Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity

PubMed Central

Mooney, Catherine; Haslam, Niall J.; Pollastri, Gianluca; Shields, Denis C.

2012-01-01

The conventional wisdom is that certain classes of bioactive peptides have specific structural features that endow their particular functions. Accordingly, predictions of bioactivity have focused on particular subgroups, such as antimicrobial peptides. We hypothesized that bioactive peptides may share more general features, and assessed this by contrasting the predictive power of existing antimicrobial predictors as well as a novel general predictor, PeptideRanker, across different classes of peptides. We observed that existing antimicrobial predictors had reasonable predictive power to identify peptides of certain other classes i.e. toxin and venom peptides. We trained two general predictors of peptide bioactivity, one focused on short peptides (4–20 amino acids) and one focused on long peptides ( amino acids). These general predictors had performance that was typically as good as, or better than, that of specific predictors. We noted some striking differences in the features of short peptide and long peptide predictions, in particular, high scoring short peptides favour phenylalanine. This is consistent with the hypothesis that short and long peptides have different functional constraints, perhaps reflecting the difficulty for typical short peptides in supporting independent tertiary structure. We conclude that there are general shared features of bioactive peptides across different functional classes, indicating that computational prediction may accelerate the discovery of novel bioactive peptides and aid in the improved design of existing peptides, across many functional classes. An implementation of the predictive method, PeptideRanker, may be used to identify among a set of peptides those that may be more likely to be bioactive. PMID:23056189

Predicting New Materials for Hydrogen Storage Application

PubMed Central

Vajeeston, Ponniah; Ravindran, Ponniah; Fjellvåg, Helmer

2009-01-01

Knowledge about the ground-state crystal structure is a prerequisite for the rational understanding of solid-state properties of new materials. To act as an efficient energy carrier, hydrogen should be absorbed and desorbed in materials easily and in high quantities. Owing to the complexity in structural arrangements and difficulties involved in establishing hydrogen positions by x-ray diffraction methods, the structural information of hydrides are very limited compared to other classes of materials (like oxides, intermetallics, etc.). This can be overcome by conducting computational simulations combined with selected experimental study which can save environment, money, and man power. The predicting capability of first-principles density functional theory (DFT) is already well recognized and in many cases structural and thermodynamic properties of single/multi component system are predicted. This review will focus on possible new classes of materials those have high hydrogen content, demonstrate the ability of DFT to predict crystal structure, and search for potential meta-stable phases. Stabilization of such meta-stable phases is also discussed.

Sexual risk behavior among youth: modeling the influence of prosocial activities and socioeconomic factors.

PubMed

Ramirez-Valles, J; Zimmerman, M A; Newcomb, M D

1998-09-01

Sexual activity among high-school-aged youths has steadily increased since the 1970s, emerging as a significant public health concern. Yet, patterns of youth sexual risk behavior are shaped by social class, race, and gender. Based on sociological theories of financial deprivation and collective socialization, we develop and test a model of the relationships among neighborhood poverty; family structure and social class position; parental involvement; prosocial activities; race; and gender as they predict youth sexual risk behavior. We employ structural equation modeling to test this model on a cross-sectional sample of 370 sexually active high-school students from a midwestern city; 57 percent (n = 209) are males and 86 percent are African American. We find that family structure indirectly predicts sexual risk behavior through neighborhood poverty, parental involvement, and prosocial activities. In addition, family class position indirectly predicts sexual risk behavior through neighborhood poverty and prosocial activities. We address implications for theory and health promotion.

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

PubMed

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

2009-04-21

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

Structural Revisions of a Class of Natural Products: Scaffolds of Aglycon Analogues of Fusicoccins and Cotylenins Isolated from Fungi.

PubMed

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

2016-03-14

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

An O(n(5)) algorithm for MFE prediction of kissing hairpins and 4-chains in nucleic acids.

PubMed

Chen, Ho-Lin; Condon, Anne; Jabbari, Hosna

2009-06-01

Efficient methods for prediction of minimum free energy (MFE) nucleic secondary structures are widely used, both to better understand structure and function of biological RNAs and to design novel nano-structures. Here, we present a new algorithm for MFE secondary structure prediction, which significantly expands the class of structures that can be handled in O(n(5)) time. Our algorithm can handle H-type pseudoknotted structures, kissing hairpins, and chains of four overlapping stems, as well as nested substructures of these types.

A dynamical system view of cerebellar function

NASA Astrophysics Data System (ADS)

Keeler, James D.

1990-06-01

First some previous theories of cerebellar function are reviewed, and deficiencies in how they map onto the neurophysiological structure are pointed out. I hypothesize that the cerebellar cortex builds an internal model, or prediction, of the dynamics of the animal. A class of algorithms for doing prediction based on local reconstruction of attractors are described, and it is shown how this class maps very well onto the structure of the cerebellar cortex. I hypothesize that the climbing fibers multiplex between different trajectories corresponding to different modes of operation. Then the vestibulo-ocular reflex is examined, and experiments to test the proposed model are suggested. The purpose of the presentation here is twofold: (1) To enlighten physiologists to the mathematics of a class of prediction algorithms that map well onto cerebellar architecture. (2) To enlighten dynamical system theorists to the physiological and anatomical details of the cerebellum.

Design, implementation and evaluation of a practical pseudoknot folding algorithm based on thermodynamics

PubMed Central

Reeder, Jens; Giegerich, Robert

2004-01-01

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

Uncertainty in age-specific harvest estimates and consequences for white-tailed deer management

USGS Publications Warehouse

Collier, B.A.; Krementz, D.G.

2007-01-01

Age structure proportions (proportion of harvested individuals within each age class) are commonly used as support for regulatory restrictions and input for deer population models. Such use requires critical evaluation when harvest regulations force hunters to selectively harvest specific age classes, due to impact on the underlying population age structure. We used a stochastic population simulation model to evaluate the impact of using harvest proportions to evaluate changes in population age structure under a selective harvest management program at two scales. Using harvest proportions to parameterize the age-specific harvest segment of the model for the local scale showed that predictions of post-harvest age structure did not vary dependent upon whether selective harvest criteria were in use or not. At the county scale, yearling frequency in the post-harvest population increased, but model predictions indicated that post-harvest population size of 2.5 years old males would decline below levels found before implementation of the antler restriction, reducing the number of individuals recruited into older age classes. Across the range of age-specific harvest rates modeled, our simulation predicted that underestimation of age-specific harvest rates has considerable influence on predictions of post-harvest population age structure. We found that the consequence of uncertainty in harvest rates corresponds to uncertainty in predictions of residual population structure, and this correspondence is proportional to scale. Our simulations also indicate that regardless of use of harvest proportions or harvest rates, at either the local or county scale the modeled SHC had a high probability (>0.60 and >0.75, respectively) of eliminating recruitment into >2.5 years old age classes. Although frequently used to increase population age structure, our modeling indicated that selective harvest criteria can decrease or eliminate the number of white-tailed deer recruited into older age classes. Thus, we suggest that using harvest proportions for management planning and evaluation should be viewed with caution. In addition, we recommend that managers focus more attention on estimation of age-specific harvest rates, and modeling approaches which combine harvest rates with information from harvested individuals to further increase their ability to effectively manage deer populations under selective harvest programs. ?? 2006 Elsevier B.V. All rights reserved.

Using RNA Sequence and Structure for the Prediction of Riboswitch Aptamer: A Comprehensive Review of Available Software and Tools

PubMed Central

Antunes, Deborah; Jorge, Natasha A. N.; Caffarena, Ernesto R.; Passetti, Fabio

2018-01-01

RNA molecules are essential players in many fundamental biological processes. Prokaryotes and eukaryotes have distinct RNA classes with specific structural features and functional roles. Computational prediction of protein structures is a research field in which high confidence three-dimensional protein models can be proposed based on the sequence alignment between target and templates. However, to date, only a few approaches have been developed for the computational prediction of RNA structures. Similar to proteins, RNA structures may be altered due to the interaction with various ligands, including proteins, other RNAs, and metabolites. A riboswitch is a molecular mechanism, found in the three kingdoms of life, in which the RNA structure is modified by the binding of a metabolite. It can regulate multiple gene expression mechanisms, such as transcription, translation initiation, and mRNA splicing and processing. Due to their nature, these entities also act on the regulation of gene expression and detection of small metabolites and have the potential to helping in the discovery of new classes of antimicrobial agents. In this review, we describe software and web servers currently available for riboswitch aptamer identification and secondary and tertiary structure prediction, including applications. PMID:29403526

Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou's PseAAC.

PubMed

Contreras-Torres, Ernesto

2018-06-02

In this study, I introduce novel global and local 0D-protein descriptors based on a statistical quantity named Total Sum of Squares (TSS). This quantity represents the sum of the squares differences of amino acid properties from the arithmetic mean property. As an extension, the amino acid-types and amino acid-groups formalisms are used for describing zones of interest in proteins. To assess the effectiveness of the proposed descriptors, a Nearest Neighbor model for predicting the major four protein structural classes was built. This model has a success rate of 98.53% on the jackknife cross-validation test; this performance being superior to other reported methods despite the simplicity of the predictor. Additionally, this predictor has an average success rate of 98.35% in different cross-validation tests performed. A value of 0.98 for the Kappa statistic clearly discriminates this model from a random predictor. The results obtained by the Nearest Neighbor model demonstrated the ability of the proposed descriptors not only to reflect relevant biochemical information related to the structural classes of proteins but also to allow appropriate interpretability. It can thus be expected that the current method may play a supplementary role to other existing approaches for protein structural class prediction and other protein attributes. Copyright © 2018 Elsevier Ltd. All rights reserved.

From molecule to solid: The prediction of organic crystal structures

NASA Astrophysics Data System (ADS)

Dzyabchenko, A. V.

2008-10-01

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

Prediction of bacterial small RNAs in the RsmA (CsrA) and ToxT pathways: a machine learning approach.

PubMed

Fakhry, Carl Tony; Kulkarni, Prajna; Chen, Ping; Kulkarni, Rahul; Zarringhalam, Kourosh

2017-08-22

Small RNAs (sRNAs) constitute an important class of post-transcriptional regulators that control critical cellular processes in bacteria. Recent research using high-throughput transcriptomic approaches has led to a dramatic increase in the discovery of bacterial sRNAs. However, it is generally believed that the currently identified sRNAs constitute a limited subset of the bacterial sRNA repertoire. In several cases, sRNAs belonging to a specific class are already known and the challenge is to identify additional sRNAs belonging to the same class. In such cases, machine-learning approaches can be used to predict novel sRNAs in a given class. In this work, we develop novel bioinformatics approaches that integrate sequence and structure-based features to train machine-learning models for the discovery of bacterial sRNAs. We show that features derived from recurrent structural motifs in the ensemble of low energy secondary structures can distinguish the RNA classes with high accuracy. We apply this approach to predict new members in two broad classes of bacterial small RNAs: 1) sRNAs that bind to the RNA-binding protein RsmA/CsrA in diverse bacterial species and 2) sRNAs regulated by the master regulator of virulence, ToxT, in Vibrio cholerae. The involvement of sRNAs in bacterial adaptation to changing environments is an increasingly recurring theme in current research in microbiology. It is likely that future research, combining experimental and computational approaches, will discover many more examples of sRNAs as components of critical regulatory pathways in bacteria. We have developed a novel approach for prediction of small RNA regulators in important bacterial pathways. This approach can be applied to specific classes of sRNAs for which several members have been identified and the challenge is to identify additional sRNAs.

Informative priors based on transcription factor structural class improve de novo motif discovery.

PubMed

Narlikar, Leelavati; Gordân, Raluca; Ohler, Uwe; Hartemink, Alexander J

2006-07-15

An important problem in molecular biology is to identify the locations at which a transcription factor (TF) binds to DNA, given a set of DNA sequences believed to be bound by that TF. In previous work, we showed that information in the DNA sequence of a binding site is sufficient to predict the structural class of the TF that binds it. In particular, this suggests that we can predict which locations in any DNA sequence are more likely to be bound by certain classes of TFs than others. Here, we argue that traditional methods for de novo motif finding can be significantly improved by adopting an informative prior probability that a TF binding site occurs at each sequence location. To demonstrate the utility of such an approach, we present priority, a powerful new de novo motif finding algorithm. Using data from TRANSFAC, we train three classifiers to recognize binding sites of basic leucine zipper, forkhead, and basic helix loop helix TFs. These classifiers are used to equip priority with three class-specific priors, in addition to a default prior to handle TFs of other classes. We apply priority and a number of popular motif finding programs to sets of yeast intergenic regions that are reported by ChIP-chip to be bound by particular TFs. priority identifies motifs the other methods fail to identify, and correctly predicts the structural class of the TF recognizing the identified binding sites. Supplementary material and code can be found at http://www.cs.duke.edu/~amink/.

Screening and structure-based modeling of T-cell epitopes of Nipah virus proteome: an immunoinformatic approach for designing peptide-based vaccine.

PubMed

Kamthania, Mohit; Sharma, D K

2015-12-01

Identification of Nipah virus (NiV) T-cell-specific antigen is urgently needed for appropriate diagnostic and vaccination. In the present study, prediction and modeling of T-cell epitopes of Nipah virus antigenic proteins nucleocapsid, phosphoprotein, matrix, fusion, glycoprotein, L protein, W protein, V protein and C protein followed by the binding simulation studies of predicted highest binding scorers with their corresponding MHC class I alleles were done. Immunoinformatic tool ProPred1 was used to predict the promiscuous MHC class I epitopes of viral antigenic proteins. The molecular modelings of the epitopes were done by PEPstr server. And alleles structure were predicted by MODELLER 9.10. Molecular dynamics (MD) simulation studies were performed through the NAMD graphical user interface embedded in visual molecular dynamics. Epitopes VPATNSPEL, NPTAVPFTL and LLFVFGPNL of Nucleocapsid, V protein and Fusion protein have considerable binding energy and score with HLA-B7, HLA-B*2705 and HLA-A2MHC class I allele, respectively. These three predicted peptides are highly potential to induce T-cell-mediated immune response and are expected to be useful in designing epitope-based vaccines against Nipah virus after further testing by wet laboratory studies.

Prediction-error variance in Bayesian model updating: a comparative study

NASA Astrophysics Data System (ADS)

Asadollahi, Parisa; Li, Jian; Huang, Yong

2017-04-01

In Bayesian model updating, the likelihood function is commonly formulated by stochastic embedding in which the maximum information entropy probability model of prediction error variances plays an important role and it is Gaussian distribution subject to the first two moments as constraints. The selection of prediction error variances can be formulated as a model class selection problem, which automatically involves a trade-off between the average data-fit of the model class and the information it extracts from the data. Therefore, it is critical for the robustness in the updating of the structural model especially in the presence of modeling errors. To date, three ways of considering prediction error variances have been seem in the literature: 1) setting constant values empirically, 2) estimating them based on the goodness-of-fit of the measured data, and 3) updating them as uncertain parameters by applying Bayes' Theorem at the model class level. In this paper, the effect of different strategies to deal with the prediction error variances on the model updating performance is investigated explicitly. A six-story shear building model with six uncertain stiffness parameters is employed as an illustrative example. Transitional Markov Chain Monte Carlo is used to draw samples of the posterior probability density function of the structure model parameters as well as the uncertain prediction variances. The different levels of modeling uncertainty and complexity are modeled through three FE models, including a true model, a model with more complexity, and a model with modeling error. Bayesian updating is performed for the three FE models considering the three aforementioned treatments of the prediction error variances. The effect of number of measurements on the model updating performance is also examined in the study. The results are compared based on model class assessment and indicate that updating the prediction error variances as uncertain parameters at the model class level produces more robust results especially when the number of measurement is small.

A hybrid method for prediction and repositioning of drug Anatomical Therapeutic Chemical classes.

