant in predicting peak pressure under the midfoot and MTH1, while both structure and function were important at the heel and hallux. The predictive models developed in this study give insight into potential etiological factors associated with elevated plantar pressure. They also provide direction for future studies designed to reduce elevated pressure in "at-risk" patients.

Salient value similarity, social trust and attitudes toward wildland fire management strategies

Treesearch

J.J. Vaske; J.D. Absher; A.D. Bright

2007-01-01

We predicted that social trust in the USDA Forest Service would mediate the relationship between shared value similarity (SVS) and attitudes toward prescribed burning and mechanical thinning. Data were obtained from a mail survey (n = 532) of rural Colorado residents living in the wildland urban interface (WUI). A structural equation analysis was used to assess the...

[Preoperative CT Scan in middle ear cholesteatoma].

PubMed

Sethom, Anissa; Akkari, Khemaies; Dridi, Inès; Tmimi, S; Mardassi, Ali; Benzarti, Sonia; Miled, Imed; Chebbi, Mohamed Kamel

2011-03-01

To compare preoperative CT scan finding and per-operative lesions in patients operated for middle ear cholesteatoma, A retrospective study including 60 patients with cholesteatoma otitis diagnosed and treated within a period of 5 years, from 2001 to 2005, at ENT department of Military Hospital of Tunis. All patients had computed tomography of the middle and inner ear. High resolution CT scan imaging was performed using millimetric incidences (3 to 5 millimetres). All patients had surgical removal of their cholesteatoma using down wall technic. We evaluated sensitivity, specificity and predictive value of CT-scan comparing otitic damages and CT finding, in order to examine the real contribution of computed tomography in cholesteatoma otitis. CT scan analysis of middle ear bone structures shows satisfaction (with 83% of sensibility). The rate of sensibility decrease (63%) for the tympanic raff. Predictive value of CT scan for the diagnosis of cholesteatoma was low. However, we have noticed an excellent sensibility in the analysis of ossicular damages (90%). Comparative frontal incidence seems to be less sensible for the detection of facial nerve lesions (42%). But when evident on CT scan findings, lesions of facial nerve were usually observed preoperatively (spécificity 78%). Predictive value of computed tomography for the diagnosis of perilymphatic fistulae (FL) was low. In fact, CT scan imaging have showed FL only for four patients among eight. Best results can be obtained if using inframillimetric incidences with performed high resolution computed tomography. Preoperative computed tomography is necessary for the diagnosis and the evaluation of chronic middle ear cholesteatoma in order to show extending lesion and to detect complications. This CT analysis and surgical correlation have showed that sensibility, specificity and predictive value of CT-scan depend on the anatomic structure implicated in cholesteatoma damages.

Robust prediction of consensus secondary structures using averaged base pairing probability matrices.

PubMed

Kiryu, Hisanori; Kin, Taishin; Asai, Kiyoshi

2007-02-15

Recent transcriptomic studies have revealed the existence of a considerable number of non-protein-coding RNA transcripts in higher eukaryotic cells. To investigate the functional roles of these transcripts, it is of great interest to find conserved secondary structures from multiple alignments on a genomic scale. Since multiple alignments are often created using alignment programs that neglect the special conservation patterns of RNA secondary structures for computational efficiency, alignment failures can cause potential risks of overlooking conserved stem structures. We investigated the dependence of the accuracy of secondary structure prediction on the quality of alignments. We compared three algorithms that maximize the expected accuracy of secondary structures as well as other frequently used algorithms. We found that one of our algorithms, called McCaskill-MEA, was more robust against alignment failures than others. The McCaskill-MEA method first computes the base pairing probability matrices for all the sequences in the alignment and then obtains the base pairing probability matrix of the alignment by averaging over these matrices. The consensus secondary structure is predicted from this matrix such that the expected accuracy of the prediction is maximized. We show that the McCaskill-MEA method performs better than other methods, particularly when the alignment quality is low and when the alignment consists of many sequences. Our model has a parameter that controls the sensitivity and specificity of predictions. We discussed the uses of that parameter for multi-step screening procedures to search for conserved secondary structures and for assigning confidence values to the predicted base pairs. The C++ source code that implements the McCaskill-MEA algorithm and the test dataset used in this paper are available at http://www.ncrna.org/papers/McCaskillMEA/. Supplementary data are available at Bioinformatics online.

Functional classification of protein structures by local structure matching in graph representation.

PubMed

Mills, Caitlyn L; Garg, Rohan; Lee, Joslynn S; Tian, Liang; Suciu, Alexandru; Cooperman, Gene; Beuning, Penny J; Ondrechen, Mary Jo

2018-03-31

As a result of high-throughput protein structure initiatives, over 14,400 protein structures have been solved by structural genomics (SG) centers and participating research groups. While the totality of SG data represents a tremendous contribution to genomics and structural biology, reliable functional information for these proteins is generally lacking. Better functional predictions for SG proteins will add substantial value to the structural information already obtained. Our method described herein, Graph Representation of Active Sites for Prediction of Function (GRASP-Func), predicts quickly and accurately the biochemical function of proteins by representing residues at the predicted local active site as graphs rather than in Cartesian coordinates. We compare the GRASP-Func method to our previously reported method, structurally aligned local sites of activity (SALSA), using the ribulose phosphate binding barrel (RPBB), 6-hairpin glycosidase (6-HG), and Concanavalin A-like Lectins/Glucanase (CAL/G) superfamilies as test cases. In each of the superfamilies, SALSA and the much faster method GRASP-Func yield similar correct classification of previously characterized proteins, providing a validated benchmark for the new method. In addition, we analyzed SG proteins using our SALSA and GRASP-Func methods to predict function. Forty-one SG proteins in the RPBB superfamily, nine SG proteins in the 6-HG superfamily, and one SG protein in the CAL/G superfamily were successfully classified into one of the functional families in their respective superfamily by both methods. This improved, faster, validated computational method can yield more reliable predictions of function that can be used for a wide variety of applications by the community. © 2018 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

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

NASA Astrophysics Data System (ADS)

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

2018-02-01

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

Basic numerical competences in large-scale assessment data: Structure and long-term relevance.

PubMed

Hirsch, Stefa; Lambert, Katharina; Coppens, Karien; Moeller, Korbinian

2018-03-01

Basic numerical competences are seen as building blocks for later numerical and mathematical achievement. The current study aimed at investigating the structure of early numeracy reflected by different basic numerical competences in kindergarten and its predictive value for mathematical achievement 6 years later using data from large-scale assessment. This allowed analyses based on considerably large sample sizes (N > 1700). A confirmatory factor analysis indicated that a model differentiating five basic numerical competences at the end of kindergarten fitted the data better than a one-factor model of early numeracy representing a comprehensive number sense. In addition, these basic numerical competences were observed to reliably predict performance in a curricular mathematics test in Grade 6 even after controlling for influences of general cognitive ability. Thus, our results indicated a differentiated view on early numeracy considering basic numerical competences in kindergarten reflected in large-scale assessment data. Consideration of different basic numerical competences allows for evaluating their specific predictive value for later mathematical achievement but also mathematical learning difficulties. Copyright © 2017 Elsevier Inc. All rights reserved.

QSPR models for predicting generator-column-derived octanol/water and octanol/air partition coefficients of polychlorinated biphenyls.

PubMed

Yuan, Jintao; Yu, Shuling; Zhang, Ting; Yuan, Xuejie; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

2016-06-01

Octanol/water (K(OW)) and octanol/air (K(OA)) partition coefficients are two important physicochemical properties of organic substances. In current practice, K(OW) and K(OA) values of some polychlorinated biphenyls (PCBs) are measured using generator column method. Quantitative structure-property relationship (QSPR) models can serve as a valuable alternative method of replacing or reducing experimental steps in the determination of K(OW) and K(OA). In this paper, two different methods, i.e., multiple linear regression based on dragon descriptors and hologram quantitative structure-activity relationship, were used to predict generator-column-derived log K(OW) and log K(OA) values of PCBs. The predictive ability of the developed models was validated using a test set, and the performances of all generated models were compared with those of three previously reported models. All results indicated that the proposed models were robust and satisfactory and can thus be used as alternative models for the rapid assessment of the K(OW) and K(OA) of PCBs. Copyright © 2016 Elsevier Inc. All rights reserved.

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).

Synthetic cannabinoids: In silico prediction of the cannabinoid receptor 1 affinity by a quantitative structure-activity relationship model.

PubMed

Paulke, Alexander; Proschak, Ewgenij; Sommer, Kai; Achenbach, Janosch; Wunder, Cora; Toennes, Stefan W

2016-03-14

The number of new synthetic psychoactive compounds increase steadily. Among the group of these psychoactive compounds, the synthetic cannabinoids (SCBs) are most popular and serve as a substitute of herbal cannabis. More than 600 of these substances already exist. For some SCBs the in vitro cannabinoid receptor 1 (CB1) affinity is known, but for the majority it is unknown. A quantitative structure-activity relationship (QSAR) model was developed, which allows the determination of the SCBs affinity to CB1 (expressed as binding constant (Ki)) without reference substances. The chemically advance template search descriptor was used for vector representation of the compound structures. The similarity between two molecules was calculated using the Feature-Pair Distribution Similarity. The Ki values were calculated using the Inverse Distance Weighting method. The prediction model was validated using a cross validation procedure. The predicted Ki values of some new SCBs were in a range between 20 (considerably higher affinity to CB1 than THC) to 468 (considerably lower affinity to CB1 than THC). The present QSAR model can serve as a simple, fast and cheap tool to get a first hint of the biological activity of new synthetic cannabinoids or of other new psychoactive compounds. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Yield of MRI, high-density electric source imaging (HD-ESI), SPECT and PET in epilepsy surgery candidates.

PubMed

Lascano, Agustina M; Perneger, Thomas; Vulliemoz, Serge; Spinelli, Laurent; Garibotto, Valentina; Korff, Christian M; Vargas, Maria I; Michel, Christoph M; Seeck, Margitta

2016-01-01

Preoperative workup aims at localizing the epileptogenic focus to achieve postoperative seizure-freedom. We studied the predictive value of non-invasive techniques, i.e. structural magnetic resonance imaging [MRI], high-density electric source imaging [HD-ESI] and metabolic imaging (positron emission tomography [PET]; single-photon emission computed tomography [SPECT]), in surgically treated patients. A prospective study of 190 epileptic operated patients, with >12 months follow-up and analyzed with state-of-the-art algorithms. 58 patients underwent all techniques. We computed sensitivity, specificity, predictive value and diagnostic odds ratio (OR) in relation to postoperative outcome. Of 190 patients, 148 (77.9%) were seizure-free at follow-up. Resection of the epileptogenic focus was associated with favorable postsurgical outcome (p<0.05). Among 58 patients who underwent all tests, only MRI and HD-ESI were favorable outcome predictors (MRI: OR 10.9, p=0.004; HD-ESI: OR 13.1, p=0.004). Patients with concordant structural MRI and HD-ESI results had 92.3% (24/26) probability of favorable outcome. When both results were negative, probability was 0% (0/5); and when they disagreed, it was 63.0% (17/27). Combination of MRI and HD-ESI offered the highest predictive value for postoperative seizure-freedom. This finding highlights the added value of HD-ESI in the presurgical workup, in particular in combination with an informative MRI. Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Organic particulate matter formation at varying relative humidity using surrogate secondary and primary organic compounds with activity corrections in the condensed phase obtained using a method based on the Wilson equation

NASA Astrophysics Data System (ADS)

Chang, E. I.; Pankow, J. F.

2008-01-01

Secondary organic aerosol (SOA) formation in the atmosphere is currently often modeled using a multiple lumped "two-product" (N·2p) approach. The N·2p approach neglects: 1) variation of activity coefficient (ζi) values and mean molecular weight MW in the particulate matter (PM) phase; 2) water uptake into the PM; and 3) the possibility of phase separation in the PM. This study considers these effects by adopting an (N·2p)ζ, MW ,θ approach (θ is a phase index). Specific chemical structures are assigned to 25 lumped SOA compounds and to 15 representative primary organic aerosol (POA) compounds to allow calculation of ζi and MW values. The SOA structure assignments are based on chamber-derived 2p gas/particle partition coefficient values coupled with known effects of structure on vapor pressure pL,i° (atm). To facilitate adoption of the (N·2p)ζ, MW, θ approach in large-scale models, this study also develops CP-Wilson.1, a group-contribution ζi-prediction method that is more computationally economical than the UNIFAC model of Fredenslund et al. (1975). Group parameter values required by CP-Wilson.1 are obtained by fitting ζi values to predictions from UNIFAC. The (N·2p)ζ,MW, θ approach is applied (using CP-Wilson.1) to several real α-pinene/O3 chamber cases for high reacted hydrocarbon levels (ΔHC≍400 to 1000 μg m-3) when relative humidity (RH) ≍50%. Good agreement between the chamber and predicted results is obtained using both the (N·2p)ζ, MW, θ and N·2p approaches, indicating relatively small water effects under these conditions. However, for a hypothetical α-pinene/O3 case at ΔHC=30 μg m-3 and RH=50%, the (N·2p)ζ, MW, θ approach predicts that water uptake will lead to an organic PM level that is more double that predicted by the N·2p approach. Adoption of the (N·2p)ζ, MW, θ approach using reasonable lumped structures for SOA and POA compounds is recommended for ambient PM modeling.

VERIFICATION AND VALIDATION OF THE SPARC MODEL

EPA Science Inventory

Mathematical models for predicting the transport and fate of pollutants in the environment require reactivity parameter values--that is, the physical and chemical constants that govern reactivity. Although empirical structure-activity relationships that allow estimation of some ...

Bioinformatics analysis of the predicted polyprenol reductase genes in higher plants

NASA Astrophysics Data System (ADS)

Basyuni, M.; Wati, R.

2018-03-01

The present study evaluates the bioinformatics methods to analyze twenty-four predicted polyprenol reductase genes from higher plants on GenBank as well as predicted the structure, composition, similarity, subcellular localization, and phylogenetic. The physicochemical properties of plant polyprenol showed diversity among the observed genes. The percentage of the secondary structure of plant polyprenol genes followed the ratio order of α helix > random coil > extended chain structure. The values of chloroplast but not signal peptide were too low, indicated that few chloroplast transit peptide in plant polyprenol reductase genes. The possibility of the potential transit peptide showed variation among the plant polyprenol reductase, suggested the importance of understanding the variety of peptide components of plant polyprenol genes. To clarify this finding, a phylogenetic tree was drawn. The phylogenetic tree shows several branches in the tree, suggested that plant polyprenol reductase genes grouped into divergent clusters in the tree.

Surface Protonation at the Rutile (110) Interface: Explicit Incorporation of Solvation Structure within the Refined MUSIC Model Framework

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

Machesky, Michael L.; Predota, M.; Wesolowski, David J

The detailed solvation structure at the (110) surface of rutile ({alpha}-TiO{sub 2}) in contact with bulk liquid water has been obtained primarily from experimentally verified classical molecular dynamics (CMD) simulations of the ab initio-optimized surface in contact with SPC/E water. The results are used to explicitly quantify H-bonding interactions, which are then used within the refined MUSIC model framework to predict surface oxygen protonation constants. Quantum mechanical molecular dynamics (QMD) simulations in the presence of freely dissociable water molecules produced H-bond distributions around deprotonated surface oxygens very similar to those obtained by CMD with nondissociable SPC/E water, thereby confirming thatmore » the less computationally intensive CMD simulations provide accurate H-bond information. Utilizing this H-bond information within the refined MUSIC model, along with manually adjusted Ti-O surface bond lengths that are nonetheless within 0.05 {angstrom} of those obtained from static density functional theory (DFT) calculations and measured in X-ray reflectivity experiments (as well as bulk crystal values), give surface protonation constants that result in a calculated zero net proton charge pH value (pHznpc) at 25 C that agrees quantitatively with the experimentally determined value (5.4 {+-} 0.2) for a specific rutile powder dominated by the (110) crystal face. Moreover, the predicted pH{sub znpc} values agree to within 0.1 pH unit with those measured at all temperatures between 10 and 250 C. A slightly smaller manual adjustment of the DFT-derived Ti-O surface bond lengths was sufficient to bring the predicted pH{sub znpc} value of the rutile (110) surface at 25 C into quantitative agreement with the experimental value (4.8 {+-} 0.3) obtained from a polished and annealed rutile (110) single crystal surface in contact with dilute sodium nitrate solutions using second harmonic generation (SHG) intensity measurements as a function of ionic strength. Additionally, the H-bond interactions between protolyzable surface oxygen groups and water were found to be stronger than those between bulk water molecules at all temperatures investigated in our CMD simulations (25, 150 and 250 C). Comparison with the protonation scheme previously determined for the (110) surface of isostructural cassiterite ({alpha}-SnO{sub 2}) reveals that the greater extent of H-bonding on the latter surface, and in particular between water and the terminal hydroxyl group (Sn-OH) results in the predicted protonation constant for that group being lower than for the bridged oxygen (Sn-O-Sn), while the reverse is true for the rutile (110) surface. These results demonstrate the importance of H-bond structure in dictating surface protonation behavior, and that explicit use of this solvation structure within the refined MUSIC model framework results in predicted surface protonation constants that are also consistent with a variety of other experimental and computational data.« less

Multiphase flow predictions from carbonate pore space images using extracted network models

NASA Astrophysics Data System (ADS)

Al-Kharusi, Anwar S.; Blunt, Martin J.

2008-06-01

A methodology to extract networks from pore space images is used to make predictions of multiphase transport properties for subsurface carbonate samples. The extraction of the network model is based on the computation of the location and sizes of pores and throats to create a topological representation of the void space of three-dimensional (3-D) rock images, using the concept of maximal balls. In this work, we follow a multistaged workflow. We start with a 2-D thin-section image; convert it statistically into a 3-D representation of the pore space; extract a network model from this image; and finally, simulate primary drainage, waterflooding, and secondary drainage flow processes using a pore-scale simulator. We test this workflow for a reservoir carbonate rock. The network-predicted absolute permeability is similar to the core plug measured value and the value computed on the 3-D void space image using the lattice Boltzmann method. The predicted capillary pressure during primary drainage agrees well with a mercury-air experiment on a core sample, indicating that we have an adequate representation of the rock's pore structure. We adjust the contact angles in the network to match the measured waterflood and secondary drainage capillary pressures. We infer a significant degree of contact angle hysteresis. We then predict relative permeabilities for primary drainage, waterflooding, and secondary drainage that agree well with laboratory measured values. This approach can be used to predict multiphase transport properties when wettability and pore structure vary in a reservoir, where experimental data is scant or missing. There are shortfalls to this approach, however. We compare results from three networks, one of which was derived from a section of the rock containing vugs. Our method fails to predict properties reliably when an unrepresentative image is processed to construct the 3-D network model. This occurs when the image volume is not sufficient to represent the geological variations observed in a core plug sample.

Advective transport in heterogeneous aquifers: Are proxy models predictive?

NASA Astrophysics Data System (ADS)

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

2015-12-01

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

Accuracy of topographic index models at identifying ephemeral gully trajectories on agricultural fields

NASA Astrophysics Data System (ADS)

Sheshukov, Aleksey Y.; Sekaluvu, Lawrence; Hutchinson, Stacy L.

2018-04-01

Topographic index (TI) models have been widely used to predict trajectories and initiation points of ephemeral gullies (EGs) in agricultural landscapes. Prediction of EGs strongly relies on the selected value of critical TI threshold, and the accuracy depends on topographic features, agricultural management, and datasets of observed EGs. This study statistically evaluated the predictions by TI models in two paired watersheds in Central Kansas that had different levels of structural disturbances due to implemented conservation practices. Four TI models with sole dependency on topographic factors of slope, contributing area, and planform curvature were used in this study. The observed EGs were obtained by field reconnaissance and through the process of hydrological reconditioning of digital elevation models (DEMs). The Kernel Density Estimation analysis was used to evaluate TI distribution within a 10-m buffer of the observed EG trajectories. The EG occurrence within catchments was analyzed using kappa statistics of the error matrix approach, while the lengths of predicted EGs were compared with the observed dataset using the Nash-Sutcliffe Efficiency (NSE) statistics. The TI frequency analysis produced bi-modal distribution of topographic indexes with the pixels within the EG trajectory having a higher peak. The graphs of kappa and NSE versus critical TI threshold showed similar profile for all four TI models and both watersheds with the maximum value representing the best comparison with the observed data. The Compound Topographic Index (CTI) model presented the overall best accuracy with NSE of 0.55 and kappa of 0.32. The statistics for the disturbed watershed showed higher best critical TI threshold values than for the undisturbed watershed. Structural conservation practices implemented in the disturbed watershed reduced ephemeral channels in headwater catchments, thus producing less variability in catchments with EGs. The variation in critical thresholds for all TI models suggested that TI models tend to predict EG occurrence and length over a range of thresholds rather than find a single best value.

Construct validity of the abbreviated mental test in older medical inpatients.

PubMed

Antonelli Incalzi, R; Cesari, M; Pedone, C; Carosella, L; Carbonin, P U

2003-01-01

To evaluate validity and internal structure of the Abbreviated Mental Test (AMT), and to assess the dependence of the internal structure upon the characteristics of the patients examined. Cross-sectional examination using data from the Italian Group of Pharmacoepidemiology in the Elderly (GIFA) database. Twenty-four acute care wards of Geriatrics or General Medicine. Two thousand eight hundred and eight patients consecutively admitted over a 4-month period. Demographic characteristics, functional status, medical conditions and performance on AMT were collected at discharge. Sensitivity, specificity and predictive values of the AMT <7 versus a diagnosis of dementia made according to DSM-III-R criteria were computed. The internal structure of AMT was assessed by principal component analysis. The analysis was performed on the whole population and stratified for age (<65, 65-80 and >80 years), gender, education (<6 or >5 years) and presence of congestive heart failure (CHF). AMT achieved high sensitivity (81%), specificity (84%) and negative predictive value (99%), but a low positive predictive value of 25%. The principal component analysis isolated two components: the former component represents the orientation to time and space and explains 45% of AMT variance; the latter is linked to memory and attention and explains 13% of variance. Comparable results were obtained after stratification by age, gender or education. In patients with CHF, only 48.3% of the cumulative variance was explained; the factor accounting for most (34.6%) of the variance explained was mainly related to the three items assessing memory. AMT >6 rules out dementia very reliably, whereas AMT <7 requires a second level cognitive assessment to confirm dementia. AMT is bidimensional and maintains the same internal structure across classes defined by selected social and demographic characteristics, but not in CHF patients. It is likely that its internal structure depends on the type of patients. The use of a sum-score could conceal some part of the information provided by the AMT. Copyright 2003 S. Karger AG, Basel

The utility of QSARs in predicting acute fish toxicity of pesticide metabolites: A retrospective validation approach.

PubMed

Burden, Natalie; Maynard, Samuel K; Weltje, Lennart; Wheeler, James R

2016-10-01

The European Plant Protection Products Regulation 1107/2009 requires that registrants establish whether pesticide metabolites pose a risk to the environment. Fish acute toxicity assessments may be carried out to this end. Considering the total number of pesticide (re-) registrations, the number of metabolites can be considerable, and therefore this testing could use many vertebrates. EFSA's recent "Guidance on tiered risk assessment for plant protection products for aquatic organisms in edge-of-field surface waters" outlines opportunities to apply non-testing methods, such as Quantitative Structure Activity Relationship (QSAR) models. However, a scientific evidence base is necessary to support the use of QSARs in predicting acute fish toxicity of pesticide metabolites. Widespread application and subsequent regulatory acceptance of such an approach would reduce the numbers of animals used. The work presented here intends to provide this evidence base, by means of retrospective data analysis. Experimental fish LC50 values for 150 metabolites were extracted from the Pesticide Properties Database (http://sitem.herts.ac.uk/aeru/ppdb/en/atoz.htm). QSAR calculations were performed to predict fish acute toxicity values for these metabolites using the US EPA's ECOSAR software. The most conservative predicted LC50 values generated by ECOSAR were compared with experimental LC50 values. There was a significant correlation between predicted and experimental fish LC50 values (Spearman rs = 0.6304, p < 0.0001). For 62% of metabolites assessed, the QSAR predicted values are equal to or lower than their respective experimental values. Refined analysis, taking into account data quality and experimental variation considerations increases the proportion of sufficiently predictive estimates to 91%. For eight of the nine outliers, there are plausible explanation(s) for the disparity between measured and predicted LC50 values. Following detailed consideration of the robustness of this non-testing approach, it can be concluded there is a strong data driven rationale for the applicability of QSAR models in the metabolite assessment scheme recommended by EFSA. As such there is value in further refining this approach, to improve the method and enable its future incorporation into regulatory guidance and practice. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

A Probabilistic Approach to Model Update

NASA Technical Reports Server (NTRS)

Horta, Lucas G.; Reaves, Mercedes C.; Voracek, David F.

2001-01-01

Finite element models are often developed for load validation, structural certification, response predictions, and to study alternate design concepts. In rare occasions, models developed with a nominal set of parameters agree with experimental data without the need to update parameter values. Today, model updating is generally heuristic and often performed by a skilled analyst with in-depth understanding of the model assumptions. Parameter uncertainties play a key role in understanding the model update problem and therefore probabilistic analysis tools, developed for reliability and risk analysis, may be used to incorporate uncertainty in the analysis. In this work, probability analysis (PA) tools are used to aid the parameter update task using experimental data and some basic knowledge of potential error sources. Discussed here is the first application of PA tools to update parameters of a finite element model for a composite wing structure. Static deflection data at six locations are used to update five parameters. It is shown that while prediction of individual response values may not be matched identically, the system response is significantly improved with moderate changes in parameter values.

Enhanced power factor via the control of structural phase transition in SnSe

PubMed Central

Yu, Hulei; Dai, Shuai; Chen, Yue

2016-01-01

Tin selenide has attracted much research interest due to its unprecedentedly high thermoelectric figure of merit (ZT). For real applications, it is desirable to increase the ZT value in the lower-temperature range, as the peak ZT value currently exists near the melting point. It is shown in this paper that the structural phase transition plays an important role in boosting the ZT value of SnSe in the lower-temperature range, as the Cmcm phase is found to have a much higher power factor than the Pnma phase. Furthermore, hydrostatic pressure is predicted to be extremely effective in tuning the phase transition temperature based on ab-initio molecular dynamic simulations; a remarkable decrease in the phase transition temperature is found when a hydrostatic pressure is applied. Dynamical stabilities are investigated based on phonon calculations, providing deeper insight into the pressure effects. Accurate band structures are obtained using the modified Becke-Johnson correction, allowing reliable prediction of the electrical transport properties. The effects of hydrostatic pressure on the thermal transport properties are also discussed. Hydrostatic pressure is shown to be efficient in manipulating the transport properties via the control of phase transition temperature in SnSe, paving a new path for enhancing its thermoelectric efficiency. PMID:27193260

Extraction of business relationships in supply networks using statistical learning theory.

PubMed

Zuo, Yi; Kajikawa, Yuya; Mori, Junichiro

2016-06-01

Supply chain management represents one of the most important scientific streams of operations research. The supply of energy, materials, products, and services involves millions of transactions conducted among national and local business enterprises. To deliver efficient and effective support for supply chain design and management, structural analyses and predictive models of customer-supplier relationships are expected to clarify current enterprise business conditions and to help enterprises identify innovative business partners for future success. This article presents the outcomes of a recent structural investigation concerning a supply network in the central area of Japan. We investigated the effectiveness of statistical learning theory to express the individual differences of a supply chain of enterprises within a certain business community using social network analysis. In the experiments, we employ support vector machine to train a customer-supplier relationship model on one of the main communities extracted from a supply network in the central area of Japan. The prediction results reveal an F-value of approximately 70% when the model is built by using network-based features, and an F-value of approximately 77% when the model is built by using attribute-based features. When we build the model based on both, F-values are improved to approximately 82%. The results of this research can help to dispel the implicit design space concerning customer-supplier relationships, which can be explored and refined from detailed topological information provided by network structures rather than from traditional and attribute-related enterprise profiles. We also investigate and discuss differences in the predictive accuracy of the model for different sizes of enterprises and types of business communities.

On the predictions of the 11B solid state NMR parameters

NASA Astrophysics Data System (ADS)

Czernek, Jiří; Brus, Jiří

2016-07-01

The set of boron containing compounds has been subject to the prediction of the 11B solid state NMR spectral parameters using DFT-GIPAW methods properly treating the solid phase effects. The quantification of the differences between measured and theoretical values has been presented, which is directly applicable in structural studies involving 11B nuclei. In particular, a simple scheme has been proposed, which is expected to provide for an estimate of the 11B chemical shift within ±2.0 ppm from the experimental value. The computer program, INFOR, enabling the visualization of concomitant Euler rotations related to the tensorial transformations has been presented.

Prediction of acute mammalian toxicity using QSAR methods: a case study of sulfur mustard and its breakdown products.

PubMed

Ruiz, Patricia; Begluitti, Gino; Tincher, Terry; Wheeler, John; Mumtaz, Moiz

2012-07-27

Predicting toxicity quantitatively, using Quantitative Structure Activity Relationships (QSAR), has matured over recent years to the point that the predictions can be used to help identify missing comparison values in a substance's database. In this manuscript we investigate using the lethal dose that kills fifty percent of a test population (LD₅₀) for determining relative toxicity of a number of substances. In general, the smaller the LD₅₀ value, the more toxic the chemical, and the larger the LD₅₀ value, the lower the toxicity. When systemic toxicity and other specific toxicity data are unavailable for the chemical(s) of interest, during emergency responses, LD₅₀ values may be employed to determine the relative toxicity of a series of chemicals. In the present study, a group of chemical warfare agents and their breakdown products have been evaluated using four available rat oral QSAR LD₅₀ models. The QSAR analysis shows that the breakdown products of Sulfur Mustard (HD) are predicted to be less toxic than the parent compound as well as other known breakdown products that have known toxicities. The QSAR estimated break down products LD₅₀ values ranged from 299 mg/kg to 5,764 mg/kg. This evaluation allows for the ranking and toxicity estimation of compounds for which little toxicity information existed; thus leading to better risk decision making in the field.

Prediction of Solvent Physical Properties using the Hierarchical Clustering Method

EPA Science Inventory

Recently a QSAR (Quantitative Structure Activity Relationship) method, the hierarchical clustering method, was developed to estimate acute toxicity values for large, diverse datasets. This methodology has now been applied to the estimate solvent physical properties including sur...

Predicting structured metadata from unstructured metadata.

PubMed

Posch, Lisa; Panahiazar, Maryam; Dumontier, Michel; Gevaert, Olivier

2016-01-01

Enormous amounts of biomedical data have been and are being produced by investigators all over the world. However, one crucial and limiting factor in data reuse is accurate, structured and complete description of the data or data about the data-defined as metadata. We propose a framework to predict structured metadata terms from unstructured metadata for improving quality and quantity of metadata, using the Gene Expression Omnibus (GEO) microarray database. Our framework consists of classifiers trained using term frequency-inverse document frequency (TF-IDF) features and a second approach based on topics modeled using a Latent Dirichlet Allocation model (LDA) to reduce the dimensionality of the unstructured data. Our results on the GEO database show that structured metadata terms can be the most accurately predicted using the TF-IDF approach followed by LDA both outperforming the majority vote baseline. While some accuracy is lost by the dimensionality reduction of LDA, the difference is small for elements with few possible values, and there is a large improvement over the majority classifier baseline. Overall this is a promising approach for metadata prediction that is likely to be applicable to other datasets and has implications for researchers interested in biomedical metadata curation and metadata prediction. © The Author(s) 2016. Published by Oxford University Press.

Predicting structured metadata from unstructured metadata

PubMed Central

Posch, Lisa; Panahiazar, Maryam; Dumontier, Michel; Gevaert, Olivier

2016-01-01

Enormous amounts of biomedical data have been and are being produced by investigators all over the world. However, one crucial and limiting factor in data reuse is accurate, structured and complete description of the data or data about the data—defined as metadata. We propose a framework to predict structured metadata terms from unstructured metadata for improving quality and quantity of metadata, using the Gene Expression Omnibus (GEO) microarray database. Our framework consists of classifiers trained using term frequency-inverse document frequency (TF-IDF) features and a second approach based on topics modeled using a Latent Dirichlet Allocation model (LDA) to reduce the dimensionality of the unstructured data. Our results on the GEO database show that structured metadata terms can be the most accurately predicted using the TF-IDF approach followed by LDA both outperforming the majority vote baseline. While some accuracy is lost by the dimensionality reduction of LDA, the difference is small for elements with few possible values, and there is a large improvement over the majority classifier baseline. Overall this is a promising approach for metadata prediction that is likely to be applicable to other datasets and has implications for researchers interested in biomedical metadata curation and metadata prediction. Database URL: http://www.yeastgenome.org/ PMID:28637268

Prediction of blood-brain partitioning: a model based on molecular electronegativity distance vector descriptors.

PubMed

Zhang, Yong-Hong; Xia, Zhi-Ning; Qin, Li-Tang; Liu, Shu-Shen

2010-09-01

The objective of this paper is to build a reliable model based on the molecular electronegativity distance vector (MEDV) descriptors for predicting the blood-brain barrier (BBB) permeability and to reveal the effects of the molecular structural segments on the BBB permeability. Using 70 structurally diverse compounds, the partial least squares regression (PLSR) models between the BBB permeability and the MEDV descriptors were developed and validated by the variable selection and modeling based on prediction (VSMP) technique. The estimation ability, stability, and predictive power of a model are evaluated by the estimated correlation coefficient (r), leave-one-out (LOO) cross-validation correlation coefficient (q), and predictive correlation coefficient (R(p)). It has been found that PLSR model has good quality, r=0.9202, q=0.7956, and R(p)=0.6649 for M1 model based on the training set of 57 samples. To search the most important structural factors affecting the BBB permeability of compounds, we performed the values of the variable importance in projection (VIP) analysis for MEDV descriptors. It was found that some structural fragments in compounds, such as -CH(3), -CH(2)-, =CH-, =C, triple bond C-, -CH<, =C<, =N-, -NH-, =O, and -OH, are the most important factors affecting the BBB permeability. (c) 2010. Published by Elsevier Inc.

Structure-based methods to predict mutational resistance to diarylpyrimidine non-nucleoside reverse transcriptase inhibitors.

PubMed

Azeem, Syeda Maryam; Muwonge, Alecia N; Thakkar, Nehaben; Lam, Kristina W; Frey, Kathleen M

2018-01-01

Resistance to non-nucleoside reverse transcriptase inhibitors (NNRTIs) is a leading cause of HIV treatment failure. Often included in antiviral therapy, NNRTIs are chemically diverse compounds that bind an allosteric pocket of enzyme target reverse transcriptase (RT). Several new NNRTIs incorporate flexibility in order to compensate for lost interactions with amino acid conferring mutations in RT. Unfortunately, even successful inhibitors such as diarylpyrimidine (DAPY) inhibitor rilpivirine are affected by mutations in RT that confer resistance. In order to aid drug design efforts, it would be efficient and cost effective to pre-evaluate NNRTI compounds in development using a structure-based computational approach. As proof of concept, we applied a residue scan and molecular dynamics strategy using RT crystal structures to predict mutations that confer resistance to DAPYs rilpivirine, etravirine, and investigational microbicide dapivirine. Our predictive values, changes in affinity and stability, are correlative with fold-resistance data for several RT mutants. Consistent with previous studies, mutation K101P is predicted to confer high-level resistance to DAPYs. These findings were further validated using structural analysis, molecular dynamics, and an enzymatic reverse transcription assay. Our results confirm that changes in affinity and stability for mutant complexes are predictive parameters of resistance as validated by experimental and clinical data. In future work, we believe that this computational approach may be useful to predict resistance mutations for inhibitors in development. Published by Elsevier Inc.

18F-FDG PET/CT in Detecting Metastatic Infection in Children.

PubMed

Kouijzer, Ilse J E; Blokhuis, Gijsbert J; Draaisma, Jos M T; Oyen, Wim J G; de Geus-Oei, Lioe-Fee; Bleeker-Rovers, Chantal P

2016-04-01

Metastatic infection is a severe complication of bacteremia with high morbidity and mortality. The aim of this study was to investigate the diagnostic value of 18F-FDG PET combined with CT (FDG PET/CT) in children suspected of having metastatic infection. The results of FDG PET/CT scans performed in children because of suspected metastatic infection from September 2003 to June 2013 were analyzed retrospectively. The results were compared with the final clinical diagnosis. FDG PET/CT was performed in 13 children with suspected metastatic infection. Of the total number of FDG PET/CT scans, 38% were clinically helpful. Positive predictive value of FDG PET/CT was 71%, and negative predictive value was 100%. FDG PET/CT appears to be a valuable diagnostic technique in children with suspected metastatic infection. Prospective studies of FDG PET/CT as part of a structured diagnostic protocol are needed to assess the exact additional diagnostic value.

Multiple receptor conformation docking and dock pose clustering as tool for CoMFA and CoMSIA analysis - a case study on HIV-1 protease inhibitors.

PubMed

Sivan, Sree Kanth; Manga, Vijjulatha

2012-02-01

Multiple receptors conformation docking (MRCD) and clustering of dock poses allows seamless incorporation of receptor binding conformation of the molecules on wide range of ligands with varied structural scaffold. The accuracy of the approach was tested on a set of 120 cyclic urea molecules having HIV-1 protease inhibitory activity using 12 high resolution X-ray crystal structures and one NMR resolved conformation of HIV-1 protease extracted from protein data bank. A cross validation was performed on 25 non-cyclic urea HIV-1 protease inhibitor having varied structures. The comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) models were generated using 60 molecules in the training set by applying leave one out cross validation method, r (loo) (2) values of 0.598 and 0.674 for CoMFA and CoMSIA respectively and non-cross validated regression coefficient r(2) values of 0.983 and 0.985 were obtained for CoMFA and CoMSIA respectively. The predictive ability of these models was determined using a test set of 60 cyclic urea molecules that gave predictive correlation (r (pred) (2) ) of 0.684 and 0.64 respectively for CoMFA and CoMSIA indicating good internal predictive ability. Based on this information 25 non-cyclic urea molecules were taken as a test set to check the external predictive ability of these models. This gave remarkable out come with r (pred) (2) of 0.61 and 0.53 for CoMFA and CoMSIA respectively. The results invariably show that this method is useful for performing 3D QSAR analysis on molecules having different structural motifs.

NWP model forecast skill optimization via closure parameter variations

NASA Astrophysics Data System (ADS)

Järvinen, H.; Ollinaho, P.; Laine, M.; Solonen, A.; Haario, H.

2012-04-01

We present results of a novel approach to tune predictive skill of numerical weather prediction (NWP) models. These models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. The current practice is to specify manually the numerical parameter values, based on expert knowledge. We developed recently a concept and method (QJRMS 2011) for on-line estimation of the NWP model parameters via closure parameter variations. The method called EPPES ("Ensemble prediction and parameter estimation system") utilizes ensemble prediction infra-structure for parameter estimation in a very cost-effective way: practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating an ensemble of predictions so that each member uses different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In this presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an ensemble prediction system emulator, based on the ECHAM5 atmospheric GCM show that the model tuning capability of EPPES scales up to realistic models and ensemble prediction systems. Finally, preliminary results of EPPES in the context of ECMWF forecasting system are presented.

Composite laminate failure parameter optimization through four-point flexure experimentation and analysis

DOE PAGES

Nelson, Stacy; English, Shawn; Briggs, Timothy

2016-05-06

Fiber-reinforced composite materials offer light-weight solutions to many structural challenges. In the development of high-performance composite structures, a thorough understanding is required of the composite materials themselves as well as methods for the analysis and failure prediction of the relevant composite structures. However, the mechanical properties required for the complete constitutive definition of a composite material can be difficult to determine through experimentation. Therefore, efficient methods are necessary that can be used to determine which properties are relevant to the analysis of a specific structure and to establish a structure's response to a material parameter that can only be definedmore » through estimation. The objectives of this paper deal with demonstrating the potential value of sensitivity and uncertainty quantification techniques during the failure analysis of loaded composite structures; and the proposed methods are applied to the simulation of the four-point flexural characterization of a carbon fiber composite material. Utilizing a recently implemented, phenomenological orthotropic material model that is capable of predicting progressive composite damage and failure, a sensitivity analysis is completed to establish which material parameters are truly relevant to a simulation's outcome. Then, a parameter study is completed to determine the effect of the relevant material properties' expected variations on the simulated four-point flexural behavior as well as to determine the value of an unknown material property. This process demonstrates the ability to formulate accurate predictions in the absence of a rigorous material characterization effort. Finally, the presented results indicate that a sensitivity analysis and parameter study can be used to streamline the material definition process as the described flexural characterization was used for model validation.« less

Antibody-protein interactions: benchmark datasets and prediction tools evaluation

PubMed Central

Ponomarenko, Julia V; Bourne, Philip E

2007-01-01

Background The ability to predict antibody binding sites (aka antigenic determinants or B-cell epitopes) for a given protein is a precursor to new vaccine design and diagnostics. Among the various methods of B-cell epitope identification X-ray crystallography is one of the most reliable methods. Using these experimental data computational methods exist for B-cell epitope prediction. As the number of structures of antibody-protein complexes grows, further interest in prediction methods using 3D structure is anticipated. This work aims to establish a benchmark for 3D structure-based epitope prediction methods. Results Two B-cell epitope benchmark datasets inferred from the 3D structures of antibody-protein complexes were defined. The first is a dataset of 62 representative 3D structures of protein antigens with inferred structural epitopes. The second is a dataset of 82 structures of antibody-protein complexes containing different structural epitopes. Using these datasets, eight web-servers developed for antibody and protein binding sites prediction have been evaluated. In no method did performance exceed a 40% precision and 46% recall. The values of the area under the receiver operating characteristic curve for the evaluated methods were about 0.6 for ConSurf, DiscoTope, and PPI-PRED methods and above 0.65 but not exceeding 0.70 for protein-protein docking methods when the best of the top ten models for the bound docking were considered; the remaining methods performed close to random. The benchmark datasets are included as a supplement to this paper. Conclusion It may be possible to improve epitope prediction methods through training on datasets which include only immune epitopes and through utilizing more features characterizing epitopes, for example, the evolutionary conservation score. Notwithstanding, overall poor performance may reflect the generality of antigenicity and hence the inability to decipher B-cell epitopes as an intrinsic feature of the protein. It is an open question as to whether ultimately discriminatory features can be found. PMID:17910770

Uncertainty quantification and propagation in dynamic models using ambient vibration measurements, application to a 10-story building

NASA Astrophysics Data System (ADS)

Behmanesh, Iman; Yousefianmoghadam, Seyedsina; Nozari, Amin; Moaveni, Babak; Stavridis, Andreas

2018-07-01

This paper investigates the application of Hierarchical Bayesian model updating for uncertainty quantification and response prediction of civil structures. In this updating framework, structural parameters of an initial finite element (FE) model (e.g., stiffness or mass) are calibrated by minimizing error functions between the identified modal parameters and the corresponding parameters of the model. These error functions are assumed to have Gaussian probability distributions with unknown parameters to be determined. The estimated parameters of error functions represent the uncertainty of the calibrated model in predicting building's response (modal parameters here). The focus of this paper is to answer whether the quantified model uncertainties using dynamic measurement at building's reference/calibration state can be used to improve the model prediction accuracies at a different structural state, e.g., damaged structure. Also, the effects of prediction error bias on the uncertainty of the predicted values is studied. The test structure considered here is a ten-story concrete building located in Utica, NY. The modal parameters of the building at its reference state are identified from ambient vibration data and used to calibrate parameters of the initial FE model as well as the error functions. Before demolishing the building, six of its exterior walls were removed and ambient vibration measurements were also collected from the structure after the wall removal. These data are not used to calibrate the model; they are only used to assess the predicted results. The model updating framework proposed in this paper is applied to estimate the modal parameters of the building at its reference state as well as two damaged states: moderate damage (removal of four walls) and severe damage (removal of six walls). Good agreement is observed between the model-predicted modal parameters and those identified from vibration tests. Moreover, it is shown that including prediction error bias in the updating process instead of commonly-used zero-mean error function can significantly reduce the prediction uncertainties.