PubMed

Chen, Lei; Lu, Jing; Zhang, Ning; Huang, Tao; Cai, Yu-Dong

2014-04-01

In the Anatomical Therapeutic Chemical (ATC) classification system, therapeutic drugs are divided into 14 main classes according to the organ or system on which they act and their chemical, pharmacological and therapeutic properties. This system, recommended by the World Health Organization (WHO), provides a global standard for classifying medical substances and serves as a tool for international drug utilization research to improve quality of drug use. In view of this, it is necessary to develop effective computational prediction methods to identify the ATC-class of a given drug, which thereby could facilitate further analysis of this system. In this study, we initiated an attempt to develop a prediction method and to gain insights from it by utilizing ontology information of drug compounds. Since only about one-fourth of drugs in the ATC classification system have ontology information, a hybrid prediction method combining the ontology information, chemical interaction information and chemical structure information of drug compounds was proposed for the prediction of drug ATC-classes. As a result, by using the Jackknife test, the 1st prediction accuracies for identifying the 14 main ATC-classes in the training dataset, the internal validation dataset and the external validation dataset were 75.90%, 75.70% and 66.36%, respectively. Analysis of some samples with false-positive predictions in the internal and external validation datasets indicated that some of them may even have a relationship with the false-positive predicted ATC-class, suggesting novel uses of these drugs. It was conceivable that the proposed method could be used as an efficient tool to identify ATC-classes of novel drugs or to discover novel uses of known drugs.

A combination of feature extraction methods with an ensemble of different classifiers for protein structural class prediction problem.

PubMed

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

2013-01-01

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

The role of population inertia in predicting the outcome of stage-structured biological invasions.

PubMed

Guiver, Chris; Dreiwi, Hanan; Filannino, Donna-Maria; Hodgson, Dave; Lloyd, Stephanie; Townley, Stuart

2015-07-01

Deterministic dynamic models for coupled resident and invader populations are considered with the purpose of finding quantities that are effective at predicting when the invasive population will become established asymptotically. A key feature of the models considered is the stage-structure, meaning that the populations are described by vectors of discrete developmental stage- or age-classes. The vector structure permits exotic transient behaviour-phenomena not encountered in scalar models. Analysis using a linear Lyapunov function demonstrates that for the class of population models considered, a large so-called population inertia is indicative of successful invasion. Population inertia is an indicator of transient growth or decline. Furthermore, for the class of models considered, we find that the so-called invasion exponent, an existing index used in models for invasion, is not always a reliable comparative indicator of successful invasion. We highlight these findings through numerical examples and a biological interpretation of why this might be the case is discussed. Copyright © 2015. Published by Elsevier Inc.

RNAstructure: software for RNA secondary structure prediction and analysis.

PubMed

Reuter, Jessica S; Mathews, David H

2010-03-15

To understand an RNA sequence's mechanism of action, the structure must be known. Furthermore, target RNA structure is an important consideration in the design of small interfering RNAs and antisense DNA oligonucleotides. RNA secondary structure prediction, using thermodynamics, can be used to develop hypotheses about the structure of an RNA sequence. RNAstructure is a software package for RNA secondary structure prediction and analysis. It uses thermodynamics and utilizes the most recent set of nearest neighbor parameters from the Turner group. It includes methods for secondary structure prediction (using several algorithms), prediction of base pair probabilities, bimolecular structure prediction, and prediction of a structure common to two sequences. This contribution describes new extensions to the package, including a library of C++ classes for incorporation into other programs, a user-friendly graphical user interface written in JAVA, and new Unix-style text interfaces. The original graphical user interface for Microsoft Windows is still maintained. The extensions to RNAstructure serve to make RNA secondary structure prediction user-friendly. The package is available for download from the Mathews lab homepage at http://rna.urmc.rochester.edu/RNAstructure.html.

Shark class II invariant chain reveals ancient conserved relationships with cathepsins and MHC class II.

PubMed

Criscitiello, Michael F; Ohta, Yuko; Graham, Matthew D; Eubanks, Jeannine O; Chen, Patricia L; Flajnik, Martin F

2012-03-01

The invariant chain (Ii) is the critical third chain required for the MHC class II heterodimer to be properly guided through the cell, loaded with peptide, and expressed on the surface of antigen presenting cells. Here, we report the isolation of the nurse shark Ii gene, and the comparative analysis of Ii splice variants, expression, genomic organization, predicted structure, and function throughout vertebrate evolution. Alternative splicing to yield Ii with and without the putative protease-protective, thyroglobulin-like domain is as ancient as the MHC-based adaptive immune system, as our analyses in shark and lizard further show conservation of this mechanism in all vertebrate classes except bony fish. Remarkable coordinate expression of Ii and class II was found in shark tissues. Conserved Ii residues and cathepsin L orthologs suggest their long co-evolution in the antigen presentation pathway, and genomic analyses suggest 450 million years of conserved Ii exon/intron structure. Other than an extended linker preceding the thyroglobulin-like domain in cartilaginous fish, the Ii gene and protein are predicted to have largely similar physiology from shark to man. Duplicated Ii genes found only in teleosts appear to have become sub-functionalized, as one form is predicted to play the same role as that mediated by Ii mRNA alternative splicing in all other vertebrate classes. No Ii homologs or potential ancestors of any of the functional Ii domains were found in the jawless fish or lower chordates. Copyright © 2011 Elsevier Ltd. All rights reserved.

Predicted structure of MIF/CD74 and RTL1000/CD74 complexes.

PubMed

Meza-Romero, Roberto; Benedek, Gil; Leng, Lin; Bucala, Richard; Vandenbark, Arthur A

2016-04-01

Macrophage migration inhibitory factor (MIF) is a key cytokine in autoimmune and inflammatory diseases that attracts and then retains activated immune cells from the periphery to the tissues. MIF exists as a homotrimer and its effects are mediated through its primary receptor, CD74 (the class II invariant chain that exhibits a highly structured trimerization domain), present on class II expressing cells. Although a number of binding residues have been identified between MIF and CD74 trimers, their spatial orientation has not been established. Using a docking program in silico, we have modeled binding interactions between CD74 and MIF as well as CD74 and a competitive MIF inhibitor, RTL1000, a partial MHC class II construct that is currently in clinical trials for multiple sclerosis. These analyses revealed 3 binding sites on the MIF trimer that each were predicted to bind one CD74 trimer through interactions with two distinct 5 amino acid determinants. Surprisingly, predicted binding of one CD74 trimer to a single RTL1000 antagonist utilized the same two 5 residue determinants, providing strong suggestive evidence in support of the MIF binding regions on CD74. Taken together, our structural modeling predicts a new MIF(CD74)3 dodecamer that may provide the basis for increased MIF potency and the requirement for ~3-fold excess RTL1000 to achieve full antagonism.

The predicted secondary structures of class I fructose-bisphosphate aldolases.

PubMed Central

Sawyer, L; Fothergill-Gilmore, L A; Freemont, P S

1988-01-01

The results of several secondary-structure prediction programs were combined to produce an estimate of the regions of alpha-helix, beta-sheet and reverse turns for fructose-bisphosphate aldolases from human and rat muscle and liver, from Trypanosoma brucei and from Drosophila melanogaster. All the aldolase sequences gave essentially the same pattern of secondary-structure predictions despite having sequences up to 50% different. One exception to this pattern was an additional strongly predicted helix in the rat liver and Drosophila enzymes. Regions of relatively high sequence variation generally were predicted as reverse turns, and probably occur as surface loops. Most of the positions corresponding to exon boundaries are located between regions predicted to have secondary-structural elements consistent with a compact structure. The predominantly alternating alpha/beta structure predicted is consistent with the alpha/beta-barrel structure indicated by preliminary high-resolution X-ray diffraction studies on rabbit muscle aldolase [Sygusch, Beaudry & Allaire (1986) Biophys. J. 49, 287a]. Images Fig. 1. (cont.) Fig. 1. PMID:3128269

Recovery of known T-cell epitopes by computational scanning of a viral genome

NASA Astrophysics Data System (ADS)

Logean, Antoine; Rognan, Didier

2002-04-01

A new computational method (EpiDock) is proposed for predicting peptide binding to class I MHC proteins, from the amino acid sequence of any protein of immunological interest. Starting from the primary structure of the target protein, individual three-dimensional structures of all possible MHC-peptide (8-, 9- and 10-mers) complexes are obtained by homology modelling. A free energy scoring function (Fresno) is then used to predict the absolute binding free energy of all possible peptides to the class I MHC restriction protein. Assuming that immunodominant epitopes are usually found among the top MHC binders, the method can thus be applied to predict the location of immunogenic peptides on the sequence of the protein target. When applied to the prediction of HLA-A*0201-restricted T-cell epitopes from the Hepatitis B virus, EpiDock was able to recover 92% of known high affinity binders and 80% of known epitopes within a filtered subset of all possible nonapeptides corresponding to about one tenth of the full theoretical list. The proposed method is fully automated and fast enough to scan a viral genome in less than an hour on a parallel computing architecture. As it requires very few starting experimental data, EpiDock can be used: (i) to predict potential T-cell epitopes from viral genomes (ii) to roughly predict still unknown peptide binding motifs for novel class I MHC alleles.

Innovation network

PubMed Central

Acemoglu, Daron; Akcigit, Ufuk; Kerr, William R.

2016-01-01

Technological progress builds upon itself, with the expansion of invention in one domain propelling future work in linked fields. Our analysis uses 1.8 million US patents and their citation properties to map the innovation network and its strength. Past innovation network structures are calculated using citation patterns across technology classes during 1975–1994. The interaction of this preexisting network structure with patent growth in upstream technology fields has strong predictive power on future innovation after 1995. This pattern is consistent with the idea that when there is more past upstream innovation for a particular technology class to build on, then that technology class innovates more. PMID:27681628

Tertiary model of a plant cellulose synthase

PubMed Central

Sethaphong, Latsavongsakda; Haigler, Candace H.; Kubicki, James D.; Zimmer, Jochen; Bonetta, Dario; DeBolt, Seth; Yingling, Yaroslava G.

2013-01-01

A 3D atomistic model of a plant cellulose synthase (CESA) has remained elusive despite over forty years of experimental effort. Here, we report a computationally predicted 3D structure of 506 amino acids of cotton CESA within the cytosolic region. Comparison of the predicted plant CESA structure with the solved structure of a bacterial cellulose-synthesizing protein validates the overall fold of the modeled glycosyltransferase (GT) domain. The coaligned plant and bacterial GT domains share a six-stranded β-sheet, five α-helices, and conserved motifs similar to those required for catalysis in other GT-2 glycosyltransferases. Extending beyond the cross-kingdom similarities related to cellulose polymerization, the predicted structure of cotton CESA reveals that plant-specific modules (plant-conserved region and class-specific region) fold into distinct subdomains on the periphery of the catalytic region. Computational results support the importance of the plant-conserved region and/or class-specific region in CESA oligomerization to form the multimeric cellulose–synthesis complexes that are characteristic of plants. Relatively high sequence conservation between plant CESAs allowed mapping of known mutations and two previously undescribed mutations that perturb cellulose synthesis in Arabidopsis thaliana to their analogous positions in the modeled structure. Most of these mutation sites are near the predicted catalytic region, and the confluence of other mutation sites supports the existence of previously undefined functional nodes within the catalytic core of CESA. Overall, the predicted tertiary structure provides a platform for the biochemical engineering of plant CESAs. PMID:23592721

Dynamics of Opinion Forming in Structurally Balanced Social Networks

PubMed Central

Altafini, Claudio

2012-01-01

A structurally balanced social network is a social community that splits into two antagonistic factions (typical example being a two-party political system). The process of opinion forming on such a community is most often highly predictable, with polarized opinions reflecting the bipartition of the network. The aim of this paper is to suggest a class of dynamical systems, called monotone systems, as natural models for the dynamics of opinion forming on structurally balanced social networks. The high predictability of the outcome of a decision process is explained in terms of the order-preserving character of the solutions of this class of dynamical systems. If we represent a social network as a signed graph in which individuals are the nodes and the signs of the edges represent friendly or hostile relationships, then the property of structural balance corresponds to the social community being splittable into two antagonistic factions, each containing only friends. PMID:22761667

Design of Critical Components

NASA Technical Reports Server (NTRS)

Hendricks, Robert C.; Zaretsky, Erwin V.

2001-01-01

Critical component design is based on minimizing product failures that results in loss of life. Potential catastrophic failures are reduced to secondary failures where components removed for cause or operating time in the system. Issues of liability and cost of component removal become of paramount importance. Deterministic design with factors of safety and probabilistic design address but lack the essential characteristics for the design of critical components. In deterministic design and fabrication there are heuristic rules and safety factors developed over time for large sets of structural/material components. These factors did not come without cost. Many designs failed and many rules (codes) have standing committees to oversee their proper usage and enforcement. In probabilistic design, not only are failures a given, the failures are calculated; an element of risk is assumed based on empirical failure data for large classes of component operations. Failure of a class of components can be predicted, yet one can not predict when a specific component will fail. The analogy is to the life insurance industry where very careful statistics are book-kept on classes of individuals. For a specific class, life span can be predicted within statistical limits, yet life-span of a specific element of that class can not be predicted.

Predicting Item Difficulty in a Reading Comprehension Test with an Artificial Neural Network.

ERIC Educational Resources Information Center

Perkins, Kyle; And Others

1995-01-01

This article reports the results of using a three-layer back propagation artificial neural network to predict item difficulty in a reading comprehension test. Three classes of variables were examined: text structure, propositional analysis, and cognitive demand. Results demonstrate that the networks can consistently predict item difficulty. (JL)

Prediction of enzyme classes from 3D structure: a general model and examples of experimental-theoretic scoring of peptide mass fingerprints of Leishmania proteins.

PubMed

Concu, Riccardo; Dea-Ayuela, Maria A; Perez-Montoto, Lazaro G; Bolas-Fernández, Francisco; Prado-Prado, Francisco J; Podda, Gianni; Uriarte, Eugenio; Ubeira, Florencio M; González-Díaz, Humberto

2009-09-01

The number of protein and peptide structures included in Protein Data Bank (PDB) and Gen Bank without functional annotation has increased. Consequently, there is a high demand for theoretical models to predict these functions. Here, we trained and validated, with an external set, a Markov Chain Model (MCM) that classifies proteins by their possible mechanism of action according to Enzyme Classification (EC) number. The methodology proposed is essentially new, and enables prediction of all EC classes with a single equation without the need for an equation for each class or nonlinear models with multiple outputs. In addition, the model may be used to predict whether one peptide presents a positive or negative contribution of the activity of the same EC class. The model predicts the first EC number for 106 out of 151 (70.2%) oxidoreductases, 178/178 (100%) transferases, 223/223 (100%) hydrolases, 64/85 (75.3%) lyases, 74/74 (100%) isomerases, and 100/100 (100%) ligases, as well as 745/811 (91.9%) nonenzymes. It is important to underline that this method may help us predict new enzyme proteins or select peptide candidates that improve enzyme activity, which may be of interest for the prediction of new drugs or drug targets. To illustrate the model's application, we report the 2D-Electrophoresis (2DE) isolation from Leishmania infantum as well as MADLI TOF Mass Spectra characterization and theoretical study of the Peptide Mass Fingerprints (PMFs) of a new protein sequence. The theoretical study focused on MASCOT, BLAST alignment, and alignment-free QSAR prediction of the contribution of 29 peptides found in the PMF of the new protein to specific enzyme action. This combined strategy may be used to identify and predict peptides of prokaryote and eukaryote parasites and their hosts as well as other superior organisms, which may be of interest in drug development or target identification.

Shock spectra applications to a class of multiple degree-of-freedom structures system

NASA Technical Reports Server (NTRS)

Hwang, Shoi Y.

1988-01-01

The demand on safety performance of launching structure and equipment system from impulsive excitations necessitates a study which predicts the maximum response of the system as well as the maximum stresses in the system. A method to extract higher modes and frequencies for a class of multiple degree-of-freedom (MDOF) Structure system is proposed. And, along with the shock spectra derived from a linear oscillator model, a procedure to obtain upper bound solutions for maximum displacement and maximum stresses in the MDOF system is presented.

Learning to Predict Combinatorial Structures

NASA Astrophysics Data System (ADS)

Vembu, Shankar

2009-12-01

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

Relations between Classroom Goal Structures and Students' Goal Orientations in Mathematics Classes: When Is a Mastery Goal Structure Adaptive?

ERIC Educational Resources Information Center

Skaalvik, Einar M.; Federici, Roger A.

2016-01-01

The purpose of this study was to test possible interactions between mastery and performance goal structures in mathematics classrooms when predicting students' goal orientations. More specifically, we tested if the degree of performance goal structure moderated the associations between mastery goal structure and students' goal orientations.…

Density Functional Theory and Beyond for Band-Gap Screening: Performance for Transition-Metal Oxides and Dichalcogenides.

PubMed

Li, Wenqing; Walther, Christian F J; Kuc, Agnieszka; Heine, Thomas

2013-07-09

The performance of a wide variety of commonly used density functionals, as well as two screened hybrid functionals (HSE06 and TB-mBJ), on predicting electronic structures of a large class of en vogue materials, such as metal oxides, chalcogenides, and nitrides, is discussed in terms of band gaps, band structures, and projected electronic densities of states. Contrary to GGA, hybrid functionals and GGA+U, both HSE06 and TB-mBJ are able to predict band gaps with an appreciable accuracy of 25% and thus allow the screening of various classes of transition-metal-based compounds, i.e., mixed or doped materials, at modest computational cost. The calculated electronic structures are largely unaffected by the choice of basis functions and software implementation, however, might be subject to the treatment of the core electrons.

Class Anxiety in Secondary Education: Exploring Structural Relations with Perceived Control, Engagement, Disaffection, and Performance.

PubMed

González, Antonio; Faílde Garrido, José María; Rodríguez Castro, Yolanda; Carrera Rodríguez, María Victoria

2015-09-14

The aim of this study was to assess the relationships between class-related anxiety with perceived control, teacher-reported behavioral engagement, behavioral disaffection, and academic performance. Participants were 355 compulsory secondary students (9th and 10th grades; Mean age = 15.2 years; SD = 1.8 years). Structural equation models revealed performance was predicted by perceived control, anxiety, disaffection, and engagement. Perceived control predicted anxiety, disaffection, and engagement. Anxiety predicted disaffection and engagement, and partially mediated the effects from control on disaffection (β = -.277, p < .005; CI = -.378, -.197) and engagement (β = .170, p < .002; CI = .103 .258). The negative association between anxiety and performance was mediated by engagement and disaffection (β = -.295, p < .002; CI = -.439, -.182). Anxiety, engagement, and disaffection mediated the effects of control on performance (β = .352, p < .003; CI = .279, .440). The implications of these results are discussed in the light of current theory and educational interventions.

Self-Determination and Meaningful Work: Exploring Socioeconomic Constraints.

PubMed

Allan, Blake A; Autin, Kelsey L; Duffy, Ryan D

2016-01-01

This study examined a model of meaningful work among a diverse sample of working adults. From the perspectives of Self-Determination Theory and the Psychology of Working Framework, we tested a structural model with social class and work volition predicting SDT motivation variables, which in turn predicted meaningful work. Partially supporting hypotheses, work volition was positively related to internal regulation and negatively related to amotivation, whereas social class was positively related to external regulation and amotivation. In turn, internal regulation was positively related to meaningful work, whereas external regulation and amotivation were negatively related to meaningful work. Indirect effects from work volition to meaningful work via internal regulation and amotivation were significant, and indirect effects from social class to meaningful work via external regulation and amotivation were significant. This study highlights the important relations between SDT motivation variables and meaningful work, especially the large positive relation between internal regulation and meaningful work. However, results also reveal that work volition and social class may play critical roles in predicting internal regulation, external regulation, and amotivation.

Prediction of a new class of half-metallic ferromagnets from first principles [A new class of half-metallic ferromagnets from first principles

DOE PAGES

Griffin, Sinead M.; Neaton, Jeffrey B.

2017-09-12

Half-metallic ferromagnetism (HMFM) occurs rarely in materials and yet offers great potential for spintronic devices. Recent experiments suggest a class of compounds with the `ThCrmore » $$_{2}$$Si$$_{2}$$' (122) structure -- isostructural and containing elements common with Fe pnictide-based superconductors -- can exhibit HMFM. Here we use $ab$ $initio$ density-functional theory calculations to understand the onset of half-metallicity in this family of materials and explain the appearance of ferromagnetism at a quantum critical point. We also predict new candidate materials with HMFM and high Curie temperatures through A-site alloying.« less

WeFold: A Coopetition for Protein Structure Prediction

PubMed Central

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

2014-01-01

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

Ab-initio conformational epitope structure prediction using genetic algorithm and SVM for vaccine design.

PubMed

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

2018-01-01

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

Abstract shapes of RNA.

PubMed

Giegerich, Robert; Voss, Björn; Rehmsmeier, Marc

2004-01-01

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

Epitope predictions indicate the presence of two distinct types of epitope-antibody-reactivities determined by epitope profiling of intravenous immunoglobulins.

PubMed

Luštrek, Mitja; Lorenz, Peter; Kreutzer, Michael; Qian, Zilliang; Steinbeck, Felix; Wu, Di; Born, Nadine; Ziems, Bjoern; Hecker, Michael; Blank, Miri; Shoenfeld, Yehuda; Cao, Zhiwei; Glocker, Michael O; Li, Yixue; Fuellen, Georg; Thiesen, Hans-Jürgen

2013-01-01

Epitope-antibody-reactivities (EAR) of intravenous immunoglobulins (IVIGs) determined for 75,534 peptides by microarray analysis demonstrate that roughly 9% of peptides derived from 870 different human protein sequences react with antibodies present in IVIG. Computational prediction of linear B cell epitopes was conducted using machine learning with an ensemble of classifiers in combination with position weight matrix (PWM) analysis. Machine learning slightly outperformed PWM with area under the curve (AUC) of 0.884 vs. 0.849. Two different types of epitope-antibody recognition-modes (Type I EAR and Type II EAR) were found. Peptides of Type I EAR are high in tyrosine, tryptophan and phenylalanine, and low in asparagine, glutamine and glutamic acid residues, whereas for peptides of Type II EAR it is the other way around. Representative crystal structures present in the Protein Data Bank (PDB) of Type I EAR are PDB 1TZI and PDB 2DD8, while PDB 2FD6 and 2J4W are typical for Type II EAR. Type I EAR peptides share predicted propensities for being presented by MHC class I and class II complexes. The latter interaction possibly favors T cell-dependent antibody responses including IgG class switching. Peptides of Type II EAR are predicted not to be preferentially presented by MHC complexes, thus implying the involvement of T cell-independent IgG class switch mechanisms. The high extent of IgG immunoglobulin reactivity with human peptides implies that circulating IgG molecules are prone to bind to human protein/peptide structures under non-pathological, non-inflammatory conditions. A webserver for predicting EAR of peptide sequences is available at www.sysmed-immun.eu/EAR.

A two-step nearest neighbors algorithm using satellite imagery for predicting forest structure within species composition classes

Treesearch

Ronald E. McRoberts

2009-01-01

Nearest neighbors techniques have been shown to be useful for predicting multiple forest attributes from forest inventory and Landsat satellite image data. However, in regions lacking good digital land cover information, nearest neighbors selected to predict continuous variables such as tree volume must be selected without regard to relevant categorical variables such...

MOAtox: A comprehensive mode of action and acute aquatic toxicity database for predictive model development (SETAC abstract)

EPA Science Inventory

The mode of toxic action (MOA) has been recognized as a key determinant of chemical toxicity and as an alternative to chemical class-based predictive toxicity modeling. However, the development of quantitative structure activity relationship (QSAR) and other models has been limit...

MOAtox: A Comprehensive Mode of Action and Acute Aquatic Toxicity Database for Predictive Model Development

EPA Science Inventory

tThe mode of toxic action (MOA) has been recognized as a key determinant of chemical toxicity andas an alternative to chemical class-based predictive toxicity modeling. However, the development ofquantitative structure activity relationship (QSAR) and other models has been limite...

Application of long-range order to predict unfolding rates of two-state proteins.

PubMed

Harihar, B; Selvaraj, S

2011-03-01

Predicting the experimental unfolding rates of two-state proteins and models describing the unfolding rates of these proteins is quite limited because of the complexity present in the unfolding mechanism and the lack of experimental unfolding data compared with folding data. In this work, 25 two-state proteins characterized by Maxwell et al. (Protein Sci 2005;14:602–616) using a consensus set of experimental conditions were taken, and the parameter long-range order (LRO) derived from their three-dimensional structures were related with their experimental unfolding rates ln(k(u)). From the total data set of 30 proteins used by Maxwell et al. (Protein Sci 2005;14:602–616), five slow-unfolding proteins with very low unfolding rates were considered to be outliers and were not included in our data set. Except all beta structural class, LRO of both the all-alpha and mixed-class proteins showed a strong inverse correlation of r = -0.99 and -0.88, respectively, with experimental ln(k(u)). LRO shows a correlation of -0.62 with experimental ln(k(u)) for all-beta proteins. For predicting the unfolding rates, a simple statistical method has been used and linear regression equations were developed for individual structural classes of proteins using LRO, and the results obtained showed a better agreement with experimental results. Copyright © 2010 Wiley-Liss, Inc.

Comparative structural modeling of six old yellow enzymes (OYEs) from the necrotrophic fungus Ascochyta rabiei: insight into novel OYE classes with differences in cofactor binding, organization of active site residues and stereopreferences.

PubMed

Nizam, Shadab; Gazara, Rajesh Kumar; Verma, Sandhya; Singh, Kunal; Verma, Praveen Kumar

2014-01-01

Old Yellow Enzyme (OYE1) was the first flavin-dependent enzyme identified and characterized in detail by the entire range of physical techniques. Irrespective of this scrutiny, true physiological role of the enzyme remains a mystery. In a recent study, we systematically identified OYE proteins from various fungi and classified them into three classes viz. Class I, II and III. However, there is no information about the structural organization of Class III OYEs, eukaryotic Class II OYEs and Class I OYEs of filamentous fungi. Ascochyta rabiei, a filamentous phytopathogen which causes Ascochyta blight (AB) in chickpea possesses six OYEs (ArOYE1-6) belonging to the three OYE classes. Here we carried out comparative homology modeling of six ArOYEs representing all the three classes to get an in depth idea of structural and functional aspects of fungal OYEs. The predicted 3D structures of A. rabiei OYEs were refined and evaluated using various validation tools for their structural integrity. Analysis of FMN binding environment of Class III OYE revealed novel residues involved in interaction. The ligand para-hydroxybenzaldehyde (PHB) was docked into the active site of the enzymes and interacting residues were analyzed. We observed a unique active site organization of Class III OYE in comparison to Class I and II OYEs. Subsequently, analysis of stereopreference through structural features of ArOYEs was carried out, suggesting differences in R/S selectivity of these proteins. Therefore, our comparative modeling study provides insights into the FMN binding, active site organization and stereopreference of different classes of ArOYEs and indicates towards functional differences of these enzymes. This study provides the basis for future investigations towards the biochemical and functional characterization of these enigmatic enzymes.

Support vector machine prediction of enzyme function with conjoint triad feature and hierarchical context.

PubMed

Wang, Yong-Cui; Wang, Yong; Yang, Zhi-Xia; Deng, Nai-Yang

2011-06-20

Enzymes are known as the largest class of proteins and their functions are usually annotated by the Enzyme Commission (EC), which uses a hierarchy structure, i.e., four numbers separated by periods, to classify the function of enzymes. Automatically categorizing enzyme into the EC hierarchy is crucial to understand its specific molecular mechanism. In this paper, we introduce two key improvements in predicting enzyme function within the machine learning framework. One is to introduce the efficient sequence encoding methods for representing given proteins. The second one is to develop a structure-based prediction method with low computational complexity. In particular, we propose to use the conjoint triad feature (CTF) to represent the given protein sequences by considering not only the composition of amino acids but also the neighbor relationships in the sequence. Then we develop a support vector machine (SVM)-based method, named as SVMHL (SVM for hierarchy labels), to output enzyme function by fully considering the hierarchical structure of EC. The experimental results show that our SVMHL with the CTF outperforms SVMHL with the amino acid composition (AAC) feature both in predictive accuracy and Matthew's correlation coefficient (MCC). In addition, SVMHL with the CTF obtains the accuracy and MCC ranging from 81% to 98% and 0.82 to 0.98 when predicting the first three EC digits on a low-homologous enzyme dataset. We further demonstrate that our method outperforms the methods which do not take account of hierarchical relationship among enzyme categories and alternative methods which incorporate prior knowledge about inter-class relationships. Our structure-based prediction model, SVMHL with the CTF, reduces the computational complexity and outperforms the alternative approaches in enzyme function prediction. Therefore our new method will be a useful tool for enzyme function prediction community.

Impact analysis of automotive structures with distributed smart material systems

NASA Astrophysics Data System (ADS)

Peelamedu, Saravanan M.; Naganathan, Ganapathy; Buckley, Stephen J.

1999-06-01

New class of automobiles has structural skins that are quite different from their current designs. Particularly, new families of composite skins are developed with new injection molding processes. These skins while support the concept of lighter vehicles of the future, are also susceptible to damage upon impact. It is important that their design should be based on a better understanding on the type of impact loads and the resulting strains and damage. It is possible that these skins can be integrally designed with active materials to counter damages. This paper presents a preliminary analysis of a new class of automotive skins, using piezoceramic as a smart material. The main objective is to consider the complex system with, the skin to be modeled as a layered plate structure involving a lightweight material with foam and active materials imbedded on them. To begin with a cantilever beam structure is subjected to a load through piezoceramic and the resulting strain at the active material site is predicted accounting for the material properties, piezoceramic thickness, adhesive thickness including the effect of adhesives. A finite element analysis is carried out to compare experimental work. Further work in this direction would provide an analytical tool that will provide the basis for algorithms to predict and counter impacts on the future class of automobiles.

TMFoldWeb: a web server for predicting transmembrane protein fold class.

PubMed

Kozma, Dániel; Tusnády, Gábor E

2015-09-17

Here we present TMFoldWeb, the web server implementation of TMFoldRec, a transmembrane protein fold recognition algorithm. TMFoldRec uses statistical potentials and utilizes topology filtering and a gapless threading algorithm. It ranks template structures and selects the most likely candidates and estimates the reliability of the obtained lowest energy model. The statistical potential was developed in a maximum likelihood framework on a representative set of the PDBTM database. According to the benchmark test the performance of TMFoldRec is about 77 % in correctly predicting fold class for a given transmembrane protein sequence. An intuitive web interface has been developed for the recently published TMFoldRec algorithm. The query sequence goes through a pipeline of topology prediction and a systematic sequence to structure alignment (threading). Resulting templates are ordered by energy and reliability values and are colored according to their significance level. Besides the graphical interface, a programmatic access is available as well, via a direct interface for developers or for submitting genome-wide data sets. The TMFoldWeb web server is unique and currently the only web server that is able to predict the fold class of transmembrane proteins while assigning reliability scores for the prediction. This method is prepared for genome-wide analysis with its easy-to-use interface, informative result page and programmatic access. Considering the info-communication evolution in the last few years, the developed web server, as well as the molecule viewer, is responsive and fully compatible with the prevalent tablets and mobile devices.

hi-class: Horndeski in the Cosmic Linear Anisotropy Solving System

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

Zumalacárregui, Miguel; Bellini, Emilio; Sawicki, Ignacy

We present the public version of hi-class (www.hiclass-code.net), an extension of the Boltzmann code CLASS to a broad ensemble of modifications to general relativity. In particular, hi-class can calculate predictions for models based on Horndeski's theory, which is the most general scalar-tensor theory described by second-order equations of motion and encompasses any perfect-fluid dark energy, quintessence, Brans-Dicke, f ( R ) and covariant Galileon models. hi-class has been thoroughly tested and can be readily used to understand the impact of alternative theories of gravity on linear structure formation as well as for cosmological parameter extraction.

The first crystal structures of a family 19 class IV chitinase: the enzyme from Norway spruce.

PubMed

Ubhayasekera, Wimal; Rawat, Reetika; Ho, Sharon Wing Tak; Wiweger, Malgorzata; Von Arnold, Sara; Chye, Mee-Len; Mowbray, Sherry L

2009-10-01

Chitinases help plants defend themselves against fungal attack, and play roles in other processes, including development. The catalytic modules of most plant chitinases belong to glycoside hydrolase family 19. We report here x-ray structures of such a module from a Norway spruce enzyme, the first for any family 19 class IV chitinase. The bi-lobed structure has a wide cleft lined by conserved residues; the most interesting for catalysis are Glu113, the proton donor, and Glu122, believed to be a general base that activate a catalytic water molecule. Comparisons to class I and II enzymes show that loop deletions in the class IV proteins make the catalytic cleft shorter and wider; from modeling studies, it is predicted that only three N-acetylglucosamine-binding subsites exist in class IV. Further, the structural comparisons suggest that the family 19 enzymes become more closed on substrate binding. Attempts to solve the structure of the complete protein including the associated chitin-binding module failed, however, modeling studies based on close relatives indicate that the binding module recognizes at most three N-acetylglucosamine units. The combined results suggest that the class IV enzymes are optimized for shorter substrates than the class I and II enzymes, or alternatively, that they are better suited for action on substrates where only small regions of chitin chain are accessible. Intact spruce chitinase is shown to possess antifungal activity, which requires the binding module; removing this module had no effect on measured chitinase activity.