Design and prediction of new anticoagulants as a selective Factor IXa inhibitor via three-dimensional quantitative structure-property relationships of amidinobenzothiophene derivatives.

PubMed

Gao, Jia-Suo; Tong, Xu-Peng; Chang, Yi-Qun; He, Yu-Xuan; Mei, Yu-Dan; Tan, Pei-Hong; Guo, Jia-Liang; Liao, Guo-Chao; Xiao, Gao-Keng; Chen, Wei-Min; Zhou, Shu-Feng; Sun, Ping-Hua

2015-01-01

Factor IXa (FIXa), a blood coagulation factor, is specifically inhibited at the initiation stage of the coagulation cascade, promising an excellent approach for developing selective and safe anticoagulants. Eighty-four amidinobenzothiophene antithrombotic derivatives targeting FIXa were selected to establish three-dimensional quantitative structure-activity relationship (3D-QSAR) and three-dimensional quantitative structure-selectivity relationship (3D-QSSR) models using comparative molecular field analysis and comparative similarity indices analysis methods. Internal and external cross-validation techniques were investigated as well as region focusing and bootstrapping. The satisfactory q (2) values of 0.753 and 0.770, and r (2) values of 0.940 and 0.965 for 3D-QSAR and 3D-QSSR, respectively, indicated that the models are available to predict both the inhibitory activity and selectivity on FIXa against Factor Xa, the activated status of Factor X. This work revealed that the steric, hydrophobic, and H-bond factors should appropriately be taken into account in future rational design, especially the modifications at the 2'-position of the benzene and the 6-position of the benzothiophene in the R group, providing helpful clues to design more active and selective FIXa inhibitors for the treatment of thrombosis. On the basis of the three-dimensional quantitative structure-property relationships, 16 new potent molecules have been designed and are predicted to be more active and selective than Compound 33, which has the best activity as reported in the literature.

Semi-empirical proton binding constants for natural organic matter

NASA Astrophysics Data System (ADS)

Matynia, Anthony; Lenoir, Thomas; Causse, Benjamin; Spadini, Lorenzo; Jacquet, Thierry; Manceau, Alain

2010-03-01

Average proton binding constants ( KH,i) for structure models of humic (HA) and fulvic (FA) acids were estimated semi-empirically by breaking down the macromolecules into reactive structural units (RSUs), and calculating KH,i values of the RSUs using linear free energy relationships (LFER) of Hammett. Predicted log KH,COOH and log KH,Ph-OH are 3.73 ± 0.13 and 9.83 ± 0.23 for HA, and 3.80 ± 0.20 and 9.87 ± 0.31 for FA. The predicted constants for phenolic-type sites (Ph-OH) are generally higher than those derived from potentiometric titrations, but the difference may not be significant in view of the considerable uncertainty of the acidity constants determined from acid-base measurements at high pH. The predicted constants for carboxylic-type sites agree well with titration data analyzed with Model VI (4.10 ± 0.16 for HA, 3.20 ± 0.13 for FA; Tipping, 1998), the Impermeable Sphere model (3.50-4.50 for HA; Avena et al., 1999), and the Stockholm Humic Model (4.10 ± 0.20 for HA, 3.50 ± 0.40 for FA; Gustafsson, 2001), but differ by about one log unit from those obtained by Milne et al. (2001) with the NICA-Donnan model (3.09 ± 0.51 for HA, 2.65 ± 0.43 for FA), and used to derive recommended generic values. To clarify this ambiguity, 10 high-quality titration data from Milne et al. (2001) were re-analyzed with the new predicted equilibrium constants. The data are described equally well with the previous and new sets of values ( R2 ⩾ 0.98), not necessarily because the NICA-Donnan model is overparametrized, but because titration lacks the sensitivity needed to quantify the full binding properties of humic substances. Correlations between NICA-Donnan parameters are discussed, but general progress is impeded by the unknown number of independent parameters that can be varied during regression of a model fit to titration data. The high consistency between predicted and experimental KH,COOH values, excluding those of Milne et al. (2001), gives faith in the proposed semi-empirical structural approach, and its usefulness to assess the plausibility of proton stability constants derived from simulations of titration data.

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

PubMed

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

2009-01-01

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

Molecular modeling of the microstructure evolution during carbon fiber processing

NASA Astrophysics Data System (ADS)

Desai, Saaketh; Li, Chunyu; Shen, Tongtong; Strachan, Alejandro

2017-12-01

The rational design of carbon fibers with desired properties requires quantitative relationships between the processing conditions, microstructure, and resulting properties. We developed a molecular model that combines kinetic Monte Carlo and molecular dynamics techniques to predict the microstructure evolution during the processes of carbonization and graphitization of polyacrylonitrile (PAN)-based carbon fibers. The model accurately predicts the cross-sectional microstructure of the fibers with the molecular structure of the stabilized PAN fibers and physics-based chemical reaction rates as the only inputs. The resulting structures exhibit key features observed in electron microcopy studies such as curved graphitic sheets and hairpin structures. In addition, computed X-ray diffraction patterns are in good agreement with experiments. We predict the transverse moduli of the resulting fibers between 1 GPa and 5 GPa, in good agreement with experimental results for high modulus fibers and slightly lower than those of high-strength fibers. The transverse modulus is governed by sliding between graphitic sheets, and the relatively low value for the predicted microstructures can be attributed to their perfect longitudinal texture. Finally, the simulations provide insight into the relationships between chemical kinetics and the final microstructure; we observe that high reaction rates result in porous structures with lower moduli.

An economic prediction of the finer resolution level wavelet coefficients in electronic structure calculations.

PubMed

Nagy, Szilvia; Pipek, János

2015-12-21

In wavelet based electronic structure calculations, introducing a new, finer resolution level is usually an expensive task, this is why often a two-level approximation is used with very fine starting resolution level. This process results in large matrices to calculate with and a large number of coefficients to be stored. In our previous work we have developed an adaptively refined solution scheme that determines the indices, where the refined basis functions are to be included, and later a method for predicting the next, finer resolution coefficients in a very economic way. In the present contribution, we would like to determine whether the method can be applied for predicting not only the first, but also the other, higher resolution level coefficients. Also the energy expectation values of the predicted wave functions are studied, as well as the scaling behaviour of the coefficients in the fine resolution limit.

Analytical prediction of the interior noise for cylindrical models of aircraft fuselages for prescribed exterior noise fields. Phase 2: Models for sidewall trim, stiffened structures and cabin acoustics with floor partition

NASA Technical Reports Server (NTRS)

Pope, L. D.; Wilby, E. G.

1982-01-01

An airplane interior noise prediction model is developed to determine the important parameters associated with sound transmission into the interiors of airplanes, and to identify apropriate noise control methods. Models for stiffened structures, and cabin acoustics with floor partition are developed. Validation studies are undertaken using three test articles: a ring stringer stiffened cylinder, an unstiffened cylinder with floor partition, and ring stringer stiffened cylinder with floor partition and sidewall trim. The noise reductions of the three test articles are computed using the heoretical models and compared to measured values. A statistical analysis of the comparison data indicates that there is no bias in the predictions although a substantial random error exists so that a discrepancy of more than five or six dB can be expected for about one out of three predictions.

Sweetness prediction of natural compounds.

PubMed

Chéron, Jean-Baptiste; Casciuc, Iuri; Golebiowski, Jérôme; Antonczak, Serge; Fiorucci, Sébastien

2017-04-15

Based on the most exhaustive database of sweeteners with known sweetness values, a new quantitative structure-activity relationship model for sweetness prediction has been set up. Analysis of the physico-chemical properties of sweeteners in the database indicates that the structure of most potent sweeteners combines a hydrophobic scaffold functionalized by a limited number of hydrogen bond sites (less than 4 hydrogen bond donors and 10 acceptors), with a moderate molecular weight ranging from 350 to 450g·mol -1 . Prediction of sweetness, bitterness and toxicity properties of the largest database of natural compounds have been performed. In silico screening reveals that the majority of the predicted natural intense sweeteners comprise saponin or stevioside scaffolds. The model highlights that their sweetness potency is comparable to known natural sweeteners. The identified compounds provide a rational basis to initiate the design and chemosensory analysis of new low-calorie sweeteners. Copyright © 2016 Elsevier Ltd. All rights reserved.

Tree Morphologic Plasticity Explains Deviation from Metabolic Scaling Theory in Semi-Arid Conifer Forests, Southwestern USA

PubMed Central

O’Connor, Christopher D.; Lynch, Ann M.

2016-01-01

A significant concern about Metabolic Scaling Theory (MST) in real forests relates to consistent differences between the values of power law scaling exponents of tree primary size measures used to estimate mass and those predicted by MST. Here we consider why observed scaling exponents for diameter and height relationships deviate from MST predictions across three semi-arid conifer forests in relation to: (1) tree condition and physical form, (2) the level of inter-tree competition (e.g. open vs closed stand structure), (3) increasing tree age, and (4) differences in site productivity. Scaling exponent values derived from non-linear least-squares regression for trees in excellent condition (n = 381) were above the MST prediction at the 95% confidence level, while the exponent for trees in good condition were no different than MST (n = 926). Trees that were in fair or poor condition, characterized as diseased, leaning, or sparsely crowned had exponent values below MST predictions (n = 2,058), as did recently dead standing trees (n = 375). Exponent value of the mean-tree model that disregarded tree condition (n = 3,740) was consistent with other studies that reject MST scaling. Ostensibly, as stand density and competition increase trees exhibited greater morphological plasticity whereby the majority had characteristically fair or poor growth forms. Fitting by least-squares regression biases the mean-tree model scaling exponent toward values that are below MST idealized predictions. For 368 trees from Arizona with known establishment dates, increasing age had no significant impact on expected scaling. We further suggest height to diameter ratios below MST relate to vertical truncation caused by limitation in plant water availability. Even with environmentally imposed height limitation, proportionality between height and diameter scaling exponents were consistent with the predictions of MST. PMID:27391084

Tree Morphologic Plasticity Explains Deviation from Metabolic Scaling Theory in Semi-Arid Conifer Forests, Southwestern USA.

PubMed

Swetnam, Tyson L; O'Connor, Christopher D; Lynch, Ann M

2016-01-01

A significant concern about Metabolic Scaling Theory (MST) in real forests relates to consistent differences between the values of power law scaling exponents of tree primary size measures used to estimate mass and those predicted by MST. Here we consider why observed scaling exponents for diameter and height relationships deviate from MST predictions across three semi-arid conifer forests in relation to: (1) tree condition and physical form, (2) the level of inter-tree competition (e.g. open vs closed stand structure), (3) increasing tree age, and (4) differences in site productivity. Scaling exponent values derived from non-linear least-squares regression for trees in excellent condition (n = 381) were above the MST prediction at the 95% confidence level, while the exponent for trees in good condition were no different than MST (n = 926). Trees that were in fair or poor condition, characterized as diseased, leaning, or sparsely crowned had exponent values below MST predictions (n = 2,058), as did recently dead standing trees (n = 375). Exponent value of the mean-tree model that disregarded tree condition (n = 3,740) was consistent with other studies that reject MST scaling. Ostensibly, as stand density and competition increase trees exhibited greater morphological plasticity whereby the majority had characteristically fair or poor growth forms. Fitting by least-squares regression biases the mean-tree model scaling exponent toward values that are below MST idealized predictions. For 368 trees from Arizona with known establishment dates, increasing age had no significant impact on expected scaling. We further suggest height to diameter ratios below MST relate to vertical truncation caused by limitation in plant water availability. Even with environmentally imposed height limitation, proportionality between height and diameter scaling exponents were consistent with the predictions of MST.

Genetic programming based quantitative structure-retention relationships for the prediction of Kovats retention indices.

PubMed

Goel, Purva; Bapat, Sanket; Vyas, Renu; Tambe, Amruta; Tambe, Sanjeev S

2015-11-13

The development of quantitative structure-retention relationships (QSRR) aims at constructing an appropriate linear/nonlinear model for the prediction of the retention behavior (such as Kovats retention index) of a solute on a chromatographic column. Commonly, multi-linear regression and artificial neural networks are used in the QSRR development in the gas chromatography (GC). In this study, an artificial intelligence based data-driven modeling formalism, namely genetic programming (GP), has been introduced for the development of quantitative structure based models predicting Kovats retention indices (KRI). The novelty of the GP formalism is that given an example dataset, it searches and optimizes both the form (structure) and the parameters of an appropriate linear/nonlinear data-fitting model. Thus, it is not necessary to pre-specify the form of the data-fitting model in the GP-based modeling. These models are also less complex, simple to understand, and easy to deploy. The effectiveness of GP in constructing QSRRs has been demonstrated by developing models predicting KRIs of light hydrocarbons (case study-I) and adamantane derivatives (case study-II). In each case study, two-, three- and four-descriptor models have been developed using the KRI data available in the literature. The results of these studies clearly indicate that the GP-based models possess an excellent KRI prediction accuracy and generalization capability. Specifically, the best performing four-descriptor models in both the case studies have yielded high (>0.9) values of the coefficient of determination (R(2)) and low values of root mean squared error (RMSE) and mean absolute percent error (MAPE) for training, test and validation set data. The characteristic feature of this study is that it introduces a practical and an effective GP-based method for developing QSRRs in gas chromatography that can be gainfully utilized for developing other types of data-driven models in chromatography science. Copyright © 2015 Elsevier B.V. All rights reserved.

Quantitative structure-activity relationship modeling of rat acute toxicity by oral exposure.

PubMed

Zhu, Hao; Martin, Todd M; Ye, Lin; Sedykh, Alexander; Young, Douglas M; Tropsha, Alexander

2009-12-01

Few quantitative structure-activity relationship (QSAR) studies have successfully modeled large, diverse rodent toxicity end points. In this study, a comprehensive data set of 7385 compounds with their most conservative lethal dose (LD(50)) values has been compiled. A combinatorial QSAR approach has been employed to develop robust and predictive models of acute toxicity in rats caused by oral exposure to chemicals. To enable fair comparison between the predictive power of models generated in this study versus a commercial toxicity predictor, TOPKAT (Toxicity Prediction by Komputer Assisted Technology), a modeling subset of the entire data set was selected that included all 3472 compounds used in TOPKAT's training set. The remaining 3913 compounds, which were not present in the TOPKAT training set, were used as the external validation set. QSAR models of five different types were developed for the modeling set. The prediction accuracy for the external validation set was estimated by determination coefficient R(2) of linear regression between actual and predicted LD(50) values. The use of the applicability domain threshold implemented in most models generally improved the external prediction accuracy but expectedly led to the decrease in chemical space coverage; depending on the applicability domain threshold, R(2) ranged from 0.24 to 0.70. Ultimately, several consensus models were developed by averaging the predicted LD(50) for every compound using all five models. The consensus models afforded higher prediction accuracy for the external validation data set with the higher coverage as compared to individual constituent models. The validated consensus LD(50) models developed in this study can be used as reliable computational predictors of in vivo acute toxicity.

QSAR models for thiophene and imidazopyridine derivatives inhibitors of the Polo-Like Kinase 1.

PubMed

Comelli, Nieves C; Duchowicz, Pablo R; Castro, Eduardo A

2014-10-01

The inhibitory activity of 103 thiophene and 33 imidazopyridine derivatives against Polo-Like Kinase 1 (PLK1) expressed as pIC50 (-logIC50) was predicted by QSAR modeling. Multivariate linear regression (MLR) was employed to model the relationship between 0D and 3D molecular descriptors and biological activities of molecules using the replacement method (MR) as variable selection tool. The 136 compounds were separated into several training and test sets. Two splitting approaches, distribution of biological data and structural diversity, and the statistical experimental design procedure D-optimal distance were applied to the dataset. The significance of the training set models was confirmed by statistically higher values of the internal leave one out cross-validated coefficient of determination (Q2) and external predictive coefficient of determination for the test set (Rtest2). The model developed from a training set, obtained with the D-optimal distance protocol and using 3D descriptor space along with activity values, separated chemical features that allowed to distinguish high and low pIC50 values reasonably well. Then, we verified that such model was sufficient to reliably and accurately predict the activity of external diverse structures. The model robustness was properly characterized by means of standard procedures and their applicability domain (AD) was analyzed by leverage method. Copyright © 2014 Elsevier B.V. All rights reserved.

Development of local calibration factors and design criteria values for mechanistic-empirical pavement design.

DOT National Transportation Integrated Search

2015-08-01

A mechanistic-empirical (ME) pavement design procedure allows for analyzing and selecting pavement structures based : on predicted distress progression resulting from stresses and strains within the pavement over its design life. The Virginia : Depar...

Measuring the hierarchy of feedforward networks

NASA Astrophysics Data System (ADS)

Corominas-Murtra, Bernat; Rodríguez-Caso, Carlos; Goñi, Joaquín; Solé, Ricard

2011-03-01

In this paper we explore the concept of hierarchy as a quantifiable descriptor of ordered structures, departing from the definition of three conditions to be satisfied for a hierarchical structure: order, predictability, and pyramidal structure. According to these principles, we define a hierarchical index taking concepts from graph and information theory. This estimator allows to quantify the hierarchical character of any system susceptible to be abstracted in a feedforward causal graph, i.e., a directed acyclic graph defined in a single connected structure. Our hierarchical index is a balance between this predictability and pyramidal condition by the definition of two entropies: one attending the onward flow and the other for the backward reversion. We show how this index allows to identify hierarchical, antihierarchical, and nonhierarchical structures. Our formalism reveals that departing from the defined conditions for a hierarchical structure, feedforward trees and the inverted tree graphs emerge as the only causal structures of maximal hierarchical and antihierarchical systems respectively. Conversely, null values of the hierarchical index are attributed to a number of different configuration networks; from linear chains, due to their lack of pyramid structure, to full-connected feedforward graphs where the diversity of onward pathways is canceled by the uncertainty (lack of predictability) when going backward. Some illustrative examples are provided for the distinction among these three types of hierarchical causal graphs.

Application of a Probalistic Sizing Methodology for Ceramic Structures

NASA Astrophysics Data System (ADS)

Rancurel, Michael; Behar-Lafenetre, Stephanie; Cornillon, Laurence; Leroy, Francois-Henri; Coe, Graham; Laine, Benoit

2012-07-01

Ceramics are increasingly used in the space industry to take advantage of their stability and high specific stiffness properties. Their brittle behaviour often leads to size them by increasing the safety factors that are applied on the maximum stresses. It induces to oversize the structures. This is inconsistent with the major driver in space architecture, the mass criteria. This paper presents a methodology to size ceramic structures based on their failure probability. Thanks to failure tests on samples, the Weibull law which characterizes the strength distribution of the material is obtained. A-value (Q0.0195%) and B-value (Q0.195%) are then assessed to take into account the limited number of samples. A knocked-down Weibull law that interpolates the A- & B- values is also obtained. Thanks to these two laws, a most-likely and a knocked- down prediction of failure probability are computed for complex ceramic structures. The application of this methodology and its validation by test is reported in the paper.

MoFvAb: Modeling the Fv region of antibodies

PubMed Central

Bujotzek, Alexander; Fuchs, Angelika; Qu, Changtao; Benz, Jörg; Klostermann, Stefan; Antes, Iris; Georges, Guy

2015-01-01

Knowledge of the 3-dimensional structure of the antigen-binding region of antibodies enables numerous useful applications regarding the design and development of antibody-based drugs. We present a knowledge-based antibody structure prediction methodology that incorporates concepts that have arisen from an applied antibody engineering environment. The protocol exploits the rich and continuously growing supply of experimentally derived antibody structures available to predict CDR loop conformations and the packing of heavy and light chain quickly and without user intervention. The homology models are refined by a novel antibody-specific approach to adapt and rearrange sidechains based on their chemical environment. The method achieves very competitive all-atom root mean square deviation values in the order of 1.5 Å on different evaluation datasets consisting of both known and previously unpublished antibody crystal structures. PMID:26176812

A unified relation for the solid-liquid interface free energy of pure FCC, BCC, and HCP metals.

PubMed

Wilson, S R; Mendelev, M I

2016-04-14

We study correlations between the solid-liquid interface (SLI) free energy and bulk material properties (melting temperature, latent heat, and liquid structure) through the determination of SLI free energies for bcc and hcp metals from molecular dynamics (MD) simulation. Values obtained for the bcc metals in this study were compared to values predicted by the Turnbull, Laird, and Ewing relations on the basis of previously published MD simulation data. We found that of these three empirical relations, the Ewing relation better describes the MD simulation data. Moreover, whereas the original Ewing relation contains two constants for a particular crystal structure, we found that the first coefficient in the Ewing relation does not depend on crystal structure, taking a common value for all three phases, at least for the class of the systems described by embedded-atom method potentials (which are considered to provide a reasonable approximation for metals).

A unified relation for the solid-liquid interface free energy of pure FCC, BCC, and HCP metals

NASA Astrophysics Data System (ADS)

Wilson, S. R.; Mendelev, M. I.

2016-04-01

We study correlations between the solid-liquid interface (SLI) free energy and bulk material properties (melting temperature, latent heat, and liquid structure) through the determination of SLI free energies for bcc and hcp metals from molecular dynamics (MD) simulation. Values obtained for the bcc metals in this study were compared to values predicted by the Turnbull, Laird, and Ewing relations on the basis of previously published MD simulation data. We found that of these three empirical relations, the Ewing relation better describes the MD simulation data. Moreover, whereas the original Ewing relation contains two constants for a particular crystal structure, we found that the first coefficient in the Ewing relation does not depend on crystal structure, taking a common value for all three phases, at least for the class of the systems described by embedded-atom method potentials (which are considered to provide a reasonable approximation for metals).

Predictive ecotoxicity of MoA 1 of organic chemicals using in silico approaches.

PubMed

de Morais E Silva, Luana; Alves, Mateus Feitosa; Scotti, Luciana; Lopes, Wilton Silva; Scotti, Marcus Tullius

2018-05-30

Persistent organic products are compounds used for various purposes, such as personal care products, surfactants, colorants, industrial additives, food, pesticides and pharmaceuticals. These substances are constantly introduced into the environment and many of these pollutants are difficult to degrade. Toxic compounds classified as MoA 1 (Mode of Action 1) are low toxicity compounds that comprise nonreactive chemicals. In silico methods such as Quantitative Structure-Activity Relationships (QSARs) have been used to develop important models for prediction in several areas of science, as well as aquatic toxicity studies. The aim of the present study was to build a QSAR model-based set of theoretical Volsurf molecular descriptors using the fish acute toxicity values of compounds defined as MoA 1 to identify the molecular properties related to this mechanism. The selected Partial Least Squares (PLS) results based on the values of cross-validation coefficients of determination (Q cv 2 ) show the following values: Q cv 2 = 0.793, coefficient of determination (R 2 ) = 0.823, explained variance in external prediction (Q ext 2 ) = 0.87. From the selected descriptors, not only the hydrophobicity is related to the toxicity as already mentioned in previously published studies but other physicochemical properties combined contribute to the activity of these compounds. The symmetric distribution of the hydrophobic moieties in the structure of the compounds as well as the shape, as branched chains, are important features that are related to the toxicity. This information from the model can be useful in predicting so as to minimize the toxicity of organic compounds. Copyright © 2018. Published by Elsevier Inc.

Generation of 3-D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error

USGS Publications Warehouse

Christensen, Nikolaj K; Minsley, Burke J.; Christensen, Steen

2017-01-01

We present a new methodology to combine spatially dense high-resolution airborne electromagnetic (AEM) data and sparse borehole information to construct multiple plausible geological structures using a stochastic approach. The method developed allows for quantification of the performance of groundwater models built from different geological realizations of structure. Multiple structural realizations are generated using geostatistical Monte Carlo simulations that treat sparse borehole lithological observations as hard data and dense geophysically derived structural probabilities as soft data. Each structural model is used to define 3-D hydrostratigraphical zones of a groundwater model, and the hydraulic parameter values of the zones are estimated by using nonlinear regression to fit hydrological data (hydraulic head and river discharge measurements). Use of the methodology is demonstrated for a synthetic domain having structures of categorical deposits consisting of sand, silt, or clay. It is shown that using dense AEM data with the methodology can significantly improve the estimated accuracy of the sediment distribution as compared to when borehole data are used alone. It is also shown that this use of AEM data can improve the predictive capability of a calibrated groundwater model that uses the geological structures as zones. However, such structural models will always contain errors because even with dense AEM data it is not possible to perfectly resolve the structures of a groundwater system. It is shown that when using such erroneous structures in a groundwater model, they can lead to biased parameter estimates and biased model predictions, therefore impairing the model's predictive capability.

Generation of 3-D hydrostratigraphic zones from dense airborne electromagnetic data to assess groundwater model prediction error

NASA Astrophysics Data System (ADS)

Christensen, N. K.; Minsley, B. J.; Christensen, S.

2017-02-01

We present a new methodology to combine spatially dense high-resolution airborne electromagnetic (AEM) data and sparse borehole information to construct multiple plausible geological structures using a stochastic approach. The method developed allows for quantification of the performance of groundwater models built from different geological realizations of structure. Multiple structural realizations are generated using geostatistical Monte Carlo simulations that treat sparse borehole lithological observations as hard data and dense geophysically derived structural probabilities as soft data. Each structural model is used to define 3-D hydrostratigraphical zones of a groundwater model, and the hydraulic parameter values of the zones are estimated by using nonlinear regression to fit hydrological data (hydraulic head and river discharge measurements). Use of the methodology is demonstrated for a synthetic domain having structures of categorical deposits consisting of sand, silt, or clay. It is shown that using dense AEM data with the methodology can significantly improve the estimated accuracy of the sediment distribution as compared to when borehole data are used alone. It is also shown that this use of AEM data can improve the predictive capability of a calibrated groundwater model that uses the geological structures as zones. However, such structural models will always contain errors because even with dense AEM data it is not possible to perfectly resolve the structures of a groundwater system. It is shown that when using such erroneous structures in a groundwater model, they can lead to biased parameter estimates and biased model predictions, therefore impairing the model's predictive capability.

Feasibility study: refinement of the TTC concept by additional rules based on in silico and experimental data.

PubMed

Hauge-Nilsen, Kristin; Keller, Detlef

2015-01-01

Starting from a single generic limit value, the threshold of toxicological concern (TTC) concept has been further developed over the years, e.g., by including differentiated structural classes according to the rules of Cramer et al. (Food Chem Toxicol 16: 255-276, 1978). In practice, the refined TTC concept of Munro et al. (Food Chem Toxicol 34: 829-867, 1996) is often applied. The purpose of this work was to explore the possibility of refining the concept by introducing additional structure-activity relationships and available toxicity data. Computer modeling was performed using the OECD Toolbox. No observed (adverse) effect level (NO(A)EL) data of 176 substances were collected in a basic data set. New subgroups were created applying the following criteria: extended Cramer rules, low bioavailability, low acute toxicity, no protein binding affinity, and consideration of predicted liver metabolism. The highest TTC limit value of 236 µg/kg/day was determined for a subgroup that combined the criteria "no protein binding affinity" and "predicted liver metabolism." This value was approximately eight times higher than the original Cramer class 1 limit value of 30 µg/kg/day. The results of this feasibility study indicate that inclusion of the proposed criteria may lead to improved TTC values. Thereby, the applicability of the TTC concept in risk assessment could be extended which could reduce the need to perform animal tests.

Fragment based group QSAR and molecular dynamics mechanistic studies on arylthioindole derivatives targeting the α-β interfacial site of human tubulin

PubMed Central

2014-01-01

Background A number of microtubule disassembly blocking agents and inhibitors of tubulin polymerization have been elements of great interest in anti-cancer therapy, some of them even entering into the clinical trials. One such class of tubulin assembly inhibitors is of arylthioindole derivatives which results in effective microtubule disorganization responsible for cell apoptosis by interacting with the colchicine binding site of the β-unit of tubulin close to the interface with the α unit. We modelled the human tubulin β unit (chain D) protein and performed docking studies to elucidate the detailed binding mode of actions associated with their inhibition. The activity enhancing structural aspects were evaluated using a fragment-based Group QSAR (G-QSAR) model and was validated statistically to determine its robustness. A combinatorial library was generated keeping the arylthioindole moiety as the template and their activities were predicted. Results The G-QSAR model obtained was statistically significant with r2 value of 0.85, cross validated correlation coefficient q2 value of 0.71 and pred_r2 (r2 value for test set) value of 0.89. A high F test value of 65.76 suggests robustness of the model. Screening of the combinatorial library on the basis of predicted activity values yielded two compounds HPI (predicted pIC50 = 6.042) and MSI (predicted pIC50 = 6.001) whose interactions with the D chain of modelled human tubulin protein were evaluated in detail. A toxicity evaluation resulted in MSI being less toxic in comparison to HPI. Conclusions The study provides an insight into the crucial structural requirements and the necessary chemical substitutions required for the arylthioindole moiety to exhibit enhanced inhibitory activity against human tubulin. The two reported compounds HPI and MSI showed promising anti cancer activities and thus can be considered as potent leads against cancer. The toxicity evaluation of these compounds suggests that MSI is a promising therapeutic candidate. This study provided another stepping stone in the direction of evaluating tubulin inhibition and microtubule disassembly degeneration as viable targets for development of novel therapeutics against cancer. PMID:25521775

Full hyperfine structure analysis of singly ionized molybdenum

NASA Astrophysics Data System (ADS)

Bouazza, Safa

2017-03-01

For a first time a parametric study of hyperfine structure of Mo II configuration levels is presented. The newly measured A and B hyperfine structure (hfs) constants values of Mo II 4d5, 4d45s and 4d35s2 configuration levels, for both 95 and 97 isotopes, using Fast-ion-beam laser-induced fluorescence spectroscopy [1] are gathered with other few data available in literature. A fitting procedure of an isolated set of these three lowest even-parity configuration levels has been performed by taking into account second-order of perturbation theory including the effects of closed shell-open shell excitations. Moreover the same study was done for Mo II odd-parity levels; for both parities two sets of fine structure parameters as well as the leading eigenvector percentages of levels and Landé-factor gJ, relevant for this paper are given. We present also predicted singlet, triplet and quintet positions of missing experimental levels up to 85000 cm-1. The single-electron hfs parameter values were extracted in their entirety for 97Mo II and for 95Mo II: for instance for 95Mo II, a4d 01 =-133.37 MHz and a5p 01 =-160.25 MHz for 4d45p; a4d 01 =-140.84 MHz, a5p 01 =-170.18 MHz and a5s 10 =-2898 MHz for 4d35s5p; a5s 10 =-2529 (2) MHz and a4d 01 =-135.17 (0.44) MHz for the 4d45s. These parameter values were analysed and compared with diverse ab-initio calculations. We closed this work with giving predicted values of magnetic dipole and electric quadrupole hfs constants of all known levels, whose splitting are not yet measured.

Intramolecular BSSE and dispersion affect the structure of a dipeptide conformer

NASA Astrophysics Data System (ADS)

Hameed, Rabia; Khan, Afsar; van Mourik, Tanja

2018-05-01

B3LYP and MP2 calculations with the commonly-used 6-31+G(d) basis set predict qualitatively different structures for the Tyr-Gly conformer book1, which is the most stable conformer identified in a previous study. The structures differ mainly in the ψtyr Ramachandran angle (138° in the B3LYP structure and 120° in the MP2 structure). The causes for the discrepant structures are attributed to missing dispersion in the B3LYP calculations and large intramolecular BSSE in the MP2 calculations. The correct ψtyr value is estimated to be 130°. The MP2/6-31+G(d) profile identified an additional conformer, not present on the B3LYP surface, with a ψtyr value of 96° and a more folded structure. This minimum is, however, likely an artefact of large intramolecular BSSE values. We recommend the use of basis sets of at least quadruple-zeta quality in density functional theory (DFT), DFTaugmented with an empirical dispersion term (DFT-D) and second-order Møller-Plesset perturbation theory (MP2 ) calculations in cases where intramolecular BSSE is expected to be large.

Novel composites for wing and fuselage applications

NASA Technical Reports Server (NTRS)

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

1995-01-01

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

The roles of birth inputs and outputs in predicting health, behaviour and test scores in early childhood.

PubMed

Li, Kai; Poirier, Dale J

2003-11-30

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

Subjective analysis of energy-management projects

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

Morris, R.

The most successful energy conservation projects always reflect human effort to fine-tune engineering and technological improvements. Subjective analysis is a technique for predicting and measuring human interaction before a project begins. The examples of a subjective analysis for office buildings incorporate evaluative questions that are structured to produce numeric values for computer scoring. Each project would need to develop its own pertinent questions and determine appropriate values for the answers.

Quantitative structure-property relationships for octanol-water partition coefficients of polybrominated diphenyl ethers.

PubMed

Li, Linnan; Xie, Shaodong; Cai, Hao; Bai, Xuetao; Xue, Zhao

2008-08-01

Theoretical molecular descriptors were tested against logK(OW) values for polybrominated diphenyl ethers (PBDEs) using the Partial Least-Squares Regression method which can be used to analyze data with many variables and few observations. A quantitative structure-property relationship (QSPR) model was successfully developed with a high cross-validated value (Q(cum)(2)) of 0.961, indicating a good predictive ability and stability of the model. The predictive power of the QSPR model was further cross-validated. The values of logK(OW) for PBDEs are mainly governed by molecular surface area, energy of the lowest unoccupied molecular orbital and the net atomic charges on the oxygen atom. All these descriptors have been discussed to interpret the partitioning mechanism of PBDE chemicals. The bulk property of the molecules represented by molecular surface area is the leading factor, and K(OW) values increase with the increase of molecular surface area. Higher energy of the lowest unoccupied molecular orbital and higher net atomic charge on the oxygen atom of PBDEs result in smaller K(OW). The energy of the lowest unoccupied molecular orbital and the net atomic charge on PBDEs oxygen also play important roles in affecting the partition of PBDEs between octanol and water by influencing the interactions between PBDEs and solvent molecules.

Fluid, solid and fluid-structure interaction simulations on patient-based abdominal aortic aneurysm models.

PubMed

Kelly, Sinead; O'Rourke, Malachy

2012-04-01

This article describes the use of fluid, solid and fluid-structure interaction simulations on three patient-based abdominal aortic aneurysm geometries. All simulations were carried out using OpenFOAM, which uses the finite volume method to solve both fluid and solid equations. Initially a fluid-only simulation was carried out on a single patient-based geometry and results from this simulation were compared with experimental results. There was good qualitative and quantitative agreement between the experimental and numerical results, suggesting that OpenFOAM is capable of predicting the main features of unsteady flow through a complex patient-based abdominal aortic aneurysm geometry. The intraluminal thrombus and arterial wall were then included, and solid stress and fluid-structure interaction simulations were performed on this, and two other patient-based abdominal aortic aneurysm geometries. It was found that the solid stress simulations resulted in an under-estimation of the maximum stress by up to 5.9% when compared with the fluid-structure interaction simulations. In the fluid-structure interaction simulations, flow induced pressure within the aneurysm was found to be up to 4.8% higher than the value of peak systolic pressure imposed in the solid stress simulations, which is likely to be the cause of the variation in the stress results. In comparing the results from the initial fluid-only simulation with results from the fluid-structure interaction simulation on the same patient, it was found that wall shear stress values varied by up to 35% between the two simulation methods. It was concluded that solid stress simulations are adequate to predict the maximum stress in an aneurysm wall, while fluid-structure interaction simulations should be performed if accurate prediction of the fluid wall shear stress is necessary. Therefore, the decision to perform fluid-structure interaction simulations should be based on the particular variables of interest in a given study.

Evaluation of 3D-Jury on CASP7 models.

PubMed

Kaján, László; Rychlewski, Leszek

2007-08-21

3D-Jury, the structure prediction consensus method publicly available in the Meta Server http://meta.bioinfo.pl/, was evaluated using models gathered in the 7th round of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7). 3D-Jury is an automated expert process that generates protein structure meta-predictions from sets of models obtained from partner servers. The performance of 3D-Jury was analysed for three aspects. First, we examined the correlation between the 3D-Jury score and a model quality measure: the number of correctly predicted residues. The 3D-Jury score was shown to correlate significantly with the number of correctly predicted residues, the correlation is good enough to be used for prediction. 3D-Jury was also found to improve upon the competing servers' choice of the best structure model in most cases. The value of the 3D-Jury score as a generic reliability measure was also examined. We found that the 3D-Jury score separates bad models from good models better than the reliability score of the original server in 27 cases and falls short of it in only 5 cases out of a total of 38. We report the release of a new Meta Server feature: instant 3D-Jury scoring of uploaded user models. The 3D-Jury score continues to be a good indicator of structural model quality. It also provides a generic reliability score, especially important for models that were not assigned such by the original server. Individual structure modellers can also benefit from the 3D-Jury scoring system by testing their models in the new instant scoring feature http://meta.bioinfo.pl/compare_your_model_example.pl available in the Meta Server.

Evaluation of 3D-Jury on CASP7 models

PubMed Central

Kaján, László; Rychlewski, Leszek

2007-01-01

Background 3D-Jury, the structure prediction consensus method publicly available in the Meta Server , was evaluated using models gathered in the 7th round of the Critical Assessment of Techniques for Protein Structure Prediction (CASP7). 3D-Jury is an automated expert process that generates protein structure meta-predictions from sets of models obtained from partner servers. Results The performance of 3D-Jury was analysed for three aspects. First, we examined the correlation between the 3D-Jury score and a model quality measure: the number of correctly predicted residues. The 3D-Jury score was shown to correlate significantly with the number of correctly predicted residues, the correlation is good enough to be used for prediction. 3D-Jury was also found to improve upon the competing servers' choice of the best structure model in most cases. The value of the 3D-Jury score as a generic reliability measure was also examined. We found that the 3D-Jury score separates bad models from good models better than the reliability score of the original server in 27 cases and falls short of it in only 5 cases out of a total of 38. We report the release of a new Meta Server feature: instant 3D-Jury scoring of uploaded user models. Conclusion The 3D-Jury score continues to be a good indicator of structural model quality. It also provides a generic reliability score, especially important for models that were not assigned such by the original server. Individual structure modellers can also benefit from the 3D-Jury scoring system by testing their models in the new instant scoring feature available in the Meta Server. PMID:17711571

Automatic prediction of facial trait judgments: appearance vs. structural models.

PubMed

Rojas, Mario; Masip, David; Todorov, Alexander; Vitria, Jordi

2011-01-01

Evaluating other individuals with respect to personality characteristics plays a crucial role in human relations and it is the focus of attention for research in diverse fields such as psychology and interactive computer systems. In psychology, face perception has been recognized as a key component of this evaluation system. Multiple studies suggest that observers use face information to infer personality characteristics. Interactive computer systems are trying to take advantage of these findings and apply them to increase the natural aspect of interaction and to improve the performance of interactive computer systems. Here, we experimentally test whether the automatic prediction of facial trait judgments (e.g. dominance) can be made by using the full appearance information of the face and whether a reduced representation of its structure is sufficient. We evaluate two separate approaches: a holistic representation model using the facial appearance information and a structural model constructed from the relations among facial salient points. State of the art machine learning methods are applied to a) derive a facial trait judgment model from training data and b) predict a facial trait value for any face. Furthermore, we address the issue of whether there are specific structural relations among facial points that predict perception of facial traits. Experimental results over a set of labeled data (9 different trait evaluations) and classification rules (4 rules) suggest that a) prediction of perception of facial traits is learnable by both holistic and structural approaches; b) the most reliable prediction of facial trait judgments is obtained by certain type of holistic descriptions of the face appearance; and c) for some traits such as attractiveness and extroversion, there are relationships between specific structural features and social perceptions.

Binding site and affinity prediction of general anesthetics to protein targets using docking.

PubMed

Liu, Renyu; Perez-Aguilar, Jose Manuel; Liang, David; Saven, Jeffery G

2012-05-01

The protein targets for general anesthetics remain unclear. A tool to predict anesthetic binding for potential binding targets is needed. In this study, we explored whether a computational method, AutoDock, could serve as such a tool. High-resolution crystal data of water-soluble proteins (cytochrome C, apoferritin, and human serum albumin), and a membrane protein (a pentameric ligand-gated ion channel from Gloeobacter violaceus [GLIC]) were used. Isothermal titration calorimetry (ITC) experiments were performed to determine anesthetic affinity in solution conditions for apoferritin. Docking calculations were performed using DockingServer with the Lamarckian genetic algorithm and the Solis and Wets local search method (http://www.dockingserver.com/web). Twenty general anesthetics were docked into apoferritin. The predicted binding constants were compared with those obtained from ITC experiments for potential correlations. In the case of apoferritin, details of the binding site and their interactions were compared with recent cocrystallization data. Docking calculations for 6 general anesthetics currently used in clinical settings (isoflurane, sevoflurane, desflurane, halothane, propofol, and etomidate) with known 50% effective concentration (EC(50)) values were also performed in all tested proteins. The binding constants derived from docking experiments were compared with known EC(50) values and octanol/water partition coefficients for the 6 general anesthetics. All 20 general anesthetics docked unambiguously into the anesthetic binding site identified in the crystal structure of apoferritin. The binding constants for 20 anesthetics obtained from the docking calculations correlate significantly with those obtained from ITC experiments (P = 0.04). In the case of GLIC, the identified anesthetic binding sites in the crystal structure are among the docking predicted binding sites, but not the top ranked site. Docking calculations suggest a most probable binding site located in the extracellular domain of GLIC. The predicted affinities correlated significantly with the known EC(50) values for the 6 frequently used anesthetics in GLIC for the site identified in the experimental crystal data (P = 0.006). However, predicted affinities in apoferritin, human serum albumin, and cytochrome C did not correlate with these 6 anesthetics' known experimental EC(50) values. A weak correlation between the predicted affinities and the octanol/water partition coefficients was observed for the sites in GLIC. We demonstrated that anesthetic binding sites and relative affinities can be predicted using docking calculations in an automatic docking server (AutoDock) for both water-soluble and membrane proteins. Correlation of predicted affinity and EC(50) for 6 frequently used general anesthetics was only observed in GLIC, a member of a protein family relevant to anesthetic mechanism.

Overview of T.E.S.T. (Toxicity Estimation Software Tool)

EPA Science Inventory

This talk provides an overview of T.E.S.T. (Toxicity Estimation Software Tool). T.E.S.T. predicts toxicity values and physical properties using a variety of different QSAR (quantitative structure activity relationship) approaches including hierarchical clustering, group contribut...

High pressure phase transitions in tetrahedrally coordinated semiconducting compounds

NASA Technical Reports Server (NTRS)

Yu, S. C.; Spain, I. L.; Skelton, E. F.

1978-01-01

New experimental results are reported for structural transitions at high pressure in several III-V compounds and two II-VI compounds. These data, together with earlier results, are then compared with the predictions of model calculations of Van Vechten. Experimental transition pressures are often at variance with calculated values. However, his calculation assumes that the high pressure phase is metallic, with the beta-Sn structure. The present results show that several compounds assume an ionic NaCl structure at high pressure, while others have neither the beta-Sn nor NaCl structure.