Classroom characteristics and implementation of a substance use prevention curriculum in European countries.

PubMed

Caria, Maria Paola; Faggiano, Fabrizio; Bellocco, Rino; Galanti, Maria Rosaria

2013-12-01

Partial implementation may explain modest effectiveness of many school-based preventive programmes against substance use. We studied whether specific characteristics of the class could predict the level of implementation of a curriculum delivered by class teachers in schools from some European countries. Secondary analysis of data from an evaluation trial. In seven European countries, 78 schools (173 classes) were randomly assigned to a 12-unit, interactive, standardized programme based on the comprehensive social influence model. Curriculum completeness, application fidelity, average unit duration and use of role-play were monitored using structured report forms. Predictors of implementation were measured by aggregating at class level information from the baseline student survey. Class size, gender composition, mean age, factors related to substance use and to affection to school were analysed, with associations estimated by multilevel regression models. Implementation was not significantly predicted by mean age, proportion of students with positive academic expectation or liking school. Proportion of boys was associated with a shorter time devoted to each unit [β = -0.19, 95% confidence intervals (CI) -0.32 to -0.06]. Class size was inversely related to application fidelity [Odds ratio (OR) 0.92, 95% CI 0.85 to 0.99]. Prevalence of substance use was associated with a decreased odds of implementing all the curriculum units (OR 0.81, 95% CI 0.65 to 0.99). Students' connectedness to their class was associated with increased odds of teachers using role-play (OR 1.52, 95% CI 1.03 to 2.29). Teachers' implementation of preventive programmes may be affected by structural and social characteristics of classes and therefore benefit from organizational strategies and teachers' training in class management techniques.

Self-Determination and Meaningful Work: Exploring Socioeconomic Constraints

PubMed Central

Allan, Blake A.

2016-01-01

This study examined a model of meaningful work among a diverse sample of working adults. From the perspectives of Self-Determination Theory and the Psychology of Working Framework, we tested a structural model with social class and work volition predicting SDT motivation variables, which in turn predicted meaningful work. Partially supporting hypotheses, work volition was positively related to internal regulation and negatively related to amotivation, whereas social class was positively related to external regulation and amotivation. In turn, internal regulation was positively related to meaningful work, whereas external regulation and amotivation were negatively related to meaningful work. Indirect effects from work volition to meaningful work via internal regulation and amotivation were significant, and indirect effects from social class to meaningful work via external regulation and amotivation were significant. This study highlights the important relations between SDT motivation variables and meaningful work, especially the large positive relation between internal regulation and meaningful work. However, results also reveal that work volition and social class may play critical roles in predicting internal regulation, external regulation, and amotivation. PMID:26869970

A Utility Model for Teaching Load Decisions in Academic Departments.

ERIC Educational Resources Information Center

Massey, William F.; Zemsky, Robert

1997-01-01

Presents a utility model for academic department decision making and describes the structural specifications for analyzing it. The model confirms the class-size utility asymmetry predicted by the authors' academic rachet theory, but shows that marginal utility associated with college teaching loads is always negative. Curricular structure and…

Historical Maps from Modern Images: Using Remote Sensing to Model and Map Century-Long Vegetation Change in a Fire-Prone Region

PubMed Central

Callister, Kate E.; Griffioen, Peter A.; Avitabile, Sarah C.; Haslem, Angie; Kelly, Luke T.; Kenny, Sally A.; Nimmo, Dale G.; Farnsworth, Lisa M.; Taylor, Rick S.; Watson, Simon J.; Bennett, Andrew F.; Clarke, Michael F.

2016-01-01

Understanding the age structure of vegetation is important for effective land management, especially in fire-prone landscapes where the effects of fire can persist for decades and centuries. In many parts of the world, such information is limited due to an inability to map disturbance histories before the availability of satellite images (~1972). Here, we describe a method for creating a spatial model of the age structure of canopy species that established pre-1972. We built predictive neural network models based on remotely sensed data and ecological field survey data. These models determined the relationship between sites of known fire age and remotely sensed data. The predictive model was applied across a 104,000 km2 study region in semi-arid Australia to create a spatial model of vegetation age structure, which is primarily the result of stand-replacing fires which occurred before 1972. An assessment of the predictive capacity of the model using independent validation data showed a significant correlation (rs = 0.64) between predicted and known age at test sites. Application of the model provides valuable insights into the distribution of vegetation age-classes and fire history in the study region. This is a relatively straightforward method which uses widely available data sources that can be applied in other regions to predict age-class distribution beyond the limits imposed by satellite imagery. PMID:27029046

HomPPI: a class of sequence homology based protein-protein interface prediction methods

PubMed Central

2011-01-01

Background Although homology-based methods are among the most widely used methods for predicting the structure and function of proteins, the question as to whether interface sequence conservation can be effectively exploited in predicting protein-protein interfaces has been a subject of debate. Results We studied more than 300,000 pair-wise alignments of protein sequences from structurally characterized protein complexes, including both obligate and transient complexes. We identified sequence similarity criteria required for accurate homology-based inference of interface residues in a query protein sequence. Based on these analyses, we developed HomPPI, a class of sequence homology-based methods for predicting protein-protein interface residues. We present two variants of HomPPI: (i) NPS-HomPPI (Non partner-specific HomPPI), which can be used to predict interface residues of a query protein in the absence of knowledge of the interaction partner; and (ii) PS-HomPPI (Partner-specific HomPPI), which can be used to predict the interface residues of a query protein with a specific target protein. Our experiments on a benchmark dataset of obligate homodimeric complexes show that NPS-HomPPI can reliably predict protein-protein interface residues in a given protein, with an average correlation coefficient (CC) of 0.76, sensitivity of 0.83, and specificity of 0.78, when sequence homologs of the query protein can be reliably identified. NPS-HomPPI also reliably predicts the interface residues of intrinsically disordered proteins. Our experiments suggest that NPS-HomPPI is competitive with several state-of-the-art interface prediction servers including those that exploit the structure of the query proteins. The partner-specific classifier, PS-HomPPI can, on a large dataset of transient complexes, predict the interface residues of a query protein with a specific target, with a CC of 0.65, sensitivity of 0.69, and specificity of 0.70, when homologs of both the query and the target can be reliably identified. The HomPPI web server is available at http://homppi.cs.iastate.edu/. Conclusions Sequence homology-based methods offer a class of computationally efficient and reliable approaches for predicting the protein-protein interface residues that participate in either obligate or transient interactions. For query proteins involved in transient interactions, the reliability of interface residue prediction can be improved by exploiting knowledge of putative interaction partners. PMID:21682895

Quantum-gravity predictions for the fine-structure constant

NASA Astrophysics Data System (ADS)

Eichhorn, Astrid; Held, Aaron; Wetterich, Christof

2018-07-01

Asymptotically safe quantum fluctuations of gravity can uniquely determine the value of the gauge coupling for a large class of grand unified models. In turn, this makes the electromagnetic fine-structure constant calculable. The balance of gravity and matter fluctuations results in a fixed point for the running of the gauge coupling. It is approached as the momentum scale is lowered in the transplanckian regime, leading to a uniquely predicted value of the gauge coupling at the Planck scale. The precise value of the predicted fine-structure constant depends on the matter content of the grand unified model. It is proportional to the gravitational fluctuation effects for which computational uncertainties remain to be settled.

Automated extraction and classification of RNA tertiary structure cyclic motifs

PubMed Central

Lemieux, Sébastien; Major, François

2006-01-01

A minimum cycle basis of the tertiary structure of a large ribosomal subunit (LSU) X-ray crystal structure was analyzed. Most cycles are small, as they are composed of 3- to 5 nt, and repeated across the LSU tertiary structure. We used hierarchical clustering to quantify and classify the 4 nt cycles. One class is defined by the GNRA tetraloop motif. The inspection of the GNRA class revealed peculiar instances in sequence. First is the presence of UA, CA, UC and CC base pairs that substitute the usual sheared GA base pair. Second is the revelation of GNR(Xn)A tetraloops, where Xn is bulged out of the classical GNRA structure, and of GN/RA formed by the two strands of interior-loops. We were able to unambiguously characterize the cycle classes using base stacking and base pairing annotations. The cycles identified correspond to small and cyclic motifs that compose most of the LSU RNA tertiary structure and contribute to its thermodynamic stability. Consequently, the RNA minimum cycles could well be used as the basic elements of RNA tertiary structure prediction methods. PMID:16679452

B and T Cell Epitope-Based Peptides Predicted from Evolutionarily Conserved and Whole Protein Sequences of Ebola Virus as Vaccine Targets.

PubMed

Yasmin, T; Nabi, A H M Nurun

2016-05-01

Ebola virus (EBV) has become a serious threat to public health. Different approaches were applied to predict continuous and discontinuous B cell epitopes as well as T cell epitopes from the sequence-based and available three-dimensional structural analyses of each protein of EBV. Peptides '(79) VPSATKRWGFRSGVPP(94) ' from GP1 and '(515) LHYWTTQDEGAAIGLA(530) ' from GP2 of Ebola were found to be the consensus peptidic sequences predicted as linear B cell epitope of which the latter contains a region (519) TTQDEG(524) that fulfilled all the criteria of accessibility, hydrophilicity, flexibility and beta turn region for becoming an ideal B cell epitope. Different nonamers as T cell epitopes were obtained that interacted with different numbers of MHC class I and class II alleles with a binding affinity of <100 nm. Interestingly, these alleles also bound to the MHC class I alleles mostly prevalent in African and South Asian regions. Of these, 'LANETTQAL' and 'FLYDRLAST' nonamers were predicted to be the most potent T cell epitopes and they, respectively, interacted with eight and twelve class I alleles that covered 63.79% and 54.16% of world population, respectively. These nonamers were found to be the core sequences of 15mer peptides that interacted with the most common class II allele, HLA-DRB1*01:01. They were further validated for their binding to specific class I alleles using docking technique. Thus, these predicted epitopes may be used as vaccine targets against EBV and can be validated in model hosts to verify their efficacy as vaccine. © 2016 The Foundation for the Scandinavian Journal of Immunology.

PRED-CLASS: cascading neural networks for generalized protein classification and genome-wide applications.

PubMed

Pasquier, C; Promponas, V J; Hamodrakas, S J

2001-08-15

A cascading system of hierarchical, artificial neural networks (named PRED-CLASS) is presented for the generalized classification of proteins into four distinct classes-transmembrane, fibrous, globular, and mixed-from information solely encoded in their amino acid sequences. The architecture of the individual component networks is kept very simple, reducing the number of free parameters (network synaptic weights) for faster training, improved generalization, and the avoidance of data overfitting. Capturing information from as few as 50 protein sequences spread among the four target classes (6 transmembrane, 10 fibrous, 13 globular, and 17 mixed), PRED-CLASS was able to obtain 371 correct predictions out of a set of 387 proteins (success rate approximately 96%) unambiguously assigned into one of the target classes. The application of PRED-CLASS to several test sets and complete proteomes of several organisms demonstrates that such a method could serve as a valuable tool in the annotation of genomic open reading frames with no functional assignment or as a preliminary step in fold recognition and ab initio structure prediction methods. Detailed results obtained for various data sets and completed genomes, along with a web sever running the PRED-CLASS algorithm, can be accessed over the World Wide Web at http://o2.biol.uoa.gr/PRED-CLASS.

Toward the prediction of class I and II mouse major histocompatibility complex-peptide-binding affinity: in silico bioinformatic step-by-step guide using quantitative structure-activity relationships.

PubMed

Hattotuwagama, Channa K; Doytchinova, Irini A; Flower, Darren R

2007-01-01

Quantitative structure-activity relationship (QSAR) analysis is a cornerstone of modern informatics. Predictive computational models of peptide-major histocompatibility complex (MHC)-binding affinity based on QSAR technology have now become important components of modern computational immunovaccinology. Historically, such approaches have been built around semiqualitative, classification methods, but these are now giving way to quantitative regression methods. We review three methods--a 2D-QSAR additive-partial least squares (PLS) and a 3D-QSAR comparative molecular similarity index analysis (CoMSIA) method--which can identify the sequence dependence of peptide-binding specificity for various class I MHC alleles from the reported binding affinities (IC50) of peptide sets. The third method is an iterative self-consistent (ISC) PLS-based additive method, which is a recently developed extension to the additive method for the affinity prediction of class II peptides. The QSAR methods presented here have established themselves as immunoinformatic techniques complementary to existing methodology, useful in the quantitative prediction of binding affinity: current methods for the in silico identification of T-cell epitopes (which form the basis of many vaccines, diagnostics, and reagents) rely on the accurate computational prediction of peptide-MHC affinity. We have reviewed various human and mouse class I and class II allele models. Studied alleles comprise HLA-A*0101, HLA-A*0201, HLA-A*0202, HLA-A*0203, HLA-A*0206, HLA-A*0301, HLA-A*1101, HLA-A*3101, HLA-A*6801, HLA-A*6802, HLA-B*3501, H2-K(k), H2-K(b), H2-D(b) HLA-DRB1*0101, HLA-DRB1*0401, HLA-DRB1*0701, I-A(b), I-A(d), I-A(k), I-A(S), I-E(d), and I-E(k). In this chapter we show a step-by-step guide into predicting the reliability and the resulting models to represent an advance on existing methods. The peptides used in this study are available from the AntiJen database (http://www.jenner.ac.uk/AntiJen). The PLS method is available commercially in the SYBYL molecular modeling software package. The resulting models, which can be used for accurate T-cell epitope prediction, will be made are freely available online at the URL http://www.jenner.ac.uk/MHCPred.

Extending (Q)SARs to incorporate proprietary knowledge for regulatory purposes: A case study using aromatic amine mutagenicity.

PubMed

Ahlberg, Ernst; Amberg, Alexander; Beilke, Lisa D; Bower, David; Cross, Kevin P; Custer, Laura; Ford, Kevin A; Van Gompel, Jacky; Harvey, James; Honma, Masamitsu; Jolly, Robert; Joossens, Elisabeth; Kemper, Raymond A; Kenyon, Michelle; Kruhlak, Naomi; Kuhnke, Lara; Leavitt, Penny; Naven, Russell; Neilan, Claire; Quigley, Donald P; Shuey, Dana; Spirkl, Hans-Peter; Stavitskaya, Lidiya; Teasdale, Andrew; White, Angela; Wichard, Joerg; Zwickl, Craig; Myatt, Glenn J

2016-06-01

Statistical-based and expert rule-based models built using public domain mutagenicity knowledge and data are routinely used for computational (Q)SAR assessments of pharmaceutical impurities in line with the approach recommended in the ICH M7 guideline. Knowledge from proprietary corporate mutagenicity databases could be used to increase the predictive performance for selected chemical classes as well as expand the applicability domain of these (Q)SAR models. This paper outlines a mechanism for sharing knowledge without the release of proprietary data. Primary aromatic amine mutagenicity was selected as a case study because this chemical class is often encountered in pharmaceutical impurity analysis and mutagenicity of aromatic amines is currently difficult to predict. As part of this analysis, a series of aromatic amine substructures were defined and the number of mutagenic and non-mutagenic examples for each chemical substructure calculated across a series of public and proprietary mutagenicity databases. This information was pooled across all sources to identify structural classes that activate or deactivate aromatic amine mutagenicity. This structure activity knowledge, in combination with newly released primary aromatic amine data, was incorporated into Leadscope's expert rule-based and statistical-based (Q)SAR models where increased predictive performance was demonstrated. Copyright © 2016 Elsevier Inc. All rights reserved.

Challenging the state-of-the-art in protein structure prediction: Highlights of experimental target structures for the 10th Critical Assessment of Techniques for Protein Structure Prediction Experiment CASP10

PubMed Central

Kryshtafovych, Andriy; Moult, John; Bales, Patrick; Bazan, J. Fernando; Biasini, Marco; Burgin, Alex; Chen, Chen; Cochran, Frank V.; Craig, Timothy K.; Das, Rhiju; Fass, Deborah; Garcia-Doval, Carmela; Herzberg, Osnat; Lorimer, Donald; Luecke, Hartmut; Ma, Xiaolei; Nelson, Daniel C.; van Raaij, Mark J.; Rohwer, Forest; Segall, Anca; Seguritan, Victor; Zeth, Kornelius; Schwede, Torsten

2014-01-01

For the last two decades, CASP has assessed the state of the art in techniques for protein structure prediction and identified areas which required further development. CASP would not have been possible without the prediction targets provided by the experimental structural biology community. In the latest experiment, CASP10, over 100 structures were suggested as prediction targets, some of which appeared to be extraordinarily difficult for modeling. In this paper, authors of some of the most challenging targets discuss which specific scientific question motivated the experimental structure determination of the target protein, which structural features were especially interesting from a structural or functional perspective, and to what extent these features were correctly reproduced in the predictions submitted to CASP10. Specifically, the following targets will be presented: the acid-gated urea channel, a difficult to predict trans-membrane protein from the important human pathogen Helicobacter pylori; the structure of human interleukin IL-34, a recently discovered helical cytokine; the structure of a functionally uncharacterized enzyme OrfY from Thermoproteus tenax formed by a gene duplication and a novel fold; an ORFan domain of mimivirus sulfhydryl oxidase R596; the fibre protein gp17 from bacteriophage T7; the Bacteriophage CBA-120 tailspike protein; a virus coat protein from metagenomic samples of the marine environment; and finally an unprecedented class of structure prediction targets based on engineered disulfide-rich small proteins. PMID:24318984

RepeatsDB-lite: a web server for unit annotation of tandem repeat proteins.

PubMed

Hirsh, Layla; Paladin, Lisanna; Piovesan, Damiano; Tosatto, Silvio C E

2018-05-09

RepeatsDB-lite (http://protein.bio.unipd.it/repeatsdb-lite) is a web server for the prediction of repetitive structural elements and units in tandem repeat (TR) proteins. TRs are a widespread but poorly annotated class of non-globular proteins carrying heterogeneous functions. RepeatsDB-lite extends the prediction to all TR types and strongly improves the performance both in terms of computational time and accuracy over previous methods, with precision above 95% for solenoid structures. The algorithm exploits an improved TR unit library derived from the RepeatsDB database to perform an iterative structural search and assignment. The web interface provides tools for analyzing the evolutionary relationships between units and manually refine the prediction by changing unit positions and protein classification. An all-against-all structure-based sequence similarity matrix is calculated and visualized in real-time for every user edit. Reviewed predictions can be submitted to RepeatsDB for review and inclusion.

Novel 3D bio-macromolecular bilinear descriptors for protein science: Predicting protein structural classes.

PubMed

Marrero-Ponce, Yovani; Contreras-Torres, Ernesto; García-Jacas, César R; Barigye, Stephen J; Cubillán, Néstor; Alvarado, Ysaías J

2015-06-07

In the present study, we introduce novel 3D protein descriptors based on the bilinear algebraic form in the ℝ(n) space on the coulombic matrix. For the calculation of these descriptors, macromolecular vectors belonging to ℝ(n) space, whose components represent certain amino acid side-chain properties, were used as weighting schemes. Generalization approaches for the calculation of inter-amino acidic residue spatial distances based on Minkowski metrics are proposed. The simple- and double-stochastic schemes were defined as approaches to normalize the coulombic matrix. The local-fragment indices for both amino acid-types and amino acid-groups are presented in order to permit characterizing fragments of interest in proteins. On the other hand, with the objective of taking into account specific interactions among amino acids in global or local indices, geometric and topological cut-offs are defined. To assess the utility of global and local indices a classification model for the prediction of the major four protein structural classes, was built with the Linear Discriminant Analysis (LDA) technique. The developed LDA-model correctly classifies the 92.6% and 92.7% of the proteins on the training and test sets, respectively. The obtained model showed high values of the generalized square correlation coefficient (GC(2)) on both the training and test series. The statistical parameters derived from the internal and external validation procedures demonstrate the robustness, stability and the high predictive power of the proposed model. The performance of the LDA-model demonstrates the capability of the proposed indices not only to codify relevant biochemical information related to the structural classes of proteins, but also to yield suitable interpretability. It is anticipated that the current method will benefit the prediction of other protein attributes or functions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Three-dimensional structural modelling and calculation of electrostatic potentials of HLA Bw4 and Bw6 epitopes to explain the molecular basis for alloantibody binding: toward predicting HLA antigenicity and immunogenicity.

PubMed

Mallon, Dermot H; Bradley, J Andrew; Winn, Peter J; Taylor, Craig J; Kosmoliaptsis, Vasilis

2015-02-01

We have previously shown that qualitative assessment of surface electrostatic potential of HLA class I molecules helps explain serological patterns of alloantibody binding. We have now used a novel computational approach to quantitate differences in surface electrostatic potential of HLA B-cell epitopes and applied this to explain HLA Bw4 and Bw6 antigenicity. Protein structure models of HLA class I alleles expressing either the Bw4 or Bw6 epitope (defined by sequence motifs at positions 77 to 83) were generated using comparative structure prediction. The electrostatic potential in 3-dimensional space encompassing the Bw4/Bw6 epitope was computed by solving the Poisson-Boltzmann equation and quantitatively compared in a pairwise, all-versus-all fashion to produce distance matrices that cluster epitopes with similar electrostatics properties. Quantitative comparison of surface electrostatic potential at the carboxyl terminal of the α1-helix of HLA class I alleles, corresponding to amino acid sequence motif 77 to 83, produced clustering of HLA molecules in 3 principal groups according to Bw4 or Bw6 epitope expression. Remarkably, quantitative differences in electrostatic potential reflected known patterns of serological reactivity better than Bw4/Bw6 amino acid sequence motifs. Quantitative assessment of epitope electrostatic potential allowed the impact of known amino acid substitutions (HLA-B*07:02 R79G, R82L, G83R) that are critical for antibody binding to be predicted. We describe a novel approach for quantitating differences in HLA B-cell epitope electrostatic potential. Proof of principle is provided that this approach enables better assessment of HLA epitope antigenicity than amino acid sequence data alone, and it may allow prediction of HLA immunogenicity.

Theoretical prediction of low-density hexagonal ZnO hollow structures

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

Tuoc, Vu Ngoc, E-mail: tuoc.vungoc@hust.edu.vn; Huan, Tran Doan; Thao, Nguyen Thi

2016-10-14

Along with wurtzite and zinc blende, zinc oxide (ZnO) has been found in a large number of polymorphs with substantially different properties and, hence, applications. Therefore, predicting and synthesizing new classes of ZnO polymorphs are of great significance and have been gaining considerable interest. Herein, we perform a density functional theory based tight-binding study, predicting several new series of ZnO hollow structures using the bottom-up approach. The geometry of the building blocks allows for obtaining a variety of hexagonal, low-density nanoporous, and flexible ZnO hollow structures. Their stability is discussed by means of the free energy computed within the lattice-dynamicsmore » approach. Our calculations also indicate that all the reported hollow structures are wide band gap semiconductors in the same fashion with bulk ZnO. The electronic band structures of the ZnO hollow structures are finally examined in detail.« less

Structures-of-the-Whole: Is There Any Glue to Hold the Concrete-Operational "Stage" Together?

ERIC Educational Resources Information Center

Brainerd, Charles J.

Studies concerned with the synchronous emergence prediction of Piaget's structures-of-the-whole principle are discussed in conjunction with three groups of concrete-operational skills: (1) transitivity/conservation/class inclusion; (2) double classification/double seriation; and (3) ordinal, cardinal, and natural number concepts. Findings show…

Analysis of energy-based algorithms for RNA secondary structure prediction

PubMed Central

2012-01-01

Background RNA molecules play critical roles in the cells of organisms, including roles in gene regulation, catalysis, and synthesis of proteins. Since RNA function depends in large part on its folded structures, much effort has been invested in developing accurate methods for prediction of RNA secondary structure from the base sequence. Minimum free energy (MFE) predictions are widely used, based on nearest neighbor thermodynamic parameters of Mathews, Turner et al. or those of Andronescu et al. Some recently proposed alternatives that leverage partition function calculations find the structure with maximum expected accuracy (MEA) or pseudo-expected accuracy (pseudo-MEA) methods. Advances in prediction methods are typically benchmarked using sensitivity, positive predictive value and their harmonic mean, namely F-measure, on datasets of known reference structures. Since such benchmarks document progress in improving accuracy of computational prediction methods, it is important to understand how measures of accuracy vary as a function of the reference datasets and whether advances in algorithms or thermodynamic parameters yield statistically significant improvements. Our work advances such understanding for the MFE and (pseudo-)MEA-based methods, with respect to the latest datasets and energy parameters. Results We present three main findings. First, using the bootstrap percentile method, we show that the average F-measure accuracy of the MFE and (pseudo-)MEA-based algorithms, as measured on our largest datasets with over 2000 RNAs from diverse families, is a reliable estimate (within a 2% range with high confidence) of the accuracy of a population of RNA molecules represented by this set. However, average accuracy on smaller classes of RNAs such as a class of 89 Group I introns used previously in benchmarking algorithm accuracy is not reliable enough to draw meaningful conclusions about the relative merits of the MFE and MEA-based algorithms. Second, on our large datasets, the algorithm with best overall accuracy is a pseudo MEA-based algorithm of Hamada et al. that uses a generalized centroid estimator of base pairs. However, between MFE and other MEA-based methods, there is no clear winner in the sense that the relative accuracy of the MFE versus MEA-based algorithms changes depending on the underlying energy parameters. Third, of the four parameter sets we considered, the best accuracy for the MFE-, MEA-based, and pseudo-MEA-based methods is 0.686, 0.680, and 0.711, respectively (on a scale from 0 to 1 with 1 meaning perfect structure predictions) and is obtained with a thermodynamic parameter set obtained by Andronescu et al. called BL* (named after the Boltzmann likelihood method by which the parameters were derived). Conclusions Large datasets should be used to obtain reliable measures of the accuracy of RNA structure prediction algorithms, and average accuracies on specific classes (such as Group I introns and Transfer RNAs) should be interpreted with caution, considering the relatively small size of currently available datasets for such classes. The accuracy of the MEA-based methods is significantly higher when using the BL* parameter set of Andronescu et al. than when using the parameters of Mathews and Turner, and there is no significant difference between the accuracy of MEA-based methods and MFE when using the BL* parameters. The pseudo-MEA-based method of Hamada et al. with the BL* parameter set significantly outperforms all other MFE and MEA-based algorithms on our large data sets. PMID:22296803

Analysis of energy-based algorithms for RNA secondary structure prediction.

PubMed

Hajiaghayi, Monir; Condon, Anne; Hoos, Holger H

2012-02-01

RNA molecules play critical roles in the cells of organisms, including roles in gene regulation, catalysis, and synthesis of proteins. Since RNA function depends in large part on its folded structures, much effort has been invested in developing accurate methods for prediction of RNA secondary structure from the base sequence. Minimum free energy (MFE) predictions are widely used, based on nearest neighbor thermodynamic parameters of Mathews, Turner et al. or those of Andronescu et al. Some recently proposed alternatives that leverage partition function calculations find the structure with maximum expected accuracy (MEA) or pseudo-expected accuracy (pseudo-MEA) methods. Advances in prediction methods are typically benchmarked using sensitivity, positive predictive value and their harmonic mean, namely F-measure, on datasets of known reference structures. Since such benchmarks document progress in improving accuracy of computational prediction methods, it is important to understand how measures of accuracy vary as a function of the reference datasets and whether advances in algorithms or thermodynamic parameters yield statistically significant improvements. Our work advances such understanding for the MFE and (pseudo-)MEA-based methods, with respect to the latest datasets and energy parameters. We present three main findings. First, using the bootstrap percentile method, we show that the average F-measure accuracy of the MFE and (pseudo-)MEA-based algorithms, as measured on our largest datasets with over 2000 RNAs from diverse families, is a reliable estimate (within a 2% range with high confidence) of the accuracy of a population of RNA molecules represented by this set. However, average accuracy on smaller classes of RNAs such as a class of 89 Group I introns used previously in benchmarking algorithm accuracy is not reliable enough to draw meaningful conclusions about the relative merits of the MFE and MEA-based algorithms. Second, on our large datasets, the algorithm with best overall accuracy is a pseudo MEA-based algorithm of Hamada et al. that uses a generalized centroid estimator of base pairs. However, between MFE and other MEA-based methods, there is no clear winner in the sense that the relative accuracy of the MFE versus MEA-based algorithms changes depending on the underlying energy parameters. Third, of the four parameter sets we considered, the best accuracy for the MFE-, MEA-based, and pseudo-MEA-based methods is 0.686, 0.680, and 0.711, respectively (on a scale from 0 to 1 with 1 meaning perfect structure predictions) and is obtained with a thermodynamic parameter set obtained by Andronescu et al. called BL* (named after the Boltzmann likelihood method by which the parameters were derived). Large datasets should be used to obtain reliable measures of the accuracy of RNA structure prediction algorithms, and average accuracies on specific classes (such as Group I introns and Transfer RNAs) should be interpreted with caution, considering the relatively small size of currently available datasets for such classes. The accuracy of the MEA-based methods is significantly higher when using the BL* parameter set of Andronescu et al. than when using the parameters of Mathews and Turner, and there is no significant difference between the accuracy of MEA-based methods and MFE when using the BL* parameters. The pseudo-MEA-based method of Hamada et al. with the BL* parameter set significantly outperforms all other MFE and MEA-based algorithms on our large data sets.

Predicting Significant Factors of Selective Marine Corps Reserve Marines’ Career Decisions in Response to the Force Structure Review

DTIC Science & Technology

2014-03-01

1st class and Not CFT 1st class in the final model, as shown in Table 16. A Marine is required to qualify annually with the M16 -A2 service rifle... M16 -A4 service rifle, or the M4 carbine. MCO 3574.2K identifies the requirements for qualification, scoring, and administrative matters pertaining to

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

PubMed Central

McLeish, Tom C. B.

2015-01-01

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

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

PubMed

McLeish, Tom C B

2015-12-06

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

Temperature-Correlated Changes in Phytoplankton Community Structure Are Restricted to Polar Waters.

PubMed

Ward, Ben A

2015-01-01

Globally distributed observations of size-fractionated chlorophyll a and temperature were used to incorporate temperature dependence into an existing semi-empirical model of phytoplankton community size structure. The additional temperature-dependent term significantly increased the model's ability to both reproduce and predict observations of chlorophyll a size-fractionation at temperatures below 2°C. The most notable improvements were in the smallest (picoplankton) size-class, for which overall model fit was more than doubled, and predictive skill was increased by approximately 40%. The model was subsequently applied to generate global maps for three phytoplankton size classes, on the basis of satellite-derived estimates of surface chlorophyll a and sea surface temperature. Polar waters were associated with marked decline in the chlorophyll a biomass of the smallest cells, relative to lower latitude waters of equivalent total chlorophyll a. In the same regions a complementary increase was seen in the chlorophyll a biomass of larger size classes. These findings suggest that a warming and stratifying ocean will see a poleward expansion of the habitat range of the smallest phytoplankton, with the possible displacement of some larger groups that currently dominate. There was no evidence of a strong temperature dependence in tropical or sub-tropical regions, suggesting that future direct temperature effects on community structure at lower latitudes may be small.

Characterization of cDNAs and genomic DNAs for human threonyl- and cysteinyl-tRNA synthetases

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

Cruzen, M.E.

1993-01-01

Techniques of molecular biology were used to clone, sequence and map two human aminoacyl-tRNA synthetase (aaRS) cDNAs: threonyl-tRNA synthetase (ThrRS) a class II enzyme and cysteinyl-tRNA synthetase (CysRS) a class I enzyme. The predicted protein sequence of human ThrRS is highly homologous to that of lower eukaryotic and prokaryotic ThRSs, particularly in the regions containing the three structural motifs common to all class II synthetases. Signature regions 1 and 2, which characterize the class IIa subgroup (SerRS, ThrRS and HisRS) are highly conserved from bacteria to human. Structural predictions for human ThrRS based on the known structure of the closelymore » related SerRS from E.coli implicate strongly conserved residues in the signature sequences to be important in substrate binding. The amino terminal 100 residues of the deduced amino acid sequence of ThrRS shares structural similarity to SerRS consistent with forming an antiparallel helix implicated in tRNA binding. The 5' untranslated sequence of the human ThrRS gene shares short stretches of common sequence with the gene for hamster HisRS including a binding site for the promoter specific transcription factor sp-1. The deduced amino acid sequence of human CysRS has a high degree of sequence identify to E. coli CysRS. Human CysRS possesses the classic characteristics of a class I synthetase and is most closely related to the MetRS subgroup. The amino terminal half of human CysRS can be modeled as a nucleotide binding fold and shares significant sequence and structural similarity to the other enzymes in this subgroup. The CysRS structural gene (CARS) was mapped to human chromosome 11p15.5 by fluorescent in situ hybridization. CARS is the first aaRS gene to be mapped to chromosome 11. The steady state of both CysRS and ThrRs mRNA were quantitated in several human tissues. Message levels for these enzymes appear to be subjected to differential regulation in different cell types.« less

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

PubMed

Chen, Peng; Li, Jinyan

2010-05-17

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

New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity.