Artificial neural network analysis based on genetic algorithm to predict the performance characteristics of a cross flow cooling tower

NASA Astrophysics Data System (ADS)

Wu, Jiasheng; Cao, Lin; Zhang, Guoqiang

2018-02-01

Cooling tower of air conditioning has been widely used as cooling equipment, and there will be broad application prospect if it can be reversibly used as heat source under heat pump heating operation condition. In view of the complex non-linear relationship of each parameter in the process of heat and mass transfer inside tower, In this paper, the BP neural network model based on genetic algorithm optimization (GABP neural network model) is established for the reverse use of cross flow cooling tower. The model adopts the structure of 6 inputs, 13 hidden nodes and 8 outputs. With this model, the outlet air dry bulb temperature, wet bulb temperature, water temperature, heat, sensible heat ratio and heat absorbing efficiency, Lewis number, a total of 8 the proportion of main performance parameters were predicted. Furthermore, the established network model is used to predict the water temperature and heat absorption of the tower at different inlet temperatures. The mean relative error MRE between BP predicted value and experimental value are 4.47%, 3.63%, 2.38%, 3.71%, 6.35%,3.14%, 13.95% and 6.80% respectively; the mean relative error MRE between GABP predicted value and experimental value are 2.66%, 3.04%, 2.27%, 3.02%, 6.89%, 3.17%, 11.50% and 6.57% respectively. The results show that the prediction results of GABP network model are better than that of BP network model; the simulation results are basically consistent with the actual situation. The GABP network model can well predict the heat and mass transfer performance of the cross flow cooling tower.

Probable flood predictions in ungauged coastal basins of El Salvador

USGS Publications Warehouse

Friedel, M.J.; Smith, M.E.; Chica, A.M.E.; Litke, D.

2008-01-01

A regionalization procedure is presented and used to predict probable flooding in four ungauged coastal river basins of El Salvador: Paz, Jiboa, Grande de San Miguel, and Goascoran. The flood-prediction problem is sequentially solved for two regions: upstream mountains and downstream alluvial plains. In the upstream mountains, a set of rainfall-runoff parameter values and recurrent peak-flow discharge hydrographs are simultaneously estimated for 20 tributary-basin models. Application of dissimilarity equations among tributary basins (soft prior information) permitted development of a parsimonious parameter structure subject to information content in the recurrent peak-flow discharge values derived using regression equations based on measurements recorded outside the ungauged study basins. The estimated joint set of parameter values formed the basis from which probable minimum and maximum peak-flow discharge limits were then estimated revealing that prediction uncertainty increases with basin size. In the downstream alluvial plain, model application of the estimated minimum and maximum peak-flow hydrographs facilitated simulation of probable 100-year flood-flow depths in confined canyons and across unconfined coastal alluvial plains. The regionalization procedure provides a tool for hydrologic risk assessment and flood protection planning that is not restricted to the case presented herein. ?? 2008 ASCE.

Developing predictive models for toxicity of organic chemicals to green algae based on mode of action.

PubMed

Bakire, Serge; Yang, Xinya; Ma, Guangcai; Wei, Xiaoxuan; Yu, Haiying; Chen, Jianrong; Lin, Hongjun

2018-01-01

Organic chemicals in the aquatic ecosystem may inhibit algae growth and subsequently lead to the decline of primary productivity. Growth inhibition tests are required for ecotoxicological assessments for regulatory purposes. In silico study is playing an important role in replacing or reducing animal tests and decreasing experimental expense due to its efficiency. In this work, a series of theoretical models was developed for predicting algal growth inhibition (log EC 50 ) after 72 h exposure to diverse chemicals. In total 348 organic compounds were classified into five modes of toxic action using the Verhaar Scheme. Each model was established by using molecular descriptors that characterize electronic and structural properties. The external validation and leave-one-out cross validation proved the statistical robustness of the derived models. Thus they can be used to predict log EC 50 values of chemicals that lack authorized algal growth inhibition values (72 h). This work systematically studied algal growth inhibition according to toxic modes and the developed model suite covers all five toxic modes. The outcome of this research will promote toxic mechanism analysis and be made applicable to structural diversity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Nonlinear model predictive control of a vortex-induced vibrations bladeless wind turbine

NASA Astrophysics Data System (ADS)

Azadi Yazdi, E.

2018-07-01

In this paper, a nonlinear model predictive controller (NMPC) is proposed for a vortex-induced vibrations bladeless wind turbine (BWT). The BWT consists of a long rigid cylinder mounted on a flexible beam. The nonlinear dynamic model of the transverse vibrations of the BWT is obtained under the fluctuating lift force due to periodically shedding vortices. The NMPC method is used to design a controller that achieves maximum energy production rate. It is observed that the power generation of the NMPC drops in high wind speeds due to a mismatch between the vortex shedding frequency and the structural natural frequency. Therefore, a secondary gain-scheduling (GS) controller is proposed to virtually increase the natural frequency of the structure to match the vortex shedding frequency for high winds. Although previous studies predicted the output power of the studied BWT to be less than 100 W, with the proposed GS-NMPC scheme the output power reaches the value of 1 kW. Therefore, the capability of the BWT as a renewable energy generation device was highly underestimated in the literature. The computed values of the aero-mechanical efficiency suggest the BWT as a major competitor to the conventional wind turbines.

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

PubMed

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

2018-05-25

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

Adverse Pregnancy Outcomes after Abnormal First Trimester Screening for Aneuploidy

PubMed Central

Goetzl, Laura

2010-01-01

Women with abnormal first trimester screening but with a normal karyotype are at risk for adverse pregnancy outcomes. A nuchal translucency >3.5mm is associated with an increased risk of subsequent pregnancy loss, fetal infection, fetal heart abnormalities and other structural abnormalities. Abnormal first trimester analytes are also associated with adverse pregnancy outcomes but the predictive value is less impressive. As a single marker, PAPP-A <1st%ile has a good predictive value for subsequent fetal growth restriction. Women with PAPP-A<5th%ile should undergo subsequent risk assessment with routine MSAFP screening with the possible addition of uterine artery PI assessment in the midtrimester. PMID:20638576

Electro-mechanical Properties of Carbon Nanotubes

NASA Technical Reports Server (NTRS)

Anantram, M. P.; Yang, Liu; Han, Jie; Liu, J. P.; Saubum Subhash (Technical Monitor)

1998-01-01

We present a simple picture to understand the bandgap variation of carbon nanotubes with small tensile and torsional strains, independent of chirality. Using this picture, we are able to predict a simple dependence of d(Bandoap)$/$d(strain) on the value of $(N_x-N_y)*mod 3$, for semiconducting tubes. We also predict a novel change in sign of d(Bandgap)$/$d(strain) as a function of tensile strain arising from a change in the value of $q$ corresponding to the minimum bandgap. These calculations are complemented by calculations of the change in bandgap using energy minimized structures, and some important differences are discussed. The calculations are based on the $i$ electron approximation.

Design of a nano-layered tunable optical filter

NASA Astrophysics Data System (ADS)

Banerjee, A.; Awasthi, S. K.; Malaviya, U.; Ojha, S. P.

2006-12-01

A novel theory to design tunable band pass filters using one-dimensional nano-photonic structures is proposed. Periodic structures consisting of different dielectrics and semiconductor materials are considered. A detailed mathematical analysis is presented to predict allowed and forbidden bands of wavelengths with variation of angle of incidence and lattice parameters. It is possible to get desired ranges of the electromagnetic spectrum filtered with this structure by changing the incidence angle of light and/or changing the value of the lattice parameters.

QSAR, docking and ADMET studies of artemisinin derivatives for antimalarial activity targeting plasmepsin II, a hemoglobin-degrading enzyme from P. falciparum.

PubMed

Qidwai, Tabish; Yadav, Dharmendra K; Khan, Feroz; Dhawan, Sangeeta; Bhakuni, R S

2012-01-01

This work presents the development of quantitative structure activity relationship (QSAR) model to predict the antimalarial activity of artemisinin derivatives. The structures of the molecules are represented by chemical descriptors that encode topological, geometric, and electronic structure features. Screening through QSAR model suggested that compounds A24, A24a, A53, A54, A62 and A64 possess significant antimalarial activity. Linear model is developed by the multiple linear regression method to link structures to their reported antimalarial activity. The correlation in terms of regression coefficient (r(2)) was 0.90 and prediction accuracy of model in terms of cross validation regression coefficient (rCV(2)) was 0.82. This study indicates that chemical properties viz., atom count (all atoms), connectivity index (order 1, standard), ring count (all rings), shape index (basic kappa, order 2), and solvent accessibility surface area are well correlated with antimalarial activity. The docking study showed high binding affinity of predicted active compounds against antimalarial target Plasmepsins (Plm-II). Further studies for oral bioavailability, ADMET and toxicity risk assessment suggest that compound A24, A24a, A53, A54, A62 and A64 exhibits marked antimalarial activity comparable to standard antimalarial drugs. Later one of the predicted active compound A64 was chemically synthesized, structure elucidated by NMR and in vivo tested in multidrug resistant strain of Plasmodium yoelii nigeriensis infected mice. The experimental results obtained agreed well with the predicted values.

Organic particulate matter formation at varying relative humidity using surrogate secondary and primary organic compounds with activity corrections in the condensed phase obtained using a method based on the Wilson equation

NASA Astrophysics Data System (ADS)

Chang, E. I.; Pankow, J. F.

2010-06-01

Secondary organic aerosol (SOA) formation in the atmosphere is currently often modeled using a multiple lumped "two-product" (N·2p) approach. The N·2p approach neglects: 1) variation of activity coefficient (ζi) values and mean molecular weight MW in the particulate matter (PM) phase; 2) water uptake into the PM; and 3) the possibility of phase separation in the PM. This study considers these effects by adopting an (N·2p)ζpMW,ζ approach (θ is a phase index). Specific chemical structures are assigned to 25 lumped SOA compounds and to 15 representative primary organic aerosol (POA) compounds to allow calculation of ζi and MW values. The SOA structure assignments are based on chamber-derived 2p gas/particle partition coefficient values coupled with known effects of structure on vapor pressure pL,io (atm). To facilitate adoption of the (N·2p)ζpMW,θ approach in large-scale models, this study also develops CP-Wilson.1 (Chang-Pankow-Wilson.1), a group-contribution ζi-prediction method that is more computationally economical than the UNIFAC model of Fredenslund et al. (1975). Group parameter values required by CP-Wilson.1 are obtained by fitting ζi values to predictions from UNIFAC. The (N·2p)ζpMW,θ approach is applied (using CP-Wilson.1) to several real α-pinene/O3 chamber cases for high reacted hydrocarbon levels (ΔHC≈400 to 1000 μg m-3) when relative humidity (RH) ≍50%. Good agreement between the chamber and predicted results is obtained using both the (N·2p)ζpMW,θ and N·2p approaches, indicating relatively small water effects under these conditions. However, for a hypothetical α-pinene/O3 case at ΔHC=30 μg m-3 and RH=50%, the (N·2p)ζpMW,θ approach predicts that water uptake will lead to an organic PM level that is more double that predicted by the N·2p approach. Adoption of the (N·2p)ζpMW,θ approach using reasonable lumped structures for SOA and POA compounds is recommended for ambient PM modeling.

Surface protonation at the rutile (110) interface: explicit incorporation of solvation structure within the refined MUSIC model framework.

PubMed

Machesky, Michael L; Predota, Milan; Wesolowski, David J; Vlcek, Lukas; Cummings, Peter T; Rosenqvist, Jörgen; Ridley, Moira K; Kubicki, James D; Bandura, Andrei V; Kumar, Nitin; Sofo, Jorge O

2008-11-04

The detailed solvation structure at the (110) surface of rutile (alpha-TiO2) in contact with bulk liquid water has been obtained primarily from experimentally verified classical molecular dynamics (CMD) simulations of the ab initio-optimized surface in contact with SPC/E water. The results are used to explicitly quantify H-bonding interactions, which are then used within the refined MUSIC model framework to predict surface oxygen protonation constants. Quantum mechanical molecular dynamics (QMD) simulations in the presence of freely dissociable water molecules produced H-bond distributions around deprotonated surface oxygens very similar to those obtained by CMD with nondissociable SPC/E water, thereby confirming that the less computationally intensive CMD simulations provide accurate H-bond information. Utilizing this H-bond information within the refined MUSIC model, along with manually adjusted Ti-O surface bond lengths that are nonetheless within 0.05 A of those obtained from static density functional theory (DFT) calculations and measured in X-ray reflectivity experiments (as well as bulk crystal values), give surface protonation constants that result in a calculated zero net proton charge pH value (pHznpc) at 25 degrees C that agrees quantitatively with the experimentally determined value (5.4+/-0.2) for a specific rutile powder dominated by the (110) crystal face. Moreover, the predicted pHznpc values agree to within 0.1 pH unit with those measured at all temperatures between 10 and 250 degrees C. A slightly smaller manual adjustment of the DFT-derived Ti-O surface bond lengths was sufficient to bring the predicted pHznpcvalue of the rutile (110) surface at 25 degrees C into quantitative agreement with the experimental value (4.8+/-0.3) obtained from a polished and annealed rutile (110) single crystal surface in contact with dilute sodium nitrate solutions using second harmonic generation (SHG) intensity measurements as a function of ionic strength. Additionally, the H-bond interactions between protolyzable surface oxygen groups and water were found to be stronger than those between bulk water molecules at all temperatures investigated in our CMD simulations (25, 150 and 250 degrees C). Comparison with the protonation scheme previously determined for the (110) surface of isostructural cassiterite (alpha-SnO2) reveals that the greater extent of H-bonding on the latter surface, and in particular between water and the terminal hydroxyl group (Sn-OH) results in the predicted protonation constant for that group being lower than for the bridged oxygen (Sn-O-Sn), while the reverse is true for the rutile (110) surface. These results demonstrate the importance of H-bond structure in dictating surface protonation behavior, and that explicit use of this solvation structure within the refined MUSIC model framework results in predicted surface protonation constants that are also consistent with a variety of other experimental and computational data.

Prediction of Water Binding to Protein Hydration Sites with a Discrete, Semiexplicit Solvent Model.

PubMed

Setny, Piotr

2015-12-08

Buried water molecules are ubiquitous in protein structures and are found at the interface of most protein-ligand complexes. Determining their distribution and thermodynamic effect is a challenging yet important task, of great of practical value for the modeling of biomolecular structures and their interactions. In this study, we present a novel method aimed at the prediction of buried water molecules in protein structures and estimation of their binding free energies. It is based on a semiexplicit, discrete solvation model, which we previously introduced in the context of small molecule hydration. The method is applicable to all macromolecular structures described by a standard all-atom force field, and predicts complete solvent distribution within a single run with modest computational cost. We demonstrate that it indicates positions of buried hydration sites, including those filled by more than one water molecule, and accurately differentiates them from sterically accessible to water but void regions. The obtained estimates of water binding free energies are in fair agreement with reference results determined with the double decoupling method.

Design and prediction of new acetylcholinesterase inhibitor via quantitative structure activity relationship of huprines derivatives.

PubMed

Zhang, Shuqun; Hou, Bo; Yang, Huaiyu; Zuo, Zhili

2016-05-01

Acetylcholinesterase (AChE) is an important enzyme in the pathogenesis of Alzheimer's disease (AD). Comparative quantitative structure-activity relationship (QSAR) analyses on some huprines inhibitors against AChE were carried out using comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR) methods. Three highly predictive QSAR models were constructed successfully based on the training set. The CoMFA, CoMSIA, and HQSAR models have values of r (2) = 0.988, q (2) = 0.757, ONC = 6; r (2) = 0.966, q (2) = 0.645, ONC = 5; and r (2) = 0.957, q (2) = 0.736, ONC = 6. The predictabilities were validated using an external test sets, and the predictive r (2) values obtained by the three models were 0.984, 0.973, and 0.783, respectively. The analysis was performed by combining the CoMFA and CoMSIA field distributions with the active sites of the AChE to further understand the vital interactions between huprines and the protease. On the basis of the QSAR study, 14 new potent molecules have been designed and six of them are predicted to be more active than the best active compound 24 described in the literature. The final QSAR models could be helpful in design and development of novel active AChE inhibitors.

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

NASA Astrophysics Data System (ADS)

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

2014-11-01

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

Quantitative structure-activation barrier relationship modeling for Diels-Alder ligations utilizing quantum chemical structural descriptors.

PubMed

Nandi, Sisir; Monesi, Alessandro; Drgan, Viktor; Merzel, Franci; Novič, Marjana

2013-10-30

In the present study, we show the correlation of quantum chemical structural descriptors with the activation barriers of the Diels-Alder ligations. A set of 72 non-catalysed Diels-Alder reactions were subjected to quantitative structure-activation barrier relationship (QSABR) under the framework of theoretical quantum chemical descriptors calculated solely from the structures of diene and dienophile reactants. Experimental activation barrier data were obtained from literature. Descriptors were computed using Hartree-Fock theory using 6-31G(d) basis set as implemented in Gaussian 09 software. Variable selection and model development were carried out by stepwise multiple linear regression methodology. Predictive performance of the quantitative structure-activation barrier relationship (QSABR) model was assessed by training and test set concept and by calculating leave-one-out cross-validated Q2 and predictive R2 values. The QSABR model can explain and predict 86.5% and 80% of the variances, respectively, in the activation energy barrier training data. Alternatively, a neural network model based on back propagation of errors was developed to assess the nonlinearity of the sought correlations between theoretical descriptors and experimental reaction barriers. A reasonable predictability for the activation barrier of the test set reactions was obtained, which enabled an exploration and interpretation of the significant variables responsible for Diels-Alder interaction between dienes and dienophiles. Thus, studies in the direction of QSABR modelling that provide efficient and fast prediction of activation barriers of the Diels-Alder reactions turn out to be a meaningful alternative to transition state theory based computation.

Damage tolerance and arrest characteristics of pressurized graphite/epoxy tape cylinders

NASA Technical Reports Server (NTRS)

Ranniger, Claudia U.; Lagace, Paul A.; Graves, Michael J.

1993-01-01

An investigation of the damage tolerance and damage arrest characteristics of internally-pressurized graphite/epoxy tape cylinders with axial notches was conducted. An existing failure prediction methodology, developed and verified for quasi-isotropic graphite/epoxy fabric cylinders, was investigated for applicability to general tape layups. In addition, the effect of external circumferential stiffening bands on the direction of fracture path propagation and possible damage arrest was examined. Quasi-isotropic (90/0/plus or minus 45)s and structurally anisotropic (plus or minus 45/0)s and (plus or minus 45/90)s coupons and cylinders were constructed from AS4/3501-6 graphite/epoxy tape. Notched and unnotched coupons were tested in tension and the data correlated using the equation of Mar and Lin. Cylinders with through-thickness axial slits were pressurized to failure achieving a far-field two-to-one biaxial stress state. Experimental failure pressures of the (90/0/plus or minus 45)s cylinders agreed with predicted values for all cases but the specimen with the smallest slit. However, the failure pressures of the structurally anisotropic cylinders, (plus or minus 45/0)s and (plus or minus 45/90)s, were above the values predicted utilizing the predictive methodology in all cases. Possible factors neglected by the predictive methodology include structural coupling in the laminates and axial loading of the cylindrical specimens. Furthermore, applicability of the predictive methodology depends on the similarity of initial fracture modes in the coupon specimens and the cylinder specimens of the same laminate type. The existence of splitting which may be exacerbated by the axial loading in the cylinders, shows that this condition is not always met. The circumferential stiffeners were generally able to redirect fracture propagation from longitudinal to circumferential. A quantitative assessment for stiffener effectiveness in containing the fracture, based on cylinder radius, slit size, and bending stiffnesses of the laminates, is proposed.

Z = 50 core stability in 110Sn from magnetic-moment and lifetime measurements

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

Kumbartzki, G. J.; Benczer-Koller, N.; Speidel, K. -H.

2016-04-18

In this study, the structure of the semimagic Sn50 isotopes were previously studied via measurements of B(E2;2 1 + → 0 1 +) and g factors of 2 1 + states. The values of the B(E2;2 1 +) in the isotopes below midshell at N = 66 show an enhancement in collectivity, contrary to predictions from shell-model calculations. This work presents the first measurement of the 2 1 + and 4 1 + states' magnetic moments in the unstable neutron-deficient 110Sn. The g factors provide complementary structure information to the interpretation of the observed B(E2) values.

Density functional theory studies on molecular structure and vibrational spectra of NLO crystal L-phenylalanine phenylalanium nitrate for THz application

NASA Astrophysics Data System (ADS)

Amalanathan, M.; Hubert Joe, I.; Rastogi, V. K.

2011-12-01

Molecular structure, FT-IR and Raman spectra of L-phenylalanine phenylalanium nitrate have been investigated using density functional theory calculation. The polarizability and hyperpolarizability value of the crystal is also calculated. Natural bond orbital analysis confirms the presence of intramolecular charge transfer and the hydrogen bonding interaction. Simultaneous activation of ring C sbnd C stretching modes shows the non-centrosymmetric symmetry. Terahertz time-domain spectroscopy has been used to detect the absorption spectra in the frequency range from 0.05 to 1.3 THz. Theoretically predicted β value exhibits the high nonlinear optical activity.

What is geological entropy and why measure it? A parsimonious approach for predicting transport behaviour in heterogeneous aquifers

NASA Astrophysics Data System (ADS)

Bianchi, Marco; Pedretti, Daniele

2017-04-01

We present an approach to predict non-Fickian transport behaviour in alluvial aquifers from knowledge of physical heterogeneity. This parsimonious approach is based on only two measurable parameters describing the global variability and the structure of the hydraulic conductivity (K) field: the variance of the ln(K) values (σY 2), and a newly developed index of geological entropy (HR), based on the concept of Shannon information entropy. Both σY 2 and HR can be obtained from data collected during conventional hydrogeological investigations and from the analysis of a representative model of the spatial distribution of K classes (e.g. hydrofacies) over the domain of interest. The new index HR integrates multiple characteristics of the K field, including the presence of well-connected features, into a unique metric that quantifies the degrees of spatial disorder in the K field structure. Stochastic simulations of tracer tests in synthetic K fields based on realistic distributions of hydrofacies in alluvial aquifers are conducted to identify empirical relations between HR, σY 2, and the first three central temporal moments of the resulting breakthrough curves (BTCs). Results indicate that the first and second moments tend to increase with spatial disorder (i.e, HR increasing). Conversely, high values of the third moment (i.e. skewness), which indicate significant post-peak tailing in the BTCs and non-Fickian transport behaviour, are observed in more orderly structures (i.e, HR decreasing), or for very high σY 2 values. We show that simple closed-form empirical expressions can be derived to describe the bivariate dependency between the skewness of the BTC and corresponding pairs of HR and σY 2. This dependency shows clear correlation for a broad range of structures and Kvariability levels. Therefore, it provides an effective and broadly applicable approach to explain and predict non-Fickian transport in real aquifers, such as those at the well-known MADE site and at the Lawrence Livermore National Laboratory.

Puzzle of magnetic moments of Ni clusters revisited using quantum Monte Carlo method.

PubMed

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

2017-02-28

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

Predicting Real-Valued Protein Residue Fluctuation Using FlexPred.

PubMed

Peterson, Lenna; Jamroz, Michal; Kolinski, Andrzej; Kihara, Daisuke

2017-01-01

The conventional view of a protein structure as static provides only a limited picture. There is increasing evidence that protein dynamics are often vital to protein function including interaction with partners such as other proteins, nucleic acids, and small molecules. Considering flexibility is also important in applications such as computational protein docking and protein design. While residue flexibility is partially indicated by experimental measures such as the B-factor from X-ray crystallography and ensemble fluctuation from nuclear magnetic resonance (NMR) spectroscopy as well as computational molecular dynamics (MD) simulation, these techniques are resource-intensive. In this chapter, we describe the web server and stand-alone version of FlexPred, which rapidly predicts absolute per-residue fluctuation from a three-dimensional protein structure. On a set of 592 nonredundant structures, comparing the fluctuations predicted by FlexPred to the observed fluctuations in MD simulations showed an average correlation coefficient of 0.669 and an average root mean square error of 1.07 Å. FlexPred is available at http://kiharalab.org/flexPred/ .

QSPR model for bioconcentration factors of nonpolar organic compounds using molecular electronegativity distance vector descriptors.

PubMed

Qin, Li-Tang; Liu, Shu-Shen; Liu, Hai-Ling

2010-02-01

A five-variable model (model M2) was developed for the bioconcentration factors (BCFs) of nonpolar organic compounds (NPOCs) by using molecular electronegativity distance vector (MEDV) to characterize the structures of NPOCs and variable selection and modeling based on prediction (VSMP) to select the optimum descriptors. The estimated correlation coefficient (r (2)) and the leave-one-out cross-validation correlation coefficients (q (2)) of model M2 were 0.9271 and 0.9171, respectively. The model was externally validated by splitting the whole data set into a representative training set of 85 chemicals and a validation set of 29 chemicals. The results show that the main structural factors influencing the BCFs of NPOCs are -cCc, cCcc, -Cl, and -Br (where "-" refers to a single bond and "c" refers to a conjugated bond). The quantitative structure-property relationship (QSPR) model can effectively predict the BCFs of NPOCs, and the predictions of the model can also extend the current BCF database of experimental values.

QSAR Methods.

PubMed

Gini, Giuseppina

2016-01-01

In this chapter, we introduce the basis of computational chemistry and discuss how computational methods have been extended to some biological properties and toxicology, in particular. Since about 20 years, chemical experimentation is more and more replaced by modeling and virtual experimentation, using a large core of mathematics, chemistry, physics, and algorithms. Then we see how animal experiments, aimed at providing a standardized result about a biological property, can be mimicked by new in silico methods. Our emphasis here is on toxicology and on predicting properties through chemical structures. Two main streams of such models are available: models that consider the whole molecular structure to predict a value, namely QSAR (Quantitative Structure Activity Relationships), and models that find relevant substructures to predict a class, namely SAR. The term in silico discovery is applied to chemical design, to computational toxicology, and to drug discovery. We discuss how the experimental practice in biological science is moving more and more toward modeling and simulation. Such virtual experiments confirm hypotheses, provide data for regulation, and help in designing new chemicals.

Combining first-principles and data modeling for the accurate prediction of the refractive index of organic polymers

NASA Astrophysics Data System (ADS)

Afzal, Mohammad Atif Faiz; Cheng, Chong; Hachmann, Johannes

2018-06-01

Organic materials with a high index of refraction (RI) are attracting considerable interest due to their potential application in optic and optoelectronic devices. However, most of these applications require an RI value of 1.7 or larger, while typical carbon-based polymers only exhibit values in the range of 1.3-1.5. This paper introduces an efficient computational protocol for the accurate prediction of RI values in polymers to facilitate in silico studies that can guide the discovery and design of next-generation high-RI materials. Our protocol is based on the Lorentz-Lorenz equation and is parametrized by the polarizability and number density values of a given candidate compound. In the proposed scheme, we compute the former using first-principles electronic structure theory and the latter using an approximation based on van der Waals volumes. The critical parameter in the number density approximation is the packing fraction of the bulk polymer, for which we have devised a machine learning model. We demonstrate the performance of the proposed RI protocol by testing its predictions against the experimentally known RI values of 112 optical polymers. Our approach to combine first-principles and data modeling emerges as both a successful and a highly economical path to determining the RI values for a wide range of organic polymers.

Protein single-model quality assessment by feature-based probability density functions.

PubMed

Cao, Renzhi; Cheng, Jianlin

2016-04-04

Protein quality assessment (QA) has played an important role in protein structure prediction. We developed a novel single-model quality assessment method-Qprob. Qprob calculates the absolute error for each protein feature value against the true quality scores (i.e. GDT-TS scores) of protein structural models, and uses them to estimate its probability density distribution for quality assessment. Qprob has been blindly tested on the 11th Critical Assessment of Techniques for Protein Structure Prediction (CASP11) as MULTICOM-NOVEL server. The official CASP result shows that Qprob ranks as one of the top single-model QA methods. In addition, Qprob makes contributions to our protein tertiary structure predictor MULTICOM, which is officially ranked 3rd out of 143 predictors. The good performance shows that Qprob is good at assessing the quality of models of hard targets. These results demonstrate that this new probability density distribution based method is effective for protein single-model quality assessment and is useful for protein structure prediction. The webserver of Qprob is available at: http://calla.rnet.missouri.edu/qprob/. The software is now freely available in the web server of Qprob.

Resistance Predictions for a High-Speed Sealift Trimaran

DTIC Science & Technology

2007-10-01

layout and operability, rather than de- sults for both the absolute value and the relative tails of resistance and structural strength. The pre- change...interferences between the circumstances, the sidehulls may be referred to as sidehulls and the centerhull, which can create an outriggers , adverse effect (that...uration 4 and Configuration 5; as a result, an exces- .. sive amount of spray impacted on the bridging cross structure connecting the three subhulls

Track Structure and the Biological Effectiveness of Accelerated Particles for the Induction of Chromosome Damage

NASA Technical Reports Server (NTRS)

George, K.; Hada, M.; Chappell, L.; Cucinotta, F. A.

2011-01-01

Track structure models predict that at a fixed value of LET, particles with lower charge number, Z will have a higher biological effectiveness compared to particles with a higher Z. In this report we investigated how track structure effects induction of chromosomal aberration in human cells. Human lymphocytes were irradiated in vitro with various energies of accelerated iron, silicon, neon, or titanium ions and chromosome damage was assessed in using three color FISH chromosome painting in chemically induced PCC samples collected a first cell division post irradiation. The LET values for these ions ranged from 30 to195 keV/micron. Of the particles studied, Neon ions have the highest biological effectiveness for induction of total chromosome damage, which is consistent with track structure model predictions. For complex-type exchanges 64 MeV/ u Neon and 450 MeV/u Iron were equally effective and induced the most complex damage. In addition we present data on chromosomes exchanges induced by six different energies of protons (5 MeV/u to 2.5 GeV/u). The linear dose response term was similar for all energies of protons suggesting that the effect of the higher LET at low proton energies is balanced by the production of nuclear secondaries from the high energy protons.

Prediction Of pKa From Chemical Structure Using Free And Open-Source Tools

EPA Science Inventory

The ionization state of a chemical, reflected in pKa values, affects lipophilicity, solubility, protein binding and the ability of a chemical to cross the plasma membrane. These properties govern the pharmacokinetic parameters such as absorption, distribution, metabolism, excreti...

Machine Learning Techniques for Prediction of Early Childhood Obesity.

PubMed

Dugan, T M; Mukhopadhyay, S; Carroll, A; Downs, S

2015-01-01

This paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA. Analyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created. Of the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model. This study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.

Studying the properties of a predicted tetragonal silicon by first principles

NASA Astrophysics Data System (ADS)

Xue, Han-Yu; Zhang, Can; Pang, Dong-Dong; Huang, Xue-Qian; Lv, Zhen-Long; Duan, Man-Yi

2018-03-01

Silicon is a very important material in many technological fields. It also has a complicated phase diagram of scientific interest. Here we reported a new allotrope of silicon obtained from crystal structure prediction. We studied its electronic, vibrational, dielectric, elastic and hardness properties by first-principles calculations. The results indicate that it is an indirect narrow-band-gap semiconductor. It is dynamically stable with a doubly degenerate infrared-active mode at its Brillouin zone center. Born effective charges of the constituent element are very small, resulting in a negligible ionic dielectric contribution. Calculated elasticity-related quantities imply that it is mechanically stable but anisotropic. There exist slowly increasing stages in the stress-strain curves of this crystal, which make it difficult to estimate the hardness of the crystal by calculating its ideal strengths. Taking advantage of the hardness model proposed by Šimůnek, we obtained a value of 12.0 GPa as its hardness. This value is lower than that of the cubic diamond-structural Si by about 5.5%.

Residue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds.

PubMed

Simkovic, Felix; Thomas, Jens M H; Keegan, Ronan M; Winn, Martyn D; Mayans, Olga; Rigden, Daniel J

2016-07-01

For many protein families, the deluge of new sequence information together with new statistical protocols now allow the accurate prediction of contacting residues from sequence information alone. This offers the possibility of more accurate ab initio (non-homology-based) structure prediction. Such models can be used in structure solution by molecular replacement (MR) where the target fold is novel or is only distantly related to known structures. Here, AMPLE, an MR pipeline that assembles search-model ensembles from ab initio structure predictions ('decoys'), is employed to assess the value of contact-assisted ab initio models to the crystallographer. It is demonstrated that evolutionary covariance-derived residue-residue contact predictions improve the quality of ab initio models and, consequently, the success rate of MR using search models derived from them. For targets containing β-structure, decoy quality and MR performance were further improved by the use of a β-strand contact-filtering protocol. Such contact-guided decoys achieved 14 structure solutions from 21 attempted protein targets, compared with nine for simple Rosetta decoys. Previously encountered limitations were superseded in two key respects. Firstly, much larger targets of up to 221 residues in length were solved, which is far larger than the previously benchmarked threshold of 120 residues. Secondly, contact-guided decoys significantly improved success with β-sheet-rich proteins. Overall, the improved performance of contact-guided decoys suggests that MR is now applicable to a significantly wider range of protein targets than were previously tractable, and points to a direct benefit to structural biology from the recent remarkable advances in sequencing.

Residue contacts predicted by evolutionary covariance extend the application of ab initio molecular replacement to larger and more challenging protein folds

PubMed Central

Simkovic, Felix; Thomas, Jens M. H.; Keegan, Ronan M.; Winn, Martyn D.; Mayans, Olga; Rigden, Daniel J.

2016-01-01

For many protein families, the deluge of new sequence information together with new statistical protocols now allow the accurate prediction of contacting residues from sequence information alone. This offers the possibility of more accurate ab initio (non-homology-based) structure prediction. Such models can be used in structure solution by molecular replacement (MR) where the target fold is novel or is only distantly related to known structures. Here, AMPLE, an MR pipeline that assembles search-model ensembles from ab initio structure predictions (‘decoys’), is employed to assess the value of contact-assisted ab initio models to the crystallographer. It is demonstrated that evolutionary covariance-derived residue–residue contact predictions improve the quality of ab initio models and, consequently, the success rate of MR using search models derived from them. For targets containing β-structure, decoy quality and MR performance were further improved by the use of a β-strand contact-filtering protocol. Such contact-guided decoys achieved 14 structure solutions from 21 attempted protein targets, compared with nine for simple Rosetta decoys. Previously encountered limitations were superseded in two key respects. Firstly, much larger targets of up to 221 residues in length were solved, which is far larger than the previously benchmarked threshold of 120 residues. Secondly, contact-guided decoys significantly improved success with β-sheet-rich proteins. Overall, the improved performance of contact-guided decoys suggests that MR is now applicable to a significantly wider range of protein targets than were previously tractable, and points to a direct benefit to structural biology from the recent remarkable advances in sequencing. PMID:27437113

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.

Sensitivity and specificity of cone beam computed tomography in thin bony structures in maxillofacial surgery – A clinical trial.

PubMed

Thönissen, P; Ermer, M A; Schmelzeisen, R; Gutwald, R; Metzger, M C; Bittermann, G

2015-09-01

Cone-Beam Computed Tomography (CBCT) has become widely used in dentistry and maxillofacial surgery. Accuracy, sensitivity and specificity of thin bony structures below 0.5 mm have been subject of some in vitro studies. This prospective in vivo study investigates the correlation between preoperative CBCT-imaging and intraoperative clinical examination of thin bony structures. We hereby present results from daily clinical routine. A total number of 80 sites in 64 patients has been examined to differentiate between preoperative 3D imaging and clinical measurements on cystic lesions in maxilla and mandible. Different CBCT-devices with a voxel size ranging from 0.08 mm to 0.4 mm were used. Overall-specificity found for detecting thin bony structures of the human jaw is 13.89%, overall sensitivity is 100%, positive predictive value (PPV) is 58.67% and negative predictive value (NPV) is 100%. Image quality is the key to make use of additional information CBCT provides and depends on spatial, temporal and contrast resolution. CBCT does not depict reliably thin bony structures of the jaw, even if high voxel resolution is used. In selected cases using high resolution protocols should be considered despite affecting the patient with higher doses of radiation. Copyright © 2015 European Association for Cranio-Maxillo-Facial Surgery. Published by Elsevier Ltd. All rights reserved.

Prediction of genotoxic potential of cosmetic ingredients by an in silico battery system consisting of a combination of an expert rule-based system and a statistics-based system.

PubMed

Aiba née Kaneko, Maki; Hirota, Morihiko; Kouzuki, Hirokazu; Mori, Masaaki

2015-02-01

Genotoxicity is the most commonly used endpoint to predict the carcinogenicity of chemicals. The International Conference on Harmonization (ICH) M7 Guideline on Assessment and Control of DNA Reactive (Mutagenic) Impurities in Pharmaceuticals to Limit Potential Carcinogenic Risk offers guidance on (quantitative) structure-activity relationship ((Q)SAR) methodologies that predict the outcome of bacterial mutagenicity assay for actual and potential impurities. We examined the effectiveness of the (Q)SAR approach with the combination of DEREK NEXUS as an expert rule-based system and ADMEWorks as a statistics-based system for the prediction of not only mutagenic potential in the Ames test, but also genotoxic potential in mutagenicity and clastogenicity tests, using a data set of 342 chemicals extracted from the literature. The prediction of mutagenic potential or genotoxic potential by DEREK NEXUS or ADMEWorks showed high values of sensitivity and concordance, while prediction by the combination of DEREK NEXUS and ADMEWorks (battery system) showed the highest values of sensitivity and concordance among the three methods, but the lowest value of specificity. The number of false negatives was reduced with the battery system. We also separately predicted the mutagenic potential and genotoxic potential of 41 cosmetic ingredients listed in the International Nomenclature of Cosmetic Ingredients (INCI) among the 342 chemicals. Although specificity was low with the battery system, sensitivity and concordance were high. These results suggest that the battery system consisting of DEREK NEXUS and ADMEWorks is useful for prediction of genotoxic potential of chemicals, including cosmetic ingredients.

Testing the hypothesis of hierarchical predictability in ecological restoration and succession.

PubMed

Abella, Scott R; Schetter, Timothy A; Walters, Timothy L

2018-02-01

To advance predictive ecology, the hypothesis of hierarchical predictability proposes that community measures for which species are interchangeable (e.g., structure and species richness) are more predictable than measures for which species identity matters (e.g., community composition). Predictability is hypothesized to decrease for response measures in order of the following categories: structure, species richness, function, and species composition. We tested this hypothesis using a 14-year, oak savanna-prairie restoration experiment that removed non-native pine plantations at 24 sites in northwestern Ohio, USA. Based on 24 response measures, the data showed minimal support for the hypothesis, because response measures varied in predictability within categories. Half of response measures had over half their variability modeled using fixed (restoration treatment and year) and random plot effects, and these "predictable" measures occurred in all four categories. Pine basal area, environment (e.g., soil texture), and antecedent vegetation accounted for over half the variation in change within the first three post-restoration years for 77% of response measures. Change between the 3rd and 14th years was less predictable, but most restoration measures increased favorably via sites achieving them in unique ways. We propose that variation will not conform with the hypothesis of hierarchical predictability in ecosystems with vegetation dynamics driven by stochastic processes such as seed dispersal, or where vegetation structure and species richness are influenced by species composition. The ability to predict a community measure may be more driven by the number of combinations of casual factors affecting a measure than by the number of values it can have.

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

PubMed

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

2014-02-01

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

Predicting who will major in a science discipline: Expectancy-value theory as part of an ecological model for studying academic communities

NASA Astrophysics Data System (ADS)

Sullins, Ellen S.; Hernandez, Delia; Fuller, Carol; Shiro Tashiro, Jay

Research on factors that shape recruitment and retention in undergraduate science majors currently is highly fragmented and in need of an integrative research framework. Such a framework should incorporate analyses of the various levels of organization that characterize academic communities (i.e., the broad institutional level, the departmental level, and the student level), and should also provide ways to study the interactions occurring within and between these structural levels. We propose that academic communities are analogous to ecosystems, and that the research paradigms of modern community ecology can provide the necessary framework, as well as new and innovative approaches to a very complex area. This article also presents the results of a pilot study that demonstrates the promise of this approach at the student level. We administered a questionnaire based on expectancy-value theory to undergraduates enrolled in introductory biology courses. Itself an integrative approach, expectancy-value theory views achievement-related behavior as a joint function of the person's expectancy of success in the behavior and the subjective value placed on such success. Our results indicated: (a) significant gender differences in the underlying factor structures of expectations and values related to the discipline of biology, (b) expectancy-value factors significantly distinguished biology majors from nonmajors, and (c) expectancy-value factors significantly predicted students' intent to enroll in future biology courses. We explore the expectancy-value framework as an operationally integrative framework in our ecological model for studying academic communities, especially in the context of assessing the underrepresentation of women and minorities in the sciences. Future research directions as well as practical implications are also discussed.

Statistical Analysis of the Ionosphere based on Singular Value Decomposition

NASA Astrophysics Data System (ADS)

Demir, Uygar; Arikan, Feza; Necat Deviren, M.; Toker, Cenk

2016-07-01

Ionosphere is made up of a spatio-temporally varying trend structure and secondary variations due to solar, geomagnetic, gravitational and seismic activities. Hence, it is important to monitor the ionosphere and acquire up-to-date information about its state in order both to better understand the physical phenomena that cause the variability and also to predict the effect of the ionosphere on HF and satellite communications, and satellite-based positioning systems. To charaterise the behaviour of the ionosphere, we propose to apply Singular Value Decomposition (SVD) to Total Electron Content (TEC) maps obtained from the TNPGN-Active (Turkish National Permanent GPS Network) CORS network. TNPGN-Active network consists of 146 GNSS receivers spread over Turkey. IONOLAB-TEC values estimated from each station are spatio-temporally interpolated using a Universal Kriging based algorithm with linear trend, namely IONOLAB-MAP, with very high spatial resolution. It is observed that the dominant singular value of TEC maps is an indicator of the trend structure of the ionosphere. The diurnal, seasonal and annual variability of the most dominant value is the representation of solar effect on ionosphere in midlatitude range. Secondary and smaller singular values are indicators of secondary variation which can have significance especially during geomagnetic storms or seismic disturbances. The dominant singular values are related to the physical basis vectors where ionosphere can be fully reconstructed using these vectors. Therefore, the proposed method can be used both for the monitoring of the current state of a region and also for the prediction and tracking of future states of ionosphere using singular values and singular basis vectors. This study is supported by by TUBITAK 115E915 and Joint TUBITAK 114E092 and AS CR14/001 projects.

An empirical potential for simulating vacancy clusters in tungsten.

PubMed

Mason, D R; Nguyen-Manh, D; Becquart, C S

2017-12-20

We present an empirical interatomic potential for tungsten, particularly well suited for simulations of vacancy-type defects. We compare energies and structures of vacancy clusters generated with the empirical potential with an extensive new database of values computed using density functional theory, and show that the new potential predicts low-energy defect structures and formation energies with high accuracy. A significant difference to other popular embedded-atom empirical potentials for tungsten is the correct prediction of surface energies. Interstitial properties and short-range pairwise behaviour remain similar to the Ackford-Thetford potential on which it is based, making this potential well-suited to simulations of microstructural evolution following irradiation damage cascades. Using atomistic kinetic Monte Carlo simulations, we predict vacancy cluster dissociation in the range 1100-1300 K, the temperature range generally associated with stage IV recovery.

Using CV-GLUE procedure in analysis of wetland model predictive uncertainty.

PubMed

Huang, Chun-Wei; Lin, Yu-Pin; Chiang, Li-Chi; Wang, Yung-Chieh

2014-07-01

This study develops a procedure that is related to Generalized Likelihood Uncertainty Estimation (GLUE), called the CV-GLUE procedure, for assessing the predictive uncertainty that is associated with different model structures with varying degrees of complexity. The proposed procedure comprises model calibration, validation, and predictive uncertainty estimation in terms of a characteristic coefficient of variation (characteristic CV). The procedure first performed two-stage Monte-Carlo simulations to ensure predictive accuracy by obtaining behavior parameter sets, and then the estimation of CV-values of the model outcomes, which represent the predictive uncertainties for a model structure of interest with its associated behavior parameter sets. Three commonly used wetland models (the first-order K-C model, the plug flow with dispersion model, and the Wetland Water Quality Model; WWQM) were compared based on data that were collected from a free water surface constructed wetland with paddy cultivation in Taipei, Taiwan. The results show that the first-order K-C model, which is simpler than the other two models, has greater predictive uncertainty. This finding shows that predictive uncertainty does not necessarily increase with the complexity of the model structure because in this case, the more simplistic representation (first-order K-C model) of reality results in a higher uncertainty in the prediction made by the model. The CV-GLUE procedure is suggested to be a useful tool not only for designing constructed wetlands but also for other aspects of environmental management. Copyright © 2014 Elsevier Ltd. All rights reserved.

Decoupling effect and forecasting of economic growth and energy structure under the peak constraint of carbon emissions in China.

PubMed

Wang, Shijin; Li, Cunfang; Yang, Lizhu

2018-06-26

The decoupling effect between economic growth and energy structure was quantitatively analyzed from 1999 to 2014 across China. The results showed it existed weak decoupling effects in most regions. Based on the analysis of the influence of energy structure on carbon intensity, using scenario simulation methods and Markov chain modeling, the carbon intensity was predicted for China in 2020. The impact of energy structure adjustment on the carbon intensity to meet China's carbon target by 18 possible scenarios are calculated. Furthermore, the peak value of carbon emissions was also calculated in 2030. The results showed that the carbon intensity predicted for China in 2020 can be achieved regardless of whether the energy structure was adjusted or not when energy saving and carbon reduction policies maintained with economic growth at 6-7%. Moreover, given fixed energy structure growth, for each 1% of economic growth, the carbon intensity will decrease by about 3.5%. Given fixed economic growth, the decrease of energy intensity will be greater if the control of energy consumption is stronger. The effect of energy structure adjustment on the decreasing of carbon intensity will be 4% higher under constraints than without constraints. On average, the contribution of energy structure adjustment to achieving the carbon intensity target was calculated as 4% higher than that with constraints. In addition, given relatively fixed economic growth at 6-7%, the peak value of carbon emission in 2030 was calculated as 13.209 billion tons with constraints and 14.38 billion tons without constraints.

A Cognitive-Social Model of Fertility Intentions

PubMed Central

Bachrach, Christine A.; Morgan, S. Philip

2014-01-01

We examine the use and value of fertility intentions against the backdrop of theory and research in the cognitive and social sciences. First, we draw on recent brain and cognition research to contextualize fertility intentions within a broader set of conscious and unconscious mechanisms that contribute to mental function. Next, we integrate this research with social theory. Our conceptualizations suggest that people do not necessarily have fertility intentions; they form them only when prompted by specific situations. Intention formation draws on the current situation and on schemas of childbearing and parenthood learned through previous experience, imbued by affect, and organized by self-representation. Using this conceptualization, we review apparently discordant knowledge about the value of fertility intentions in predicting fertility. Our analysis extends and deepens existing explanations for the weak predictive validity of fertility intentions at the individual level and provides a social-cognitive explanation for why intentions predict as well as they do. When focusing on the predictive power of intentions at the aggregate level, our conceptualizations lead us to focus on how social structures frustrate or facilitate intentions and how the structural environment contributes to the formation of reported intentions in the first place. Our analysis suggests that existing measures of fertility intentions are useful but to varying extents and in many cases despite their failure to capture what they seek to measure. PMID:25132695

Improved predictions of atmospheric icing in Norway

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Predicting gaseous reaction rates of short chain chlorinated paraffins with ·OH: overcoming the difficulty in experimental determination.

PubMed

Li, Chao; Xie, Hong-Bin; Chen, Jingwen; Yang, Xianhai; Zhang, Yifei; Qiao, Xianliang

2014-12-02

Short chain chlorinated paraffins (SCCPs) are under evaluation for inclusion in the Stockholm Convention on persistent organic pollutants. However, information on their reaction rate constants with gaseous ·OH (kOH) is unavailable, limiting the evaluation of their persistence in the atmosphere. Experimental determination of kOH is confined by the unavailability of authentic chemical standards for some SCCP congeners. In this study, we evaluated and selected density functional theory (DFT) methods to predict kOH of SCCPs, by comparing the experimental kOH values of six polychlorinated alkanes (PCAs) with those calculated by the different theoretical methods. We found that the M06-2X/6-311+G(3df,2pd)//B3LYP/6-311 +G(d,p) method is time-effective and can be used to predict kOH of PCAs. Moreover, based on the calculated kOH of nine SCCPs and available experimental kOH values of 22 PCAs with low carbon chain, a quantitative structure-activity relationship (QSAR) model was developed. The molecular structural characteristics determining the ·OH reaction rate were discussed. logkOH was found to negatively correlate with the percentage of chlorine substitutions (Cl%). The DFT calculation method and the QSAR model are important alternatives to the conventional experimental determination of kOH for SCCPs, and are prospective in predicting their persistence in the atmosphere.

Prediction of local proximal tibial subchondral bone structural stiffness using subject-specific finite element modeling: Effect of selected density-modulus relationship.

PubMed

Nazemi, S Majid; Amini, Morteza; Kontulainen, Saija A; Milner, Jaques S; Holdsworth, David W; Masri, Bassam A; Wilson, David R; Johnston, James D

2015-08-01

Quantitative computed tomography based subject-specific finite element modeling has potential to clarify the role of subchondral bone alterations in knee osteoarthritis initiation, progression, and pain initiation. Calculation of bone elastic moduli from image data is a basic step when constructing finite element models. However, different relationships between elastic moduli and imaged density (known as density-modulus relationships) have been reported in the literature. The objective of this study was to apply seven different trabecular-specific and two cortical-specific density-modulus relationships from the literature to finite element models of proximal tibia subchondral bone, and identify the relationship(s) that best predicted experimentally measured local subchondral structural stiffness with highest explained variance and least error. Thirteen proximal tibial compartments were imaged via quantitative computed tomography. Imaged bone mineral density was converted to elastic moduli using published density-modulus relationships and mapped to corresponding finite element models. Proximal tibial structural stiffness values were compared to experimentally measured stiffness values from in-situ macro-indentation testing directly on the subchondral bone surface (47 indentation points). Regression lines between experimentally measured and finite element calculated stiffness had R(2) values ranging from 0.56 to 0.77. Normalized root mean squared error varied from 16.6% to 337.6%. Of the 21 evaluated density-modulus relationships in this study, Goulet combined with Snyder and Schneider or Rho appeared most appropriate for finite element modeling of local subchondral bone structural stiffness. Though, further studies are needed to optimize density-modulus relationships and improve finite element estimates of local subchondral bone structural stiffness. Copyright © 2015 Elsevier Ltd. All rights reserved.

Protein charge ladders reveal that the net charge of ALS-linked superoxide dismutase can be different in sign and magnitude from predicted values

PubMed Central

Shi, Yunhua; Abdolvahabi, Alireza; Shaw, Bryan F

2014-01-01

This article utilized “protein charge ladders”—chemical derivatives of proteins with similar structure, but systematically altered net charge—to quantify how missense mutations that cause amyotrophic lateral sclerosis (ALS) affect the net negative charge (Z) of superoxide dismutase-1 (SOD1) as a function of subcellular pH and Zn2+ stoichiometry. Capillary electrophoresis revealed that the net charge of ALS-variant SOD1 can be different in sign and in magnitude—by up to 7.4 units per dimer at lysosomal pH—than values predicted from standard pKa values of amino acids and formal oxidation states of metal ions. At pH 7.4, the G85R, D90A, and G93R substitutions diminished the net negative charge of dimeric SOD1 by up to +2.29 units more than predicted; E100K lowered net charge by less than predicted. The binding of a single Zn2+ to mutant SOD1 lowered its net charge by an additional +2.33 ± 0.01 to +3.18 ± 0.02 units, however, each protein regulated net charge when binding a second, third, or fourth Zn2+ (ΔZ < 0.44 ± 0.07 per additional Zn2+). Both metalated and apo-SOD1 regulated net charge across subcellular pH, without inverting from negative to positive at the theoretical pI. Differential scanning calorimetry, hydrogen-deuterium exchange, and inductively coupled plasma mass spectrometry confirmed that the structure, stability, and metal content of mutant proteins were not significantly affected by lysine acetylation. Measured values of net charge should be used when correlating the biophysical properties of a specific ALS-variant SOD1 protein with its observed aggregation propensity or clinical phenotype. PMID:25052939

Asymmetric bagging and feature selection for activities prediction of drug molecules.

PubMed

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

2008-05-28

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

Factors influencing protein tyrosine nitration – structure-based predictive models

PubMed Central

Bayden, Alexander S.; Yakovlev, Vasily A.; Graves, Paul R.; Mikkelsen, Ross B.; Kellogg, Glen E.

2010-01-01

Models for exploring tyrosine nitration in proteins have been created based on 3D structural features of 20 proteins for which high resolution X-ray crystallographic or NMR data are available and for which nitration of 35 total tyrosines has been experimentally proven under oxidative stress. Factors suggested in previous work to enhance nitration were examined with quantitative structural descriptors. The role of neighboring acidic and basic residues is complex: for the majority of tyrosines that are nitrated the distance to the heteroatom of the closest charged sidechain corresponds to the distance needed for suspected nitrating species to form hydrogen bond bridges between the tyrosine and that charged amino acid. This suggests that such bridges play a very important role in tyrosine nitration. Nitration is generally hindered for tyrosines that are buried and for those tyrosines where there is insufficient space for the nitro group. For in vitro nitration, closed environments with nearby heteroatoms or unsaturated centers that can stabilize radicals are somewhat favored. Four quantitative structure-based models, depending on the conditions of nitration, have been developed for predicting site-specific tyrosine nitration. The best model, relevant for both in vitro and in vivo cases predicts 30 of 35 tyrosine nitrations (positive predictive value) and has a sensitivity of 60/71 (11 false positives). PMID:21172423

Three-dimensional quantitative structure-activity relationship analysis for human pregnane X receptor for the prediction of CYP3A4 induction in human hepatocytes: structure-based comparative molecular field analysis.

PubMed

Handa, Koichi; Nakagome, Izumi; Yamaotsu, Noriyuki; Gouda, Hiroaki; Hirono, Shuichi

2015-01-01

The pregnane X receptor [PXR (NR1I2)] induces the expression of xenobiotic metabolic genes and transporter genes. In this study, we aimed to establish a computational method for quantifying the enzyme-inducing potencies of different compounds via their ability to activate PXR, for the application in drug discovery and development. To achieve this purpose, we developed a three-dimensional quantitative structure-activity relationship (3D-QSAR) model using comparative molecular field analysis (CoMFA) for predicting enzyme-inducing potencies, based on computer-ligand docking to multiple PXR protein structures sampled from the trajectory of a molecular dynamics simulation. Molecular mechanics-generalized born/surface area scores representing the ligand-protein-binding free energies were calculated for each ligand. As a result, the predicted enzyme-inducing potencies for compounds generated by the CoMFA model were in good agreement with the experimental values. Finally, we concluded that this 3D-QSAR model has the potential to predict the enzyme-inducing potencies of novel compounds with high precision and therefore has valuable applications in the early stages of the drug discovery process. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

Synthesis, spectroscopic and electrochemical characterization of secnidazole esters

NASA Astrophysics Data System (ADS)

Shahid, Hafiz Abdullah; Jahangir, Sajid; Hanif, Muddasir; Xiong, Tianrou; Muhammad, Haji; Wahid, Sana; Yousuf, Sammer; Qureshi, Naseem

2017-12-01

We report a low-cost, less toxic to environment and simple method for the esterification of secnidazole. This is first comprehensive structural characterization of novel secnidazole esters by the spectroscopic and electrochemical methods. The important EIMS fragmentation analysis showed unique contribution of heteroatom bonds explained by the fragmentation patterns. These peaks originate from the loss of single electron, loss of HCN, M-O, M-NO, M-NO2, M-C7H10N3O3, and M-C8H10N3O4. The comparison of 13C NMR predicted values with the experimental values showed that ChemBioDraw Ultra 14.0 has advantage of predicting aromatic (sp2) carbons, while MestReNova 6.1 predicts sp3 hybrid carbons more accurately. The electrochemical properties indicated an irreversible oxidation process and reversible reduction process in these ester molecules similar to the parent secnidazole.

Gaussian covariance graph models accounting for correlated marker effects in genome-wide prediction.

PubMed

Martínez, C A; Khare, K; Rahman, S; Elzo, M A

2017-10-01

Several statistical models used in genome-wide prediction assume uncorrelated marker allele substitution effects, but it is known that these effects may be correlated. In statistics, graphical models have been identified as a useful tool for covariance estimation in high-dimensional problems and it is an area that has recently experienced a great expansion. In Gaussian covariance graph models (GCovGM), the joint distribution of a set of random variables is assumed to be Gaussian and the pattern of zeros of the covariance matrix is encoded in terms of an undirected graph G. In this study, methods adapting the theory of GCovGM to genome-wide prediction were developed (Bayes GCov, Bayes GCov-KR and Bayes GCov-H). In simulated data sets, improvements in correlation between phenotypes and predicted breeding values and accuracies of predicted breeding values were found. Our models account for correlation of marker effects and permit to accommodate general structures as opposed to models proposed in previous studies, which consider spatial correlation only. In addition, they allow incorporation of biological information in the prediction process through its use when constructing graph G, and their extension to the multi-allelic loci case is straightforward. © 2017 Blackwell Verlag GmbH.

Effects of the microbial siderophore DFO-B on Pb and Cd speciation in aqueous solution.

PubMed

Mishra, Bhoopesh; Haack, Elizabeth A; Maurice, Patricia A; Bunker, Bruce A

2009-01-01

This study investigates the complexation environments of aqueous Pb and Cd in the presence of the trihydroxamate microbial siderophore, desferrioxamine-B (DFO-B) as a function of pH. Complexation of aqueous Pb and Cd with DFO-B was predicted using equilibrium speciation calculation. Synchrotron-based X-ray absorption fine structure (XAFS) spectroscopy at Pb L(III) edge and Cd K edge was used to characterize Pb and Cd-DFO-B complexes at pH values predicted to best represent each of the metal-siderophore complexes. Pb was not found to be complexed measurably by DFO-B at pH 3.0, but was complexed by all three hydroxamate groups to form a totally "caged" hexadentate structure at pH 7.5-9.0. At the intermediate pH value (pH 4.8), a mixture of Pb-DFOB complexes involving binding of the metal through one and two hydroxamate groups was observed. Cd, on the other hand, remained as hydrated Cd2+ at pH 5.0, occurred as a mixture of Cd-DFOB and inorganic species at pH 8.0, and was bound by three hydroxamate groups from DFO-B at pH 9.0. Overall, the solution species observed with EXAFS were consistent with those predicted thermodynamically. However, Pb speciation at higher pH values differed from that predicted and suggests that published constants underestimate the binding constant for complexation of Pb with all three hydroxamate groups of the DFO-B ligand. This molecular-level understanding of metal-siderophore solution coordination provides physical evidence for complexes of Pb and Cd with DFO-B, and is an important first step toward understanding processes at the microbial- and/or mineral-water interface in the presence of siderophores.

Toxicity prediction of ionic liquids based on Daphnia magna by using density functional theory

NASA Astrophysics Data System (ADS)

Nu’aim, M. N.; Bustam, M. A.

2018-04-01

By using a model called density functional theory, the toxicity of ionic liquids can be predicted and forecast. It is a theory that allowing the researcher to have a substantial tool for computation of the quantum state of atoms, molecules and solids, and molecular dynamics which also known as computer simulation method. It can be done by using structural feature based quantum chemical reactivity descriptor. The identification of ionic liquids and its Log[EC50] data are from literature data that available in Ismail Hossain thesis entitled “Synthesis, Characterization and Quantitative Structure Toxicity Relationship of Imidazolium, Pyridinium and Ammonium Based Ionic Liquids”. Each cation and anion of the ionic liquids were optimized and calculated. The geometry optimization and calculation from the software, produce the value of highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO). From the value of HOMO and LUMO, the value for other toxicity descriptors were obtained according to their formulas. The toxicity descriptor that involves are electrophilicity index, HOMO, LUMO, energy gap, chemical potential, hardness and electronegativity. The interrelation between the descriptors are being determined by using a multiple linear regression (MLR). From this MLR, all descriptors being analyzed and the descriptors that are significant were chosen. In order to develop the finest model equation for toxicity prediction of ionic liquids, the selected descriptors that are significant were used. The validation of model equation was performed with the Log[EC50] data from the literature and the final model equation was developed. A bigger range of ionic liquids which nearly 108 of ionic liquids can be predicted from this model equation.

Hysteretic energy prediction method for mainshock-aftershock sequences

NASA Astrophysics Data System (ADS)

Zhai, Changhai; Ji, Duofa; Wen, Weiping; Li, Cuihua; Lei, Weidong; Xie, Lili

2018-04-01

Structures located in seismically active regions may be subjected to mainshock-aftershock (MSAS) sequences. Strong aftershocks significantly affect the hysteretic energy demand of structures. The hysteretic energy, E H,seq, is normalized by mass m and expressed in terms of the equivalent velocity, V D,seq, to quantitatively investigate aftershock effects on the hysteretic energy of structures. The equivalent velocity, V D,seq, is computed by analyzing the response time-history of an inelastic single-degree-of-freedom (SDOF) system with a varying vibration period subjected to 309 MSAS sequences. The present study selected two kinds of MSAS sequences, with one aftershock and two aftershocks, respectively. The aftershocks are scaled to maintain different relative intensities. The variation of the equivalent velocity, V D,seq, is studied for consideration of the ductility values, site conditions, relative intensities, number of aftershocks, hysteretic models, and damping ratios. The MSAS sequence with one aftershock exhibited a 10% to 30% hysteretic energy increase, whereas the MSAS sequence with two aftershocks presented a 20% to 40% hysteretic energy increase. Finally, a hysteretic energy prediction equation is proposed as a function of the vibration period, ductility value, and damping ratio to estimate hysteretic energy for mainshock-aftershock sequences.

Local thermodynamic mapping for effective liquid density-functional theory

NASA Technical Reports Server (NTRS)

Kyrlidis, Agathagelos; Brown, Robert A.

1992-01-01

The structural-mapping approximation introduced by Lutsko and Baus (1990) in the generalized effective-liquid approximation is extended to include a local thermodynamic mapping based on a spatially dependent effective density for approximating the solid phase in terms of the uniform liquid. This latter approximation, called the local generalized effective-liquid approximation (LGELA) yields excellent predictions for the free energy of hard-sphere solids and for the conditions of coexistence of a hard-sphere fcc solid with a liquid. Moreover, the predicted free energy remains single valued for calculations with more loosely packed crystalline structures, such as the diamond lattice. The spatial dependence of the weighted density makes the LGELA useful in the study of inhomogeneous solids.

Structure and magnetization of Co4N thin film

NASA Astrophysics Data System (ADS)

Pandey, Nidhi; Gupta, Mukul; Gupta, Rachana; Rajput, Parasmani; Stahn, Jochen

2018-02-01

In this work, we studied the local structure and the magnetization of Co4N thin films deposited by a reactive dc magnetron sputtering process. The interstitial incorporation of N atoms in a fcc Co lattice is expected to expand the structure. This expansion yields interesting magnetic properties e.g. a larger magnetic moment (than Co) and a very high value of spin polarization ratio in Co4N . By optimizing the growth conditions, we prepared Co4N film having lattice parameter close to its theoretically predicted value. The N concentration was measured using secondary ion mass spectroscopy. Detailed magnetization measurements using bulk magnetization method and polarized neutron reflectivity confirm that the magnetic moment of Co in Co4N is higher than that of Co.

Examining the mediational role of psychological flexibility, pain catastrophizing, and visceral sensitivity in the relationship between psychological distress, irritable bowel symptom frequency, and quality of life.

PubMed

Cassar, G E; Knowles, S; Youssef, G J; Moulding, R; Uiterwijk, D; Waters, L; Austin, D W

2018-06-08

The aim of the current study was to use Structural Equation Modelling (SEM) to examine whether psychological flexibility (i.e. mindfulness, acceptance, valued-living) mediates the relationship between distress, irritable bowel syndrome (IBS) symptom frequency, and quality of life (QoL). Ninety-two individuals participated in the study (12 male, 80 female, M age  = 36.24) by completing an online survey including measures of visceral sensitivity, distress, IBS-related QoL, mindfulness, bowel symptoms, pain catastrophizing, acceptance, and valued-living. A final model with excellent fit was identified. Psychological distress significantly and directly predicted pain catastrophizing, valued-living, and IBS symptom frequency. Pain catastrophizing directly predicted visceral sensitivity and acceptance, while visceral sensitivity significantly and directly predicted IBS symptom frequency and QoL. Symptom frequency also had a direct and significant relationship with QoL. The current findings suggest that interventions designed to address unhelpful cognitive processes related to visceral sensitivity, pain catastrophizing, and psychological distress may be of most benefit to IBS-related QoL.

Testing Small Variance Priors Using Prior-Posterior Predictive p Values.

PubMed

Hoijtink, Herbert; van de Schoot, Rens

2017-04-03

Muthén and Asparouhov (2012) propose to evaluate model fit in structural equation models based on approximate (using small variance priors) instead of exact equality of (combinations of) parameters to zero. This is an important development that adequately addresses Cohen's (1994) The Earth is Round (p < .05), which stresses that point null-hypotheses are so precise that small and irrelevant differences from the null-hypothesis may lead to their rejection. It is tempting to evaluate small variance priors using readily available approaches like the posterior predictive p value and the DIC. However, as will be shown, both are not suited for the evaluation of models based on small variance priors. In this article, a well behaving alternative, the prior-posterior predictive p value, will be introduced. It will be shown that it is consistent, the distributions under the null and alternative hypotheses will be elaborated, and it will be applied to testing whether the difference between 2 means and the size of a correlation are relevantly different from zero. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Novel Use of Natural Language Processing (NLP) to Predict Suicidal Ideation and Psychiatric Symptoms in a Text-Based Mental Health Intervention in Madrid.

PubMed

Cook, Benjamin L; Progovac, Ana M; Chen, Pei; Mullin, Brian; Hou, Sherry; Baca-Garcia, Enrique

2016-01-01

Natural language processing (NLP) and machine learning were used to predict suicidal ideation and heightened psychiatric symptoms among adults recently discharged from psychiatric inpatient or emergency room settings in Madrid, Spain. Participants responded to structured mental and physical health instruments at multiple follow-up points. Outcome variables of interest were suicidal ideation and psychiatric symptoms (GHQ-12). Predictor variables included structured items (e.g., relating to sleep and well-being) and responses to one unstructured question, "how do you feel today?" We compared NLP-based models using the unstructured question with logistic regression prediction models using structured data. The PPV, sensitivity, and specificity for NLP-based models of suicidal ideation were 0.61, 0.56, and 0.57, respectively, compared to 0.73, 0.76, and 0.62 of structured data-based models. The PPV, sensitivity, and specificity for NLP-based models of heightened psychiatric symptoms (GHQ-12 ≥ 4) were 0.56, 0.59, and 0.60, respectively, compared to 0.79, 0.79, and 0.85 in structured models. NLP-based models were able to generate relatively high predictive values based solely on responses to a simple general mood question. These models have promise for rapidly identifying persons at risk of suicide or psychological distress and could provide a low-cost screening alternative in settings where lengthy structured item surveys are not feasible.

20180318 - Prediction Of pKa From Chemical Structure Using Free And Open-Source Tools (ACS Spring)

EPA Science Inventory

The ionization state of a chemical, reflected in pKa values, affects lipophilicity, solubility, protein binding and the ability of a chemical to cross the plasma membrane. These properties govern the pharmacokinetic parameters such as absorption, distribution, metabolism, excreti...

77 FR 21721 - Sierra National Forest, Bass Lake Ranger District, California, Whisky Ecosystem Restoration Project

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-11

... to predicted warmer temperatures and longer periods of depleted soil moisture. Stocking levels (stand... ecological processes, biodiversity, wildlife habitat, and structural heterogeneity. The impacts of past... culturally gathered plant material; Protect the historic values and characteristics of archaeological and...

Importance of ligand reorganization free energy in protein-ligand binding-affinity prediction.

PubMed

Yang, Chao-Yie; Sun, Haiying; Chen, Jianyong; Nikolovska-Coleska, Zaneta; Wang, Shaomeng

2009-09-30

Accurate prediction of the binding affinities of small-molecule ligands to their biological targets is fundamental for structure-based drug design but remains a very challenging task. In this paper, we have performed computational studies to predict the binding models of 31 small-molecule Smac (the second mitochondria-derived activator of caspase) mimetics to their target, the XIAP (X-linked inhibitor of apoptosis) protein, and their binding affinities. Our results showed that computational docking was able to reliably predict the binding models, as confirmed by experimentally determined crystal structures of some Smac mimetics complexed with XIAP. However, all the computational methods we have tested, including an empirical scoring function, two knowledge-based scoring functions, and MM-GBSA (molecular mechanics and generalized Born surface area), yield poor to modest prediction for binding affinities. The linear correlation coefficient (r(2)) value between the predicted affinities and the experimentally determined affinities was found to be between 0.21 and 0.36. Inclusion of ensemble protein-ligand conformations obtained from molecular dynamic simulations did not significantly improve the prediction. However, major improvement was achieved when the free-energy change for ligands between their free- and bound-states, or "ligand-reorganization free energy", was included in the MM-GBSA calculation, and the r(2) value increased from 0.36 to 0.66. The prediction was validated using 10 additional Smac mimetics designed and evaluated by an independent group. This study demonstrates that ligand reorganization free energy plays an important role in the overall binding free energy between Smac mimetics and XIAP. This term should be evaluated for other ligand-protein systems and included in the development of new scoring functions. To our best knowledge, this is the first computational study to demonstrate the importance of ligand reorganization free energy for the prediction of protein-ligand binding free energy.

Solute transport in crystalline rocks at Äspö — II: Blind predictions, inverse modelling and lessons learnt from test STT1

NASA Astrophysics Data System (ADS)

Jakob, Andreas; Mazurek, Martin; Heer, Walter

2003-03-01

Based on the results from detailed structural and petrological characterisation and on up-scaled laboratory values for sorption and diffusion, blind predictions were made for the STT1 dipole tracer test performed in the Swedish Äspö Hard Rock Laboratory. The tracers used were nonsorbing, such as uranine and tritiated water, weakly sorbing 22Na +, 85Sr 2+, 47Ca 2+and more strongly sorbing 86Rb +, 133Ba 2+, 137Cs +. Our model consists of two parts: (1) a flow part based on a 2D-streamtube formalism accounting for the natural background flow field and with an underlying homogeneous and isotropic transmissivity field and (2) a transport part in terms of the dual porosity medium approach which is linked to the flow part by the flow porosity. The calibration of the model was done using the data from one single uranine breakthrough (PDT3). The study clearly showed that matrix diffusion into a highly porous material, fault gouge, had to be included in our model evidenced by the characteristic shape of the breakthrough curve and in line with geological observations. After the disclosure of the measurements, it turned out that, in spite of the simplicity of our model, the prediction for the nonsorbing and weakly sorbing tracers was fairly good. The blind prediction for the more strongly sorbing tracers was in general less accurate. The reason for the good predictions is deemed to be the result of the choice of a model structure strongly based on geological observation. The breakthrough curves were inversely modelled to determine in situ values for the transport parameters and to draw consequences on the model structure applied. For good fits, only one additional fracture family in contact with cataclasite had to be taken into account, but no new transport mechanisms had to be invoked. The in situ values for the effective diffusion coefficient for fault gouge are a factor of 2-15 larger than the laboratory data. For cataclasite, both data sets have values comparable to laboratory data. The extracted Kd values for the weakly sorbing tracers are larger than Swedish laboratory data by a factor of 25-60, but agree within a factor of 3-5 for the more strongly sorbing nuclides. The reason for the inconsistency concerning Kds is the use of fresh granite in the laboratory studies, whereas tracers in the field experiments interact only with fracture fault gouge and to a lesser extent with cataclasite both being mineralogically very different (e.g. clay-bearing) from the intact wall rock.

modPDZpep: a web resource for structure based analysis of human PDZ-mediated interaction networks.

PubMed

Sain, Neetu; Mohanty, Debasisa

2016-09-21

PDZ domains recognize short sequence stretches usually present in C-terminal of their interaction partners. Because of the involvement of PDZ domains in many important biological processes, several attempts have been made for developing bioinformatics tools for genome-wide identification of PDZ interaction networks. Currently available tools for prediction of interaction partners of PDZ domains utilize machine learning approach. Since, they have been trained using experimental substrate specificity data for specific PDZ families, their applicability is limited to PDZ families closely related to the training set. These tools also do not allow analysis of PDZ-peptide interaction interfaces. We have used a structure based approach to develop modPDZpep, a program to predict the interaction partners of human PDZ domains and analyze structural details of PDZ interaction interfaces. modPDZpep predicts interaction partners by using structural models of PDZ-peptide complexes and evaluating binding energy scores using residue based statistical pair potentials. Since, it does not require training using experimental data on peptide binding affinity, it can predict substrates for diverse PDZ families. Because of the use of simple scoring function for binding energy, it is also fast enough for genome scale structure based analysis of PDZ interaction networks. Benchmarking using artificial as well as real negative datasets indicates good predictive power with ROC-AUC values in the range of 0.7 to 0.9 for a large number of human PDZ domains. Another novel feature of modPDZpep is its ability to map novel PDZ mediated interactions in human protein-protein interaction networks, either by utilizing available experimental phage display data or by structure based predictions. In summary, we have developed modPDZpep, a web-server for structure based analysis of human PDZ domains. It is freely available at http://www.nii.ac.in/modPDZpep.html or http://202.54.226.235/modPDZpep.html . This article was reviewed by Michael Gromiha and Zoltán Gáspári.

Using discharge data to reduce structural deficits in a hydrological model with a Bayesian inference approach and the implications for the prediction of critical source areas

NASA Astrophysics Data System (ADS)

Frey, M. P.; Stamm, C.; Schneider, M. K.; Reichert, P.

2011-12-01

A distributed hydrological model was used to simulate the distribution of fast runoff formation as a proxy for critical source areas for herbicide pollution in a small agricultural catchment in Switzerland. We tested to what degree predictions based on prior knowledge without local measurements could be improved upon relying on observed discharge. This learning process consisted of five steps: For the prior prediction (step 1), knowledge of the model parameters was coarse and predictions were fairly uncertain. In the second step, discharge data were used to update the prior parameter distribution. Effects of uncertainty in input data and model structure were accounted for by an autoregressive error model. This step decreased the width of the marginal distributions of parameters describing the lower boundary (percolation rates) but hardly affected soil hydraulic parameters. Residual analysis (step 3) revealed model structure deficits. We modified the model, and in the subsequent Bayesian updating (step 4) the widths of the posterior marginal distributions were reduced for most parameters compared to those of the prior. This incremental procedure led to a strong reduction in the uncertainty of the spatial prediction. Thus, despite only using spatially integrated data (discharge), the spatially distributed effect of the improved model structure can be expected to improve the spatially distributed predictions also. The fifth step consisted of a test with independent spatial data on herbicide losses and revealed ambiguous results. The comparison depended critically on the ratio of event to preevent water that was discharged. This ratio cannot be estimated from hydrological data only. The results demonstrate that the value of local data is strongly dependent on a correct model structure. An iterative procedure of Bayesian updating, model testing, and model modification is suggested.

Implications of next generation attenuation ground motion prediction equations for site coefficients used in earthquake resistant design

USGS Publications Warehouse

Borcherdt, Roger D.

2014-01-01

Proposals are developed to update Tables 11.4-1 and 11.4-2 of Minimum Design Loads for Buildings and Other Structures published as American Society of Civil Engineers Structural Engineering Institute standard 7-10 (ASCE/SEI 7–10). The updates are mean next generation attenuation (NGA) site coefficients inferred directly from the four NGA ground motion prediction equations used to derive the maximum considered earthquake response maps adopted in ASCE/SEI 7–10. Proposals include the recommendation to use straight-line interpolation to infer site coefficients at intermediate values of (average shear velocity to 30-m depth). The NGA coefficients are shown to agree well with adopted site coefficients at low levels of input motion (0.1 g) and those observed from the Loma Prieta earthquake. For higher levels of input motion, the majority of the adopted values are within the 95% epistemic-uncertainty limits implied by the NGA estimates with the exceptions being the mid-period site coefficient, Fv, for site class D and the short-period coefficient, Fa, for site class C, both of which are slightly less than the corresponding 95% limit. The NGA data base shows that the median value  of 913 m/s for site class B is more typical than 760 m/s as a value to characterize firm to hard rock sites as the uniform ground condition for future maximum considered earthquake response ground motion estimates. Future updates of NGA ground motion prediction equations can be incorporated easily into future adjustments of adopted site coefficients using procedures presented herein. 

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

Young's Modulus of Wurtzite and Zinc Blende InP Nanowires.

PubMed

Dunaevskiy, Mikhail; Geydt, Pavel; Lähderanta, Erkki; Alekseev, Prokhor; Haggrén, Tuomas; Kakko, Joona-Pekko; Jiang, Hua; Lipsanen, Harri

2017-06-14

The Young's modulus of thin conical InP nanowires with either wurtzite or mixed "zinc blende/wurtzite" structures was measured. It has been shown that the value of Young's modulus obtained for wurtzite InP nanowires (E [0001] = 130 ± 30 GPa) was similar to the theoretically predicted value for the wurtzite InP material (E [0001] = 120 ± 10 GPa). The Young's modulus of mixed "zinc blende/wurtzite" InP nanowires (E [111] = 65 ± 10 GPa) appeared to be 40% less than the theoretically predicted value for the zinc blende InP material (E [111] = 110 GPa). An advanced method for measuring the Young's modulus of thin and flexible nanostructures is proposed. It consists of measuring the flexibility (the inverse of stiffness) profiles 1/k(x) by the scanning probe microscopy with precise control of loading force in nanonewton range followed by simulations.

SeqRate: sequence-based protein folding type classification and rates prediction

PubMed Central

2010-01-01

Background Protein folding rate is an important property of a protein. Predicting protein folding rate is useful for understanding protein folding process and guiding protein design. Most previous methods of predicting protein folding rate require the tertiary structure of a protein as an input. And most methods do not distinguish the different kinetic nature (two-state folding or multi-state folding) of the proteins. Here we developed a method, SeqRate, to predict both protein folding kinetic type (two-state versus multi-state) and real-value folding rate using sequence length, amino acid composition, contact order, contact number, and secondary structure information predicted from only protein sequence with support vector machines. Results We systematically studied the contributions of individual features to folding rate prediction. On a standard benchmark dataset, the accuracy of folding kinetic type classification is 80%. The Pearson correlation coefficient and the mean absolute difference between predicted and experimental folding rates (sec-1) in the base-10 logarithmic scale are 0.81 and 0.79 for two-state protein folders, and 0.80 and 0.68 for three-state protein folders. SeqRate is the first sequence-based method for protein folding type classification and its accuracy of fold rate prediction is improved over previous sequence-based methods. Its performance can be further enhanced with additional information, such as structure-based geometric contacts, as inputs. Conclusions Both the web server and software of predicting folding rate are publicly available at http://casp.rnet.missouri.edu/fold_rate/index.html. PMID:20438647

Ab initio predictions of structural and elastic properties of struvite: contribution to urinary stone research.

PubMed

Piechota, Jacek; Prywer, Jolanta; Torzewska, Agnieszka

2012-01-01

In the present work, we carried out density functional calculations of struvite--the main component of the so-called infectious urinary stones--to study its structural and elastic properties. Using a local density approximation and a generalised gradient approximation, we calculated the equilibrium structural parameters and elastic constants C(ijkl). At present, there is no experimental data for these elastic constants C (ijkl) for comparison. Besides the elastic constants, we also present the calculated macroscopic mechanical parameters, namely the bulk modulus (K), the shear modulus (G) and Young's modulus (E). The values of these moduli are found to be in good agreement with available experimental data. Our results imply that the mechanical stability of struvite is limited by the shear modulus, G. The study also explores the energy-band structure to understand the obtained values of the elastic constants.

Fine- and hyperfine structure investigations of the even-parity configuration system of the atomic holmium

NASA Astrophysics Data System (ADS)

Stefanska, D.; Ruczkowski, J.; Elantkowska, M.; Furmann, B.

2018-04-01

In this work new experimental results concerning the hyperfine structure (hfs) for the even-parity level system of the holmium atom (Ho I) were obtained; additionally, hfs data obtained recently as a by-product in investigations of the odd-parity level system were summarized. In the present work the values of the magnetic dipole and the electric quadrupole hfs constants A and B were determined for 24 even-parity levels, for 14 of them for the first time. On the basis of these results, as well as on available literature data, a parametric study of the fine structure and the hyperfine structure for the even-parity configurations of atomic holmium was performed. A multi-configuration fit of 7 configurations was carried out, taking into account second-order of the perturbation theory. For unknown electronic levels predicted values of the level energies and hfs constants are given, which can facilitate further experimental investigations.

Development of an in Silico Model of DPPH• Free Radical Scavenging Capacity: Prediction of Antioxidant Activity of Coumarin Type Compounds.

PubMed

Goya Jorge, Elizabeth; Rayar, Anita Maria; Barigye, Stephen J; Jorge Rodríguez, María Elisa; Sylla-Iyarreta Veitía, Maité

2016-06-07

A quantitative structure-activity relationship (QSAR) study of the 2,2-diphenyl-l-picrylhydrazyl (DPPH•) radical scavenging ability of 1373 chemical compounds, using DRAGON molecular descriptors (MD) and the neural network technique, a technique based on the multilayer multilayer perceptron (MLP), was developed. The built model demonstrated a satisfactory performance for the training ( R 2 = 0.713 ) and test set ( Q ext 2 = 0.654 ) , respectively. To gain greater insight on the relevance of the MD contained in the MLP model, sensitivity and principal component analyses were performed. Moreover, structural and mechanistic interpretation was carried out to comprehend the relationship of the variables in the model with the modeled property. The constructed MLP model was employed to predict the radical scavenging ability for a group of coumarin-type compounds. Finally, in order to validate the model's predictions, an in vitro assay for one of the compounds (4-hydroxycoumarin) was performed, showing a satisfactory proximity between the experimental and predicted pIC50 values.

Anatomic features of the neck as predictive markers of difficult direct laryngoscopy in men and women: A prospective study

PubMed Central

Chara, Liaskou; Eleftherios, Vouzounerakis; Maria, Moirasgenti; Anastasia, Trikoupi; Chryssoula, Staikou

2014-01-01

Background and Aims: Difficult airway assessment is based on various anatomic parameters of upper airway, much of it being concentrated on oral cavity and the pharyngeal structures. The diagnostic value of tests based on neck anatomy in predicting difficult laryngoscopy was assessed in this prospective, open cohort study. Methods: We studied 341 adult patients scheduled to receive general anaesthesia. Thyromental distance (TMD), sternomental distance (STMD), ratio of height to thyromental distance (RHTMD) and neck circumference (NC) were measured pre-operatively. The laryngoscopic view was classified according to the Cormack–Lehane Grade (1-4). Difficult laryngoscopy was defined as Cormack–Lehane Grade 3 or 4. The optimal cut-off points for each variable were identified by using receiver operating characteristic analysis. Sensitivity, specificity and positive predictive value and negative predictive value (NPV) were calculated for each test. Multivariate analysis with logistic regression, including all variables, was used to create a predictive model. Comparisons between genders were also performed. Results: Laryngoscopy was difficult in 12.6% of the patients. The cut-off values were: TMD ≤7 cm, STMD ≤15 cm, RHTMD >18.4 and NC >37.5 cm. The RHTMD had the highest sensitivity (88.4%) and NPV (95.2%), while TMD had the highest specificity (83.9%). The area under curve (AUC) for the TMD, STMD, RHTMD and NC was 0.63, 0.64, 0.62 and 0.54, respectively. The predictive model exhibited a higher and statistically significant diagnostic accuracy (AUC: 0.68, P < 0.001). Gender-specific cut-off points improved the predictive accuracy of NC in women (AUC: 0.65). Conclusions: The TMD, STMD, RHTMD and NC were found to be poor single predictors of difficult laryngoscopy, while a model including all four variables had a significant predictive accuracy. Among the studied tests, gender-specific cut-off points should be used for NC. PMID:24963183

Anatomic features of the neck as predictive markers of difficult direct laryngoscopy in men and women: A prospective study.

PubMed

Liaskou, Chara; Chara, Liaskou; Vouzounerakis, Eleftherios; Eleftherios, Vouzounerakis; Moirasgenti, Maria; Maria, Moirasgenti; Trikoupi, Anastasia; Anastasia, Trikoupi; Staikou, Chryssoula; Chryssoula, Staikou

2014-03-01

Difficult airway assessment is based on various anatomic parameters of upper airway, much of it being concentrated on oral cavity and the pharyngeal structures. The diagnostic value of tests based on neck anatomy in predicting difficult laryngoscopy was assessed in this prospective, open cohort study. We studied 341 adult patients scheduled to receive general anaesthesia. Thyromental distance (TMD), sternomental distance (STMD), ratio of height to thyromental distance (RHTMD) and neck circumference (NC) were measured pre-operatively. The laryngoscopic view was classified according to the Cormack-Lehane Grade (1-4). Difficult laryngoscopy was defined as Cormack-Lehane Grade 3 or 4. The optimal cut-off points for each variable were identified by using receiver operating characteristic analysis. Sensitivity, specificity and positive predictive value and negative predictive value (NPV) were calculated for each test. Multivariate analysis with logistic regression, including all variables, was used to create a predictive model. Comparisons between genders were also performed. Laryngoscopy was difficult in 12.6% of the patients. The cut-off values were: TMD ≤7 cm, STMD ≤15 cm, RHTMD >18.4 and NC >37.5 cm. The RHTMD had the highest sensitivity (88.4%) and NPV (95.2%), while TMD had the highest specificity (83.9%). The area under curve (AUC) for the TMD, STMD, RHTMD and NC was 0.63, 0.64, 0.62 and 0.54, respectively. The predictive model exhibited a higher and statistically significant diagnostic accuracy (AUC: 0.68, P < 0.001). Gender-specific cut-off points improved the predictive accuracy of NC in women (AUC: 0.65). The TMD, STMD, RHTMD and NC were found to be poor single predictors of difficult laryngoscopy, while a model including all four variables had a significant predictive accuracy. Among the studied tests, gender-specific cut-off points should be used for NC.

Modelling oxygen transfer using dynamic alpha factors.

PubMed

Jiang, Lu-Man; Garrido-Baserba, Manel; Nolasco, Daniel; Al-Omari, Ahmed; DeClippeleir, Haydee; Murthy, Sudhir; Rosso, Diego

2017-11-01

Due to the importance of wastewater aeration in meeting treatment requirements and due to its elevated energy intensity, it is important to describe the real nature of an aeration system to improve design and specification, performance prediction, energy consumption, and process sustainability. Because organic loadings drive aeration efficiency to its lowest value when the oxygen demand (energy) is the highest, the implications of considering their dynamic nature on energy costs are of utmost importance. A dynamic model aimed at identifying conservation opportunities is presented. The model developed describes the correlation between the COD concentration and the α factor in activated sludge. Using the proposed model, the aeration efficiency is calculated as a function of the organic loading (i.e. COD). This results in predictions of oxygen transfer values that are more realistic than the traditional method of assuming constant α values. The model was applied to two water resource recovery facilities, and was calibrated and validated with time-sensitive databases. Our improved aeration model structure increases the quality of prediction of field data through the recognition of the dynamic nature of the alpha factor (α) as a function of the applied oxygen demand. For the cases presented herein, the model prediction of airflow improved by 20-35% when dynamic α is used. The proposed model offers a quantitative tool for the prediction of energy demand and for minimizing aeration design uncertainty. Copyright © 2017 Elsevier Ltd. All rights reserved.