PubMed

Hattotuwagama, Channa K; Guan, Pingping; Doytchinova, Irini A; Flower, Darren R

2004-11-21

Quantitative structure-activity relationship (QSAR) analysis is a main cornerstone of modern informatic disciplines. Predictive computational models, based on QSAR technology, of peptide-major histocompatibility complex (MHC) binding affinity have now become a vital component of modern day computational immunovaccinology. Historically, such approaches have been built around semi-qualitative, classification methods, but these are now giving way to quantitative regression methods. The additive method, an established immunoinformatics technique for the quantitative prediction of peptide-protein affinity, was used here to identify the sequence dependence of peptide binding specificity for three mouse class I MHC alleles: H2-D(b), H2-K(b) and H2-K(k). As we show, in terms of reliability the resulting models represent a significant advance on existing methods. They can be used for the accurate prediction of T-cell epitopes and are freely available online ( http://www.jenner.ac.uk/MHCPred).

Challenging the state of the art in protein structure prediction: Highlights of experimental target structures for the 10th Critical Assessment of Techniques for Protein Structure Prediction Experiment CASP10.

PubMed

Kryshtafovych, Andriy; Moult, John; Bales, Patrick; Bazan, J Fernando; Biasini, Marco; Burgin, Alex; Chen, Chen; Cochran, Frank V; Craig, Timothy K; Das, Rhiju; Fass, Deborah; Garcia-Doval, Carmela; Herzberg, Osnat; Lorimer, Donald; Luecke, Hartmut; Ma, Xiaolei; Nelson, Daniel C; van Raaij, Mark J; Rohwer, Forest; Segall, Anca; Seguritan, Victor; Zeth, Kornelius; Schwede, Torsten

2014-02-01

For the last two decades, CASP has assessed the state of the art in techniques for protein structure prediction and identified areas which required further development. CASP would not have been possible without the prediction targets provided by the experimental structural biology community. In the latest experiment, CASP10, more than 100 structures were suggested as prediction targets, some of which appeared to be extraordinarily difficult for modeling. In this article, authors of some of the most challenging targets discuss which specific scientific question motivated the experimental structure determination of the target protein, which structural features were especially interesting from a structural or functional perspective, and to what extent these features were correctly reproduced in the predictions submitted to CASP10. Specifically, the following targets will be presented: the acid-gated urea channel, a difficult to predict transmembrane protein from the important human pathogen Helicobacter pylori; the structure of human interleukin (IL)-34, a recently discovered helical cytokine; the structure of a functionally uncharacterized enzyme OrfY from Thermoproteus tenax formed by a gene duplication and a novel fold; an ORFan domain of mimivirus sulfhydryl oxidase R596; the fiber protein gene product 17 from bacteriophage T7; the bacteriophage CBA-120 tailspike protein; a virus coat protein from metagenomic samples of the marine environment; and finally, an unprecedented class of structure prediction targets based on engineered disulfide-rich small proteins. Copyright © 2013 The Authors. Wiley Periodicals, Inc.

TargetSpy: a supervised machine learning approach for microRNA target prediction.

PubMed

Sturm, Martin; Hackenberg, Michael; Langenberger, David; Frishman, Dmitrij

2010-05-28

Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences.In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila, suggesting that it may be applicable to a broad range of species. Moreover, we have demonstrated that the application of machine learning techniques in combination with upcoming deep sequencing data results in a powerful microRNA target site prediction tool http://www.targetspy.org.

TargetSpy: a supervised machine learning approach for microRNA target prediction

PubMed Central

2010-01-01

Background Virtually all currently available microRNA target site prediction algorithms require the presence of a (conserved) seed match to the 5' end of the microRNA. Recently however, it has been shown that this requirement might be too stringent, leading to a substantial number of missed target sites. Results We developed TargetSpy, a novel computational approach for predicting target sites regardless of the presence of a seed match. It is based on machine learning and automatic feature selection using a wide spectrum of compositional, structural, and base pairing features covering current biological knowledge. Our model does not rely on evolutionary conservation, which allows the detection of species-specific interactions and makes TargetSpy suitable for analyzing unconserved genomic sequences. In order to allow for an unbiased comparison of TargetSpy to other methods, we classified all algorithms into three groups: I) no seed match requirement, II) seed match requirement, and III) conserved seed match requirement. TargetSpy predictions for classes II and III are generated by appropriate postfiltering. On a human dataset revealing fold-change in protein production for five selected microRNAs our method shows superior performance in all classes. In Drosophila melanogaster not only our class II and III predictions are on par with other algorithms, but notably the class I (no-seed) predictions are just marginally less accurate. We estimate that TargetSpy predicts between 26 and 112 functional target sites without a seed match per microRNA that are missed by all other currently available algorithms. Conclusion Only a few algorithms can predict target sites without demanding a seed match and TargetSpy demonstrates a substantial improvement in prediction accuracy in that class. Furthermore, when conservation and the presence of a seed match are required, the performance is comparable with state-of-the-art algorithms. TargetSpy was trained on mouse and performs well in human and drosophila, suggesting that it may be applicable to a broad range of species. Moreover, we have demonstrated that the application of machine learning techniques in combination with upcoming deep sequencing data results in a powerful microRNA target site prediction tool http://www.targetspy.org. PMID:20509939

Customizing G Protein-coupled receptor models for structure-based virtual screening.

PubMed

de Graaf, Chris; Rognan, Didier

2009-01-01

This review will focus on the construction, refinement, and validation of G Protein-coupled receptor models for the purpose of structure-based virtual screening. Practical tips and tricks derived from concrete modeling and virtual screening exercises to overcome the problems and pitfalls associated with the different steps of the receptor modeling workflow will be presented. These examples will not only include rhodopsin-like (class A), but also secretine-like (class B), and glutamate-like (class C) receptors. In addition, the review will present a careful comparative analysis of current crystal structures and their implication on homology modeling. The following themes will be discussed: i) the use of experimental anchors in guiding the modeling procedure; ii) amino acid sequence alignments; iii) ligand binding mode accommodation and binding cavity expansion; iv) proline-induced kinks in transmembrane helices; v) binding mode prediction and virtual screening by receptor-ligand interaction fingerprint scoring; vi) extracellular loop modeling; vii) virtual filtering schemes. Finally, an overview of several successful structure-based screening shows that receptor models, despite structural inaccuracies, can be efficiently used to find novel ligands.

Structural Basis for Catalysis of a Tetrameric Class IIa Fructose 1,6-Bisphosphate Aldolase from Mycobacterium tuberculosis

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

Pegan, Scott D.; Ruskseree, Kamolchanok; Franzblau, Scott G.

2009-03-04

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), currently infects one-third of the world's population in its latent form. The emergence of multidrug-resistant and extensive drug-resistant strains has highlighted the need for new pharmacological targets within M. tuberculosis. The class IIa fructose 1,6-bisphosphate aldolase (FBA) enzyme from M. tuberculosis (MtFBA) has been proposed as one such target since it is upregulated in latent TB. Since the structure of MtFBA has not been determined and there is little information available on its reaction mechanism, we sought to determine the X-ray structure of MtFBA in complex with its substrates. By lowering themore » pH of the enzyme in the crystalline state, we were able to determine a series of high-resolution X-ray structures of MtFBA bound to dihydroxyacetone phosphate, glyceraldehyde 3-phosphate, and fructose 1,6-bisphosphate at 1.5, 2.1, and 1.3 {angstrom}, respectively. Through these structures, it was discovered that MtFBA belongs to a novel tetrameric class of type IIa FBAs. The molecular details at the interface of the tetramer revealed important information for better predictability of the quaternary structures among the FBAs based on their primary sequences. These X-ray structures also provide interesting and new details on the reaction mechanism of class II FBAs. Substrates and products were observed in geometries poised for catalysis; in addition, unexpectedly, the hydroxyl-enolate intermediate of dihydroxyacetone phosphate was also captured and resolved structurally. These concise new details offer a better understanding of the reaction mechanisms for FBAs in general and provide a structural basis for inhibitor design efforts aimed at this class of enzymes.« less

Structural basis for catalysis of a tetrameric, class IIa fructose 1,6-bisphosphate aldolase from M. tuberculosis

PubMed Central

Pegan, Scott D.; Rukseree, Kamolchanok; Franzblau, Scott G.; Mesecar, Andrew D.

2009-01-01

Mycobacterium tuberculosis, the causative agent of tuberculosis (TB), currently infects one third of the world’s population in its latent form. The emergence of multidrug resistant strains, MDR-TB and XDR-TB has highlighted the need for new pharmacological targets within M. tuberculosis. The Class IIa fructose 1,6-bisphosphate aldolase (FBA) enzyme from M. tuberculosis (MtFBA) has been proposed as one such target since its upregulated in latent TB. Since the structure of MtFBA had not been determined and since there was little information available on its reaction mechanism, we sought to determine the X-ray structure of MtFBA in complex with its substrates. By lowering the pH of the enzyme in the crystalline state, we were able to determine a series of high-resolution X-ray structures of MtFBA bound to dihydroxyacetonephosphate (DHAP), glyceraldehyde-3-phosphate (G3P), and fructose 1,6-bisphosphate (FBP) at 1.5 Å, 2.1 Å, and 1.3 Å respectively. Through these structures it was discovered that MtFBA belongs to a novel tetrameric class of the type IIa FBAs. The molecular details at the interface of the tetramer revealed important information for being able to better predict the quaternary structures among the FBAs based on their primary sequences. These X-ray structures also provide interesting and new details on the reaction mechanism of class II FBAs. Not only were the substrates and products observed in geometries poised for catalysis, but unexpectedly the hydroxyl enolate intermediate of DHAP was also captured and resolved structurally. These concise new details provide a better understanding of the reaction mechanisms for FBAs in general and provide a structural basis for inhibitor design efforts aimed at this class of enzymes. PMID:19167403

Local Structural Differences in Homologous Proteins: Specificities in Different SCOP Classes

PubMed Central

Joseph, Agnel Praveen; Valadié, Hélène; Srinivasan, Narayanaswamy; de Brevern, Alexandre G.

2012-01-01

The constant increase in the number of solved protein structures is of great help in understanding the basic principles behind protein folding and evolution. 3-D structural knowledge is valuable in designing and developing methods for comparison, modelling and prediction of protein structures. These approaches for structure analysis can be directly implicated in studying protein function and for drug design. The backbone of a protein structure favours certain local conformations which include α-helices, β-strands and turns. Libraries of limited number of local conformations (Structural Alphabets) were developed in the past to obtain a useful categorization of backbone conformation. Protein Block (PB) is one such Structural Alphabet that gave a reasonable structure approximation of 0.42 Å. In this study, we use PB description of local structures to analyse conformations that are preferred sites for structural variations and insertions, among group of related folds. This knowledge can be utilized in improving tools for structure comparison that work by analysing local structure similarities. Conformational differences between homologous proteins are known to occur often in the regions comprising turns and loops. Interestingly, these differences are found to have specific preferences depending upon the structural classes of proteins. Such class-specific preferences are mainly seen in the all-β class with changes involving short helical conformations and hairpin turns. A test carried out on a benchmark dataset also indicates that the use of knowledge on the class specific variations can improve the performance of a PB based structure comparison approach. The preference for the indel sites also seem to be confined to a few backbone conformations involving β-turns and helix C-caps. These are mainly associated with short loops joining the regular secondary structures that mediate a reversal in the chain direction. Rare β-turns of type I’ and II’ are also identified as preferred sites for insertions. PMID:22745680

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

PubMed

Kirschner, Andreas; Frishman, Dmitrij

2008-10-01

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

Laboratory test of a novel structural model of anxiety sensitivity and panic vulnerability.

PubMed

Bernstein, Amit; Zvolensky, Michael J; Zvolensky, Michael J; Schmidt, Norman B

2009-06-01

The current study evaluated a novel latent structural model of anxiety sensitivity (AS) in relation to panic vulnerability among a sample of young adults (N=216). AS was measured using the 16-item Anxiety Sensitivity Index (ASI; Reiss, Peterson, Gursky, & McNally, 1986), and panic vulnerability was indexed by panic attack responding to a single administration of a 4-minute, 10% CO(2) challenge. As predicted, vulnerability for panic attack responding to biological challenge was associated with dichotomous individual differences between taxonic AS classes and continuous within-taxon class individual differences in AS physical concerns. Findings supported the AS taxonic-dimensional hypothesis of AS latent structure and panic vulnerability. These findings are discussed in terms of their theoretical and clinical implications.

Measuring and Predicting the Internal Structure of Semiconductor Nanocrystals through Raman Spectroscopy.

PubMed

Mukherjee, Prabuddha; Lim, Sung Jun; Wrobel, Tomasz P; Bhargava, Rohit; Smith, Andrew M

2016-08-31

Nanocrystals composed of mixed chemical domains have diverse properties that are driving their integration in next-generation electronics, light sources, and biosensors. However, the precise spatial distribution of elements within these particles is difficult to measure and control, yet profoundly impacts their quality and performance. Here we synthesized a unique series of 42 different quantum dot nanocrystals, composed of two chemical domains (CdS:CdSe), arranged in 7 alloy and (core)shell structural classes. Chemometric analyses of far-field Raman spectra accurately classified their internal structures from their vibrational signatures. These classifications provide direct insight into the elemental arrangement of the alloy as well as an independent prediction of fluorescence quantum yield. This nondestructive, rapid approach can be broadly applied to greatly enhance our capacity to measure, predict and monitor multicomponent nanomaterials for precise tuning of their structures and properties.

Observing golden-mean universality class in the scaling of thermal transport.

PubMed

Xiong, Daxing

2018-02-01

We address the issue of whether the golden-mean [ψ=(sqrt[5]+1)/2≃1.618] universality class, as predicted by several theoretical models, can be observed in the dynamical scaling of thermal transport. Remarkably, we show strong evidence that ψ appears to be the scaling exponent of heat mode correlation in a purely quartic anharmonic chain. This observation seems to somewhat deviate from the previous expectation and we explain it by the unusual slow decay of the cross correlation between heat and sound modes. Whenever the cubic anharmonicity is included, this cross correlation gradually dies out and another universality class with scaling exponent γ=5/3, as commonly predicted by theories, seems recovered. However, this recovery is accompanied by two interesting phase transition processes characterized by a change of symmetry of the potential and a clear variation of the dynamic structure factor, respectively. Due to these transitions, an additional exponent close to γ≃1.580 emerges. All this evidence suggests that, to gain a full prediction of the scaling of thermal transport, more ingredients should be taken into account.

Observing golden-mean universality class in the scaling of thermal transport

NASA Astrophysics Data System (ADS)

Xiong, Daxing

2018-02-01

We address the issue of whether the golden-mean [ψ =(√{5 }+1 ) /2 ≃1.618 ] universality class, as predicted by several theoretical models, can be observed in the dynamical scaling of thermal transport. Remarkably, we show strong evidence that ψ appears to be the scaling exponent of heat mode correlation in a purely quartic anharmonic chain. This observation seems to somewhat deviate from the previous expectation and we explain it by the unusual slow decay of the cross correlation between heat and sound modes. Whenever the cubic anharmonicity is included, this cross correlation gradually dies out and another universality class with scaling exponent γ =5 /3 , as commonly predicted by theories, seems recovered. However, this recovery is accompanied by two interesting phase transition processes characterized by a change of symmetry of the potential and a clear variation of the dynamic structure factor, respectively. Due to these transitions, an additional exponent close to γ ≃1.580 emerges. All this evidence suggests that, to gain a full prediction of the scaling of thermal transport, more ingredients should be taken into account.