Protein Solvent-Accessibility Prediction by a Stacked Deep Bidirectional Recurrent Neural Network.

PubMed

Zhang, Buzhong; Li, Linqing; Lü, Qiang

2018-05-25

Residue solvent accessibility is closely related to the spatial arrangement and packing of residues. Predicting the solvent accessibility of a protein is an important step to understand its structure and function. In this work, we present a deep learning method to predict residue solvent accessibility, which is based on a stacked deep bidirectional recurrent neural network applied to sequence profiles. To capture more long-range sequence information, a merging operator was proposed when bidirectional information from hidden nodes was merged for outputs. Three types of merging operators were used in our improved model, with a long short-term memory network performing as a hidden computing node. The trained database was constructed from 7361 proteins extracted from the PISCES server using a cut-off of 25% sequence identity. Sequence-derived features including position-specific scoring matrix, physical properties, physicochemical characteristics, conservation score and protein coding were used to represent a residue. Using this method, predictive values of continuous relative solvent-accessible area were obtained, and then, these values were transformed into binary states with predefined thresholds. Our experimental results showed that our deep learning method improved prediction quality relative to current methods, with mean absolute error and Pearson's correlation coefficient values of 8.8% and 74.8%, respectively, on the CB502 dataset and 8.2% and 78%, respectively, on the Manesh215 dataset.

Improved RMR Rock Mass Classification Using Artificial Intelligence Algorithms

NASA Astrophysics Data System (ADS)

Gholami, Raoof; Rasouli, Vamegh; Alimoradi, Andisheh

2013-09-01

Rock mass classification systems such as rock mass rating (RMR) are very reliable means to provide information about the quality of rocks surrounding a structure as well as to propose suitable support systems for unstable regions. Many correlations have been proposed to relate measured quantities such as wave velocity to rock mass classification systems to limit the associated time and cost of conducting the sampling and mechanical tests conventionally used to calculate RMR values. However, these empirical correlations have been found to be unreliable, as they usually overestimate or underestimate the RMR value. The aim of this paper is to compare the results of RMR classification obtained from the use of empirical correlations versus machine-learning methodologies based on artificial intelligence algorithms. The proposed methods were verified based on two case studies located in northern Iran. Relevance vector regression (RVR) and support vector regression (SVR), as two robust machine-learning methodologies, were used to predict the RMR for tunnel host rocks. RMR values already obtained by sampling and site investigation at one tunnel were taken into account as the output of the artificial networks during training and testing phases. The results reveal that use of empirical correlations overestimates the predicted RMR values. RVR and SVR, however, showed more reliable results, and are therefore suggested for use in RMR classification for design purposes of rock structures.

Measuring Sperm DNA Fragmentation and Clinical Outcomes of Medically Assisted Reproduction: A Systematic Review and Meta-Analysis.

PubMed

Cissen, Maartje; Wely, Madelon van; Scholten, Irma; Mansell, Steven; Bruin, Jan Peter de; Mol, Ben Willem; Braat, Didi; Repping, Sjoerd; Hamer, Geert

2016-01-01

Sperm DNA fragmentation has been associated with reduced fertilization rates, embryo quality, pregnancy rates and increased miscarriage rates. Various methods exist to test sperm DNA fragmentation such as the sperm chromatin structure assay (SCSA), the sperm chromatin dispersion (SCD) test, the terminal deoxynucleotidyl transferase mediated deoxyuridine triphosphate nick end labelling (TUNEL) assay and the single cell gel electrophoresis (Comet) assay. We performed a systematic review and meta-analysis to assess the value of measuring sperm DNA fragmentation in predicting chance of ongoing pregnancy with IVF or ICSI. Out of 658 unique studies, 30 had extractable data and were thus included in the meta-analysis. Overall, the sperm DNA fragmentation tests had a reasonable to good sensitivity. A wide variety of other factors may also affect the IVF/ICSI outcome, reflected by limited to very low specificity. The constructed hierarchical summary receiver operating characteristic (HSROC) curve indicated a fair discriminatory capacity of the TUNEL assay (area under the curve (AUC) of 0.71; 95% CI 0.66 to 0.74) and Comet assay (AUC of 0.73; 95% CI 0.19 to 0.97). The SCSA and the SCD test had poor predictive capacity. Importantly, for the TUNEL assay, SCD test and Comet assay, meta-regression showed no differences in predictive value between IVF and ICSI. For the SCSA meta-regression indicated the predictive values for IVF and ICSI were different. The present review suggests that current sperm DNA fragmentation tests have limited capacity to predict the chance of pregnancy in the context of MAR. Furthermore, sperm DNA fragmentation tests have little or no difference in predictive value between IVF and ICSI. At this moment, there is insufficient evidence to recommend the routine use of sperm DNA fragmentation tests in couples undergoing MAR both for the prediction of pregnancy and for the choice of treatment. Given the significant limitations of the evidence and the methodological weakness and design of the included studies, we do urge for further research on the predictive value of sperm DNA fragmentation for the chance of pregnancy after MAR, also in comparison with other predictors of pregnancy after MAR.

Some aspects of forecasting the post-mining substratum deformation for evaluation of its influence on constructions

NASA Astrophysics Data System (ADS)

Strzałkowski, Piotr; Ścigała, Roman; Szafulera, Katarzyna

2018-04-01

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

Structure-based CoMFA as a predictive model - CYP2C9 inhibitors as a test case.

PubMed

Yasuo, Kazuya; Yamaotsu, Noriyuki; Gouda, Hiroaki; Tsujishita, Hideki; Hirono, Shuichi

2009-04-01

In this study, we tried to establish a general scheme to create a model that could predict the affinity of small compounds to their target proteins. This scheme consists of a search for ligand-binding sites on a protein, a generation of bound conformations (poses) of ligands in each of the sites by docking, identifications of the correct poses of each ligand by consensus scoring and MM-PBSA analysis, and a construction of a CoMFA model with the obtained poses to predict the affinity of the ligands. By using a crystal structure of CYP 2C9 and the twenty known CYP inhibitors as a test case, we obtained a CoMFA model with a good statistics, which suggested that the classification of the binding sites as well as the predicted bound poses of the ligands should be reasonable enough. The scheme described here would give a method to predict the affinity of small compounds with a reasonable accuracy, which is expected to heighten the value of computational chemistry in the drug design process.

Predicting the Influence of Nano-Scale Material Structure on the In-Plane Buckling of Orthotropic Plates

NASA Technical Reports Server (NTRS)

Gates, Thomas S.; Odegard, Gregory M.; Nemeth, Michael P.; Frankland, Sarah-Jane V.

2004-01-01

A multi-scale analysis of the structural stability of a carbon nanotube-polymer composite material is developed. The influence of intrinsic molecular structure, such as nanotube length, volume fraction, orientation and chemical functionalization, is investigated by assessing the relative change in critical, in-plane buckling loads. The analysis method relies on elastic properties predicted using the hierarchical, constitutive equations developed from the equivalent-continuum modeling technique applied to the buckling analysis of an orthotropic plate. The results indicate that for the specific composite materials considered in this study, a composite with randomly orientated carbon nanotubes consistently provides the highest values of critical buckling load and that for low volume fraction composites, the non-functionalized nanotube material provides an increase in critical buckling stability with respect to the functionalized system.

Transcriptome mining and in silico structural and functional analysis of ascorbic acid and tartaric acid biosynthesis pathway enzymes in rose-scanted geranium.

PubMed

Narnoliya, Lokesh K; Sangwan, Rajender S; Singh, Sudhir P

2018-06-01

Rose-scented geranium (Pelargonium sp.) is widely known as aromatic and medicinal herb, accumulating specialized metabolites of high economic importance, such as essential oils, ascorbic acid, and tartaric acid. Ascorbic acid and tartaric acid are multifunctional metabolites of human value to be used as vital antioxidants and flavor enhancing agents in food products. No information is available related to the structural and functional properties of the enzymes involved in ascorbic acid and tartaric acid biosynthesis in rose-scented geranium. In the present study, transcriptome mining was done to identify full-length genes, followed by their bioinformatic and molecular modeling investigations and understanding of in silico structural and functional properties of these enzymes. Evolutionary conserved domains were identified in the pathway enzymes. In silico physicochemical characterization of the catalytic enzymes revealed isoelectric point (pI), instability index, aliphatic index, and grand average hydropathy (GRAVY) values of the enzymes. Secondary structural prediction revealed abundant proportion of alpha helix and random coil confirmations in the pathway enzymes. Three-dimensional homology models were developed for these enzymes. The predicted structures showed significant structural similarity with their respective templates in root mean square deviation analysis. Ramachandran plot analysis of the modeled enzymes revealed that more than 84% of the amino acid residues were within the favored regions. Further, functionally important residues were identified corresponding to catalytic sites located in the enzymes. To, our best knowledge, this is the first report which provides a foundation on functional annotation and structural determination of ascorbic acid and tartaric acid pathway enzymes in rose-scanted geranium.

Factors influencing accuracy of cortical thickness in the diagnosis of Alzheimer's disease.

PubMed

Belathur Suresh, Mahanand; Fischl, Bruce; Salat, David H

2018-04-01

There is great value to use of structural neuroimaging in the assessment of Alzheimer's disease (AD). However, to date, predictive value of structural imaging tend to range between 80% and 90% in accuracy and it is unclear why this is the case given that structural imaging should parallel the pathologic processes of AD. There is a possibility that clinical misdiagnosis relative to the gold standard pathologic diagnosis and/or additional brain pathologies are confounding factors contributing to reduced structural imaging classification accuracy. We examined potential factors contributing to misclassification of individuals with clinically diagnosed AD purely from cortical thickness measures. Correctly classified and incorrectly classified groups were compared across a range of demographic, biological, and neuropsychological data including cerebrospinal fluid biomarkers, amyloid imaging, white matter hyperintensity (WMH) volume, cognitive, and genetic factors. Individual subject analyses suggested that at least a portion of the control individuals misclassified as AD from structural imaging additionally harbor substantial AD biomarker pathology and risk, yet are relatively resistant to cognitive symptoms, likely due to "cognitive reserve," and therefore clinically unimpaired. In contrast, certain clinical control individuals misclassified as AD from cortical thickness had increased WMH volume relative to other controls in the sample, suggesting that vascular conditions may contribute to classification accuracy from cortical thickness measures. These results provide examples of factors that contribute to the accuracy of structural imaging in predicting a clinical diagnosis of AD, and provide important information about considerations for future work aimed at optimizing structural based diagnostic classifiers for AD. © 2017 Wiley Periodicals, Inc.

Development of an artificial neural network model for risk assessment of skin sensitization using human cell line activation test, direct peptide reactivity assay, KeratinoSens™ and in silico structure alert parameter.

PubMed

Hirota, Morihiko; Ashikaga, Takao; Kouzuki, Hirokazu

2018-04-01

It is important to predict the potential of cosmetic ingredients to cause skin sensitization, and in accordance with the European Union cosmetic directive for the replacement of animal tests, several in vitro tests based on the adverse outcome pathway have been developed for hazard identification, such as the direct peptide reactivity assay, KeratinoSens™ and the human cell line activation test. Here, we describe the development of an artificial neural network (ANN) prediction model for skin sensitization risk assessment based on the integrated testing strategy concept, using direct peptide reactivity assay, KeratinoSens™, human cell line activation test and an in silico or structure alert parameter. We first investigated the relationship between published murine local lymph node assay EC3 values, which represent skin sensitization potency, and in vitro test results using a panel of about 134 chemicals for which all the required data were available. Predictions based on ANN analysis using combinations of parameters from all three in vitro tests showed a good correlation with local lymph node assay EC3 values. However, when the ANN model was applied to a testing set of 28 chemicals that had not been included in the training set, predicted EC3s were overestimated for some chemicals. Incorporation of an additional in silico or structure alert descriptor (obtained with TIMES-M or Toxtree software) in the ANN model improved the results. Our findings suggest that the ANN model based on the integrated testing strategy concept could be useful for evaluating the skin sensitization potential. Copyright © 2017 John Wiley & Sons, Ltd.

A Quantitative Description of Suicide Inhibition of Dichloroacetic Acid in Rats and Mice

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

Keys, Deborah A.; Schultz, Irv R.; Mahle, Deirdre A.

Dichloroacetic acid (DCA), a minor metabolite of trichloroethylene (TCE) and water disinfection byproduct, remains an important risk assessment issue because of its carcinogenic potency. DCA has been shown to inhibit its own metabolism by irreversibly inactivating glutathione transferase zeta (GSTzeta). To better predict internal dosimetry of DCA, a physiologically based pharmacokinetic (PBPK) model of DCA was developed. Suicide inhibition was described dynamically by varying the rate of maximal GSTzeta mediated metabolism of DCA (Vmax) over time. Resynthesis (zero-order) and degradation (first-order) of metabolic activity were described. Published iv pharmacokinetic studies in native rats were used to estimate an initial Vmaxmore » value, with Km set to an in vitro determined value. Degradation and resynthesis rates were set to estimated values from a published immunoreactive GSTzeta protein time course. The first-order inhibition rate, kd, was estimated to this same time course. A secondary, linear non-GSTzeta-mediated metabolic pathway is proposed to fit DCA time courses following treatment with DCA in drinking water. The PBPK model predictions were validated by comparing predicted DCA concentrations to measured concentrations in published studies of rats pretreated with DCA following iv exposure to 0.05 to 20 mg/kg DCA. The same model structure was parameterized to simulate DCA time courses following iv exposure in native and pretreated mice. Blood and liver concentrations during and postexposure to DCA in drinking water were predicted. Comparisons of PBPK model predicted to measured values were favorable, lending support for the further development of this model for application to DCA or TCE human health risk assessment.« less

Bundle branch block after ablation for Wolff-Parkinson-White syndrome.

PubMed

Fuenmayor A, Abdel J; Rodríguez S, Yenny A

2013-09-20

Bundle branch block (BBB) is a difficult diagnosis in the Wolff-Parkinson-White syndrome (WPW). We investigated the clinical implications of BBB that appears after performing an accessory pathway (AP) ablation. We studied 199 patients with WPW who were submitted to AP ablation. Thirty (15%) exhibited BBB after the ablation. Twenty-two patients had right BBB and 8 had left BBB. Thirteen patients had right-sided AP and 17 had left-sided AP. They were compared with 82 similar patients without BBB after the AP ablation. Among the patients with BBB, 86.66% showed delays in the middle part of the QRS in the ECG recorded before ablation vs. 18.29% of the patients without BBB (p<0.05) (sensitivity 86%, specificity 81%, positive predictive value 67% and negative predictive value 93%). Forty-four percent of the patients with BBB had BBB morphology during orthodromic tachycardia vs. 10% of the patients without BBB (p<0.05) (sensitivity 44%, specificity 89%, positive predictive value 57% and negative predictive value 82%). No relationship was found between AP location and the site of the BBB. Ejection fraction was normal before (0.61 ± 0.03) and upon completion of follow-up (0.61 ± 0.07). BBB disappeared in 95.3% of the patients. Delays in the middle portion of the QRS may predict BBB after AP ablation. BBB after performing AP ablation is frequent, transient, benign, and not related to either the ablation lesion location or progression to structural heart disease. BBB after AP ablation may be related to cardiac memory. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

A unified relation for the solid-liquid interface free energy of pure FCC, BCC, and HCP metals

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

Wilson, S. R.; Mendelev, M. I., E-mail: mendelev@ameslab.gov

2016-04-14

We study correlations between the solid-liquid interface (SLI) free energy and bulk material properties (melting temperature, latent heat, and liquid structure) through the determination of SLI free energies for bcc and hcp metals from molecular dynamics (MD) simulation. Values obtained for the bcc metals in this study were compared to values predicted by the Turnbull, Laird, and Ewing relations on the basis of previously published MD simulation data. We found that of these three empirical relations, the Ewing relation better describes the MD simulation data. Moreover, whereas the original Ewing relation contains two constants for a particular crystal structure, wemore » found that the first coefficient in the Ewing relation does not depend on crystal structure, taking a common value for all three phases, at least for the class of the systems described by embedded-atom method potentials (which are considered to provide a reasonable approximation for metals).« less

KABAM Version 1.0 User's Guide and Technical Documentation - Appendix C - Explanation of Default Values Representing Biotic Characteristics of Aquatic Ecosystem, Including Food Web Structure

EPA Pesticide Factsheets

Information relevant to KABAM and explanations of default parameters used to define the 7 trophic levels. KABAM is a simulation model used to predict pesticide concentrations in aquatic regions for use in exposure assessments.

Predicting Homework Effort: Support for a Domain-Specific, Multilevel Homework Model

ERIC Educational Resources Information Center

Trautwein, Ulrich; Ludtke, Oliver; Schnyder, Inge; Niggli, Alois

2006-01-01

According to the domain-specific, multilevel homework model proposed in the present study, students' homework effort is influenced by expectancy and value beliefs, homework characteristics, parental homework behavior, and conscientiousness. The authors used structural equation modeling and hierarchical linear modeling analyses to test the model in…

PREDICTING SOIL SORPTION COEFFICIENTS OF ORGANIC CHEMICALS USING A NEURAL NETWORK MODEL

EPA Science Inventory

The soil/sediment adsorption partition coefficient normalized to organic carbon (K_oc) is extensively used to assess the fate of organic chemicals in hazardous waste sites. Several attempts have been made to estimate the value of K_oc from chemical structure ...

A distinct correlation between the vibrational and thermal transport properties of group VA monolayer crystals.

PubMed

Kocabaş, Tuğbey; Çakır, Deniz; Gülseren, Oğuz; Ay, Feridun; Kosku Perkgöz, Nihan; Sevik, Cem

2018-04-26

The investigation of thermal transport properties of novel two-dimensional materials is crucially important in order to assess their potential to be used in future technological applications, such as thermoelectric power generation. In this respect, the lattice thermal transport properties of the monolayer structures of group VA elements (P, As, Sb, Bi, PAs, PSb, PBi, AsSb, AsBi, SbBi, P3As1, P3Sb1, P1As3, and As3Sb1) with a black phosphorus like puckered structure were systematically investigated by first-principles calculations and an iterative solution of the phonon Boltzmann transport equation. Phosphorene was found to have the highest lattice thermal conductivity, κ, due to its low average atomic mass and strong interatomic bonding character. As a matter of course, anisotropic κ was obtained for all the considered materials, owing to anisotropy in frequency values and phonon group velocities calculated for these structures. However, the determined linear correlation between the anisotropy in the κ values of P, As, and Sb is significant. The results corresponding to the studied compound structures clearly point out that thermal (electronic) conductivity of pristine monolayers might be suppressed (improved) by alloying them with the same group elements. For instance, the room temperature κ of PBi along the armchair direction was predicted to be as low as 1.5 W m-1 K-1, whereas that of P was predicted to be 21 W m-1 K-1. In spite of the apparent differences in structural and vibrational properties, we peculiarly revealed an intriguing correlation between the κ values of all the considered materials as κ = c1 + c2/m2, in particular along the zigzag direction. Furthermore, our calculations on compound structures clearly showed that the thermoelectric potential of these materials can be improved by suppressing their thermal properties. The presence of ultra-low κ values and high electrical conductivity (especially along the armchair direction) makes this class of monolayers promising candidates for thermoelectric applications.

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

NASA Technical Reports Server (NTRS)

Boyd, D. Douglas, Jr.

2009-01-01

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

Great interactions: How binding incorrect partners can teach us about protein recognition and function.

PubMed

Vamparys, Lydie; Laurent, Benoist; Carbone, Alessandra; Sacquin-Mora, Sophie

2016-10-01

Protein-protein interactions play a key part in most biological processes and understanding their mechanism is a fundamental problem leading to numerous practical applications. The prediction of protein binding sites in particular is of paramount importance since proteins now represent a major class of therapeutic targets. Amongst others methods, docking simulations between two proteins known to interact can be a useful tool for the prediction of likely binding patches on a protein surface. From the analysis of the protein interfaces generated by a massive cross-docking experiment using the 168 proteins of the Docking Benchmark 2.0, where all possible protein pairs, and not only experimental ones, have been docked together, we show that it is also possible to predict a protein's binding residues without having any prior knowledge regarding its potential interaction partners. Evaluating the performance of cross-docking predictions using the area under the specificity-sensitivity ROC curve (AUC) leads to an AUC value of 0.77 for the complete benchmark (compared to the 0.5 AUC value obtained for random predictions). Furthermore, a new clustering analysis performed on the binding patches that are scattered on the protein surface show that their distribution and growth will depend on the protein's functional group. Finally, in several cases, the binding-site predictions resulting from the cross-docking simulations will lead to the identification of an alternate interface, which corresponds to the interaction with a biomolecular partner that is not included in the original benchmark. Proteins 2016; 84:1408-1421. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. © 2016 The Authors Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc.

The influence of computational strategy on prediction of mechanical stress in carotid atherosclerotic plaques: comparison of 2D structure-only, 3D structure-only, one-way and fully coupled fluid-structure interaction analyses.

PubMed

Huang, Yuan; Teng, Zhongzhao; Sadat, Umar; Graves, Martin J; Bennett, Martin R; Gillard, Jonathan H

2014-04-11

Compositional and morphological features of carotid atherosclerotic plaques provide complementary information to luminal stenosis in predicting clinical presentations. However, they alone cannot predict cerebrovascular risk. Mechanical stress within the plaque induced by cyclical changes in blood pressure has potential to assess plaque vulnerability. Various modeling strategies have been employed to predict stress, including 2D and 3D structure-only, 3D one-way and fully coupled fluid-structure interaction (FSI) simulations. However, differences in stress predictions using different strategies have not been assessed. Maximum principal stress (Stress-P1) within 8 human carotid atherosclerotic plaques was calculated based on geometry reconstructed from in vivo computerized tomography and high resolution, multi-sequence magnetic resonance images. Stress-P1 within the diseased region predicted by 2D and 3D structure-only, and 3D one-way FSI simulations were compared to 3D fully coupled FSI analysis. Compared to 3D fully coupled FSI, 2D structure-only simulation significantly overestimated stress level (94.1 kPa [65.2, 117.3] vs. 85.5 kPa [64.4, 113.6]; median [inter-quartile range], p=0.0004). However, when slices around the bifurcation region were excluded, stresses predicted by 2D structure-only simulations showed a good correlation (R(2)=0.69) with values obtained from 3D fully coupled FSI analysis. 3D structure-only model produced a small yet statistically significant stress overestimation compared to 3D fully coupled FSI (86.8 kPa [66.3, 115.8] vs. 85.5 kPa [64.4, 113.6]; p<0.0001). In contrast, one-way FSI underestimated stress compared to 3D fully coupled FSI (78.8 kPa [61.1, 100.4] vs. 85.5 kPa [64.4, 113.7]; p<0.0001). A 3D structure-only model seems to be a computationally inexpensive yet reasonably accurate approximation for stress within carotid atherosclerotic plaques with mild to moderate luminal stenosis as compared to fully coupled FSI analysis. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

New determination of the fine structure constant from the electron value and QED.

PubMed

Gabrielse, G; Hanneke, D; Kinoshita, T; Nio, M; Odom, B

2006-07-21

Quantum electrodynamics (QED) predicts a relationship between the dimensionless magnetic moment of the electron (g) and the fine structure constant (alpha). A new measurement of g using a one-electron quantum cyclotron, together with a QED calculation involving 891 eighth-order Feynman diagrams, determine alpha(-1)=137.035 999 710 (96) [0.70 ppb]. The uncertainties are 10 times smaller than those of nearest rival methods that include atom-recoil measurements. Comparisons of measured and calculated g test QED most stringently, and set a limit on internal electron structure.

3D-quantitative structure-activity relationship studies on benzothiadiazepine hydroxamates as inhibitors of tumor necrosis factor-alpha converting enzyme.

PubMed

Murumkar, Prashant R; Giridhar, Rajani; Yadav, Mange Ram

2008-04-01

A set of 29 benzothiadiazepine hydroxamates having selective tumor necrosis factor-alpha converting enzyme inhibitory activity were used to compare the quality and predictive power of 3D-quantitative structure-activity relationship, comparative molecular field analysis, and comparative molecular similarity indices models for the atom-based, centroid/atom-based, data-based, and docked conformer-based alignment. Removal of two outliers from the initial training set of molecules improved the predictivity of models. Among the 3D-quantitative structure-activity relationship models developed using the above four alignments, the database alignment provided the optimal predictive comparative molecular field analysis model for the training set with cross-validated r(2) (q(2)) = 0.510, non-cross-validated r(2) = 0.972, standard error of estimates (s) = 0.098, and F = 215.44 and the optimal comparative molecular similarity indices model with cross-validated r(2) (q(2)) = 0.556, non-cross-validated r(2) = 0.946, standard error of estimates (s) = 0.163, and F = 99.785. These models also showed the best test set prediction for six compounds with predictive r(2) values of 0.460 and 0.535, respectively. The contour maps obtained from 3D-quantitative structure-activity relationship studies were appraised for activity trends for the molecules analyzed. The comparative molecular similarity indices models exhibited good external predictivity as compared with that of comparative molecular field analysis models. The data generated from the present study helped us to further design and report some novel and potent tumor necrosis factor-alpha converting enzyme inhibitors.

The electronic structure, elastic and optical properties of Cu2ZnGe(SexS1 - x)4 alloys: density functional calculations

NASA Astrophysics Data System (ADS)

Shen, Kesheng; Jia, Guangrui; Zhang, Xianzhou; Jiao, Zhaoyong

2016-10-01

The electronic structure, elastic and optical properties of Cu2ZnGe(SexS1 - x)4 alloys are systematically analysed using first-principles calculations. The lattice parameters agree well with the theoretical and experimental values which are searched as complete as possible indicating our calculations are reliable. The elastic properties are investigated first and are compared with the similar compounds CZTS and CZTSe due to the unavailable experimental data currently. The variation of the optical properties caused by the increase of Se/S ratio is discussed. The static optical constants are calculated and the corrected values are also predicted according to the available experimental data.

Predictable and unpredictable modes of seasonal mean precipitation over Northeast China

NASA Astrophysics Data System (ADS)

Ying, Kairan; Frederiksen, Carsten S.; Zhao, Tianbao; Zheng, Xiaogu; Xiong, Zhe; Yi, Xue; Li, Chunxiang

2018-04-01

This study investigates the patterns of interannual variability that arise from the potentially predictable (slow) and unpredictable (intraseasonal) components of seasonal mean precipitation over Northeast (NE) China, using observations from a network of 162 meteorological stations for the period 1961-2014. A variance decomposition method is applied to identify the sources of predictability, as well as the sources of prediction uncertainty, for January-February-March (JFM), April-May-June (AMJ), July-August-September (JAS) and October-November-December (OND). The averaged potential predictability (ratio of slow to total variance) of NE China precipitation has the highest value of 0.32 during JAS and lowest value of 0.1 in AMJ. Possible sources of seasonal prediction for the leading predictable precipitation EOF modes come from the SST anomalies in the Japan Sea, as well as the North Atlantic during JFM, the Indian Ocean SST in AMJ, and the eastern tropical Pacific SST in JAS and OND. The prolonged linear trend, which is seen in the principal component time series of the leading predictable mode in JFM and OND, may also serve as a source of predictability. The Polar-Eurasia and Northern Annular Mode atmospheric teleconnection patterns are closely connected with the leading and the second predictable mode of JAS, respectively. The Hadley cell circulation is closely related to the leading predictable mode of OND. The leading/second unpredictable precipitation modes for all these four seasons show a similar monopole/dipole structure, and can be largely attributed to the intraseasonal variabilities of the atmosphere.

Survey of predictors of propensity for protein production and crystallization with application to predict resolution of crystal structures

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

Gao, Jianzhao; Wu, Zhonghua; Hu, Gang

Selection of proper targets for the X-ray crystallography will benefit biological research community immensely. Several computational models were proposed to predict propensity of successful protein production and diffraction quality crystallization from protein sequences. We reviewed a comprehensive collection of 22 such predictors that were developed in the last decade. We found that almost all of these models are easily accessible as webservers and/or standalone software and we demonstrated that some of them are widely used by the research community. We empirically evaluated and compared the predictive performance of seven representative methods. The analysis suggests that these methods produce quite accuratemore » propensities for the diffraction-quality crystallization. We also summarized results of the first study of the relation between these predictive propensities and the resolution of the crystallizable proteins. We found that the propensities predicted by several methods are significantly higher for proteins that have high resolution structures compared to those with the low resolution structures. Moreover, we tested a new meta-predictor, MetaXXC, which averages the propensities generated by the three most accurate predictors of the diffraction-quality crystallization. MetaXXC generates putative values of resolution that have modest levels of correlation with the experimental resolutions and it offers the lowest mean absolute error when compared to the seven considered methods. We conclude that protein sequences can be used to fairly accurately predict whether their corresponding protein structures can be solved using X-ray crystallography. Moreover, we also ascertain that sequences can be used to reasonably well predict the resolution of the resulting protein crystals.« less

Identifying type 1 and type 2 diabetic cases using administrative data: a tree-structured model.

PubMed

Lo-Ciganic, Weihsuan; Zgibor, Janice C; Ruppert, Kristine; Arena, Vincent C; Stone, Roslyn A

2011-05-01

To date, few administrative diabetes mellitus (DM) registries have distinguished type 1 diabetes mellitus (T1DM) from type 2 diabetes mellitus (T2DM). Using a classification tree model, a prediction rule was developed to distinguish T1DM from T2DM in a large administrative database. The Medical Archival Retrieval System at the University of Pittsburgh Medical Center included administrative and clinical data from January 1, 2000, through September 30, 2009, for 209,647 DM patients aged ≥18 years. Probable cases (8,173 T1DM and 125,111 T2DM) were identified by applying clinical criteria to administrative data. Nonparametric classification tree models were fit using TIBCO Spotfire S+ 8.1 (TIBCO Software), with model size based on 10-fold cross validation. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of T1DM were estimated. The main predictors that distinguished T1DM from T2DM are age <40 years; International Classification of Disease, 9th revision, codes of T1DM or T2DM diagnosis; inpatient oral hypoglycemic agent use; inpatient insulin use; and episode(s) of diabetic ketoacidosis diagnosis. Compared with a complex clinical algorithm, the tree-structured model to predict T1DM had 92.8% sensitivity, 99.3% specificity, 89.5% PPV, and 99.5% NPV. The preliminary predictive rule appears to be promising. Being able to distinguish between DM subtypes in administrative databases will allow large-scale subtype-specific analyses of medical care costs, morbidity, and mortality. © 2011 Diabetes Technology Society.

Remote sensing techniques for prediction of watershed runoff

NASA Technical Reports Server (NTRS)

Blanchard, B. J.

1975-01-01

Hydrologic parameters of watersheds for use in mathematical models and as design criteria for flood detention structures are sometimes difficult to quantify using conventional measuring systems. The advent of remote sensing devices developed in the past decade offers the possibility that watershed characteristics such as vegetative cover, soils, soil moisture, etc., may be quantified rapidly and economically. Experiments with visible and near infrared data from the LANDSAT-1 multispectral scanner indicate a simple technique for calibration of runoff equation coefficients is feasible. The technique was tested on 10 watersheds in the Chickasha area and test results show more accurate runoff coefficients were obtained than with conventional methods. The technique worked equally as well using a dry fall scene. The runoff equation coefficients were then predicted for 22 subwatersheds with flood detention structures. Predicted values were again more accurate than coefficients produced by conventional methods.

An expert system for prediction of chemical toxicity

USGS Publications Warehouse

Hickey, James P.; Aldridge, Andrew J.; Passino-Reader, Dora R.; Frank, Anthony M.

1992-01-01

The National Fisheries Research Center- Great Lakes has developed an interactive computer program that uses the structure of an organic molecule to predict its acute toxicity to four aquatic species. The expert system software, written in the muLISP language, identifies the skeletal structures and substituent groups of an organic molecule from a user-supplied standard chemical notation known as a SMILES string, and then generates values for four solvatochromic parameters. Multiple regression equations relate these parameters to the toxicities (expressed as log10LC50s and log10EC50s, along with 95% confidence intervals) for four species. The system is demonstrated by prediction of toxicity for anilide-type pesticides to the fathead minnow (Pimephales promelas). This software is designed for use on an IBM-compatible personal computer by personnel with minimal toxicology background for rapid estimation of chemical toxicity. The system has numerous applications, with much potential for use in the pharmaceutical industry

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

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

Prediction of 1-octanol solubilities using data from the Open Notebook Science Challenge.

PubMed

Buonaiuto, Michael A; Lang, Andrew S I D

2015-12-01

1-Octanol solubility is important in a variety of applications involving pharmacology and environmental chemistry. Current models are linear in nature and often require foreknowledge of either melting point or aqueous solubility. Here we extend the range of applicability of 1-octanol solubility models by creating a random forest model that can predict 1-octanol solubilities directly from structure. We created a random forest model using CDK descriptors that has an out-of-bag (OOB) R 2 value of 0.66 and an OOB mean squared error of 0.34. The model has been deployed for general use as a Shiny application. The 1-octanol solubility model provides reasonably accurate predictions of the 1-octanol solubility of organic solutes directly from structure. The model was developed under Open Notebook Science conditions which makes it open, reproducible, and as useful as possible.Graphical abstract.

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

NASA Technical Reports Server (NTRS)

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

2011-01-01

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

Structure-Activity Relationship Models for Rat Carcinogenesis and Assessing the Role Mutagens Play in Model Predictivity

PubMed Central

Carrasquer, C. Alex; Batey, Kaylind; Qamar, Shahid; Cunningham, Albert R.; Cunningham, Suzanne L.

2016-01-01

We previously demonstrated that fragment based cat-SAR carcinogenesis models consisting solely of mutagenic or non-mutagenic carcinogens varied greatly in terms of their predictive accuracy. This led us to investigate how well the rat cancer cat-SAR model predicted mutagens and non-mutagens in their learning set. Four rat cancer cat-SAR models were developed: Complete Rat, Transgender Rat, Male Rat, and Female Rat, with leave-one-out (LOO) validation concordance values of 69%, 74%, 67%, and 73%, respectively. The mutagenic carcinogens produced concordance values in the range of 69–76% as compared to only 47–53% for non-mutagenic carcinogens. As a surrogate for mutagenicity comparisons between single site and multiple site carcinogen SAR models was analyzed. The LOO concordance values for models consisting of 1-site, 2-site, and 4+-site carcinogens were 66%, 71%, and 79%, respectively. As expected, the proportion of mutagens to non-mutagens also increased, rising from 54% for 1-site to 80% for 4+-site carcinogens. This study demonstrates that mutagenic chemicals, in both SAR learning sets and test sets, are influential in assessing model accuracy. This suggests that SAR models for carcinogens may require a two-step process in which mutagenicity is first determined before carcinogenicity can be accurately predicted. PMID:24697549

Accelerated Discovery of High-Refractive-Index Polymers Using First-Principles Modeling, Virtual High-Throughput Screening, and Data Mining

NASA Astrophysics Data System (ADS)

Afzal, Mohammad Atif Faiz; Cheng, Chong; Hachmann, Johannes

Organic materials with refractive index (RI) values higher than 1.7 have attracted considerable interest in recent years due to the tremendous potential for their application in optical, optometric, and optoelectronic devices, and thus for shaping technological innovation in numerous related areas. Our work is concerned with creating predictive models for the optical properties of organic polymers, which will guide our experimentalist partners and allow them to target the most promising candidates. The RI model is developed based on a synergistic combination of first-principles electronic structure theory and machine learning techniques. The RI values predicted for common polymers using this model are in very good agreement with the experimental values. We also benchmark different DFT approximations along with various basis sets for their predictive performance in this model. We demonstrate that this combination of first-principles and data modeling is both successful and highly economical in determining the RI values of a wide range of organic polymers. To accelerate the development process, we cast this modeling approach into the high-throughput screening, materials informatics, and rational design framework that is developed in the group. This framework is a powerful tool and has shown to be highly promising for rapidly identifying polymer candidates with exceptional RI values as well as discovering design rules for advanced materials.

Confirmation of general relativity on large scales from weak lensing and galaxy velocities.

PubMed

Reyes, Reinabelle; Mandelbaum, Rachel; Seljak, Uros; Baldauf, Tobias; Gunn, James E; Lombriser, Lucas; Smith, Robert E

2010-03-11

Although general relativity underlies modern cosmology, its applicability on cosmological length scales has yet to be stringently tested. Such a test has recently been proposed, using a quantity, E(G), that combines measures of large-scale gravitational lensing, galaxy clustering and structure growth rate. The combination is insensitive to 'galaxy bias' (the difference between the clustering of visible galaxies and invisible dark matter) and is thus robust to the uncertainty in this parameter. Modified theories of gravity generally predict values of E(G) different from the general relativistic prediction because, in these theories, the 'gravitational slip' (the difference between the two potentials that describe perturbations in the gravitational metric) is non-zero, which leads to changes in the growth of structure and the strength of the gravitational lensing effect. Here we report that E(G) = 0.39 +/- 0.06 on length scales of tens of megaparsecs, in agreement with the general relativistic prediction of E(G) approximately 0.4. The measured value excludes a model within the tensor-vector-scalar gravity theory, which modifies both Newtonian and Einstein gravity. However, the relatively large uncertainty still permits models within f(R) theory, which is an extension of general relativity. A fivefold decrease in uncertainty is needed to rule out these models.

Confirmation of general relativity on large scales from weak lensing and galaxy velocities

NASA Astrophysics Data System (ADS)

Reyes, Reinabelle; Mandelbaum, Rachel; Seljak, Uros; Baldauf, Tobias; Gunn, James E.; Lombriser, Lucas; Smith, Robert E.

2010-03-01

Although general relativity underlies modern cosmology, its applicability on cosmological length scales has yet to be stringently tested. Such a test has recently been proposed, using a quantity, EG, that combines measures of large-scale gravitational lensing, galaxy clustering and structure growth rate. The combination is insensitive to `galaxy bias' (the difference between the clustering of visible galaxies and invisible dark matter) and is thus robust to the uncertainty in this parameter. Modified theories of gravity generally predict values of EG different from the general relativistic prediction because, in these theories, the `gravitational slip' (the difference between the two potentials that describe perturbations in the gravitational metric) is non-zero, which leads to changes in the growth of structure and the strength of the gravitational lensing effect. Here we report that EG = 0.39+/-0.06 on length scales of tens of megaparsecs, in agreement with the general relativistic prediction of EG~0.4. The measured value excludes a model within the tensor-vector-scalar gravity theory, which modifies both Newtonian and Einstein gravity. However, the relatively large uncertainty still permits models within f() theory, which is an extension of general relativity. A fivefold decrease in uncertainty is needed to rule out these models.

Alcohol Use among Recent Immigrant Latino/a Youth: Acculturation, Gender, and the Theory of Reasoned Action

PubMed Central

Lorenzo-Blanco, Elma I.; Schwartz, Seth J.; Unger, Jennifer B.; Zamboanga, Byron L.; Rosiers, Sabrina E. Des; Baezconde-Garbanati, Lourdes; Huang, Shi; Villamar, Juan A.; Soto, Daniel; Pattarroyo, Monica

2016-01-01

Objective Latino/a youth are at risk for alcohol use. This risk seems to rise with increasing U.S. cultural orientation and decreasing Latino cultural orientation, especially among girls. To ascertain how acculturation may influence Latino/a youth alcohol use, in this study we integrated an expanded multi-domain model of acculturation with the Theory of Reasoned Action. Design Participants were 302 recent Latino/a immigrant youth (141 girls, 160 boys; 152 from Miami, 150 from Los Angeles) who completed surveys at 4 time points. Youth completed measures of acculturation (measured in terms of Latino/a practices, Latino/a identity, collectivistic values; U.S. cultural practices, U.S. identity, and individualistic values), attitudes toward drinking, perceived subjective norms regarding alcohol use, intention to drink, and alcohol use. Results Structural equation modeling indicated that collectivistic values predicted more perceived disapproval of drinking, which negatively predicted intention to drink. Intention to drink predicted elevated alcohol use. Conclusion Although the association between collectivistic values and social disapproval of drinking was relatively small (β=.19, p < .05), findings suggest that collectivistic values may help protect Latino/a immigrant youth from alcohol use by influencing their perceived social disapproval of drinking, leading to lower intention to drink. Educational preventive interventions aimed at reducing or preventing alcohol use in recent Latino/a immigrant youth could promote collectivistic values and disseminate messages about the negative consequences of drinking. PMID:27220730

Experimental and ab initio studies on sub-lattice ordering and magnetism in Co{sub 2}Fe(Ge{sub 1−x}Si{sub x}) alloys

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

Deka, Bhargab; Kundu, Ashis; Ghosh, Subhradip

2015-10-07

Crystallographic and magnetic properties of bulk Co{sub 2}Fe(Ge{sub 1−x}Si{sub x}) alloys with 0 ≤ x ≤ 1, synthesized by arc melting method, have been studied. Co{sub 2}FeSi alloy has been found to crystallize with L2{sub 1} structure, but the super-lattice peaks are absent in the X-ray diffraction patterns of alloys containing high Ge concentration. Unit cell volume of this series of alloys decreased from 185.2 to 178.5 Å{sup 3} as Si content was increased from 0 to 1.00. All alloy compositions exhibit ferromagnetic behavior with a high Curie temperature (T{sub C}). T{sub C} showed a systematic variation with x. A comparison between the valuesmore » of saturation magnetization (M{sub s}) and effective moment per magnetic atom p{sub c} estimated from the temperature dependent susceptibility data above T{sub C}, shows that the alloys have half-metallic character. The alloy with x = 0 follows Slater-Pauling (S-P) rule with M{sub s} of 5.99μ{sub B}. However, M{sub s} for the alloy with x = 1.00 was found to be 5.42μ{sub B}, which is lower than the value of 6.0μ{sub B} predicted by S-P rule. Since atomic disorder is known to affect the M{sub s} and electronic structure of these alloys, ab initio calculations were carried out to explain the deviation in observed M{sub s} from S-P rule prediction and the half-metallic character of the alloys. Ab initio calculations reveal that alloys with L2{sub 1} structure have M{sub s} value as predicted by S-P rule. However, introduction of 12.5% DO{sub 3} disorder, which occurs due to swapping of Co and Fe atoms in the unit cell, decreases M{sub s} of alloys with x > 0 from the S-P prediction to values obtained experimentally. The results analyzed from the view point of electronic structure of the alloys in different ordered states bring out the influence of disorder on the observed magnetic properties of these technologically important alloys.« less

Impact of the variation in dynamic vehicle load on flexible pavement responses

NASA Astrophysics Data System (ADS)

Ahsanuzzaman, Md

The purpose of this research was to evaluate the dynamic variation in asphalt pavement critical responses due to dynamic tire load variations. An attempt was also made to develop generalized regression equations to predict the dynamic response variation in flexible pavement under various dynamic load conditions. The study used an extensive database of computed pavement response histories for five different types of sites (smooth, rough, medium rough, very rough and severely rough), two different asphalt pavement structures (thin and thick) at two temperatures (70 °F and 104 °F), subjected to a tandem axle dual tire at three speeds 25, 37 and 50 mph (40, 60 and 80 km/h). All pavement responses were determined using the 3D-Move Analysis program (Version 1.2) developed by University of Nevada, Reno. A new term called Dynamic Response Coefficient (DRC) was introduced in this study to address the variation in critical pavement responses due to dynamic loads as traditionally measured by the Dynamic Load Coefficient (DLC). While DLC represents the additional varying component of the tire load, DRC represents the additional varying component of the response value (standard deviation divided by mean response). In this study, DRC was compared with DLC for five different sites based on the roughness condition of the sites. Previous studies showed that DLC varies with vehicle speed and suspension types, and assumes a constant value for the whole pavement structure (lateral and vertical directions). On the other hand, in this study, DRC was found to be significantly varied with the asphalt pavement and function of pavement structure, road roughness conditions, temperatures, vehicle speeds, suspension types, and locations of the point of interest in the pavement. A major contribution of the study is that the variation of pavement responses due to dynamic load in a flexible pavement system can be predicted with generalized regression equations. Fitting parameters (R2) in the rage of 0.60 to 0.87 were observed the DRC predictive equations. In addition, verification of those generalized equations was evaluated using different sets of asphalt pavement structures and pavement materials. The differences between calculated and predicted values were found to be within +/-20% for the maximum tensile strain and +/-30% for the maximum compressive strain in the asphalt layer.

Multivariate functions for predicting the sorption of 2,4,6-trinitrotoluene (TNT) and 1,3,5-trinitro-1,3,5-tricyclohexane (RDX) among taxonomically distinct soils.

PubMed

Katseanes, Chelsea K; Chappell, Mark A; Hopkins, Bryan G; Durham, Brian D; Price, Cynthia L; Porter, Beth E; Miller, Lesley F

2016-11-01

After nearly a century of use in numerous munition platforms, TNT and RDX contamination has turned up largely in the environment due to ammunition manufacturing or as part of releases from low-order detonations during training activities. Although the basic knowledge governing the environmental fate of TNT and RDX are known, accurate predictions of TNT and RDX persistence in soil remain elusive, particularly given the universal heterogeneity of pedomorphic soil types. In this work, we proposed a new solution for modeling the sorption and persistence of these munition constituents as multivariate mathematical functions correlating soil attribute data over a variety of taxonomically distinct soil types to contaminant behavior, instead of a single constant or parameter of a specific absolute value. To test this idea, we conducted experiments measuring the sorption of TNT and RDX on taxonomically different soil types that were extensively physical and chemically characterized. Statistical decomposition of the log-transformed, and auto-scaled soil characterization data using the dimension-reduction technique PCA (principal component analysis) revealed a strong latent structure based in the multiple pairwise correlations among the soil properties. TNT and RDX sorption partitioning coefficients (KD-TNT and KD-RDX) were regressed against this latent structure using partial least squares regression (PLSR), generating a 3-factor, multivariate linear functions. Here, PLSR models predicted KD-TNT and KD-RDX values based on attributes contributing to endogenous alkaline/calcareous and soil fertility criteria, respectively, exhibited among the different soil types: We hypothesized that the latent structure arising from the strong covariance of full multivariate geochemical matrix describing taxonomically distinguished soil types may provide the means for potentially predicting complex phenomena in soils. The development of predictive multivariate models tuned to a local soil's taxonomic designation would have direct benefit to military range managers seeking to anticipate the environmental risks of training activities on impact sites. Published by Elsevier Ltd.

Human Brain Modeling with Its Anatomical Structure and Realistic Material Properties for Brain Injury Prediction.

PubMed

Atsumi, Noritoshi; Nakahira, Yuko; Tanaka, Eiichi; Iwamoto, Masami

2018-05-01

Impairments of executive brain function after traumatic brain injury (TBI) due to head impacts in traffic accidents need to be obviated. Finite element (FE) analyses with a human brain model facilitate understanding of the TBI mechanisms. However, conventional brain FE models do not suitably describe the anatomical structure in the deep brain, which is a critical region for executive brain function, and the material properties of brain parenchyma. In this study, for better TBI prediction, a novel brain FE model with anatomical structure in the deep brain was developed. The developed model comprises a constitutive model of brain parenchyma considering anisotropy and strain rate dependency. Validation was performed against postmortem human subject test data associated with brain deformation during head impact. Brain injury analyses were performed using head acceleration curves obtained from reconstruction analysis of rear-end collision with a human whole-body FE model. The difference in structure was found to affect the regions of strain concentration, while the difference in material model contributed to the peak strain value. The injury prediction result by the proposed model was consistent with the characteristics in the neuroimaging data of TBI patients due to traffic accidents.

Alcohol use among recent immigrant Latino/a youth: acculturation, gender, and the Theory of Reasoned Action.

PubMed

Lorenzo-Blanco, Elma I; Schwartz, Seth J; Unger, Jennifer B; Zamboanga, Byron L; Des Rosiers, Sabrina E; Baezconde-Garbanati, Lourdes; Huang, Shi; Villamar, Juan A; Soto, Daniel; Pattarroyo, Monica

2016-12-01

Latino/a youth are at risk for alcohol use. This risk seems to rise with increasing US cultural orientation and decreasing Latino cultural orientation, especially among girls. To ascertain how acculturation may influence Latino/a youth alcohol use, we integrated an expanded multi-domain model of acculturation with the Theory of Reasoned Action. Participants were 302 recent Latino/a immigrant youth (141 girls, 160 boys; 152 from Miami, 150 from Los Angeles) who completed surveys at 4 time points. Youth completed measures of acculturation, attitudes toward drinking, perceived subjective norms regarding alcohol use, intention to drink, and alcohol use. Structural equation modeling indicated that collectivistic values predicted more perceived disapproval of drinking, which negatively predicted intention to drink. Intention to drink predicted elevated alcohol use. Although the association between collectivistic values and social disapproval of drinking was relatively small (β = .19, p < .05), findings suggest that collectivistic values may help protect Latino/a immigrant youth from alcohol use by influencing their perceived social disapproval of drinking, leading to lower intention to drink. Educational preventive interventions aimed at reducing or preventing alcohol use in recent Latino/a immigrant youth could promote collectivistic values and disseminate messages about the negative consequences of drinking.

Association Rule-based Predictive Model for Machine Failure in Industrial Internet of Things

NASA Astrophysics Data System (ADS)

Kwon, Jung-Hyok; Lee, Sol-Bee; Park, Jaehoon; Kim, Eui-Jik

2017-09-01

This paper proposes an association rule-based predictive model for machine failure in industrial Internet of things (IIoT), which can accurately predict the machine failure in real manufacturing environment by investigating the relationship between the cause and type of machine failure. To develop the predictive model, we consider three major steps: 1) binarization, 2) rule creation, 3) visualization. The binarization step translates item values in a dataset into one or zero, then the rule creation step creates association rules as IF-THEN structures using the Lattice model and Apriori algorithm. Finally, the created rules are visualized in various ways for users’ understanding. An experimental implementation was conducted using R Studio version 3.3.2. The results show that the proposed predictive model realistically predicts machine failure based on association rules.

Computational analysis of histidine mutations on the structural stability of human tyrosinases leading to albinism insurgence.

PubMed

Hassan, Mubashir; Abbas, Qamar; Raza, Hussain; Moustafa, Ahmed A; Seo, Sung-Yum

2017-07-25

Misfolding and structural alteration in proteins lead to serious malfunctions and cause various diseases in humans. Mutations at the active binding site in tyrosinase impair structural stability and cause lethal albinism by abolishing copper binding. To evaluate the histidine mutational effect, all mutated structures were built using homology modelling. The protein sequence was retrieved from the UniProt database, and 3D models of original and mutated human tyrosinase sequences were predicted by changing the residual positions within the target sequence separately. Structural and mutational analyses were performed to interpret the significance of mutated residues (N 180 , R 202 , Q 202 , R 211 , Y 363 , R 367 , Y 367 and D 390 ) at the active binding site of tyrosinases. CSpritz analysis depicted that 23.25% residues actively participate in the instability of tyrosinase. The accuracy of predicted models was confirmed through online servers ProSA-web, ERRAT score and VERIFY 3D values. The theoretical pI and GRAVY generated results also showed the accuracy of the predicted models. The CCA negative correlation results depicted that the replacement of mutated residues at His within the active binding site disturbs the structural stability of tyrosinases. The predicted CCA scores of Tyr 367 (-0.079) and Q/R 202 (0.032) revealed that both mutations have more potential to disturb the structural stability. MD simulation analyses of all predicted models justified that Gln 202 , Arg 202 , Tyr 367 and D 390 replacement made the protein structures more susceptible to destabilization. Mutational results showed that the replacement of His with Q/R 202 and Y/R 363 has a lethal effect and may cause melanin associated diseases such as OCA1. Taken together, our computational analysis depicts that the mutated residues such as Q/R 202 and Y/R 363 actively participate in instability and misfolding of tyrosinases, which may govern OCA1 through disturbing the melanin biosynthetic pathway.

Mechanical behavior of regular open-cell porous biomaterials made of diamond lattice unit cells.

PubMed

Ahmadi, S M; Campoli, G; Amin Yavari, S; Sajadi, B; Wauthle, R; Schrooten, J; Weinans, H; Zadpoor, A A

2014-06-01

Cellular structures with highly controlled micro-architectures are promising materials for orthopedic applications that require bone-substituting biomaterials or implants. The availability of additive manufacturing techniques has enabled manufacturing of biomaterials made of one or multiple types of unit cells. The diamond lattice unit cell is one of the relatively new types of unit cells that are used in manufacturing of regular porous biomaterials. As opposed to many other types of unit cells, there is currently no analytical solution that could be used for prediction of the mechanical properties of cellular structures made of the diamond lattice unit cells. In this paper, we present new analytical solutions and closed-form relationships for predicting the elastic modulus, Poisson׳s ratio, critical buckling load, and yield (plateau) stress of cellular structures made of the diamond lattice unit cell. The mechanical properties predicted using the analytical solutions are compared with those obtained using finite element models. A number of solid and porous titanium (Ti6Al4V) specimens were manufactured using selective laser melting. A series of experiments were then performed to determine the mechanical properties of the matrix material and cellular structures. The experimentally measured mechanical properties were compared with those obtained using analytical solutions and finite element (FE) models. It has been shown that, for small apparent density values, the mechanical properties obtained using analytical and numerical solutions are in agreement with each other and with experimental observations. The properties estimated using an analytical solution based on the Euler-Bernoulli theory markedly deviated from experimental results for large apparent density values. The mechanical properties estimated using FE models and another analytical solution based on the Timoshenko beam theory better matched the experimental observations. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

PubMed Central

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

2014-01-01

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

Adolescent-perceived parent and teacher overestimation of mathematics ability: Developmental implications for students' mathematics task values.

PubMed

Gniewosz, Burkhard; Watt, Helen M G

2017-07-01

This study examines whether and how student-perceived parents' and teachers' overestimation of students' own perceived mathematical ability can explain trajectories for adolescents' mathematical task values (intrinsic and utility) controlling for measured achievement, following expectancy-value and self-determination theories. Longitudinal data come from a 3-cohort (mean ages 13.25, 12.36, and 14.41 years; Grades 7-10), 4-wave data set of 1,271 Australian secondary school students. Longitudinal structural equation models revealed positive effects of student-perceived overestimation of math ability by parents and teachers on students' intrinsic and utility math task values development. Perceived parental overestimations predicted intrinsic task value changes between all measurement occasions, whereas utility task value changes only were predicted between Grades 9 and 10. Parental influences were stronger for intrinsic than utility task values. Teacher influences were similar for both forms of task values and commenced after the curricular school transition in Grade 8. Results support the assumptions that the perceived encouragement conveyed by student-perceived mathematical ability beliefs of parents and teachers, promote positive mathematics task values development. Moreover, results point to different mechanisms underlying parents' and teachers' support. Finally, the longitudinal changes indicate transition-related increases in the effects of student-perceived overestimations and stronger effects for intrinsic than utility values. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Spectral and structural studies of the anti-cancer drug Flutamide by density functional theoretical method

NASA Astrophysics Data System (ADS)

Mariappan, G.; Sundaraganesan, N.

2014-01-01

A comprehensive screening of the more recent DFT theoretical approach to structural analysis is presented in this section of theoretical structural analysis. The chemical name of 2-methyl-N-[4-nitro-3-(trifluoromethyl)phenyl]-propanamide is usually called as Flutamide (In the present study it is abbreviated as FLT) and is an important and efficacious drug in the treatment of anti-cancer resistant. The molecular geometry, vibrational spectra, electronic and NMR spectral interpretation of Flutamide have been studied with the aid of density functional theory method (DFT). The vibrational assignments of the normal modes were performed on the basis of the PED calculations using the VEDA 4 program. Comparison of computational results with X-ray diffraction results of Flutamide allowed the evaluation of structure predictions and confirmed B3LYP/6-31G(d,p) as accurate for structure determination. Application of scaling factors for IR and Raman frequency predictions showed good agreement with experimental values. This is supported the assignment of the major contributors of the vibration modes of the title compound. Stability of the molecule arising from hyperconjugative interactions leading to its bioactivity, charge delocalization have been analyzed using natural bond orbital (NBO) analysis. NMR chemical shifts of the molecule were calculated using the gauge independent atomic orbital (GIAO) method. The comparison of measured FTIR, FT-Raman, and UV-Visible data to calculated values allowed assignment of major spectral features of the title molecule. Besides, Frontier molecular orbital analyze was also investigated using theoretical calculations.

Using multi-species occupancy models in structured decision making on managed lands

USGS Publications Warehouse

Sauer, John R.; Blank, Peter J.; Zipkin, Elise F.; Fallon, Jane E.; Fallon, Frederick W.

2013-01-01

Land managers must balance the needs of a variety of species when manipulating habitats. Structured decision making provides a systematic means of defining choices and choosing among alternative management options; implementation of a structured decision requires quantitative approaches to predicting consequences of management on the relevant species. Multi-species occupancy models provide a convenient framework for making structured decisions when the management objective is focused on a collection of species. These models use replicate survey data that are often collected on managed lands. Occupancy can be modeled for each species as a function of habitat and other environmental features, and Bayesian methods allow for estimation and prediction of collective responses of groups of species to alternative scenarios of habitat management. We provide an example of this approach using data from breeding bird surveys conducted in 2008 at the Patuxent Research Refuge in Laurel, Maryland, evaluating the effects of eliminating meadow and wetland habitats on scrub-successional and woodland-breeding bird species using summed total occupancy of species as an objective function. Removal of meadows and wetlands decreased value of an objective function based on scrub-successional species by 23.3% (95% CI: 20.3–26.5), but caused only a 2% (0.5, 3.5) increase in value of an objective function based on woodland species, documenting differential effects of elimination of meadows and wetlands on these groups of breeding birds. This approach provides a useful quantitative tool for managers interested in structured decision making.

Monitoring muscle optical scattering properties during rigor mortis

NASA Astrophysics Data System (ADS)

Xia, J.; Ranasinghesagara, J.; Ku, C. W.; Yao, G.

2007-09-01

Sarcomere is the fundamental functional unit in skeletal muscle for force generation. In addition, sarcomere structure is also an important factor that affects the eating quality of muscle food, the meat. The sarcomere structure is altered significantly during rigor mortis, which is the critical stage involved in transforming muscle to meat. In this paper, we investigated optical scattering changes during the rigor process in Sternomandibularis muscles. The measured optical scattering parameters were analyzed along with the simultaneously measured passive tension, pH value, and histology analysis. We found that the temporal changes of optical scattering, passive tension, pH value and fiber microstructures were closely correlated during the rigor process. These results suggested that sarcomere structure changes during rigor mortis can be monitored and characterized by optical scattering, which may find practical applications in predicting meat quality.

[Zn(C 7H 3O 5N)] n · nH 2O: A third-order NLO Zn coordination polymer with spiroconjugated structure

NASA Astrophysics Data System (ADS)

Zhou, Guo-Wei; Lan, You-Zhao; Zheng, Fa-Kun; Zhang, Xin; Lin, Meng-Hai; Guo, Guo-Cong; Huang, Jin-Shun

2006-08-01

[Zn(C 7H 3O 5N)] n · nH 2O ( 1) possesses an anticlockwise windmill-like framework structure and formats spiroconjugation over the infinite molecular layer that is predicted to have large static third-order polarizability and the convergence value of γxxxx reaches 6.86 × 10 -33 esu in the case of zero input photon energy. The third-order NLO properties of 1 were investigated via Z-scan techniques at wavelength of 532 nm. It showed strong third-order NLO absorptive properties, and its n2 value was calculated to be 4.15 × 10 -11 esu. The relationship between the spiroconjugated structure and the NLO property has been discussed, which supposed to be more valuable for the NLO research.

Energy hyperspace for stacking interaction in AU/AU dinucleotide step: Dispersion-corrected density functional theory study.

PubMed

Mukherjee, Sanchita; Kailasam, Senthilkumar; Bansal, Manju; Bhattacharyya, Dhananjay

2014-01-01

Double helical structures of DNA and RNA are mostly determined by base pair stacking interactions, which give them the base sequence-directed features, such as small roll values for the purine-pyrimidine steps. Earlier attempts to characterize stacking interactions were mostly restricted to calculations on fiber diffraction geometries or optimized structure using ab initio calculations lacking variation in geometry to comment on rather unusual large roll values observed in AU/AU base pair step in crystal structures of RNA double helices. We have generated stacking energy hyperspace by modeling geometries with variations along the important degrees of freedom, roll, and slide, which were chosen via statistical analysis as maximally sequence dependent. Corresponding energy contours were constructed by several quantum chemical methods including dispersion corrections. This analysis established the most suitable methods for stacked base pair systems despite the limitation imparted by number of atom in a base pair step to employ very high level of theory. All the methods predict negative roll value and near-zero slide to be most favorable for the purine-pyrimidine steps, in agreement with Calladine's steric clash based rule. Successive base pairs in RNA are always linked by sugar-phosphate backbone with C3'-endo sugars and this demands C1'-C1' distance of about 5.4 Å along the chains. Consideration of an energy penalty term for deviation of C1'-C1' distance from the mean value, to the recent DFT-D functionals, specifically ωB97X-D appears to predict reliable energy contour for AU/AU step. Such distance-based penalty improves energy contours for the other purine-pyrimidine sequences also. © 2013 Wiley Periodicals, Inc. Biopolymers 101: 107-120, 2014. Copyright © 2013 Wiley Periodicals, Inc.

Satellite-based prediction of rainfall interception by tropical forest stands of a human-dominated landscape in Central Sulawesi, Indonesia

NASA Astrophysics Data System (ADS)

Nieschulze, Jens; Erasmi, Stefan; Dietz, Johannes; Hölscher, Dirk

2009-01-01

SummaryRainforest conversion to other land use types drastically alters the hydrological cycle in which changes in rainfall interception contribute significantly to the observed differences. However, little is known about the effects of more gradual changes in forest structure and at regional scales. We studied land use types ranging from natural forest over selectively-logged forest to cacao agroforest in a lower montane region in Central Sulawesi, Indonesia, and tested the suitability of high-resolution optical satellite imagery for modeling observed interception patterns. Investigated characteristics indicating canopy structure were mean and standard deviation of reflectance values, local maxima, and self-similarity measures based on the grey level co-occurrence matrix and geostatistical variogram analysis. Previously studied and published rainfall interception data comprised twelve plots and median values per land use type ranged from 30% in natural forest to 18% in cacao agroforests. A linear regression model with local maxima, mean contrast and normalized digital vegetation index (NDVI) as regressors was able to explain more than 84% ( Radj2) of the variation encountered in the data. Other investigated characteristics did not prove significant in the regression analysis. The model yielded stable results with respect to cross-validation and also produced realistic values and spatial patterns when applied at the landscape level (783.6 ha). High values of interception were rare and localized in natural forest stands distant to villages, whereas low interception characterized the intensively used sites close to settlements. We conclude that forest use intensity significantly reduced rainfall interception and satellite image analysis can successfully be applied for its regional prediction, and most forest in the study region has already been subject to human-induced structural changes.

Helical magnetic structure and the anomalous and topological Hall effects in epitaxial B20 Fe1 -yCoyGe films

NASA Astrophysics Data System (ADS)

Spencer, Charles S.; Gayles, Jacob; Porter, Nicholas A.; Sugimoto, Satoshi; Aslam, Zabeada; Kinane, Christian J.; Charlton, Timothy R.; Freimuth, Frank; Chadov, Stanislav; Langridge, Sean; Sinova, Jairo; Felser, Claudia; Blügel, Stefan; Mokrousov, Yuriy; Marrows, Christopher H.

2018-06-01

Epitaxial films of the B20-structure compound Fe1 -yCoyGe were grown by molecular beam epitaxy on Si (111) substrates. The magnetization varied smoothly from the bulklike values of one Bohr magneton per Fe atom for FeGe to zero for nonmagnetic CoGe. The chiral lattice structure leads to a Dzyaloshinskii-Moriya interaction (DMI), and the films' helical magnetic ground state was confirmed using polarized neutron reflectometry measurements. The pitch of the spin helix, measured by this method, varies with Co content y and diverges at y ˜0.45 . This indicates a zero crossing of the DMI, which we reproduced in calculations using first-principles methods. We also measured the longitudinal and Hall resistivity of our films as a function of magnetic field, temperature, and Co content y . The Hall resistivity is expected to contain contributions from the ordinary, anomalous, and topological Hall effects. Both the anomalous and topological Hall resistivities show peaks around y ˜0.5 . Our first-principles calculations show a peak in the topological Hall constant at this value of y , related to the strong spin polarization predicted for intermediate values of y . Our calculations predict half-metallicity for y =0.6 , consistent with the experimentally observed linear magnetoresistance at this composition, and potentially related to the other unusual transport properties for intermediate value of y . While it is possible to reconcile theory with experiment for the various Hall effects for FeGe, the large topological Hall resistivities for y ˜0.5 are much larger than expected when the very small emergent fields associated with the divergence in the DMI are taken into account.

Assessing cannabis use in adolescents and young adults: what do urine screen and parental report tell you?

PubMed

Gignac, Martin; Wilens, Timothy E; Biederman, Joseph; Kwon, A; Mick, E; Swezey, A

2005-10-01

Our analysis compares three approaches to detect the most common drug abused in early adulthood, cannabis: (1) report on direct structured interview; (2) indirect parental report; and (3) urine toxicology screen. We examined data on 207 subjects (36% also met criteria for alcohol abuse; 9% for alcohol dependence) derived from two prospective and ongoing family studies of boys and girls with or without attention-deficit/hyperactivity disorder (ADHD). Assessments relied on the Schedule for Affective Disorders and Schizophrenia (K-SADS-E; under 18 years of age) and on the Structured Clinical Interview for DSM-IV (SCID-IV; over 18 years of age). Urine samples were analyzed with Auccusign DOA5 (on-site screening assay). Ninety-seven percent (97%) of individuals, who reported no use of cannabis within the past month, had a negative urine screening and 79% of individuals, who endorsed cannabis abuse/dependence, had a positive urine screening. The sensitivity of the direct structured interview report was 91%, the specificity 87%, the positive predicting value 67%, and the negative predictive value 97%. Indirect parental reports were found to be less informative on cannabis use than direct report. Direct report of cannabis use, abuse, or dependence during the structured interview is both sensitive and specific when compared to urine toxicology screens and indirect parental reports.

WallGen, software to construct layered cellulose-hemicellulose networks and predict their small deformation mechanics.

PubMed

Kha, Hung; Tuble, Sigrid C; Kalyanasundaram, Shankar; Williamson, Richard E

2010-02-01

We understand few details about how the arrangement and interactions of cell wall polymers produce the mechanical properties of primary cell walls. Consequently, we cannot quantitatively assess if proposed wall structures are mechanically reasonable or assess the effectiveness of proposed mechanisms to change mechanical properties. As a step to remedying this, we developed WallGen, a Fortran program (available on request) building virtual cellulose-hemicellulose networks by stochastic self-assembly whose mechanical properties can be predicted by finite element analysis. The thousands of mechanical elements in the virtual wall are intended to have one-to-one spatial and mechanical correspondence with their real wall counterparts of cellulose microfibrils and hemicellulose chains. User-defined inputs set the properties of the two polymer types (elastic moduli, dimensions of microfibrils and hemicellulose chains, hemicellulose molecular weight) and their population properties (microfibril alignment and volume fraction, polymer weight percentages in the network). This allows exploration of the mechanical consequences of variations in nanostructure that might occur in vivo and provides estimates of how uncertainties regarding certain inputs will affect WallGen's mechanical predictions. We summarize WallGen's operation and the choice of values for user-defined inputs and show that predicted values for the elastic moduli of multinet walls subject to small displacements overlap measured values. "Design of experiment" methods provide systematic exploration of how changed input values affect mechanical properties and suggest that changing microfibril orientation and/or the number of hemicellulose cross-bridges could change wall mechanical anisotropy.

Using Plasticity Values Determined from Systematic Hardness Indentation Measurements for Predicting Impact Behavior in Structural Ceramics: A New, Simple Screening Technique

DTIC Science & Technology

2008-12-01

oxynitride (AlON), silicon carbide, aluminum oxide and boron carbide. A power-law equation (H = kFc ) is shown to fit the Knoop data quite well. A plot...20 22 24 26 0 20 40 60 80 100 120 140 a/F + b kFc HK= 24.183 F-0.0699 R2= 0.97 H K (G Pa ) Load (N) HK = a/F + b ErrorValue 0.919483.7367a

The structure of premixed particle-cloud flames

NASA Technical Reports Server (NTRS)

Seshadri, K.; Berlad, A. L.; Tangirala, V.

1992-01-01

The structure of premixed flames propagating in combustible systems, containing uniformly distributed volatile fuel particles, in an oxidizing gas mixture, is analyzed. It is presumed that the fuel particles vaporize first to yield a gaseous fuel of known chemical structure, which is subsequently oxidized in the gas phase. The analysis is performed in the asymptotic limit, where the value of the characteristic Zeldovich number, based on the gas-phase oxidation of the gaseous fuel is large, and for values of phi(u) greater than or equal to 1.0, where phi(u) is the equivalence ratio based on the fuel available in the fuel particles. The structure of the flame is presumed to consist of a preheat vaporization zone where the rate of the gas-phase chemical reaction is small, a reaction zone where convection and the rate of vaporization of the fuel particles are small and a convection zone where diffusive terms in the conservation equations are small. For given values phi(u) the analysis yields results for the burning velocity and phi(g) where phi(g) is the effective equivalence ratio in the reaction zone. The analysis shows that even though phi(u) greater than or equal to 1.0, for certain cases the calculated value of phi(g) is less than unity. This prediction is in agreement with experimental observations.

Numerical study of the defect adamantine compound CuGaGeSe4

NASA Astrophysics Data System (ADS)

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

2018-06-01

The electronic structure, elastic and optical properties of the defect adamantine compound CuGaGeSe4 in ? structure are systematically investigated using first-principles calculations. Through detailed calculation and comparison, we obtain three independent atomic arrangements and predict the most stable atomic arrangement according to the lattice constants and enthalpy formation energies. The elastic constants are calculated, which can be used to predict the axial thermal expansion coefficients accurately. The optical properties of compound CuGaGeSe4, including the dielectric function, refractive index and absorption spectrum, are depicted for a more intuitive understanding. Our calculated zero-frequency limits ɛ1(0) and n(0) are very close to the other theoretical values, which proves that our calculations are reliable.

QSAR modeling for predicting mutagenic toxicity of diverse chemicals for regulatory purposes.

PubMed

Basant, Nikita; Gupta, Shikha

2017-06-01

The safety assessment process of chemicals requires information on their mutagenic potential. The experimental determination of mutagenicity of a large number of chemicals is tedious and time and cost intensive, thus compelling for alternative methods. We have established local and global QSAR models for discriminating low and high mutagenic compounds and predicting their mutagenic activity in a quantitative manner in Salmonella typhimurium (TA) bacterial strains (TA98 and TA100). The decision treeboost (DTB)-based classification QSAR models discriminated among two categories with accuracies of >96% and the regression QSAR models precisely predicted the mutagenic activity of diverse chemicals yielding high correlations (R 2 ) between the experimental and model-predicted values in the respective training (>0.96) and test (>0.94) sets. The test set root mean squared error (RMSE) and mean absolute error (MAE) values emphasized the usefulness of the developed models for predicting new compounds. Relevant structural features of diverse chemicals that were responsible and influence the mutagenic activity were identified. The applicability domains of the developed models were defined. The developed models can be used as tools for screening new chemicals for their mutagenicity assessment for regulatory purpose.

Competitive Dynamics on Complex Networks

PubMed Central

Zhao, Jiuhua; Liu, Qipeng; Wang, Xiaofan

2014-01-01

We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states, and is independent of the initial states of agents. This implies that the competition result is fully determined by the network structure and positions of competitors in the network. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another agent in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with seven node centrality measures to predict the winner. We find that the competitor with higher Katz Centrality in an undirected network or higher PageRank in a directed network is most likely to be the winner. These findings may shed new light on the role of network structure in competition and to what extent could competitors adjust network structure so as to win the competition. PMID:25068622

Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes?

DOE PAGES

Graham, Emily B.; Knelman, Joseph E.; Schindlbacher, Andreas; ...

2016-02-24

In this study, microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of processmore » rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.« less

Microbes as engines of ecosystem function: When does community structure enhance predictions of ecosystem processes?

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

Graham, Emily B.; Knelman, Joseph E.; Schindlbacher, Andreas

In this study, microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of processmore » rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.« less

Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?

PubMed Central

Graham, Emily B.; Knelman, Joseph E.; Schindlbacher, Andreas; Siciliano, Steven; Breulmann, Marc; Yannarell, Anthony; Beman, J. M.; Abell, Guy; Philippot, Laurent; Prosser, James; Foulquier, Arnaud; Yuste, Jorge C.; Glanville, Helen C.; Jones, Davey L.; Angel, Roey; Salminen, Janne; Newton, Ryan J.; Bürgmann, Helmut; Ingram, Lachlan J.; Hamer, Ute; Siljanen, Henri M. P.; Peltoniemi, Krista; Potthast, Karin; Bañeras, Lluís; Hartmann, Martin; Banerjee, Samiran; Yu, Ri-Qing; Nogaro, Geraldine; Richter, Andreas; Koranda, Marianne; Castle, Sarah C.; Goberna, Marta; Song, Bongkeun; Chatterjee, Amitava; Nunes, Olga C.; Lopes, Ana R.; Cao, Yiping; Kaisermann, Aurore; Hallin, Sara; Strickland, Michael S.; Garcia-Pausas, Jordi; Barba, Josep; Kang, Hojeong; Isobe, Kazuo; Papaspyrou, Sokratis; Pastorelli, Roberta; Lagomarsino, Alessandra; Lindström, Eva S.; Basiliko, Nathan; Nemergut, Diana R.

2016-01-01

Microorganisms are vital in mediating the earth’s biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: ‘When do we need to understand microbial community structure to accurately predict function?’ We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology. PMID:26941732

Microbes as Engines of Ecosystem Function: When Does Community Structure Enhance Predictions of Ecosystem Processes?

PubMed

Graham, Emily B; Knelman, Joseph E; Schindlbacher, Andreas; Siciliano, Steven; Breulmann, Marc; Yannarell, Anthony; Beman, J M; Abell, Guy; Philippot, Laurent; Prosser, James; Foulquier, Arnaud; Yuste, Jorge C; Glanville, Helen C; Jones, Davey L; Angel, Roey; Salminen, Janne; Newton, Ryan J; Bürgmann, Helmut; Ingram, Lachlan J; Hamer, Ute; Siljanen, Henri M P; Peltoniemi, Krista; Potthast, Karin; Bañeras, Lluís; Hartmann, Martin; Banerjee, Samiran; Yu, Ri-Qing; Nogaro, Geraldine; Richter, Andreas; Koranda, Marianne; Castle, Sarah C; Goberna, Marta; Song, Bongkeun; Chatterjee, Amitava; Nunes, Olga C; Lopes, Ana R; Cao, Yiping; Kaisermann, Aurore; Hallin, Sara; Strickland, Michael S; Garcia-Pausas, Jordi; Barba, Josep; Kang, Hojeong; Isobe, Kazuo; Papaspyrou, Sokratis; Pastorelli, Roberta; Lagomarsino, Alessandra; Lindström, Eva S; Basiliko, Nathan; Nemergut, Diana R

2016-01-01

Microorganisms are vital in mediating the earth's biogeochemical cycles; yet, despite our rapidly increasing ability to explore complex environmental microbial communities, the relationship between microbial community structure and ecosystem processes remains poorly understood. Here, we address a fundamental and unanswered question in microbial ecology: 'When do we need to understand microbial community structure to accurately predict function?' We present a statistical analysis investigating the value of environmental data and microbial community structure independently and in combination for explaining rates of carbon and nitrogen cycling processes within 82 global datasets. Environmental variables were the strongest predictors of process rates but left 44% of variation unexplained on average, suggesting the potential for microbial data to increase model accuracy. Although only 29% of our datasets were significantly improved by adding information on microbial community structure, we observed improvement in models of processes mediated by narrow phylogenetic guilds via functional gene data, and conversely, improvement in models of facultative microbial processes via community diversity metrics. Our results also suggest that microbial diversity can strengthen predictions of respiration rates beyond microbial biomass parameters, as 53% of models were improved by incorporating both sets of predictors compared to 35% by microbial biomass alone. Our analysis represents the first comprehensive analysis of research examining links between microbial community structure and ecosystem function. Taken together, our results indicate that a greater understanding of microbial communities informed by ecological principles may enhance our ability to predict ecosystem process rates relative to assessments based on environmental variables and microbial physiology.

A multivariate prediction model for Rho-dependent termination of transcription.

PubMed

Nadiras, Cédric; Eveno, Eric; Schwartz, Annie; Figueroa-Bossi, Nara; Boudvillain, Marc

2018-06-21

Bacterial transcription termination proceeds via two main mechanisms triggered either by simple, well-conserved (intrinsic) nucleic acid motifs or by the motor protein Rho. Although bacterial genomes can harbor hundreds of termination signals of either type, only intrinsic terminators are reliably predicted. Computational tools to detect the more complex and diversiform Rho-dependent terminators are lacking. To tackle this issue, we devised a prediction method based on Orthogonal Projections to Latent Structures Discriminant Analysis [OPLS-DA] of a large set of in vitro termination data. Using previously uncharacterized genomic sequences for biochemical evaluation and OPLS-DA, we identified new Rho-dependent signals and quantitative sequence descriptors with significant predictive value. Most relevant descriptors specify features of transcript C>G skewness, secondary structure, and richness in regularly-spaced 5'CC/UC dinucleotides that are consistent with known principles for Rho-RNA interaction. Descriptors collectively warrant OPLS-DA predictions of Rho-dependent termination with a ∼85% success rate. Scanning of the Escherichia coli genome with the OPLS-DA model identifies significantly more termination-competent regions than anticipated from transcriptomics and predicts that regions intrinsically refractory to Rho are primarily located in open reading frames. Altogether, this work delineates features important for Rho activity and describes the first method able to predict Rho-dependent terminators in bacterial genomes.

Aromatase inhibitory activity of 1,4-naphthoquinone derivatives and QSAR study

PubMed Central

Prachayasittikul, Veda; Pingaew, Ratchanok; Worachartcheewan, Apilak; Sitthimonchai, Somkid; Nantasenamat, Chanin; Prachayasittikul, Supaluk; Ruchirawat, Somsak; Prachayasittikul, Virapong

2017-01-01

A series of 2-amino(chloro)-3-chloro-1,4-naphthoquinone derivatives (1-11) were investigated for their aromatase inhibitory activities. 1,4-Naphthoquinones 1 and 4 were found to be the most potent compounds affording IC50 values 5.2 times lower than the reference drug, ketoconazole. A quantitative structure-activity relationship (QSAR) model provided good predictive performance (R2CV = 0.9783 and RMSECV = 0.0748) and indicated mass (Mor04m and H8m), electronegativity (Mor08e), van der Waals volume (G1v) and structural information content index (SIC2) descriptors as key descriptors governing the activity. To investigate the effects of structural modifications on aromatase inhibitory activity, the model was employed to predict the activities of an additional set of 39 structurally modified compounds constructed in silico. The prediction suggested that the 2,3-disubstitution of 1,4-naphthoquinone ring with halogen atoms (i.e., Br, I and F) is the most effective modification for potent activity (1a, 1b and 1c). Importantly, compound 1b was predicted to be more potent than its parent compound 1 (11.90-fold) and the reference drug, letrozole (1.03-fold). The study suggests the 1,4-naphthoquinone derivatives as promising compounds to be further developed as a novel class of aromatase inhibitors. PMID:28827987

Factors influencing protein tyrosine nitration--structure-based predictive models.

PubMed

Bayden, Alexander S; Yakovlev, Vasily A; Graves, Paul R; Mikkelsen, Ross B; Kellogg, Glen E

2011-03-15

Models for exploring tyrosine nitration in proteins have been created based on 3D structural features of 20 proteins for which high-resolution X-ray crystallographic or NMR data are available and for which nitration of 35 total tyrosines has been experimentally proven under oxidative stress. Factors suggested in previous work to enhance nitration were examined with quantitative structural descriptors. The role of neighboring acidic and basic residues is complex: for the majority of tyrosines that are nitrated the distance to the heteroatom of the closest charged side chain corresponds to the distance needed for suspected nitrating species to form hydrogen bond bridges between the tyrosine and that charged amino acid. This suggests that such bridges play a very important role in tyrosine nitration. Nitration is generally hindered for tyrosines that are buried and for those tyrosines for which there is insufficient space for the nitro group. For in vitro nitration, closed environments with nearby heteroatoms or unsaturated centers that can stabilize radicals are somewhat favored. Four quantitative structure-based models, depending on the conditions of nitration, have been developed for predicting site-specific tyrosine nitration. The best model, relevant for both in vitro and in vivo cases, predicts 30 of 35 tyrosine nitrations (positive predictive value) and has a sensitivity of 60/71 (11 false positives). Copyright © 2010 Elsevier Inc. All rights reserved.

Thermodynamic and Structural Properties of Methanol-Water Solutions Using Non-Additive Interaction Models

PubMed Central

Zhong, Yang; Warren, G. Lee; Patel, Sandeep

2014-01-01

We study bulk structural and thermodynamic properties of methanol-water solutions via molecular dynamics simulations using novel interaction potentials based on the charge equilibration (fluctuating charge) formalism to explicitly account for molecular polarization at the atomic level. The study uses the TIP4P-FQ potential for water-water interactions, and the CHARMM-based (Chemistry at HARvard Molecular Mechanics) fluctuating charge potential for methanol-methanol and methanol-water interactions. In terms of bulk solution properties, we discuss liquid densities, enthalpies of mixing, dielectric constants, self-diffusion constants, as well as structural properties related to local hydrogen bonding structure as manifested in radial distribution functions and cluster analysis. We further explore the electronic response of water and methanol in the differing local environments established by the interaction of each species predominantly with molecules of the other species. The current force field for the alcohol-water interaction performs reasonably well for most properties, with the greatest deviation from experiment observed for the excess mixing enthalpies, which are predicted to be too favorable. This is qualitatively consistent with the overestimation of the methanol-water gas-phase interaction energy for the lowest-energy conformer (methanol as proton donor). Hydration free energies for methanol in TIP4P-FQ water are predicted to be −5.6±0.2 kcal/mole, in respectable agreement with the experimental value of −5.1 kcal/mole. With respect to solution micro-structure, the present cluster analysis suggests that the micro-scale environment for concentrations where select thermodynamic quantities reach extremal values is described by a bi-percolating network structure. PMID:18074339

Evaluation of non-negative matrix factorization of grey matter in age prediction.

PubMed

Varikuti, Deepthi P; Genon, Sarah; Sotiras, Aristeidis; Schwender, Holger; Hoffstaedter, Felix; Patil, Kaustubh R; Jockwitz, Christiane; Caspers, Svenja; Moebus, Susanne; Amunts, Katrin; Davatzikos, Christos; Eickhoff, Simon B

2018-06-01

The relationship between grey matter volume (GMV) patterns and age can be captured by multivariate pattern analysis, allowing prediction of individuals' age based on structural imaging. Raw data, voxel-wise GMV and non-sparse factorization (with Principal Component Analysis, PCA) show good performance but do not promote relatively localized brain components for post-hoc examinations. Here we evaluated a non-negative matrix factorization (NNMF) approach to provide a reduced, but also interpretable representation of GMV data in age prediction frameworks in healthy and clinical populations. This examination was performed using three datasets: a multi-site cohort of life-span healthy adults, a single site cohort of older adults and clinical samples from the ADNI dataset with healthy subjects, participants with Mild Cognitive Impairment and patients with Alzheimer's disease (AD) subsamples. T1-weighted images were preprocessed with VBM8 standard settings to compute GMV values after normalization, segmentation and modulation for non-linear transformations only. Non-negative matrix factorization was computed on the GM voxel-wise values for a range of granularities (50-690 components) and LASSO (Least Absolute Shrinkage and Selection Operator) regression were used for age prediction. First, we compared the performance of our data compression procedure (i.e., NNMF) to various other approaches (i.e., uncompressed VBM data, PCA-based factorization and parcellation-based compression). We then investigated the impact of the granularity on the accuracy of age prediction, as well as the transferability of the factorization and model generalization across datasets. We finally validated our framework by examining age prediction in ADNI samples. Our results showed that our framework favorably compares with other approaches. They also demonstrated that the NNMF based factorization derived from one dataset could be efficiently applied to compress VBM data of another dataset and that granularities between 300 and 500 components give an optimal representation for age prediction. In addition to the good performance in healthy subjects our framework provided relatively localized brain regions as the features contributing to the prediction, thereby offering further insights into structural changes due to brain aging. Finally, our validation in clinical populations showed that our framework is sensitive to deviance from normal structural variations in pathological aging. Copyright © 2018 Elsevier Inc. All rights reserved.

Cosmological velocity correlations - Observations and model predictions

NASA Technical Reports Server (NTRS)

Gorski, Krzysztof M.; Davis, Marc; Strauss, Michael A.; White, Simon D. M.; Yahil, Amos

1989-01-01

By applying the present simple statistics for two-point cosmological peculiar velocity-correlation measurements to the actual data sets of the Local Supercluster spiral galaxy of Aaronson et al. (1982) and the elliptical galaxy sample of Burstein et al. (1987), as well as to the velocity field predicted by the distribution of IRAS galaxies, a coherence length of 1100-1600 km/sec is obtained. Coherence length is defined as that separation at which the correlations drop to half their zero-lag value. These results are compared with predictions from two models of large-scale structure formation: that of cold dark matter and that of baryon isocurvature proposed by Peebles (1980). N-body simulations of these models are performed to check the linear theory predictions and measure sampling fluctuations.

Can multivariate models based on MOAKS predict OA knee pain? Data from the Osteoarthritis Initiative

NASA Astrophysics Data System (ADS)

Luna-Gómez, Carlos D.; Zanella-Calzada, Laura A.; Galván-Tejada, Jorge I.; Galván-Tejada, Carlos E.; Celaya-Padilla, José M.

2017-03-01

Osteoarthritis is the most common rheumatic disease in the world. Knee pain is the most disabling symptom in the disease, the prediction of pain is one of the targets in preventive medicine, this can be applied to new therapies or treatments. Using the magnetic resonance imaging and the grading scales, a multivariate model based on genetic algorithms is presented. Using a predictive model can be useful to associate minor structure changes in the joint with the future knee pain. Results suggest that multivariate models can be predictive with future knee chronic pain. All models; T0, T1 and T2, were statistically significant, all p values were < 0.05 and all AUC > 0.60.

Solution x-ray scattering and structure formation in protein dynamics

NASA Astrophysics Data System (ADS)

Nasedkin, Alexandr; Davidsson, Jan; Niemi, Antti J.; Peng, Xubiao

2017-12-01

We propose a computationally effective approach that builds on Landau mean-field theory in combination with modern nonequilibrium statistical mechanics to model and interpret protein dynamics and structure formation in small- to wide-angle x-ray scattering (S/WAXS) experiments. We develop the methodology by analyzing experimental data in the case of Engrailed homeodomain protein as an example. We demonstrate how to interpret S/WAXS data qualitatively with a good precision and over an extended temperature range. We explain experimental observations in terms of protein phase structure, and we make predictions for future experiments and for how to analyze data at different ambient temperature values. We conclude that the approach we propose has the potential to become a highly accurate, computationally effective, and predictive tool for analyzing S/WAXS data. For this, we compare our results with those obtained previously in an all-atom molecular dynamics simulation.