Network control principles predict neuron function in the Caenorhabditis elegans connectome

PubMed Central

Chew, Yee Lian; Walker, Denise S.; Schafer, William R.; Barabási, Albert-László

2017-01-01

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social and technological networks1–3. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode C. elegans4–6, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires twelve neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation7–13, as well as one previously uncharacterised neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed, with single-cell ablations of DD04 or DD05, but not DD02 or DD03, specifically affecting posterior body movements. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterised connectomes. PMID:29045391

Network control principles predict neuron function in the Caenorhabditis elegans connectome

NASA Astrophysics Data System (ADS)

Yan, Gang; Vértes, Petra E.; Towlson, Emma K.; Chew, Yee Lian; Walker, Denise S.; Schafer, William R.; Barabási, Albert-László

2017-10-01

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

Network control principles predict neuron function in the Caenorhabditis elegans connectome.

PubMed

Yan, Gang; Vértes, Petra E; Towlson, Emma K; Chew, Yee Lian; Walker, Denise S; Schafer, William R; Barabási, Albert-László

2017-10-26

Recent studies on the controllability of complex systems offer a powerful mathematical framework to systematically explore the structure-function relationship in biological, social, and technological networks. Despite theoretical advances, we lack direct experimental proof of the validity of these widely used control principles. Here we fill this gap by applying a control framework to the connectome of the nematode Caenorhabditis elegans, allowing us to predict the involvement of each C. elegans neuron in locomotor behaviours. We predict that control of the muscles or motor neurons requires 12 neuronal classes, which include neuronal groups previously implicated in locomotion by laser ablation, as well as one previously uncharacterized neuron, PDB. We validate this prediction experimentally, finding that the ablation of PDB leads to a significant loss of dorsoventral polarity in large body bends. Importantly, control principles also allow us to investigate the involvement of individual neurons within each neuronal class. For example, we predict that, within the class of DD motor neurons, only three (DD04, DD05, or DD06) should affect locomotion when ablated individually. This prediction is also confirmed; single cell ablations of DD04 or DD05 specifically affect posterior body movements, whereas ablations of DD02 or DD03 do not. Our predictions are robust to deletions of weak connections, missing connections, and rewired connections in the current connectome, indicating the potential applicability of this analytical framework to larger and less well-characterized connectomes.

NetTurnP – Neural Network Prediction of Beta-turns by Use of Evolutionary Information and Predicted Protein Sequence Features

PubMed Central

Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

2010-01-01

β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC  = 0.50, Qtotal = 82.1%, sensitivity  = 75.6%, PPV  = 68.8% and AUC  = 0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17 – 0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. Conclusion The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences. PMID:21152409

NetTurnP--neural network prediction of beta-turns by use of evolutionary information and predicted protein sequence features.

PubMed

Petersen, Bent; Lundegaard, Claus; Petersen, Thomas Nordahl

2010-11-30

β-turns are the most common type of non-repetitive structures, and constitute on average 25% of the amino acids in proteins. The formation of β-turns plays an important role in protein folding, protein stability and molecular recognition processes. In this work we present the neural network method NetTurnP, for prediction of two-class β-turns and prediction of the individual β-turn types, by use of evolutionary information and predicted protein sequence features. It has been evaluated against a commonly used dataset BT426, and achieves a Matthews correlation coefficient of 0.50, which is the highest reported performance on a two-class prediction of β-turn and not-β-turn. Furthermore NetTurnP shows improved performance on some of the specific β-turn types. In the present work, neural network methods have been trained to predict β-turn or not and individual β-turn types from the primary amino acid sequence. The individual β-turn types I, I', II, II', VIII, VIa1, VIa2, VIba and IV have been predicted based on classifications by PROMOTIF, and the two-class prediction of β-turn or not is a superset comprised of all β-turn types. The performance is evaluated using a golden set of non-homologous sequences known as BT426. Our two-class prediction method achieves a performance of: MCC=0.50, Qtotal=82.1%, sensitivity=75.6%, PPV=68.8% and AUC=0.864. We have compared our performance to eleven other prediction methods that obtain Matthews correlation coefficients in the range of 0.17-0.47. For the type specific β-turn predictions, only type I and II can be predicted with reasonable Matthews correlation coefficients, where we obtain performance values of 0.36 and 0.31, respectively. The NetTurnP method has been implemented as a webserver, which is freely available at http://www.cbs.dtu.dk/services/NetTurnP/. NetTurnP is the only available webserver that allows submission of multiple sequences.

Two classes of ODE models with switch-like behavior.

PubMed

Just, Winfried; Korb, Mason; Elbert, Ben; Young, Todd

2013-12-01

In cases where the same real-world system can be modeled both by an ODE system ⅅ and a Boolean system , it is of interest to identify conditions under which the two systems will be consistent, that is, will make qualitatively equivalent predictions. In this note we introduce two broad classes of relatively simple models that provide a convenient framework for studying such questions. In contrast to the widely known class of Glass networks, the right-hand sides of our ODEs are Lipschitz-continuous. We prove that if has certain structures, consistency between ⅅ and is implied by sufficient separation of time scales in one class of our models. Namely, if the trajectories of are "one-stepping" then we prove a strong form of consistency and if has a certain monotonicity property then there is a weaker consistency between ⅅ and . These results appear to point to more general structure properties that favor consistency between ODE and Boolean models.

ECOSAR model performance with a large test set of industrial chemicals.

PubMed

Reuschenbach, Peter; Silvani, Maurizio; Dammann, Martina; Warnecke, Dietmar; Knacker, Thomas

2008-05-01

The widely used ECOSAR computer programme for QSAR prediction of chemical toxicity towards aquatic organisms was evaluated by using large data sets of industrial chemicals with varying molecular structures. Experimentally derived toxicity data covering acute effects on fish, Daphnia and green algae growth inhibition of in total more than 1,000 randomly selected substances were compared to the prediction results of the ECOSAR programme in order (1) to assess the capability of ECOSAR to correctly classify the chemicals into defined classes of aquatic toxicity according to rules of EU regulation and (2) to determine the number of correct predictions within tolerance factors from 2 to 1,000. Regarding ecotoxicity classification, 65% (fish), 52% (Daphnia) and 49% (algae) of the substances were correctly predicted into the classes "not harmful", "harmful", "toxic" and "very toxic". At all trophic levels about 20% of the chemicals were underestimated in their toxicity. The class of "not harmful" substances (experimental LC/EC(50)>100 mg l(-1)) represents nearly half of the whole data set. The percentages for correct predictions of toxic effects on fish, Daphnia and algae growth inhibition were 69%, 64% and 60%, respectively, when a tolerance factor of 10 was allowed. Focussing on those experimental results which were verified by analytically measured concentrations, the predictability for Daphnia and algae toxicity was improved by approximately three percentage points, whereas for fish no improvement was determined. The calculated correlation coefficients demonstrated poor correlation when the complete data set was taken, but showed good results for some of the ECOSAR chemical classes. The results are discussed in the context of literature data on the performance of ECOSAR and other QSAR models.

Improved pan-specific MHC class I peptide-binding predictions using a novel representation of the MHC-binding cleft environment.

PubMed

Carrasco Pro, S; Zimic, M; Nielsen, M

2014-02-01

Major histocompatibility complex (MHC) molecules play a key role in cell-mediated immune responses presenting bounded peptides for recognition by the immune system cells. Several in silico methods have been developed to predict the binding affinity of a given peptide to a specific MHC molecule. One of the current state-of-the-art methods for MHC class I is NetMHCpan, which has a core ingredient for the representation of the MHC class I molecule using a pseudo-sequence representation of the binding cleft amino acid environment. New and large MHC-peptide-binding data sets are constantly being made available, and also new structures of MHC class I molecules with a bound peptide have been published. In order to test if the NetMHCpan method can be improved by integrating this novel information, we created new pseudo-sequence definitions for the MHC-binding cleft environment from sequence and structural analyses of different MHC data sets including human leukocyte antigen (HLA), non-human primates (chimpanzee, macaque and gorilla) and other animal alleles (cattle, mouse and swine). From these constructs, we showed that by focusing on MHC sequence positions found to be polymorphic across the MHC molecules used to train the method, the NetMHCpan method achieved a significant increase in the predictive performance, in particular, of non-human MHCs. This study hence showed that an improved performance of MHC-binding methods can be achieved not only by the accumulation of more MHC-peptide-binding data but also by a refined definition of the MHC-binding environment including information from non-human species. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Comparing side chain packing in soluble proteins, protein-protein interfaces, and transmembrane proteins.

PubMed

Gaines, J C; Acebes, S; Virrueta, A; Butler, M; Regan, L; O'Hern, C S

2018-05-01

We compare side chain prediction and packing of core and non-core regions of soluble proteins, protein-protein interfaces, and transmembrane proteins. We first identified or created comparable databases of high-resolution crystal structures of these 3 protein classes. We show that the solvent-inaccessible cores of the 3 classes of proteins are equally densely packed. As a result, the side chains of core residues at protein-protein interfaces and in the membrane-exposed regions of transmembrane proteins can be predicted by the hard-sphere plus stereochemical constraint model with the same high prediction accuracies (>90%) as core residues in soluble proteins. We also find that for all 3 classes of proteins, as one moves away from the solvent-inaccessible core, the packing fraction decreases as the solvent accessibility increases. However, the side chain predictability remains high (80% within 30°) up to a relative solvent accessibility, rSASA≲0.3, for all 3 protein classes. Our results show that ≈40% of the interface regions in protein complexes are "core", that is, densely packed with side chain conformations that can be accurately predicted using the hard-sphere model. We propose packing fraction as a metric that can be used to distinguish real protein-protein interactions from designed, non-binding, decoys. Our results also show that cores of membrane proteins are the same as cores of soluble proteins. Thus, the computational methods we are developing for the analysis of the effect of hydrophobic core mutations in soluble proteins will be equally applicable to analyses of mutations in membrane proteins. © 2018 Wiley Periodicals, Inc.

Effects of structural marsh management and salinity on invertebrate prey of waterbirds in marsh ponds during winter on the Gulf Coast Chenier Plain

USGS Publications Warehouse

Bolduc, F.; Afton, A.D.

2003-01-01

Aquatic invertebrates are important food resources for wintering waterbirds, and prey selection generally is limited by prey size. Aquatic invertebrate communities are influenced by sediments and hydrologic characteristics of wetlands, which were affected by structural marsh management (levees, water-control structures and impoundments; SMM) and salinity on the Gulf Coast Chenier Plain of North America. Based on previous research, we tested general predictions that SMM reduces biomass of infaunal invertebrates and increases that of epifaunal invertebrates and those that tolerate low levels of dissolved oxygen (O2) and salinity. We also tested the general prediction that invertebrate biomass in freshwater, oligohaline, and mesohaline marshes are similar, except for taxa adapted to specific ranges of salinity. Finally, we investigated relationships among invertebrate biomass and sizes, sediment and hydrologic variables, and marsh types. Accordingly, we measured biomass of common invertebrate by three size classes (63 to 199 ??m, 200 to 999 ??m, and ???1000 ??m), sediment variables (carbon content, C:N ratio, hardness, particle size, and O, penetration), and hydrologic variables (salinity, water depth,temperature, 02, and turbidity) in ponds of impounded freshwater (IF), oligohaline (IO), mesohaline (IM), and unimpounded mesohaline (UM) marshes during winters 1997-1998 to 1999-2000 on Rockefeller State Wildlife Refuge, near Grand Chenier, Louisiana, USA. As predicted, an a priori multivariate analysis of variance (MANOVA) contrast indicated that biomass of an infaunal class of invertebrates (Nematoda, 63 to 199 ??m) was greater in UM marsh ponds than in those of IM marshes, and biomass of an epifaunal class of invertebrates (Ostracoda, 200 to 999 ??m) was greater in IM marsh ponds than in those of UM marshes. The observed reduction in Nematoda due to SMM also was consistent with the prediction that SMM reduces invertebrates that do not tolerate low salinity. Furthermore, as predicted, an a priori MANOVA contrast indicated that biomass of a single invertebrate class adapted to low salinity (Oligochaeta, 200 to 999 ??m) was greater in ponds of IF marshes than in those of IO and IM marshes. A canonical correspondence analysis indicated that variation in salinity and O2 penetration best explained differences among sites that maximized biomass of the common invertebrate classes. Salinity was positively correlated with the silt-clay fraction, O2, and O2 penetration, and negatively correlated with water depth, sediment hardness, carbon, and C:N. Nematoda, Foraminifera, and Copepoda generally were associated with UM marsh ponds and high salinity, whereas other invertebrate classes were distributed among impounded marsh ponds and associated with lower salinity. Our results suggest that SMM and salinity have relatively small effects on invertebrate prey of wintering waterbirds in marsh ponds because they affect biomass of Nematoda and Oligochaeta, and few waterbirds consume these invertebrates. ?? 2003, The Society of Wetland Scientists.

Drug-target interaction prediction via class imbalance-aware ensemble learning.

PubMed

Ezzat, Ali; Wu, Min; Li, Xiao-Li; Kwoh, Chee-Keong

2016-12-22

Multiple computational methods for predicting drug-target interactions have been developed to facilitate the drug discovery process. These methods use available data on known drug-target interactions to train classifiers with the purpose of predicting new undiscovered interactions. However, a key challenge regarding this data that has not yet been addressed by these methods, namely class imbalance, is potentially degrading the prediction performance. Class imbalance can be divided into two sub-problems. Firstly, the number of known interacting drug-target pairs is much smaller than that of non-interacting drug-target pairs. This imbalance ratio between interacting and non-interacting drug-target pairs is referred to as the between-class imbalance. Between-class imbalance degrades prediction performance due to the bias in prediction results towards the majority class (i.e. the non-interacting pairs), leading to more prediction errors in the minority class (i.e. the interacting pairs). Secondly, there are multiple types of drug-target interactions in the data with some types having relatively fewer members (or are less represented) than others. This variation in representation of the different interaction types leads to another kind of imbalance referred to as the within-class imbalance. In within-class imbalance, prediction results are biased towards the better represented interaction types, leading to more prediction errors in the less represented interaction types. We propose an ensemble learning method that incorporates techniques to address the issues of between-class imbalance and within-class imbalance. Experiments show that the proposed method improves results over 4 state-of-the-art methods. In addition, we simulated cases for new drugs and targets to see how our method would perform in predicting their interactions. New drugs and targets are those for which no prior interactions are known. Our method displayed satisfactory prediction performance and was able to predict many of the interactions successfully. Our proposed method has improved the prediction performance over the existing work, thus proving the importance of addressing problems pertaining to class imbalance in the data.

Primordial linkage of β2-microglobulin to the MHC.

PubMed

Ohta, Yuko; Shiina, Takashi; Lohr, Rebecca L; Hosomichi, Kazuyoshi; Pollin, Toni I; Heist, Edward J; Suzuki, Shingo; Inoko, Hidetoshi; Flajnik, Martin F

2011-03-15

β2-Microglobulin (β2M) is believed to have arisen in a basal jawed vertebrate (gnathostome) and is the essential L chain that associates with most MHC class I molecules. It contains a distinctive molecular structure called a constant-1 Ig superfamily domain, which is shared with other adaptive immune molecules including MHC class I and class II. Despite its structural similarity to class I and class II and its conserved function, β2M is encoded outside the MHC in all examined species from bony fish to mammals, but it is assumed to have translocated from its original location within the MHC early in gnathostome evolution. We screened a nurse shark bacterial artificial chromosome library and isolated clones containing β2M genes. A gene present in the MHC of all other vertebrates (ring3) was found in the bacterial artificial chromosome clone, and the close linkage of ring3 and β2M to MHC class I and class II genes was determined by single-strand conformational polymorphism and allele-specific PCR. This study satisfies the long-held conjecture that β2M was linked to the primordial MHC (Ur MHC); furthermore, the apparent stability of the shark genome may yield other genes predicted to have had a primordial association with the MHC specifically and with immunity in general.

Comprehensive analysis of T cell epitope discovery strategies using 17DD yellow fever virus structural proteins and BALB/c (H2d) mice model.