Correlation of predicted and measured thermal stresses on an advanced aircraft structure with similar materials

NASA Technical Reports Server (NTRS)

Jenkins, J. M.

1979-01-01

A laboratory heating test simulating hypersonic heating was conducted on a heat-sink type structure to provide basic thermal stress measurements. Six NASTRAN models utilizing various combinations of bar, shear panel, membrane, and plate elements were used to develop calculated thermal stresses. Thermal stresses were also calculated using a beam model. For a given temperature distribution there was very little variation in NASTRAN calculated thermal stresses when element types were interchanged for a given grid system. Thermal stresses calculated for the beam model compared similarly to the values obtained for the NASTRAN models. Calculated thermal stresses compared generally well to laboratory measured thermal stresses. A discrepancy of signifiance occurred between the measured and predicted thermal stresses in the skin areas. A minor anomaly in the laboratory skin heating uniformity resulted in inadequate temperature input data for the structural models.

Vortex wakes generated by robins Erithacus rubecula during free flight in a wind tunnel.

PubMed

Hedenström, A; Rosén, M; Spedding, G R

2006-04-22

The wakes of two individual robins were measured in digital particle image velocimetry (DPIV) experiments conducted in the Lund wind tunnel. Wake measurements were compared with each other, and with previous studies in the same facility. There was no significant individual variation in any of the measured quantities. Qualitatively, the wake structure and its gradual variation with flight speed were exactly as previously measured for the thrush nightingale. A procedure that accounts for the disparate sources of circulation spread over the complex wake structure nevertheless can account for the vertical momentum flux required to support the weight, and an example calculation is given for estimating drag from the components of horizontal momentum flux (whose net value is zero). The measured circulations of the largest structures in the wake can be predicted quite well by simple models, and expressions are given to predict these and other measurable quantities in future bird flight experiments.

Atomic scale modelling of hexagonal structured metallic fission product alloys

PubMed Central

Middleburgh, S. C.; King, D. M.; Lumpkin, G. R.

2015-01-01

Noble metal particles in the Mo-Pd-Rh-Ru-Tc system have been simulated on the atomic scale using density functional theory techniques for the first time. The composition and behaviour of the epsilon phases are consistent with high-entropy alloys (or multi-principal component alloys)—making the epsilon phase the only hexagonally close packed high-entropy alloy currently described. Configurational entropy effects were considered to predict the stability of the alloys with increasing temperatures. The variation of Mo content was modelled to understand the change in alloy structure and behaviour with fuel burnup (Mo molar content decreases in these alloys as burnup increases). The predicted structures compare extremely well with experimentally ascertained values. Vacancy formation energies and the behaviour of extrinsic defects (including iodine and xenon) in the epsilon phase were also investigated to further understand the impact that the metallic precipitates have on fuel performance. PMID:26064629

Quantifying the increase in average human heterozygosity due to urbanisation.

PubMed

Rudan, Igor; Carothers, Andrew D; Polasek, Ozren; Hayward, Caroline; Vitart, Veronique; Biloglav, Zrinka; Kolcic, Ivana; Zgaga, Lina; Ivankovic, Davor; Vorko-Jovic, Ariana; Wilson, James F; Weber, James L; Hastie, Nick; Wright, Alan; Campbell, Harry

2008-09-01

The human population is undergoing a major transition from a historical metapopulation structure of relatively isolated small communities to an outbred structure. This process is predicted to increase average individual genome-wide heterozygosity (h) and could have effects on health. We attempted to quantify this increase in mean h. We initially sampled 1001 examinees from a metapopulation of nine isolated villages on five Dalmatian islands (Croatia). Village populations had high levels of genetic differentiation, endogamy and consanguinity. We then selected 166 individuals with highly specific personal genetic histories to form six subsamples, which could be ranked a priori by their predicted level of outbreeding. The measure h was then estimated in the 166 examinees by genotyping 1184 STR/indel markers and using two different computation methods. Compared to the value of mean h in the least outbred sample, values of h in the remaining samples increased successively with predicted outbreeding by 0.023, 0.038, 0.058, 0.067 and 0.079 (P<0.0001), where these values are measured on the same scale as the inbreeding coefficient (but opposite sign). We have shown that urbanisation was associated with an average increase in h of up to 0.08-0.10 in this Croatian metapopulation, regardless of the method used. Similar levels of differentiation have been described in many populations. Therefore, changes in the level of heterozygosity across the genome of this magnitude may be common during isolate break-up in humans and could have significant health effects through the established genetic mechanism of hybrid vigour/heterosis.

[Detection of the main quality indicators in red wine with infrared spectroscopy based on FastICA and neural network].

PubMed

Fang, Li-Min; Lin, Min

2009-08-01

For the rapid detection of the ethanol, pH and rest sugar in red wine, infrared (IR) spectra of 44 wine samples were analyzed. The algorithm of fast independent component analysis (FastICA) was used to decompose the data of IR spectra, and their independent components and the mixing matrix were obtained. Then, the ICA-NNR calibration model with three-level artificial neural network (ANN) structure was built by using back-propagation (BP) algorithm. The models were used to estimate the contents of ethanol, pH and rest sugar in red wine samples for both in calibration set and predicted set. Correlation coefficient (r) of prediction and root mean square error of prediction (RMSEP) were used as the evaluation indexes. The results indicate that the r and RMSEP for the prediction of ethanol content, pH and rest sugar content are 0.953, 0.983 and 0.994, and 0.161, 0.017 and 0.181, respectively. The maximum relative deviations between the ICA-NNR method predicted value and referenced value of the 22 samples in predicted set are less than 4%. The results of this paper provide a foundation for the application and further development of IR on-line red wine analyzer.

Outstanding problems in the band structures of 152Sm

NASA Astrophysics Data System (ADS)

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

2017-09-01

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

Planning for robust reserve networks using uncertainty analysis

USGS Publications Warehouse

Moilanen, A.; Runge, M.C.; Elith, Jane; Tyre, A.; Carmel, Y.; Fegraus, E.; Wintle, B.A.; Burgman, M.; Ben-Haim, Y.

2006-01-01

Planning land-use for biodiversity conservation frequently involves computer-assisted reserve selection algorithms. Typically such algorithms operate on matrices of species presence?absence in sites, or on species-specific distributions of model predicted probabilities of occurrence in grid cells. There are practically always errors in input data?erroneous species presence?absence data, structural and parametric uncertainty in predictive habitat models, and lack of correspondence between temporal presence and long-run persistence. Despite these uncertainties, typical reserve selection methods proceed as if there is no uncertainty in the data or models. Having two conservation options of apparently equal biological value, one would prefer the option whose value is relatively insensitive to errors in planning inputs. In this work we show how uncertainty analysis for reserve planning can be implemented within a framework of information-gap decision theory, generating reserve designs that are robust to uncertainty. Consideration of uncertainty involves modifications to the typical objective functions used in reserve selection. Search for robust-optimal reserve structures can still be implemented via typical reserve selection optimization techniques, including stepwise heuristics, integer-programming and stochastic global search.

QSPR models for various physical properties of carbohydrates based on molecular mechanics and quantum chemical calculations.

PubMed

Dyekjaer, Jane Dannow; Jónsdóttir, Svava Osk

2004-01-22

Quantitative Structure-Property Relationships (QSPR) have been developed for a series of monosaccharides, including the physical properties of partial molar heat capacity, heat of solution, melting point, heat of fusion, glass-transition temperature, and solid state density. The models were based on molecular descriptors obtained from molecular mechanics and quantum chemical calculations, combined with other types of descriptors. Saccharides exhibit a large degree of conformational flexibility, therefore a methodology for selecting the energetically most favorable conformers has been developed, and was used for the development of the QSPR models. In most cases good correlations were obtained for monosaccharides. For five of the properties predictions were made for disaccharides, and the predicted values for the partial molar heat capacities were in excellent agreement with experimental values.

The Role of School Principals in Shaping Children's Values.

PubMed

Berson, Yair; Oreg, Shaul

2016-12-01

Instilling values in children is among the cornerstones of every society. There is wide agreement that beyond academic teaching, schools play an important role in shaping schoolchildren's character, imparting in them values such as curiosity, achievement, benevolence, and citizenship. Despite the importance of this topic, we know very little about whether and how schools affect children's values. In this large-scale longitudinal study, we examined school principals' roles in the development of children's values. We hypothesized that relationships exist between principals' values and changes in children's values through the mediating effect of the school climate. To test our predictions, we collected data from 252 school principals, 3,658 teachers, and 49,401 schoolchildren. A multilevel structural-equation-modeling analysis yielded overall support for our hypotheses. These findings contribute to understanding the development of children's values and the far-reaching impact of leaders' values. They also demonstrate effects of schools on children beyond those on academic achievement.

Predicting climate-driven regime shifts versus rebound potential in coral reefs.

PubMed

Graham, Nicholas A J; Jennings, Simon; MacNeil, M Aaron; Mouillot, David; Wilson, Shaun K

2015-02-05

Climate-induced coral bleaching is among the greatest current threats to coral reefs, causing widespread loss of live coral cover. Conditions under which reefs bounce back from bleaching events or shift from coral to algal dominance are unknown, making it difficult to predict and plan for differing reef responses under climate change. Here we document and predict long-term reef responses to a major climate-induced coral bleaching event that caused unprecedented region-wide mortality of Indo-Pacific corals. Following loss of >90% live coral cover, 12 of 21 reefs recovered towards pre-disturbance live coral states, while nine reefs underwent regime shifts to fleshy macroalgae. Functional diversity of associated reef fish communities shifted substantially following bleaching, returning towards pre-disturbance structure on recovering reefs, while becoming progressively altered on regime shifting reefs. We identified threshold values for a range of factors that accurately predicted ecosystem response to the bleaching event. Recovery was favoured when reefs were structurally complex and in deeper water, when density of juvenile corals and herbivorous fishes was relatively high and when nutrient loads were low. Whether reefs were inside no-take marine reserves had no bearing on ecosystem trajectory. Although conditions governing regime shift or recovery dynamics were diverse, pre-disturbance quantification of simple factors such as structural complexity and water depth accurately predicted ecosystem trajectories. These findings foreshadow the likely divergent but predictable outcomes for reef ecosystems in response to climate change, thus guiding improved management and adaptation.

Conflicts among human values and trust in institutions.

PubMed

Devos, Thierry; Spini, Dario; Schwartz, Shalom H

2002-12-01

Institutions contribute to maintaining social order and stability in society. At the same time, they restrain the freedom of individuals. Based on the theory of value structure and content (Schwartz, 1992), we hypothesized about the relations of people's trust in institutions to their value priorities. More precisely, we predicted and found that the level of trust in various institutions correlated positively with values that stress stability, protection, and preservation of traditional practices, and negatively with values that emphasize independent thought and action and favour change. In addition, we demonstrated that groups defined on the basis of religious affiliation or political orientation exhibited contrasting value priorities on the same bipolar dimension. Moreover, differences in value priorities accounted for the fact that religious individuals and right-wing supporters expressed more trust in institutions than non-religious individuals and left-wing supporters.

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

PubMed

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

2017-09-29

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

Structural insights of Staphylococcus aureus FtsZ inhibitors through molecular docking, 3D-QSAR and molecular dynamics simulations.

PubMed

Ballu, Srilata; Itteboina, Ramesh; Sivan, Sree Kanth; Manga, Vijjulatha

2018-02-01

Filamentous temperature-sensitive protein Z (FtsZ) is a protein encoded by the FtsZ gene that assembles into a Z-ring at the future site of the septum of bacterial cell division. Structurally, FtsZ is a homolog of eukaryotic tubulin but has low sequence similarity; this makes it possible to obtain FtsZ inhibitors without affecting the eukaryotic cell division. Computational studies were performed on a series of substituted 3-arylalkoxybenzamide derivatives reported as inhibitors of FtsZ activity in Staphylococcus aureus. Quantitative structure-activity relationship models (QSAR) models generated showed good statistical reliability, which is evident from r 2 ncv and r 2 loo values. The predictive ability of these models was determined and an acceptable predictive correlation (r 2 Pred ) values were obtained. Finally, we performed molecular dynamics simulations in order to examine the stability of protein-ligand interactions. This facilitated us to compare free binding energies of cocrystal ligand and newly designed molecule B1. The good concordance between the docking results and comparative molecular field analysis (CoMFA)/comparative molecular similarity indices analysis (CoMSIA) contour maps afforded obliging clues for the rational modification of molecules to design more potent FtsZ inhibitors.

Structural, electronic, mechanical, and thermoelectric properties of a novel half Heusler compound HfPtPb

NASA Astrophysics Data System (ADS)

Kaur, Kulwinder; Rai, D. P.; Thapa, R. K.; Srivastava, Sunita

2017-07-01

We explore the structural, electronic, mechanical, and thermoelectric properties of a new half Heusler compound HfPtPb, an all metallic heavy element, recently proposed to be stable [Gautier et al., Nat. Chem. 7, 308 (2015)]. In this work, we employ density functional theory and semi-classical Boltzmann transport equations with constant relaxation time approximation. The mechanical properties, such as shear modulus, Young's modulus, elastic constants, Poisson's ratio, and shear anisotropy factor, have been investigated. The elastic and phonon properties reveal that this compound is mechanically and dynamically stable. Pugh's ratio and Frantsevich's ratio demonstrate its ductile behavior, and the shear anisotropic factor reveals the anisotropic nature of HfPtPb. The band structure predicts this compound to be a semiconductor with a band gap of 0.86 eV. The thermoelectric transport parameters, such as Seebeck coefficient, electrical conductivity, electronic thermal conductivity, and lattice thermal conductivity, have been calculated as a function of temperature. The highest value of Seebeck coefficient is obtained for n-type doping at an optimal carrier concentration of 1.0 × 1020 e/cm3. We predict the maximum value of figure of merit (0.25) at 1000 K. Our investigation suggests that this material is an n-type semiconductor.

Evaluation and comparison of the ability of online available prediction programs to predict true linear B-cell epitopes.

PubMed

Costa, Juan G; Faccendini, Pablo L; Sferco, Silvano J; Lagier, Claudia M; Marcipar, Iván S

2013-06-01

This work deals with the use of predictors to identify useful B-cell linear epitopes to develop immunoassays. Experimental techniques to meet this goal are quite expensive and time consuming. Therefore, we tested 5 free, online prediction methods (AAPPred, ABCpred, BcePred, BepiPred and Antigenic) widely used for predicting linear epitopes, using the primary structure of the protein as the only input. We chose a set of 65 experimentally well documented epitopes obtained by the most reliable experimental techniques as our true positive set. To compare the quality of the predictor methods we used their positive predictive value (PPV), i.e. the proportion of the predicted epitopes that are true, experimentally confirmed epitopes, in relation to all the epitopes predicted. We conclude that AAPPred and ABCpred yield the best results as compared with the other programs and with a random prediction procedure. Our results also indicate that considering the consensual epitopes predicted by several programs does not improve the PPV.

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

Characterizing ceramics and the interfacial adhesion to resin: I - The relationship of microstructure, composition, properties and fractography.

PubMed

Della Bona, Alvaro

2005-03-01

The appeal of ceramics as structural dental materials is based on their light weight, high hardness values, chemical inertness, and anticipated unique tribological characteristics. A major goal of current ceramic research and development is to produce tough, strong ceramics that can provide reliable performance in dental applications. Quantifying microstructural parameters is important to develop structure/property relationships. Quantitative microstructural analysis provides an association among the constitution, physical properties, and structural characteristics of materials. Structural reliability of dental ceramics is a major factor in the clinical success of ceramic restorations. Complex stress distributions are present in most practical conditions and strength data alone cannot be directly extrapolated to predict structural performance.

A food-predictive cue attributed with incentive salience engages subcortical afferents and efferents of the paraventricular nucleus of the thalamus.

PubMed

Haight, Joshua L; Fuller, Zachary L; Fraser, Kurt M; Flagel, Shelly B

2017-01-06

The paraventricular nucleus of the thalamus (PVT) has been implicated in behavioral responses to reward-associated cues. However, the precise role of the PVT in these behaviors has been difficult to ascertain since Pavlovian-conditioned cues can act as both predictive and incentive stimuli. The "sign-tracker/goal-tracker" rat model has allowed us to further elucidate the role of the PVT in cue-motivated behaviors, identifying this structure as a critical component of the neural circuitry underlying individual variation in the propensity to attribute incentive salience to reward cues. The current study assessed differences in the engagement of specific PVT afferents and efferents in response to presentation of a food-cue that had been attributed with only predictive value or with both predictive and incentive value. The retrograde tracer fluorogold (FG) was injected into the PVT or the nucleus accumbens (NAc) of rats, and cue-induced c-Fos in FG-labeled cells was quantified. Presentation of a predictive stimulus that had been attributed with incentive value elicited c-Fos in PVT afferents from the lateral hypothalamus, medial amygdala (MeA), and the prelimbic cortex (PrL), as well as posterior PVT efferents to the NAc. PVT afferents from the PrL also showed elevated c-Fos levels following presentation of a predictive stimulus alone. Thus, presentation of an incentive stimulus results in engagement of subcortical brain regions; supporting a role for the hypothalamic-thalamic-striatal axis, as well as the MeA, in mediating responses to incentive stimuli; whereas activity in the PrL to PVT pathway appears to play a role in processing the predictive qualities of reward-paired stimuli. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

A food-predictive cue attributed with incentive salience engages subcortical afferents and efferents of the paraventricular nucleus of the thalamus

PubMed Central

Haight, Joshua L.; Fuller, Zachary L.; Fraser, Kurt M.; Flagel, Shelly B.

2016-01-01

The paraventricular nucleus of the thalamus (PVT) has been implicated in behavioral responses to reward-associated cues. However, the precise role of the PVT in these behaviors has been difficult to ascertain since Pavlovian-conditioned cues can act as both predictive and incentive stimuli. The “sign-tracker/goal-tracker” animal model has allowed us to further elucidate the role of the PVT in cue-motivated behaviors, identifying this structure as a critical component of the neural circuitry underlying individual variation in the propensity to attribute incentive salience to reward cues. The current study assessed differences in the engagement of specific PVT afferents and efferents in response to presentation of a food-cue that had been attributed with only predictive value or with both predictive and incentive value. The retrograde tracer fluorogold (FG) was injected into the PVT or the nucleus accumbens (NAc), and cue-induced c-Fos in FG-labeled cells was quantified. Presentation of a predictive stimulus that had been attributed with incentive value elicited c-Fos in PVT afferents from the lateral hypothalamus, medial amygdala (MeA), and the prelimbic cortex (PrL), as well as posterior PVT efferents to the NAc. PVT afferents from the PrL also showed elevated c-Fos levels following presentation of a predictive stimulus alone. Thus, presentation of an incentive stimulus results in engagement of subcortical brain regions; supporting a role for the hypothalamic-thalamic-striatal axis, as well as the MeA, in mediating responses to incentive stimuli; whereas activity in the PrL to PVT pathway appears to play a role in processing the predictive qualities of reward-paired stimuli. PMID:27793779

Could Values and Social Structures in Singapore Facilitate Attainment of Patient-Focused, Cultural, and Linguistic Competency Standards in a Patient-Centered Medical Home Pilot?

PubMed Central

Shih, Jenny A; Shiow, Sue-Anne Toh Ee; Wee, Hwee-Lin

2015-01-01

Primary care practices in the United States are transforming into patient-centered medical homes (PCMHs) at a rapid pace. Newer PCMH standards have emphasized culturally and linguistically appropriate services (CLAS), but at this time, only some states in the United States have proposed or passed cultural competency training for health care professionals. Other countries are moving to PCMH models. Singapore, a small, ethnically diverse island nation, has national values and social structures that emphasize cultural and linguistic cohesion. In this piece, we examine Singapore’s first PCMH pilot with a national academic center and primary care practice group. Features such as common shared values, self-reliance, racial and religious harmony, patient experience surveillance, and incorporation of CLAS standards in routine health care transactions may predict success for the PCMH in Singapore, with some implications for the United States. PMID:28725822

The structural, electronic and optical properties of CuGa (SexS1-x)2 compounds from first-principle calculations

NASA Astrophysics Data System (ADS)

Shen, Ke-Sheng; Jiao, Zhao-Yong; Zhang, Xian-Zhou; Huang, Xiao-Fen

2013-11-01

The structural, electronic and optical properties of the CuGa (Se x S1- x )2 alloy system have been performed systematic within generalized gradient approximation (GGA) of Perdew-Burke-Ernzerhof (PBE) implemented in the Cambridge serial total energy package (CASTEP) code. We calculate the lattice parameters and axial ratio, which agree with the experimental values quite well. The anion position parameters u are also predicted using the model of Abrahams and Bernstein and the results seem to be trustworthy as compared to the experimental and theoretical values. The total and part density of states are discussed which follow the common rule of the conventional semiconductors. The static dielectric tenser and refractive index are summarized compared with available experimental and theoretical values. Also the spectra of the dielectric functions, refractive index, reflectance, absorption coefficient and real parts of photoconductivity are discussed in details.

QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.

PubMed

Votano, Joseph R; Parham, Marc; Hall, L Mark; Hall, Lowell H; Kier, Lemont B; Oloff, Scott; Tropsha, Alexander

2006-11-30

Four modeling techniques, using topological descriptors to represent molecular structure, were employed to produce models of human serum protein binding (% bound) on a data set of 1008 experimental values, carefully screened from publicly available sources. To our knowledge, this data is the largest set on human serum protein binding reported for QSAR modeling. The data was partitioned into a training set of 808 compounds and an external validation test set of 200 compounds. Partitioning was accomplished by clustering the compounds in a structure descriptor space so that random sampling of 20% of the whole data set produced an external test set that is a good representative of the training set with respect to both structure and protein binding values. The four modeling techniques include multiple linear regression (MLR), artificial neural networks (ANN), k-nearest neighbors (kNN), and support vector machines (SVM). With the exception of the MLR model, the ANN, kNN, and SVM QSARs were ensemble models. Training set correlation coefficients and mean absolute error ranged from r2=0.90 and MAE=7.6 for ANN to r2=0.61 and MAE=16.2 for MLR. Prediction results from the validation set yielded correlation coefficients and mean absolute errors which ranged from r2=0.70 and MAE=14.1 for ANN to a low of r2=0.59 and MAE=18.3 for the SVM model. Structure descriptors that contribute significantly to the models are discussed and compared with those found in other published models. For the ANN model, structure descriptor trends with respect to their affects on predicted protein binding can assist the chemist in structure modification during the drug design process.

Validation of Design and Analysis Techniques of Tailored Composite Structures

NASA Technical Reports Server (NTRS)

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

2004-01-01

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

Earthquake recurrence models fail when earthquakes fail to reset the stress field

USGS Publications Warehouse

Tormann, Thessa; Wiemer, Stefan; Hardebeck, Jeanne L.

2012-01-01

Parkfield's regularly occurring M6 mainshocks, about every 25 years, have over two decades stoked seismologists' hopes to successfully predict an earthquake of significant size. However, with the longest known inter-event time of 38 years, the latest M6 in the series (28 Sep 2004) did not conform to any of the applied forecast models, questioning once more the predictability of earthquakes in general. Our study investigates the spatial pattern of b-values along the Parkfield segment through the seismic cycle and documents a stably stressed structure. The forecasted rate of M6 earthquakes based on Parkfield's microseismicity b-values corresponds well to observed rates. We interpret the observed b-value stability in terms of the evolution of the stress field in that area: the M6 Parkfield earthquakes do not fully unload the stress on the fault, explaining why time recurrent models fail. We present the 1989 M6.9 Loma Prieta earthquake as counter example, which did release a significant portion of the stress along its fault segment and yields a substantial change in b-values.

Extending RosettaDock with water, sugar, and pH for prediction of complex structures and affinities for CAPRI rounds 20-27.

PubMed

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

2013-12-01

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

Predicting the intrauterine fetal death of fetuses with cystic hygroma in early pregnancy.

PubMed

Shimura, Mai; Ishikawa, Hiroshi; Nagase, Hiromi; Mochizuki, Akihiko; Sekiguchi, Futoshi; Koshimizu, Naho; Itai, Toshiyuki; Odagami, Mizuha

2018-01-11

We investigated whether it was possible to predict the prognosis of fetuses with cystic hygroma in early pregnancy based on the degree of neck thickening. We retrospectively analyzed 57 singleton pregnancies with fetuses with cystic hygroma who were examined before the 22nd week of pregnancy. The fetuses were categorized according to the outcome, structural abnormalities at birth, and chromosomal abnormalities. Here, we proposed a new sonographic predictor with which we assessed neck thickening by dividing the width of the neck thickening by the biparietal diameter, which is expressed as the cystic hygroma width/biparietal diameter ratio. The median cystic hygroma width/biparietal diameter ratio in the intrauterine fetal death group (0.51) was significantly higher than that in the live birth group (0.27). No significant difference in the median cystic hygroma width/biparietal diameter ratio was found between the structural abnormalities group at birth and the no structural abnormalities group, and no significant difference in the median cystic hygroma width/biparietal diameter ratio was found between the chromosomal abnormality group and the no chromosomal abnormality group. We used receiver operating characteristic analysis to evaluate the cystic hygroma width/biparietal diameter ratio to predict intrauterine fetal death. When the cystic hygroma width/biparietal diameter ratio cut-off value was 0.5, intrauterine fetal death could be predicted with a sensitivity of 52.9% and a specificity of 100%. It is possible to predict intrauterine fetal death in fetuses with cystic hygroma in early pregnancy if cystic hygroma width/biparietal diameter ratio is measured. However, even if cystic hygroma width/biparietal diameter ratio is measured, predicting the presence or absence of a structural abnormality at birth or a chromosomal abnormality is difficult. © 2018 Japanese Teratology Society.

The implications of the COBE diffuse microwave radiation results for cosmic strings

NASA Technical Reports Server (NTRS)

Bennett, David P.; Stebbins, Albert; Bouchet, Francois R.

1992-01-01

We compare the anisotropies in the cosmic microwave background radiation measured by the COBE experiment to those predicted by cosmic string theories. We use an analytic model for the Delta T/T power spectrum that is based on our previous numerical simulations of strings, under the assumption that cosmic strings are the sole source of the measured anisotropy. This implies a value for the string mass per unit length of 1.5 +/- 0.5 x 10 exp -6 C-squared/G. This is within the range of values required for cosmic strings to successfully seed the formation of large-scale structures in the universe. These results clearly encourage further studies of Delta T/T and large-scale structure in the cosmic string model.

Nonlinear vibrational excitations in molecular crystals molecular mechanics calculations

NASA Astrophysics Data System (ADS)

Pumilia, P.; Abbate, S.; Baldini, G.; Ferro, D. R.; Tubino, R.

1992-03-01

The coupling constant for vibrational solitons χ has been examined in a molecular mechanics model for acetanilide (ACN) molecular crystal. According to A.C. Scott, solitons can form and propagate in solid acetanilide over a threshold energy value. This can be regarded as a structural model for the spines of hydrogen bond chains stabilizing the α helical structure of proteins. A one dimensional hydrogen bond chain of ACN has been built, for which we have found that, even though experimental parameters are correctly predicted, the excessive rigidity of the isolated chain prevents the formation of a localized distortion around the excitation. Yet, C=O coupling value with softer lattice modes could be rather high, allowing self-trapping to take place.

The effect of protonation on the thermal isomerization of stilbazolium betaines

NASA Astrophysics Data System (ADS)

Tavan, Paul; Schulten, Klaus

1984-09-01

MINDOC calculations have been carried out on the protonated and unprotonated forms of a stilbazolium betaine. The results show (1) a strong increase by 24 kcal/mol of the torsional barrier around the central bond upon protonation, (2) polar structures for the protonated as well as the unprotonated forms, and (3) strong alterations of the polar structure of the latter during isomerization, and predict a higher pK value for the cis isomer, particularly, in the case of less polar and less protonic solvents.

An Ab Initio Study of CuCO

NASA Technical Reports Server (NTRS)

Bauschlicher, Charles W., Jr.

1994-01-01

Modified coupled-pair functional (MCPF) calculations and coupled cluster singles and doubles calculations, which include a perturbational estimate of the connected triples [CCSD(T)], yield a bent structure for CuCO, thus, supporting the prediction of a nonlinear structure based on density functional (DF) calculations. Our best estimate for the binding energy is 4.9 +/- 1.4 kcal/mol; this is in better agreement with experiment (6.0 +/- 1.2 kcal/mol) than the DF approach which yields a value (19.6 kcal/mol) significantly larger than experiment.

What is Neptune's D/H ratio really telling us about its water abundance?

NASA Astrophysics Data System (ADS)

Ali-Dib, Mohamad; Lakhlani, Gunjan

2018-05-01

We investigate the deep-water abundance of Neptune using a simple two-component (core + envelope) toy model. The free parameters of the model are the total mass of heavy elements in the planet (Z), the mass fraction of Z in the envelope (fenv), and the D/H ratio of the accreted building blocks (D/Hbuild).We systematically search the allowed parameter space on a grid and constrain it using Neptune's bulk carbon abundance, D/H ratio, and interior structure models. Assuming solar C/O ratio and cometary D/H for the accreted building blocks are forming the planet, we can fit all of the constraints if less than ˜15 per cent of Z is in the envelope (f_{env}^{median} ˜ 7 per cent), and the rest is locked in a solid core. This model predicts a maximum bulk oxygen abundance in Neptune of 65× solar value. If we assume a C/O of 0.17, corresponding to clathrate-hydrates building blocks, we predict a maximum oxygen abundance of 200× solar value with a median value of ˜140. Thus, both cases lead to oxygen abundance significantly lower than the preferred value of Cavalié et al. (˜540× solar), inferred from model-dependent deep CO observations. Such high-water abundances are excluded by our simple but robust model. We attribute this discrepancy to our imperfect understanding of either the interior structure of Neptune or the chemistry of the primordial protosolar nebula.

Chlorosilane acute inhalation toxicity and development of an LC50 prediction model.

PubMed

Jean, Paul A; Gallavan, Robert H; Kolesar, Gary B; Siddiqui, Waheed H; Oxley, Jon A; Meeks, Robert G

2006-07-01

The acute inhalation toxicity of 10 chlorosilanes was investigated in Fischer 344 rats using a 1-h whole-body vapor inhalation exposure and a 14-day recovery period. The median lethal concentration (LC50(1)) for each material was calculated from the nominal exposure concentrations and mortality. Experimentally derived LC50(1) values for monochlorosilanes (4257-4478 ppm) were greater than those for dichlorosilanes (1785-2092 ppm), which were greater than those for trichlorosilanes (1257-1611 ppm). Apparent was a strong structure-activity relationship (r2 = .97) between chlorine content and LC50(1) value. Estimated LC50(1) values for mono-, di-, and trichlorosilanes were determined to be 3262, 1639, and 1066 ppm, respectively, utilizing this relationship and the lower limit of the 95% prediction interval. The LC50(1) values determined in this series of studies were greater than that reported for hydrogen chloride (3124 ppm), when expressed on a chlorine equivalence basis (3570-5248 ppm), demonstrating that the acute toxicity of these chlorosilanes is similar to or less than that for hydrogen chloride. The good correlation between chlorine content and LC50(1) provides a sound basis for estimation of LC50(1) for chlorosilanes not already evaluated. The use of structure-activity relationships is consistent with the chemical industry and federal agency initiatives to reduce, refine, and/or replace the use of animals in testing without compromising the quality of health and safety assessments.

Sound transmission loss of composite sandwich panels

NASA Astrophysics Data System (ADS)

Zhou, Ran

Light composite sandwich panels are increasingly used in automobiles, ships and aircraft, because of the advantages they offer of high strength-to-weight ratios. However, the acoustical properties of these light and stiff structures can be less desirable than those of equivalent metal panels. These undesirable properties can lead to high interior noise levels. A number of researchers have studied the acoustical properties of honeycomb and foam sandwich panels. Not much work, however, has been carried out on foam-filled honeycomb sandwich panels. In this dissertation, governing equations for the forced vibration of asymmetric sandwich panels are developed. An analytical expression for modal densities of symmetric sandwich panels is derived from a sixth-order governing equation. A boundary element analysis model for the sound transmission loss of symmetric sandwich panels is proposed. Measurements of the modal density, total loss factor, radiation loss factor, and sound transmission loss of foam-filled honeycomb sandwich panels with different configurations and thicknesses are presented. Comparisons between the predicted sound transmission loss values obtained from wave impedance analysis, statistical energy analysis, boundary element analysis, and experimental values are presented. The wave impedance analysis model provides accurate predictions of sound transmission loss for the thin foam-filled honeycomb sandwich panels at frequencies above their first resonance frequencies. The predictions from the statistical energy analysis model are in better agreement with the experimental transmission loss values of the sandwich panels when the measured radiation loss factor values near coincidence are used instead of the theoretical values for single-layer panels. The proposed boundary element analysis model provides more accurate predictions of sound transmission loss for the thick foam-filled honeycomb sandwich panels than either the wave impedance analysis model or the statistical energy analysis model.

Investigating the Energetic Ordering of Stable and Metastable TiO 2 Polymorphs Using DFT+ U and Hybrid Functionals

DOE PAGES

Curnan, Matthew T.; Kitchin, John R.

2015-08-12

Prediction of transition metal oxide BO 2 (B = Ti, V, etc.) polymorph energetic properties is critical to tunable material design and identifying thermodynamically accessible structures. Determining procedures capable of synthesizing particular polymorphs minimally requires prior knowledge of their relative energetic favorability. Information concerning TiO 2 polymorph relative energetic favorability has been ascertained from experimental research. In this study, the consistency of first-principles predictions and experimental results involving the relative energetic ordering of stable (rutile), metastable (anatase and brookite), and unstable (columbite) TiO 2 polymorphs is assessed via density functional theory (DFT). Considering the issues involving electron–electron interaction and chargemore » delocalization in TiO 2 calculations, relative energetic ordering predictions are evaluated over trends varying Ti Hubbard U 3d or exact exchange fraction parameter values. Energetic trends formed from varying U 3d predict experimentally consistent energetic ordering over U 3d intervals when using GGA-based functionals, regardless of pseudopotential selection. Given pertinent linear response calculated Hubbard U values, these results enable TiO 2 polymorph energetic ordering prediction. Here, the hybrid functional calculations involving rutile–anatase relative energetics, though demonstrating experimentally consistent energetic ordering over exact exchange fraction ranges, are not accompanied by predicted fractions, for a first-principles methodology capable of calculating exact exchange fractions precisely predicting TiO 2 polymorph energetic ordering is not available.« less

Numerical study of the electronic structure, elastic and optical properties of defect quaternary semiconductor CuGaSnSe4

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Analysis of the Rotational Structure in the High-Resolution Infrared Spectrum of TRANS-HEXATRIENE-1-13C1

NASA Astrophysics Data System (ADS)

Craig, Norman C.; Tian, Hengfeng; Blake, Thomas A.

2011-06-01

Hexatriene-1-13C1 was synthesized by reaction of 2,4-pentadienal and (methyl-13C)-triphenylphosphonium iodide (Wittig reagent). The trans isomer was isolated by preparative gas chromatography, and the high-resolution (0.0015 Cm-1) infrared spectrum was recorded on a Bruker IFS 125HR instrument. The rotational structure in two C-type bands was analyzed. For this species the bands at 1010.7 and 893.740 Cm-1 yielded composite ground state rotational constants of A0 = 0.872820(1), B0 = 0.0435868(4), and C0 = 0.0415314(2) Cm-1. The ground state rotational constants for the 1-13C species were also predicted with Gaussian 03 software and the B3LYP/cc-pVTZ model. After scaling by the ratio of the observed and predicted ground state rotational constants for the normal species, the predicted ground state rotational constants for the 1-13C species agreed within 0.005 % with the observed values. Similar good agreement between observed and calculated values (0.016 %) was found for the three 13C species of the cis isomer. We conclude that ground state rotational constants for single heavy atom substitution can be calculated with adequate accuracy for use in determining semi-experimental equilibrium structures of small molecules. It will be unnecessary to synthesize the other two 13C species of trans-hexatriene. R. D. Suenram, B. H. Pate, A. Lesarri, J. L. Neill, S. Shipman, R. A. Holmes, M. C. Leyden, N. C. Craig J. Phys. Chem. A 113, 1864-1868 (2009).

Galaxy Formation Efficiency and the Multiverse Explanation of the Cosmological Constant with EAGLE Simulations

NASA Astrophysics Data System (ADS)

Barnes, Luke A.; Elahi, Pascal J.; Salcido, Jaime; Bower, Richard G.; Lewis, Geraint F.; Theuns, Tom; Schaller, Matthieu; Crain, Robert A.; Schaye, Joop

2018-04-01

Models of the very early universe, including inflationary models, are argued to produce varying universe domains with different values of fundamental constants and cosmic parameters. Using the cosmological hydrodynamical simulation code from the EAGLE collaboration, we investigate the effect of the cosmological constant on the formation of galaxies and stars. We simulate universes with values of the cosmological constant ranging from Λ = 0 to Λ0 × 300, where Λ0 is the value of the cosmological constant in our Universe. Because the global star formation rate in our Universe peaks at t = 3.5 Gyr, before the onset of accelerating expansion, increases in Λ of even an order of magnitude have only a small effect on the star formation history and efficiency of the universe. We use our simulations to predict the observed value of the cosmological constant, given a measure of the multiverse. Whether the cosmological constant is successfully predicted depends crucially on the measure. The impact of the cosmological constant on the formation of structure in the universe does not seem to be a sharp enough function of Λ to explain its observed value alone.

Galaxy formation efficiency and the multiverse explanation of the cosmological constant with EAGLE simulations

NASA Astrophysics Data System (ADS)

Barnes, Luke A.; Elahi, Pascal J.; Salcido, Jaime; Bower, Richard G.; Lewis, Geraint F.; Theuns, Tom; Schaller, Matthieu; Crain, Robert A.; Schaye, Joop

2018-07-01

Models of the very early Universe, including inflationary models, are argued to produce varying universe domains with different values of fundamental constants and cosmic parameters. Using the cosmological hydrodynamical simulation code from the EAGLE collaboration, we investigate the effect of the cosmological constant on the formation of galaxies and stars. We simulate universes with values of the cosmological constant ranging from Λ = 0 to Λ0 × 300, where Λ0 is the value of the cosmological constant in our Universe. Because the global star formation rate in our Universe peaks at t = 3.5 Gyr, before the onset of accelerating expansion, increases in Λ of even an order of magnitude have only a small effect on the star formation history and efficiency of the universe. We use our simulations to predict the observed value of the cosmological constant, given a measure of the multiverse. Whether the cosmological constant is successfully predicted depends crucially on the measure. The impact of the cosmological constant on the formation of structure in the universe does not seem to be a sharp enough function of Λ to explain its observed value alone.

Structural Inelasticity (28th), A Finite Element ’Vectorization’ Method for the Solution of Crack Growth Problems in Two or Three Dimensions.

DTIC Science & Technology

1986-02-01

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

A Study of the Dependence of Microsegregation on Critical Solidification Parameters in Rapidly-Quenched Structures.

DTIC Science & Technology

1980-12-01

a formulation given in many sources (Refs. 1-3). The laser is assumed to penetrate completely through the material (making a " keyhole ") and the heat...absorbed laser power as determined from calor- imetric measurements. The analytical predictions were brought to close agree- ment with the experimental...kW power setting would be about 45 kW/cm 2. This value is close to the 50 kW/cm2 line predicted by the model. As in Fig. 13, the laser dwell time is

Materials Discovery via CALYPSO Methodology

NASA Astrophysics Data System (ADS)

Ma, Yanming

2014-03-01

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

Recent developments of the NESSUS probabilistic structural analysis computer program

NASA Technical Reports Server (NTRS)

Millwater, H.; Wu, Y.-T.; Torng, T.; Thacker, B.; Riha, D.; Leung, C. P.

1992-01-01

The NESSUS probabilistic structural analysis computer program combines state-of-the-art probabilistic algorithms with general purpose structural analysis methods to compute the probabilistic response and the reliability of engineering structures. Uncertainty in loading, material properties, geometry, boundary conditions and initial conditions can be simulated. The structural analysis methods include nonlinear finite element and boundary element methods. Several probabilistic algorithms are available such as the advanced mean value method and the adaptive importance sampling method. The scope of the code has recently been expanded to include probabilistic life and fatigue prediction of structures in terms of component and system reliability and risk analysis of structures considering cost of failure. The code is currently being extended to structural reliability considering progressive crack propagation. Several examples are presented to demonstrate the new capabilities.

Method for protein structure alignment

DOEpatents

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

2005-02-22

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

The construction phase’s influence to the moving ability of cross-sections of woven structure

NASA Astrophysics Data System (ADS)

Inogamdjanov, D.; Daminov, A.; Kasimov, O.

2017-10-01

The purpose of this study is to work out bases to predict properties for single layer flat woven fabrics depending on changes of construction phases. A structural model of cross-section of single layered fabric is described based on the Pierce’s model. Form transformation of the yarn like straight, semi-arch and arch yarn is considered according to the alteration of yarn tension under the theory of Novikov. The value contributions to movement index of warp and weft yarn and their total moving ability in cross-sections at all structure phases of fabric are summarized.

ERRATUM: 'MAPPING THE GAS TURBULENCE IN THE COMA CLUSTER: PREDICTIONS FOR ASTRO-H'

NASA Technical Reports Server (NTRS)

Zuhone, J. A.; Markevitch, M.; Zhuravleva, I.

2016-01-01

The published version of this paper contained an error in Figure 5. This figure is intended to show the effect on the structure function of subtracting the bias induced by the statistical and systematic errors on the line shift. The filled circles show the bias-subtracted structure function. The positions of these points in the left panel of the original figure were calculated incorrectly. The figure is reproduced below (with the original caption) with the correct values for the bias-subtracted structure function. No other computations or figures in the original manuscript are affected.

Acoustic structure and propagation in highly porous, layered, fibrous materials

NASA Technical Reports Server (NTRS)

Lambert, R. F.; Tesar, J. S.

1984-01-01

The acoustic structure and propagation of sound in highly porous, layered, fine fiber materials is examined. Of particular interest is the utilization of the Kozeny number for determining the static flow resistance and the static structure factor based on flow permeability measurements. In this formulation the Kozeny number is a numerical constant independent of volume porosity at high porosities. The other essential parameters are then evaluated employing techniques developed earlier for open cell foams. The attenuation and progressive phase characteristics in bulk samples are measured and compared with predicted values. The agreements on the whole are very satisfactory.

Risk and protective factors for structural brain ageing in the eighth decade of life.

PubMed

Ritchie, Stuart J; Tucker-Drob, Elliot M; Cox, Simon R; Dickie, David Alexander; Del C Valdés Hernández, Maria; Corley, Janie; Royle, Natalie A; Redmond, Paul; Muñoz Maniega, Susana; Pattie, Alison; Aribisala, Benjamin S; Taylor, Adele M; Clarke, Toni-Kim; Gow, Alan J; Starr, John M; Bastin, Mark E; Wardlaw, Joanna M; Deary, Ian J

2017-11-01

Individuals differ markedly in brain structure, and in how this structure degenerates during ageing. In a large sample of human participants (baseline n = 731 at age 73 years; follow-up n = 488 at age 76 years), we estimated the magnitude of mean change and variability in changes in MRI measures of brain macrostructure (grey matter, white matter, and white matter hyperintensity volumes) and microstructure (fractional anisotropy and mean diffusivity from diffusion tensor MRI). All indices showed significant average change with age, with considerable heterogeneity in those changes. We then tested eleven socioeconomic, physical, health, cognitive, allostatic (inflammatory and metabolic), and genetic variables for their value in predicting these differences in changes. Many of these variables were significantly correlated with baseline brain structure, but few could account for significant portions of the heterogeneity in subsequent brain change. Physical fitness was an exception, being correlated both with brain level and changes. The results suggest that only a subset of correlates of brain structure are also predictive of differences in brain ageing.

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.

Toxicity of ionic liquids: database and prediction via quantitative structure-activity relationship method.

PubMed

Zhao, Yongsheng; Zhao, Jihong; Huang, Ying; Zhou, Qing; Zhang, Xiangping; Zhang, Suojiang

2014-08-15

A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the relationship between IL's structure and its toxicity has been analyzed qualitatively. Furthermore, Quantitative Structure-Activity relationships (QSAR) model is conducted to predict the toxicities (EC50 values) of various ILs toward the Leukemia rat cell line IPC-81. Four parameters selected by the heuristic method (HM) are used to perform the studies of multiple linear regression (MLR) and support vector machine (SVM). The squared correlation coefficient (R(2)) and the root mean square error (RMSE) of training sets by two QSAR models are 0.918 and 0.959, 0.258 and 0.179, respectively. The prediction R(2) and RMSE of QSAR test sets by MLR model are 0.892 and 0.329, by SVM model are 0.958 and 0.234, respectively. The nonlinear model developed by SVM algorithm is much outperformed MLR, which indicates that SVM model is more reliable in the prediction of toxicity of ILs. This study shows that increasing the relative number of O atoms of molecules leads to decrease in the toxicity of ILs. Copyright © 2014 Elsevier B.V. All rights reserved.

Implementation of machine learning for high-volume manufacturing metrology challenges (Conference Presentation)

NASA Astrophysics Data System (ADS)

Timoney, Padraig; Kagalwala, Taher; Reis, Edward; Lazkani, Houssam; Hurley, Jonathan; Liu, Haibo; Kang, Charles; Isbester, Paul; Yellai, Naren; Shifrin, Michael; Etzioni, Yoav

2018-03-01

In recent years, the combination of device scaling, complex 3D device architecture and tightening process tolerances have strained the capabilities of optical metrology tools to meet process needs. Two main categories of approaches have been taken to address the evolving process needs. In the first category, new hardware configurations are developed to provide more spectral sensitivity. Most of this category of work will enable next generation optical metrology tools to try to maintain pace with next generation process needs. In the second category, new innovative algorithms have been pursued to increase the value of the existing measurement signal. These algorithms aim to boost sensitivity to the measurement parameter of interest, while reducing the impact of other factors that contribute to signal variability but are not influenced by the process of interest. This paper will evaluate the suitability of machine learning to address high volume manufacturing metrology requirements in both front end of line (FEOL) and back end of line (BEOL) sectors from advanced technology nodes. In the FEOL sector, initial feasibility has been demonstrated to predict the fin CD values from an inline measurement using machine learning. In this study, OCD spectra were acquired after an etch process that occurs earlier in the process flow than where the inline CD is measured. The fin hard mask etch process is known to impact the downstream inline CD value. Figure 1 shows the correlation of predicted CD vs downstream inline CD measurement obtained after the training of the machine learning algorithm. For BEOL, machine learning is shown to provide an additional source of information in prediction of electrical resistance from structures that are not compatible for direct copper height measurement. Figure 2 compares the trench height correlation to electrical resistance (Rs) and the correlation of predicted Rs to the e-test Rs value for a far back end of line (FBEOL) metallization level across 3 products. In the case of product C, it is found that the predicted Rs correlation to the e-test value is significantly improved utilizing spectra acquired at the e-test structure. This paper will explore the considerations required to enable use of machine learning derived metrology output to enable improved process monitoring and control. Further results from the FEOL and BEOL sectors will be presented, together with further discussion on future proliferation of machine learning based metrology solutions in high volume manufacturing.

Synthesis, structure, theoretical and experimental in vitro antioxidant/pharmacological properties of α-aryl, N-alkyl nitrones, as potential agents for the treatment of cerebral ischemia.

PubMed

Samadi, Abdelouahid; Soriano, Elena; Revuelta, Julia; Valderas, Carolina; Chioua, Mourad; Garrido, Ignacio; Bartolomé, Begoña; Tomassolli, Isabelle; Ismaili, Lhassane; González-Lafuente, Laura; Villarroya, Mercedes; García, Antonio G; Oset-Gasque, María J; Marco-Contelles, José

2011-01-15

The synthesis, structure, theoretical and experimental in vitro antioxidant properties using the DPPH, ORAC, and benzoic acid, as well as preliminary in vitro pharmacological activities of (Z)-α-aryl and heteroaryl N-alkyl-nitrones 6-15, 18, 19, 21, and 23, is reported. In the in vitro antioxidant activity, for the DPPH radical test, only nitrones bearing free phenol groups gave the best RSA (%) values, nitrones 13 and 14 showing the highest values in this assay. In the ORAC analysis, the most potent radical scavenger was nitrone indole 21, followed by the N-benzyl benzene-type nitrones 10 and 15. Interestingly enough, the archetypal nitrone 7 (PBN) gave a low RSA value (1.4%) in the DPPH test, or was inactive in the ORAC assay. Concerning the ability to scavenge the hydroxyl radical, all the nitrones studied proved active in this experiment, showing high values in the 94-97% range, the most potent being nitrone 14. The theoretical calculations for the prediction of the antioxidant power, and the potential of ionization confirm that nitrones 9 and 10 are among the best compounds in electron transfer processes, a result that is also in good agreement with the experimental values in the DPPH assay. The calculated energy values for the reaction of ROS (hydroxyl, peroxyl) with the nitrones predict that the most favourable adduct-spin will take place between nitrones 9, 10, and 21, a fact that would be in agreement with their experimentally observed scavenger ability. The in vitro pharmacological analysis showed that the neuroprotective profile of the target molecules was in general low, with values ranging from 0% to 18.7%, in human neuroblastoma cells stressed with a mixture of rotenone/oligomycin-A, being nitrones 18, and 6-8 the most potent, as they show values in the range 24-18.4%. Crown Copyright © 2010. Published by Elsevier Ltd. All rights reserved.

Predicting the Naturalistic Course of Major Depressive Disorder Using Clinical and Multimodal Neuroimaging Information: A Multivariate Pattern Recognition Study.

PubMed

Schmaal, Lianne; Marquand, Andre F; Rhebergen, Didi; van Tol, Marie-José; Ruhé, Henricus G; van der Wee, Nic J A; Veltman, Dick J; Penninx, Brenda W J H

2015-08-15

A chronic course of major depressive disorder (MDD) is associated with profound alterations in brain volumes and emotional and cognitive processing. However, no neurobiological markers have been identified that prospectively predict MDD course trajectories. This study evaluated the prognostic value of different neuroimaging modalities, clinical characteristics, and their combination to classify MDD course trajectories. One hundred eighteen MDD patients underwent structural and functional magnetic resonance imaging (MRI) (emotional facial expressions and executive functioning) and were clinically followed-up at 2 years. Three MDD trajectories (chronic n = 23, gradual improving n = 36, and fast remission n = 59) were identified based on Life Chart Interview measuring the presence of symptoms each month. Gaussian process classifiers were employed to evaluate prognostic value of neuroimaging data and clinical characteristics (including baseline severity, duration, and comorbidity). Chronic patients could be discriminated from patients with more favorable trajectories from neural responses to various emotional faces (up to 73% accuracy) but not from structural MRI and functional MRI related to executive functioning. Chronic patients could also be discriminated from remitted patients based on clinical characteristics (accuracy 69%) but not when age differences between the groups were taken into account. Combining different task contrasts or data sources increased prediction accuracies in some but not all cases. Our findings provide evidence that the prediction of naturalistic course of depression over 2 years is improved by considering neuroimaging data especially derived from neural responses to emotional facial expressions. Neural responses to emotional salient faces more accurately predicted outcome than clinical data. Copyright © 2015 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

Kassiopeia: a database and web application for the analysis of mutually exclusive exomes of eukaryotes

PubMed Central

2014-01-01

Background Alternative splicing is an important process in higher eukaryotes that allows obtaining several transcripts from one gene. A specific case of alternative splicing is mutually exclusive splicing, in which exactly one exon out of a cluster of neighbouring exons is spliced into the mature transcript. Recently, a new algorithm for the prediction of these exons has been developed based on the preconditions that the exons of the cluster have similar lengths, sequence homology, and conserved splice sites, and that they are translated in the same reading frame. Description In this contribution we introduce Kassiopeia, a database and web application for the generation, storage, and presentation of genome-wide analyses of mutually exclusive exomes. Currently, Kassiopeia provides access to the mutually exclusive exomes of twelve Drosophila species, the thale cress Arabidopsis thaliana, the flatworm Caenorhabditis elegans, and human. Mutually exclusive spliced exons (MXEs) were predicted based on gene reconstructions from Scipio. Based on the standard prediction values, with which 83.5% of the annotated MXEs of Drosophila melanogaster were reconstructed, the exomes contain surprisingly more MXEs than previously supposed and identified. The user can search Kassiopeia using BLAST or browse the genes of each species optionally adjusting the parameters used for the prediction to reveal more divergent or only very similar exon candidates. Conclusions We developed a pipeline to predict MXEs in the genomes of several model organisms and a web interface, Kassiopeia, for their visualization. For each gene Kassiopeia provides a comprehensive gene structure scheme, the sequences and predicted secondary structures of the MXEs, and, if available, further evidence for MXE candidates from cDNA/EST data, predictions of MXEs in homologous genes of closely related species, and RNA secondary structure predictions. Kassiopeia can be accessed at http://www.motorprotein.de/kassiopeia. PMID:24507667

Predicting acute aquatic toxicity of structurally diverse chemicals in fish using artificial intelligence approaches.

PubMed

Singh, Kunwar P; Gupta, Shikha; Rai, Premanjali

2013-09-01

The research aims to develop global modeling tools capable of categorizing structurally diverse chemicals in various toxicity classes according to the EEC and European Community directives, and to predict their acute toxicity in fathead minnow using set of selected molecular descriptors. Accordingly, artificial intelligence approach based classification and regression models, such as probabilistic neural networks (PNN), generalized regression neural networks (GRNN), multilayer perceptron neural network (MLPN), radial basis function neural network (RBFN), support vector machines (SVM), gene expression programming (GEP), and decision tree (DT) were constructed using the experimental toxicity data. Diversity and non-linearity in the chemicals' data were tested using the Tanimoto similarity index and Brock-Dechert-Scheinkman statistics. Predictive and generalization abilities of various models constructed here were compared using several statistical parameters. PNN and GRNN models performed relatively better than MLPN, RBFN, SVM, GEP, and DT. Both in two and four category classifications, PNN yielded a considerably high accuracy of classification in training (95.85 percent and 90.07 percent) and validation data (91.30 percent and 86.96 percent), respectively. GRNN rendered a high correlation between the measured and model predicted -log LC50 values both for the training (0.929) and validation (0.910) data and low prediction errors (RMSE) of 0.52 and 0.49 for two sets. Efficiency of the selected PNN and GRNN models in predicting acute toxicity of new chemicals was adequately validated using external datasets of different fish species (fathead minnow, bluegill, trout, and guppy). The PNN and GRNN models showed good predictive and generalization abilities and can be used as tools for predicting toxicities of structurally diverse chemical compounds. Copyright © 2013 Elsevier Inc. All rights reserved.

Anti-inflammatory drugs and prediction of new structures by comparative analysis.

PubMed

Bartzatt, Ronald

2012-01-01

Nonsteroidal anti-inflammatory drugs (NSAIDs) are a group of agents important for their analgesic, anti-inflammatory, and antipyretic properties. This study presents several approaches to predict and elucidate new molecular structures of NSAIDs based on 36 known and proven anti-inflammatory compounds. Based on 36 known NSAIDs the mean value of Log P is found to be 3.338 (standard deviation= 1.237), mean value of polar surface area is 63.176 Angstroms2 (standard deviation = 20.951 A2), and the mean value of molecular weight is 292.665 (standard deviation = 55.627). Nine molecular properties are determined for these 36 NSAID agents, including Log P, number of -OH and -NHn, violations of Rule of 5, number of rotatable bonds, and number of oxygens and nitrogens. Statistical analysis of these nine molecular properties provides numerical parameters to conform to in the design of novel NSAID drug candidates. Multiple regression analysis is accomplished using these properties of 36 agents followed with examples of predicted molecular weight based on minimum and maximum property values. Hierarchical cluster analysis indicated that licofelone, tolfenamic acid, meclofenamic acid, droxicam, and aspirin are substantially distinct from all remaining NSAIDs. Analysis of similarity (ANOSIM) produced R = 0.4947, which indicates low to moderate level of dissimilarity between these 36 NSAIDs. Non-hierarchical K-means cluster analysis separated the 36 NSAIDs into four groups having members of greatest similarity. Likewise, discriminant analysis divided the 36 agents into two groups indicating the greatest level of distinction (discrimination) based on nine properties. These two multivariate methods together provide investigators a means to compare and elucidate novel drug designs to 36 proven compounds and ascertain to which of those are most analogous in pharmacodynamics. In addition, artificial neural network modeling is demonstrated as an approach to predict numerous molecular properties of new drug designs that is based on neural training from 36 proven NSAIDs. Comprehensive and effective approaches are presented in this study for the design of new NSAID type agents which are so very important for inhibition of COX-2 and COX-1 isoenzymes.

Adolescents' perceptions of socializers' beliefs, career-related conversations, and motivation in mathematics.

PubMed

Lazarides, Rebecca; Rubach, Charlott; Ittel, Angela

2017-03-01

Research based on the Eccles model of parent socialization demonstrated that parents are an important source of value and ability information for their children. Little is known, however, about the bidirectional effects between students' perceptions of their parents' beliefs and behaviors and the students' own domain-specific values. This study analyzed how students' perceptions of parents' beliefs and behaviors and students' mathematics values and mathematics-related career plans affect each other bidirectionally, and analyzed the role of students' gender as a moderator of these relations. Data from 475 students in 11th and 12th grade (girls: 50.3%; 31 classrooms; 12 schools), who participated in 2 waves of the study, were analyzed. Results of longitudinal structural equation models demonstrated that students' perceptions of their parents' mathematics value beliefs at Time 1 affected the students' own mathematics utility value at Time 2. Bidirectional effects were not shown in the full sample but were identified for boys. The paths within the tested model varied for boys and girls. For example, boys', not girls', mathematics intrinsic value predicted their reported conversations with their fathers about future occupational plans. Boys', not girls', perceived parents' mathematics value predicted the mathematics utility value. Findings are discussed in relation to their implications for parents and teachers, as well as in relation to gendered motivational processes. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Entropy measure of credit risk in highly correlated markets

NASA Astrophysics Data System (ADS)

Gottschalk, Sylvia

2017-07-01

We compare the single and multi-factor structural models of corporate default by calculating the Jeffreys-Kullback-Leibler divergence between their predicted default probabilities when asset correlations are either high or low. Single-factor structural models assume that the stochastic process driving the value of a firm is independent of that of other companies. A multi-factor structural model, on the contrary, is built on the assumption that a single firm's value follows a stochastic process correlated with that of other companies. Our main results show that the divergence between the two models increases in highly correlated, volatile, and large markets, but that it is closer to zero in small markets, when asset correlations are low and firms are highly leveraged. These findings suggest that during periods of financial instability, when asset volatility and correlations increase, one of the models misreports actual default risk.

Correlation between average frequency and RA value (rise time/amplitude) for crack classification of reinforced concrete beam using acoustic emission technique

NASA Astrophysics Data System (ADS)

Noorsuhada, M. N.; Abdul Hakeem, Z.; Soffian Noor, M. S.; Noor Syafeekha, M. S.; Azmi, I.

2017-12-01

Health monitoring of structures during their service life become a vital thing as it provides crucial information regarding the performance and condition of the structures. Acoustic emission (AE) is one of the non-destructive techniques (NDTs) that could be used to monitor the performance of the structures. Reinforced concrete (RC) beam associated with AE monitoring was monotonically loaded to failure under three-point loading. Correlation between average frequency and RA value (rise time / amplitude) was computed. The relationship was established to classify the crack types that propagated in the RC beam. The crack was classified as tensile crack and shear crack. It was found that the relationship is well matched with the actual crack pattern that appeared on the beam surface. Hence, this relationship is useful for prediction of the crack occurrence in the beam and its performance can be determined.

Distribution drivers and physiological responses in geothermal bryophyte communities.

PubMed

García, Estefanía Llaneza; Rosenstiel, Todd N; Graves, Camille; Shortlidge, Erin E; Eppley, Sarah M

2016-04-01

Our ability to explain community structure rests on our ability to define the importance of ecological niches, including realized ecological niches, in shaping communities, but few studies of plant distributions have combined predictive models with physiological measures. Using field surveys and statistical modeling, we predicted distribution drivers in geothermal bryophyte (moss) communities of Lassen Volcanic National Park (California, USA). In the laboratory, we used drying and rewetting experiments to test whether the strong species-specific effects of relative humidity on distributions predicted by the models were correlated with physiological characters. We found that the three most common bryophytes in geothermal communities were significantly affected by three distinct distribution drivers: temperature, light, and relative humidity. Aulacomnium palustre, whose distribution is significantly affected by relative humidity according to our model, and which occurs in high-humidity sites, showed extreme signs of stress after drying and never recovered optimal values of PSII efficiency after rewetting. Campylopus introflexus, whose distribution is not affected by humidity according to our model, was able to maintain optimal values of PSII efficiency for 48 hr at 50% water loss and recovered optimal values of PSII efficiency after rewetting. Our results suggest that species-specific environmental stressors tightly constrain the ecological niches of geothermal bryophytes. Tests of tolerance to drying in two bryophyte species corresponded with model predictions of the comparative importance of relative humidity as distribution drivers for these species. © 2016 Botanical Society of America.

Colossal dielectric response in all-ceramic percolative composite 0.65Pb(Mg1/3Nb2/3)O3-0.35PbTiO3-Pb2Ru2O6.5

NASA Astrophysics Data System (ADS)

Bobnar, V.; Hrovat, M.; Holc, J.; Filipič, C.; Levstik, A.; Kosec, M.

2009-02-01

An exceptionally high dielectric constant was obtained by making use of the conductive percolative phenomenon in all-ceramic composite, comprising of Pb2Ru2O6.5 with high electrical conductivity denoted as the conductive phase and ferroelectric 0.65Pb(Mg1/3Nb2/3)O3-0.35PbTiO3 (PMN-PT) perovskite systems. Structural analysis revealed a uniform distribution of conductive ceramic grains within the PMN-PT matrix. Consequently, the dielectric response in the PMN-PT-Pb2Ru2O6.5 composite follows the predictions of the percolation theory. Thus, close to the percolation point exceptionally high values of the dielectric constant were obtained—values higher than 105 were detected at room temperature at 1 kHz. Fit of the data, obtained for samples of different compositions, revealed critical exponent and percolation point, which reasonably agree with the theoretically predicted values.

A reexamination of age-related variation in body weight and morphometry of Maryland nutria

USGS Publications Warehouse

Sherfy, M.H.; Mollett, T.A.; McGowan, K.R.; Daugherty, S.L.

2006-01-01

Age-related variation in morphometry has been documented for many species. Knowledge of growth patterns can be useful for modeling energetics, detecting physiological influences on populations, and predicting age. These benefits have shown value in understanding population dynamics of invasive species, particularly in developing efficient control and eradication programs. However, development and evaluation of descriptive and predictive models is a critical initial step in this process. Accordingly, we used data from necropsies of 1,544 nutria (Myocastor coypus) collected in Maryland, USA, to evaluate the accuracy of previously published models for prediction of nutria age from body weight. Published models underestimated body weights of our animals, especially for ages <3. We used cross-validation procedures to develop and evaluate models for describing nutria growth patterns and for predicting nutria age. We derived models from a randomly selected model-building data set (n = 192-193 M, 217-222 F) and evaluated them with the remaining animals (n = 487-488 M, 642-647 F). We used nonlinear regression to develop Gompertz growth-curve models relating morphometric variables to age. Predicted values of morphometric variables fell within the 95% confidence limits of their true values for most age classes. We also developed predictive models for estimating nutria age from morphometry, using linear regression of log-transformed age on morphometric variables. The evaluation data set corresponded with 95% prediction intervals from the new models. Predictive models for body weight and length provided greater accuracy and less bias than models for foot length and axillary girth. Our growth models accurately described age-related variation in nutria morphometry, and our predictive models provided accurate estimates of ages from morphometry that will be useful for live-captured individuals. Our models offer better accuracy and precision than previously published models, providing a capacity for modeling energetics and growth patterns of Maryland nutria as well as an empirical basis for determining population age structure from live-captured animals.

3D Protein structure prediction with genetic tabu search algorithm

PubMed Central

2010-01-01

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

Thermal math model analysis of FRSI test article subjected to cold soak and entry environments. [Flexible Reuseable Surface Insulation in Space Shuttle Orbiter

NASA Technical Reports Server (NTRS)

Gallegos, J. J.

1978-01-01

A multi-objective test program was conducted at the NASA/JSC Radiant Heat Test Facility in which an aluminum skin/stringer test panel insulated with FRSI (Flexible Reusable Surface Insulation) was subjected to 24 simulated Space Shuttle Orbiter ascent/entry heating cycles with a cold soak in between in the 10th and 20th cycles. A two-dimensional thermal math model was developed and utilized to predict the thermal performance of the FRSI. Results are presented which indicate that the modeling techniques and property values have been proven adequate in predicting peak structure temperatures and entry thermal responses from both an ambient and cold soak condition of an FRSI covered aluminum structure.

A Structural Evaluation of a Large-Scale Quasi-Experimental Microfinance Initiative

PubMed Central

Kaboski, Joseph P.; Townsend, Robert M.

2010-01-01

This paper uses a structural model to understand, predict, and evaluate the impact of an exogenous microcredit intervention program, the Thai Million Baht Village Fund program. We model household decisions in the face of borrowing constraints, income uncertainty, and high-yield indivisible investment opportunities. After estimation of parameters using pre-program data, we evaluate the model’s ability to predict and interpret the impact of the village fund intervention. Simulations from the model mirror the data in yielding a greater increase in consumption than credit, which is interpreted as evidence of credit constraints. A cost-benefit analysis using the model indicates that some households value the program much more than its per household cost, but overall the program costs 20 percent more than the sum of these benefits. PMID:22162594

A Structural Evaluation of a Large-Scale Quasi-Experimental Microfinance Initiative.

PubMed

Kaboski, Joseph P; Townsend, Robert M

2011-09-01

This paper uses a structural model to understand, predict, and evaluate the impact of an exogenous microcredit intervention program, the Thai Million Baht Village Fund program. We model household decisions in the face of borrowing constraints, income uncertainty, and high-yield indivisible investment opportunities. After estimation of parameters using pre-program data, we evaluate the model's ability to predict and interpret the impact of the village fund intervention. Simulations from the model mirror the data in yielding a greater increase in consumption than credit, which is interpreted as evidence of credit constraints. A cost-benefit analysis using the model indicates that some households value the program much more than its per household cost, but overall the program costs 20 percent more than the sum of these benefits.

Prediction of betavoltaic battery output parameters based on SEM measurements and Monte Carlo simulation.

PubMed

Yakimov, Eugene B

2016-06-01

An approach for a prediction of (63)Ni-based betavoltaic battery output parameters is described. It consists of multilayer Monte Carlo simulation to obtain the depth dependence of excess carrier generation rate inside the semiconductor converter, a determination of collection probability based on the electron beam induced current measurements, a calculation of current induced in the semiconductor converter by beta-radiation, and SEM measurements of output parameters using the calculated induced current value. Such approach allows to predict the betavoltaic battery parameters and optimize the converter design for any real semiconductor structure and any thickness and specific activity of beta-radiation source. Copyright © 2016 Elsevier Ltd. All rights reserved.

The Future of Medical Dosimetry

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

Adams, Robert D., E-mail: robert_adams@med.unc.edu

2015-07-01

The world of health care delivery is becoming increasingly complex. The purpose of this manuscript is to analyze current metrics and analytically predict future practices and principles of medical dosimetry. The results indicate five potential areas precipitating change factors: a) evolutionary and revolutionary thinking processes, b) social factors, c) economic factors, d) political factors, and e) technological factors. Outcomes indicate that significant changes will occur in the job structure and content of being a practicing medical dosimetrist. Discussion indicates potential variables that can occur within each process and change factor and how the predicted outcomes can deviate from normative values.more » Finally, based on predicted outcomes, future opportunities for medical dosimetrists are given.« less

Weighted linear regression using D2H and D2 as the independent variables

Treesearch

Hans T. Schreuder; Michael S. Williams

1998-01-01

Several error structures for weighted regression equations used for predicting volume were examined for 2 large data sets of felled and standing loblolly pine trees (Pinus taeda L.). The generally accepted model with variance of error proportional to the value of the covariate squared ( D2H = diameter squared times height or D...

Predicting Changes in Athletes' Well Being from Changes in Need Satisfaction over the Course of a Competitive Season

ERIC Educational Resources Information Center

Amorose, Anthony J.; Anderson-Butcher, Dawn; Cooper, Jillian

2009-01-01

It is commonly believed that participation in structured sport programs leads to positive experiences and beneficial developmental outcomes for children and adolescents. For instance, proponents of organized sport cite that participation can help build self-esteem, promote sportspersonship, encourage a valuing of physical activity, and provide a…

Primerless RTV Silicone Sealants/Adhesives PP-1135

DTIC Science & Technology

2003-04-01

Ultem ®. Structural homology recommended benzyl alcohol as a candidate fragment that might fulfill these...value for benzyl alcohol . Qualitative experiments show that benzyl alcohol wets the surface of an Ultem sample, as do some of the other solvents...isopropanol and benzyl alcohol are very nearly equal, yet the modeling predicts that benzyl alcohol interacts more favorably with Ultem than

Perceived Autonomy-Support, Expectancy, Value, Metacognitive Strategies and Performance in Chemistry: A Structural Equation Model in Undergraduates

ERIC Educational Resources Information Center

González, Antonio; Paoloni, Paola-Verónica

2015-01-01

Research in chemistry education has highlighted a number of variables that predict learning and performance, such as teacher-student interactions, academic motivation and metacognition. Most of this chemistry research has examined these variables by identifying dyadic relationships through bivariate correlations. The main purpose of this study was…

A structural model for the flexural mechanics of nonwoven tissue engineering scaffolds.

PubMed

Engelmayr, George C; Sacks, Michael S

2006-08-01

The development of methods to predict the strength and stiffness of biomaterials used in tissue engineering is critical for load-bearing applications in which the essential functional requirements are primarily mechanical. We previously quantified changes in the effective stiffness (E) of needled nonwoven polyglycolic acid (PGA) and poly-L-lactic acid (PLLA) scaffolds due to tissue formation and scaffold degradation under three-point bending. Toward predicting these changes, we present a structural model for E of a needled nonwoven scaffold in flexure. The model accounted for the number and orientation of fibers within a representative volume element of the scaffold demarcated by the needling process. The spring-like effective stiffness of the curved fibers was calculated using the sinusoidal fiber shapes. Structural and mechanical properties of PGA and PLLA fibers and PGA, PLLA, and 50:50 PGA/PLLA scaffolds were measured and compared with model predictions. To verify the general predictive capability, the predicted dependence of E on fiber diameter was compared with experimental measurements. Needled nonwoven scaffolds were found to exhibit distinct preferred (PD) and cross-preferred (XD) fiber directions, with an E ratio (PD/XD) of approximately 3:1. The good agreement between the predicted and experimental dependence of E on fiber diameter (R2 = 0.987) suggests that the structural model can be used to design scaffolds with E values more similar to native soft tissues. A comparison with previous results for cell-seeded scaffolds (Engelmayr, G. C., Jr., et al., 2005, Biomaterials, 26(2), pp. 175-187) suggests, for the first time, that the primary mechanical effect of collagen deposition is an increase in the number of fiber-fiber bond points yielding effectively stiffer scaffold fibers. This finding indicated that the effects of tissue deposition on needled nonwoven scaffold mechanics do not follow a rule-of-mixtures behavior. These important results underscore the need for structural approaches in modeling the effects of engineered tissue formation on nonwoven scaffolds, and their potential utility in scaffold design.

Residual Strength Analyses of Monolithic Structures

NASA Technical Reports Server (NTRS)

Forth, Scott (Technical Monitor); Ambur, Damodar R. (Technical Monitor); Seshadri, B. R.; Tiwari, S. N.

2003-01-01

Finite-element fracture simulation methodology predicts the residual strength of damaged aircraft structures. The methodology uses the critical crack-tip-opening-angle (CTOA) fracture criterion to characterize the fracture behavior of the material. The CTOA fracture criterion assumes that stable crack growth occurs when the crack-tip angle reaches a constant critical value. The use of the CTOA criterion requires an elastic- plastic, finite-element analysis. The critical CTOA value is determined by simulating fracture behavior in laboratory specimens, such as a compact specimen, to obtain the angle that best fits the observed test behavior. The critical CTOA value appears to be independent of loading, crack length, and in-plane dimensions. However, it is a function of material thickness and local crack-front constraint. Modeling the local constraint requires either a three-dimensional analysis or a two-dimensional analysis with an approximation to account for the constraint effects. In recent times as the aircraft industry is leaning towards monolithic structures with the intention of reducing part count and manufacturing cost, there has been a consistent effort at NASA Langley to extend critical CTOA based numerical methodology in the analysis of integrally-stiffened panels.In this regard, a series of fracture tests were conducted on both flat and curved aluminum alloy integrally-stiffened panels. These flat panels were subjected to uniaxial tension and during the test, applied load-crack extension, out-of-plane displacements and local deformations around the crack tip region were measured. Compact and middle-crack tension specimens were tested to determine the critical angle (wc) using three-dimensional code (ZIP3D) and the plane-strain core height (hJ using two-dimensional code (STAGS). These values were then used in the STAGS analysis to predict the fracture behavior of the integrally-stiffened panels. The analyses modeled stable tearing, buckling, and crack branching at the integral stiffener using different values of critical CTOA for different material thicknesses and orientation. Comparisons were made between measured and predicted load-crack extension, out-of-plane displacements and local deformations around the crack tip region. Simultaneously, three-dimensional capabilities to model crack branching and to monitor stable crack growth of multiple cracks in a large thick integrally-stiffened flat panels were implemented in three-dimensional finite element code (ZIP3D) and tested by analyzing the integrally-stiffened panels tested at Alcoa. The residual strength of the panels predicted from STAGS and ZP3D code compared very well with experimental data. In recent times, STAGS software has been updated with new features and now one can have combinations of solid and shell elements in the residual strength analysis of integrally-stiffened panels.

Multilingual Validation of the Questionnaire for Verifying Stroke-Free Status in West Africa.

PubMed

Sarfo, Fred; Gebregziabher, Mulugeta; Ovbiagele, Bruce; Akinyemi, Rufus; Owolabi, Lukman; Obiako, Reginald; Akpa, Onoja; Armstrong, Kevin; Akpalu, Albert; Adamu, Sheila; Obese, Vida; Boa-Antwi, Nana; Appiah, Lambert; Arulogun, Oyedunni; Mensah, Yaw; Adeoye, Abiodun; Tosin, Aridegbe; Adeleye, Osimhiarherhuo; Tabi-Ajayi, Eric; Phillip, Ibinaiye; Sani, Abubakar; Isah, Suleiman; Tabari, Nasir; Mande, Aliyu; Agunloye, Atinuke; Ogbole, Godwin; Akinyemi, Joshua; Laryea, Ruth; Melikam, Sylvia; Uvere, Ezinne; Adekunle, Gregory; Kehinde, Salaam; Azuh, Paschal; Dambatta, Abdul; Ishaq, Naser; Saulson, Raelle; Arnett, Donna; Tiwari, Hemnant; Jenkins, Carolyn; Lackland, Dan; Owolabi, Mayowa

2016-01-01

The Questionnaire for Verifying Stroke-Free Status (QVSFS), a method for verifying stroke-free status in participants of clinical, epidemiological, and genetic studies, has not been validated in low-income settings where populations have limited knowledge of stroke symptoms. We aimed to validate QVSFS in 3 languages, Yoruba, Hausa and Akan, for ascertainment of stroke-free status of control subjects enrolled in an on-going stroke epidemiological study in West Africa. Data were collected using a cross-sectional study design where 384 participants were consecutively recruited from neurology and general medicine clinics of 5 tertiary referral hospitals in Nigeria and Ghana. Ascertainment of stroke status was by neurologists using structured neurological examination, review of case records, and neuroimaging (gold standard). Relative performance of QVSFS without and with pictures of stroke symptoms (pictograms) was assessed using sensitivity, specificity, positive predictive value, and negative predictive value. The overall median age of the study participants was 54 years and 48.4% were males. Of 165 stroke cases identified by gold standard, 98% were determined to have had stroke, whereas of 219 without stroke 87% were determined to be stroke-free by QVSFS. Negative predictive value of the QVSFS across the 3 languages was 0.97 (range, 0.93-1.00), sensitivity, specificity, and positive predictive value were 0.98, 0.82, and 0.80, respectively. Agreement between the questionnaire with and without the pictogram was excellent/strong with Cohen k=0.92. QVSFS is a valid tool for verifying stroke-free status across culturally diverse populations in West Africa. © 2015 American Heart Association, Inc.

Validation of the Hospital Anxiety and Depression Scale in patients with epilepsy.

PubMed

Wiglusz, Mariusz S; Landowski, Jerzy; Michalak, Lidia; Cubała, Wiesław J

2016-05-01

Despite the fact that depressive disorders are the most common comorbidities among patients with epilepsy (PWEs), they often go unrecognized and untreated. The availability of validated screening instruments to detect depression in PWEs is limited. The aim of the present study was to validate the Hospital Anxiety and Depression Scale (HADS) in adult PWEs. A consecutive group of 118 outpatient PWEs was invited to participate in the study. Ninety-six patients met inclusion criteria, completed HADS, and were examined by a trained psychiatrist using Structured Clinical Interview (SCID-I) for DSM-IV-TR. Receiver operating characteristic (ROC) curves were used to determine the optimal threshold scores for the HADS depression subscale (HADS-D). Receiver operating characteristic analyses showed areas under the curve at approximately 84%. For diagnoses of MDD, the HADS-D demonstrated the best psychometric properties for a cutoff score ≥7 with sensitivity of 90.5%, specificity of 70.7%, positive predictive value of 46.3%, and negative predictive value of 96.4%. In the case of the group with 'any depressive disorder', the HADS-D optimum cutoff score was ≥6 with sensitivity of 82.5%, specificity of 73.2%, positive predictive value of 68.8%, and negative predictive value of 85.4%. The HADS-D proved to be a valid and reliable psychometric instrument in terms of screening for depressive disorders in PWEs. In the epilepsy setting, HADS-D maintains adequate sensitivity, acceptable specificity, and high NPV but low PPV for diagnosing MDD with an optimum cutoff score ≥7. Copyright © 2016 Elsevier Inc. All rights reserved.

Right hemisphere structures predict poststroke speech fluency.

PubMed

Pani, Ethan; Zheng, Xin; Wang, Jasmine; Norton, Andrea; Schlaug, Gottfried

2016-04-26

We sought to determine via a cross-sectional study the contribution of (1) the right hemisphere's speech-relevant white matter regions and (2) interhemispheric connectivity to speech fluency in the chronic phase of left hemisphere stroke with aphasia. Fractional anisotropy (FA) of white matter regions underlying the right middle temporal gyrus (MTG), precentral gyrus (PreCG), pars opercularis (IFGop) and triangularis (IFGtri) of the inferior frontal gyrus, and the corpus callosum (CC) was correlated with speech fluency measures. A region within the superior parietal lobule (SPL) was examined as a control. FA values of regions that significantly predicted speech measures were compared with FA values from healthy age- and sex-matched controls. FA values for the right MTG, PreCG, and IFGop significantly predicted speech fluency, but FA values of the IFGtri and SPL did not. A multiple regression showed that combining FA of the significant right hemisphere regions with the lesion load of the left arcuate fasciculus-a previously identified biomarker of poststroke speech fluency-provided the best model for predicting speech fluency. FA of CC fibers connecting left and right supplementary motor areas (SMA) was also correlated with speech fluency. FA of the right IFGop and PreCG was significantly higher in patients than controls, while FA of a whole CC region of interest (ROI) and the CC-SMA ROI was significantly lower in patients. Right hemisphere white matter integrity is related to speech fluency measures in patients with chronic aphasia. This may indicate premorbid anatomical variability beneficial for recovery or be the result of poststroke remodeling. © 2016 American Academy of Neurology.

Right hemisphere structures predict poststroke speech fluency

PubMed Central

Pani, Ethan; Zheng, Xin; Wang, Jasmine; Norton, Andrea

2016-01-01

Objective: We sought to determine via a cross-sectional study the contribution of (1) the right hemisphere's speech-relevant white matter regions and (2) interhemispheric connectivity to speech fluency in the chronic phase of left hemisphere stroke with aphasia. Methods: Fractional anisotropy (FA) of white matter regions underlying the right middle temporal gyrus (MTG), precentral gyrus (PreCG), pars opercularis (IFGop) and triangularis (IFGtri) of the inferior frontal gyrus, and the corpus callosum (CC) was correlated with speech fluency measures. A region within the superior parietal lobule (SPL) was examined as a control. FA values of regions that significantly predicted speech measures were compared with FA values from healthy age- and sex-matched controls. Results: FA values for the right MTG, PreCG, and IFGop significantly predicted speech fluency, but FA values of the IFGtri and SPL did not. A multiple regression showed that combining FA of the significant right hemisphere regions with the lesion load of the left arcuate fasciculus—a previously identified biomarker of poststroke speech fluency—provided the best model for predicting speech fluency. FA of CC fibers connecting left and right supplementary motor areas (SMA) was also correlated with speech fluency. FA of the right IFGop and PreCG was significantly higher in patients than controls, while FA of a whole CC region of interest (ROI) and the CC-SMA ROI was significantly lower in patients. Conclusions: Right hemisphere white matter integrity is related to speech fluency measures in patients with chronic aphasia. This may indicate premorbid anatomical variability beneficial for recovery or be the result of poststroke remodeling. PMID:27029627

High-resolution synchrotron-based Fourier transform spectroscopy of [image omitted] in the 120-350 cm-1 far-infrared region

NASA Astrophysics Data System (ADS)

Moruzzi, G.; Murphy, R. J.; Lees, R. M.; Predoi-Cross, A.; Billinghurst, B. E.

2010-09-01

The Fourier transform spectrum of the ? isotopologue of methanol has been recorded in the 120-350 cm-1 far-infrared region at a resolution of 0.00096 cm-1 using synchrotron source radiation at the Canadian Light Source. The study, motivated by astrophysical applications, is aimed at generating a sufficiently accurate set of energy level term values for the ground vibrational state to allow prediction of the centres of the quadrupole hyperfine multiplets for astronomically observable sub-millimetre transitions to within an uncertainty of a few MHz. To expedite transition identification, a new function was added to the Ritz program in which predicted spectral line positions were generated by an adjustable interpolation between the known assignments for the ? and ? isotopologues. By displaying the predictions along with the experimental spectrum on the computer monitor and adjusting the predictions to match observed features, rapid assignment of numerous ? sub-bands was possible. The least squares function of the Ritz program was then used to generate term values for the identified levels. For each torsion-K-rotation substate, the term values were fitted to a Taylor-series expansion in powers of J(J + 1) to determine the substate origin energy and effective B-value. In this first phase of the study we did not attempt a full global fit to the assigned transitions, but instead fitted the sub-band J-independent origins to a restricted Hamiltonian containing the principal torsional and K-dependent terms. These included structural and torsional potential parameters plus quartic distortional and torsion-rotation interaction terms.

On the Distribution of Protein Refractive Index Increments

PubMed Central

Zhao, Huaying; Brown, Patrick H.; Schuck, Peter

2011-01-01

The protein refractive index increment, dn/dc, is an important parameter underlying the concentration determination and the biophysical characterization of proteins and protein complexes in many techniques. In this study, we examine the widely used assumption that most proteins have dn/dc values in a very narrow range, and reappraise the prediction of dn/dc of unmodified proteins based on their amino acid composition. Applying this approach in large scale to the entire set of known and predicted human proteins, we obtain, for the first time, to our knowledge, an estimate of the full distribution of protein dn/dc values. The distribution is close to Gaussian with a mean of 0.190 ml/g (for unmodified proteins at 589 nm) and a standard deviation of 0.003 ml/g. However, small proteins <10 kDa exhibit a larger spread, and almost 3000 proteins have values deviating by more than two standard deviations from the mean. Due to the widespread availability of protein sequences and the potential for outliers, the compositional prediction should be convenient and provide greater accuracy than an average consensus value for all proteins. We discuss how this approach should be particularly valuable for certain protein classes where a high dn/dc is coincidental to structural features, or may be functionally relevant such as in proteins of the eye. PMID:21539801

On the distribution of protein refractive index increments.

PubMed

Zhao, Huaying; Brown, Patrick H; Schuck, Peter

2011-05-04

The protein refractive index increment, dn/dc, is an important parameter underlying the concentration determination and the biophysical characterization of proteins and protein complexes in many techniques. In this study, we examine the widely used assumption that most proteins have dn/dc values in a very narrow range, and reappraise the prediction of dn/dc of unmodified proteins based on their amino acid composition. Applying this approach in large scale to the entire set of known and predicted human proteins, we obtain, for the first time, to our knowledge, an estimate of the full distribution of protein dn/dc values. The distribution is close to Gaussian with a mean of 0.190 ml/g (for unmodified proteins at 589 nm) and a standard deviation of 0.003 ml/g. However, small proteins <10 kDa exhibit a larger spread, and almost 3000 proteins have values deviating by more than two standard deviations from the mean. Due to the widespread availability of protein sequences and the potential for outliers, the compositional prediction should be convenient and provide greater accuracy than an average consensus value for all proteins. We discuss how this approach should be particularly valuable for certain protein classes where a high dn/dc is coincidental to structural features, or may be functionally relevant such as in proteins of the eye. Copyright © 2011 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Structure-activity relationships to estimate the effective Henry's law coefficients of organics of atmospheric interest

NASA Astrophysics Data System (ADS)

Raventos-Duran, Teresa; Valorso, Richard; Aumont, Bernard; Camredon, Marie

2010-05-01

The oxidation of volatile organic compounds emitted in the atmosphere involves complex reaction mechanisms which leads to the formation of oxygenated organic intermediates, usually denoted as secondary organics. The fate of these secondary organics remains poorly quantified due to a lack of information about their speciation, distribution and evolution in the gas and condensed phases. A significant fraction of secondary organics may dissolve into the tropospheric aqueous phase owing to the presence of polar moieties generated during the oxidation processes. The partitioning of organics between the gas and the aqueous atmospheric phases is usually described in the basis of Henry's law. Atmospheric models require a knowledge of the Henry's law coefficient (H) for every water soluble organic species described in the chemical mechanism. Methods that can predict reliable H values for the vast number of organic compounds are therefore required. We have compiled a data set of experimental Henry's law constants for compounds bearing functional groups of atmospheric relevance. This data set was then used to develop GROMHE, a structure activity relationship to predict H values based on a group contribution approach. We assessed its performance with two other available estimation methods. The results show that for all these methods the reliability of the estimates decreases with increasing solubility. We discuss differences between methods and found that GROMHE had greater prediction ability.

Reliability analysis of composite structures

NASA Technical Reports Server (NTRS)

Kan, Han-Pin

1992-01-01

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