PubMed

Maciel, Milton; Kellathur, Srinivasan N; Chikhlikar, Pryia; Dhalia, Rafael; Sidney, John; Sette, Alessandro; August, Thomas J; Marques, Ernesto T A

2008-08-15

Immunomics research uses in silico epitope prediction, as well as in vivo and in vitro approaches. We inoculated BALB/c (H2d) mice with 17DD yellow fever vaccine to investigate the correlations between approaches used for epitope discovery: ELISPOT assays, binding assays, and prediction software. Our results showed a good agreement between ELISPOT and binding assays, which seemed to correlate with the protein immunogenicity. PREDBALB/c prediction software partially agreed with the ELISPOT and binding assay results, but presented low specificity. The use of prediction software to exclude peptides containing no epitopes, followed by high throughput screening of the remaining peptides by ELISPOT, and the use of MHC-biding assays to characterize the MHC restrictions demonstrated to be an efficient strategy. The results allowed the characterization of 2 MHC class I and 17 class II epitopes in the envelope protein of the YF virus in BALB/c (H2d) mice.

A comparison of selected parametric and imputation methods for estimating snag density and snag quality attributes

USGS Publications Warehouse

Eskelson, Bianca N.I.; Hagar, Joan; Temesgen, Hailemariam

2012-01-01

Snags (standing dead trees) are an essential structural component of forests. Because wildlife use of snags depends on size and decay stage, snag density estimation without any information about snag quality attributes is of little value for wildlife management decision makers. Little work has been done to develop models that allow multivariate estimation of snag density by snag quality class. Using climate, topography, Landsat TM data, stand age and forest type collected for 2356 forested Forest Inventory and Analysis plots in western Washington and western Oregon, we evaluated two multivariate techniques for their abilities to estimate density of snags by three decay classes. The density of live trees and snags in three decay classes (D1: recently dead, little decay; D2: decay, without top, some branches and bark missing; D3: extensive decay, missing bark and most branches) with diameter at breast height (DBH) ≥ 12.7 cm was estimated using a nonparametric random forest nearest neighbor imputation technique (RF) and a parametric two-stage model (QPORD), for which the number of trees per hectare was estimated with a Quasipoisson model in the first stage and the probability of belonging to a tree status class (live, D1, D2, D3) was estimated with an ordinal regression model in the second stage. The presence of large snags with DBH ≥ 50 cm was predicted using a logistic regression and RF imputation. Because of the more homogenous conditions on private forest lands, snag density by decay class was predicted with higher accuracies on private forest lands than on public lands, while presence of large snags was more accurately predicted on public lands, owing to the higher prevalence of large snags on public lands. RF outperformed the QPORD model in terms of percent accurate predictions, while QPORD provided smaller root mean square errors in predicting snag density by decay class. The logistic regression model achieved more accurate presence/absence classification of large snags than the RF imputation approach. Adjusting the decision threshold to account for unequal size for presence and absence classes is more straightforward for the logistic regression than for the RF imputation approach. Overall, model accuracies were poor in this study, which can be attributed to the poor predictive quality of the explanatory variables and the large range of forest types and geographic conditions observed in the data.

Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis.

PubMed

Park, In-Hee; Venable, John D; Steckler, Caitlin; Cellitti, Susan E; Lesley, Scott A; Spraggon, Glen; Brock, Ansgar

2015-09-28

Hydrogen exchange (HX) studies have provided critical insight into our understanding of protein folding, structure, and dynamics. More recently, hydrogen exchange mass spectrometry (HX-MS) has become a widely applicable tool for HX studies. The interpretation of the wealth of data generated by HX-MS experiments as well as other HX methods would greatly benefit from the availability of exchange predictions derived from structures or models for comparison with experiment. Most reported computational HX modeling studies have employed solvent-accessible-surface-area based metrics in attempts to interpret HX data on the basis of structures or models. In this study, a computational HX-MS prediction method based on classification of the amide hydrogen bonding modes mimicking the local unfolding model is demonstrated. Analysis of the NH bonding configurations from molecular dynamics (MD) simulation snapshots is used to determine partitioning over bonded and nonbonded NH states and is directly mapped into a protection factor (PF) using a logistics growth function. Predicted PFs are then used for calculating deuteration values of peptides and compared with experimental data. Hydrogen exchange MS data for fatty acid synthase thioesterase (FAS-TE) collected for a range of pHs and temperatures was used for detailed evaluation of the approach. High correlation between prediction and experiment for observable fragment peptides is observed in the FAS-TE and additional benchmarking systems that included various apo/holo proteins for which literature data were available. In addition, it is shown that HX modeling can improve experimental resolution through decomposition of in-exchange curves into rate classes, which correlate with prediction from MD. Successful rate class decompositions provide further evidence that the presented approach captures the underlying physical processes correctly at the single residue level. This assessment is further strengthened in a comparison of residue resolved protection factor predictions for staphylococcal nuclease with NMR data, which was also used to compare prediction performance with other algorithms described in the literature. The demonstrated transferable and scalable MD based HX prediction approach adds significantly to the available tools for HX-MS data interpretation based on available structures and models.

Estimation of Hydrogen-Exchange Protection Factors from MD Simulation Based on Amide Hydrogen Bonding Analysis

PubMed Central

Park, In-Hee; Venable, John D.; Steckler, Caitlin; Cellitti, Susan E.; Lesley, Scott A.; Spraggon, Glen; Brock, Ansgar

2015-01-01

Hydrogen exchange (HX) studies have provided critical insight into our understanding of protein folding, structure and dynamics. More recently, Hydrogen Exchange Mass Spectrometry (HX-MS) has become a widely applicable tool for HX studies. The interpretation of the wealth of data generated by HX-MS experiments as well as other HX methods would greatly benefit from the availability of exchange predictions derived from structures or models for comparison with experiment. Most reported computational HX modeling studies have employed solvent-accessible-surface-area based metrics in attempts to interpret HX data on the basis of structures or models. In this study, a computational HX-MS prediction method based on classification of the amide hydrogen bonding modes mimicking the local unfolding model is demonstrated. Analysis of the NH bonding configurations from Molecular Dynamics (MD) simulation snapshots is used to determine partitioning over bonded and non-bonded NH states and is directly mapped into a protection factor (PF) using a logistics growth function. Predicted PFs are then used for calculating deuteration values of peptides and compared with experimental data. Hydrogen exchange MS data for Fatty acid synthase thioesterase (FAS-TE) collected for a range of pHs and temperatures was used for detailed evaluation of the approach. High correlation between prediction and experiment for observable fragment peptides is observed in the FAS-TE and additional benchmarking systems that included various apo/holo proteins for which literature data were available. In addition, it is shown that HX modeling can improve experimental resolution through decomposition of in-exchange curves into rate classes, which correlate with prediction from MD. Successful rate class decompositions provide further evidence that the presented approach captures the underlying physical processes correctly at the single residue level. This assessment is further strengthened in a comparison of residue resolved protection factor predictions for staphylococcal nuclease with NMR data, which was also used to compare prediction performance with other algorithms described in the literature. The demonstrated transferable and scalable MD based HX prediction approach adds significantly to the available tools for HX-MS data interpretation based on available structures and models. PMID:26241692

BIOACCUMULATION AND AQUATIC SYSTEM SIMULATOR (BASS) USER'S MANUAL BETA TEST VERSION 2.1

EPA Science Inventory

BASS (Bioaccumulation and Aquatic System Simulator) is a Fortran 95 simulation program that predicts the population and bioaccumulation dynamics of age-structured fish assemblages that are exposed to hydrophobic organic pollutants and class B and borderline metals that complex wi...

Crash Simulation of a Vertical Drop Test of a Commuter-Class Aircraft

NASA Technical Reports Server (NTRS)

Jackson, Karen E.; Fasanella, Edwin L.

2004-01-01

A finite element model of an ATR42-300 commuter-class aircraft was developed and a crash simulation was executed. Analytical predictions were correlated with data obtained from a 30-ft/s (9.14-m/s) vertical drop test of the aircraft. The purpose of the test was to evaluate the structural response of the aircraft when subjected to a severe, but survivable, impact. The aircraft was configured with seats, dummies, luggage, and other ballast. The wings were filled with 8,700 lb. (3,946 kg) of water to represent the fuel. The finite element model, which consisted of 57,643 nodes and 62,979 elements, was developed from direct measurements of the airframe geometry. The seats, dummies, luggage, fuel, and other ballast were represented using concentrated masses. The model was executed in LS-DYNA, a commercial code for performing explicit transient dynamic simulations. Predictions of structural deformation and selected time-history responses were generated. The simulation was successfully validated through extensive test-analysis correlation.

Characterization and calibration of a viscoelastic simplified potential energy clock model for inorganic glasses

DOE PAGES

Chambers, Robert S.; Tandon, Rajan; Stavig, Mark E.

2015-07-07

In this study, to analyze the stresses and strains generated during the solidification of glass-forming materials, stress and volume relaxation must be predicted accurately. Although the modeling attributes required to depict physical aging in organic glassy thermosets strongly resemble the structural relaxation in inorganic glasses, the historical modeling approaches have been distinctly different. To determine whether a common constitutive framework can be applied to both classes of materials, the nonlinear viscoelastic simplified potential energy clock (SPEC) model, developed originally for glassy thermosets, was calibrated for the Schott 8061 inorganic glass and used to analyze a number of tests. A practicalmore » methodology for material characterization and model calibration is discussed, and the structural relaxation mechanism is interpreted in the context of SPEC model constitutive equations. SPEC predictions compared to inorganic glass data collected from thermal strain measurements and creep tests demonstrate the ability to achieve engineering accuracy and make the SPEC model feasible for engineering applications involving a much broader class of glassy materials.« less

DeepGO: predicting protein functions from sequence and interactions using a deep ontology-aware classifier.

PubMed

Kulmanov, Maxat; Khan, Mohammed Asif; Hoehndorf, Robert; Wren, Jonathan

2018-02-15

A large number of protein sequences are becoming available through the application of novel high-throughput sequencing technologies. Experimental functional characterization of these proteins is time-consuming and expensive, and is often only done rigorously for few selected model organisms. Computational function prediction approaches have been suggested to fill this gap. The functions of proteins are classified using the Gene Ontology (GO), which contains over 40 000 classes. Additionally, proteins have multiple functions, making function prediction a large-scale, multi-class, multi-label problem. We have developed a novel method to predict protein function from sequence. We use deep learning to learn features from protein sequences as well as a cross-species protein-protein interaction network. Our approach specifically outputs information in the structure of the GO and utilizes the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and demonstrate a significant improvement over baseline methods such as BLAST, in particular for predicting cellular locations. Web server: http://deepgo.bio2vec.net, Source code: https://github.com/bio-ontology-research-group/deepgo. robert.hoehndorf@kaust.edu.sa. Supplementary data are available at Bioinformatics online. © The Author(s) 2017. Published by Oxford University Press.

Superpixel-based classification of gastric chromoendoscopy images

NASA Astrophysics Data System (ADS)

Boschetto, Davide; Grisan, Enrico

2017-03-01

Chromoendoscopy (CH) is a gastroenterology imaging modality that involves the staining of tissues with methylene blue, which reacts with the internal walls of the gastrointestinal tract, improving the visual contrast in mucosal surfaces and thus enhancing a doctor's ability to screen precancerous lesions or early cancer. This technique helps identify areas that can be targeted for biopsy or treatment and in this work we will focus on gastric cancer detection. Gastric chromoendoscopy for cancer detection has several taxonomies available, one of which classifies CH images into three classes (normal, metaplasia, dysplasia) based on color, shape and regularity of pit patterns. Computer-assisted diagnosis is desirable to help us improve the reliability of the tissue classification and abnormalities detection. However, traditional computer vision methodologies, mainly segmentation, do not translate well to the specific visual characteristics of a gastroenterology imaging scenario. We propose the exploitation of a first unsupervised segmentation via superpixel, which groups pixels into perceptually meaningful atomic regions, used to replace the rigid structure of the pixel grid. For each superpixel, a set of features is extracted and then fed to a random forest based classifier, which computes a model used to predict the class of each superpixel. The average general accuracy of our model is 92.05% in the pixel domain (86.62% in the superpixel domain), while detection accuracies on the normal and abnormal class are respectively 85.71% and 95%. Eventually, the whole image class can be predicted image through a majority vote on each superpixel's predicted class.

A comparative study of sequence- and structure-based features of small RNAs and other RNAs of bacteria.

PubMed

Barik, Amita; Das, Santasabuj

2018-01-02

Small RNAs (sRNAs) in bacteria have emerged as key players in transcriptional and post-transcriptional regulation of gene expression. Here, we present a statistical analysis of different sequence- and structure-related features of bacterial sRNAs to identify the descriptors that could discriminate sRNAs from other bacterial RNAs. We investigated a comprehensive and heterogeneous collection of 816 sRNAs, identified by northern blotting across 33 bacterial species and compared their various features with other classes of bacterial RNAs, such as tRNAs, rRNAs and mRNAs. We observed that sRNAs differed significantly from the rest with respect to G+C composition, normalized minimum free energy of folding, motif frequency and several RNA-folding parameters like base-pairing propensity, Shannon entropy and base-pair distance. Based on the selected features, we developed a predictive model using Random Forests (RF) method to classify the above four classes of RNAs. Our model displayed an overall predictive accuracy of 89.5%. These findings would help to differentiate bacterial sRNAs from other RNAs and further promote prediction of novel sRNAs in different bacterial species.

Measuring case-mix complexity of tertiary care hospitals using DRGs.

PubMed

Park, Hayoung; Shin, Youngsoo

2004-02-01

The objectives of the study were to develop a model that measures and evaluates case-mix complexity of tertiary care hospitals, and to examine the characteristics of such a model. Physician panels defined three classes of case complexity and assigned disease categories represented by Adjacent Diagnosis Related Groups (ADRGs) to one of three case complexity classes. Three types of scores, indicating proportions of inpatients in each case complexity class standardized by the proportions at the national level, were defined to measure the case-mix complexity of a hospital. Discharge information for about 10% of inpatient episodes at 85 hospitals with bed size larger than 400 and their input structure and research and education activity were used to evaluate the case-mix complexity model. Results show its power to predict hospitals with the expected functions of tertiary care hospitals, i.e. resource intensive care, expensive input structure, and high levels of research and education activities.

Social affiliation in same-class and cross-class interactions.

PubMed

Côté, Stéphane; Kraus, Michael W; Carpenter, Nichelle C; Piff, Paul K; Beermann, Ursula; Keltner, Dacher

2017-02-01

Historically high levels of economic inequality likely have important consequences for relationships between people of the same and different social class backgrounds. Here, we test the prediction that social affiliation among same-class partners is stronger at the extremes of the class spectrum, given that these groups are highly distinctive and most separated from others by institutional and economic forces. An internal meta-analysis of 4 studies (N = 723) provided support for this hypothesis. Participant and partner social class were interactively, rather than additively, associated with social affiliation, indexed by affiliative behaviors and emotions during structured laboratory interactions and in daily life. Further, response surface analyses revealed that paired upper or lower class partners generally affiliated more than average-class pairs. Analyses with separate class indices suggested that these patterns are driven more by parental income and subjective social class than by parental education. The findings illuminate the dynamics of same- and cross-class interactions, revealing that not all same-class interactions feature the same degree of affiliation. They also reveal the importance of studying social class from an intergroup perspective. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Marsupials and monotremes possess a novel family of MHC class I genes that is lost from the eutherian lineage.

PubMed

Papenfuss, Anthony T; Feng, Zhi-Ping; Krasnec, Katina; Deakin, Janine E; Baker, Michelle L; Miller, Robert D

2015-07-22

Major histocompatibility complex (MHC) class I genes are found in the genomes of all jawed vertebrates. The evolution of this gene family is closely tied to the evolution of the vertebrate genome. Family members are frequently found in four paralogous regions, which were formed in two rounds of genome duplication in the early vertebrates, but in some species class Is have been subject to additional duplication or translocation, creating additional clusters. The gene family is traditionally grouped into two subtypes: classical MHC class I genes that are usually MHC-linked, highly polymorphic, expressed in a broad range of tissues and present endogenously-derived peptides to cytotoxic T-cells; and non-classical MHC class I genes generally have lower polymorphism, may have tissue-specific expression and have evolved to perform immune-related or non-immune functions. As immune genes can evolve rapidly and are subject to different selection pressure, we hypothesised that there may be divergent, as yet unannotated or uncharacterised class I genes. Application of a novel method of sensitive genome searching of available vertebrate genome sequences revealed a new, extensive sub-family of divergent MHC class I genes, denoted as UT, which has not previously been characterized. These class I genes are found in both American and Australian marsupials, and in monotremes, at an evolutionary chromosomal breakpoint, but are not present in non-mammalian genomes and have been lost from the eutherian lineage. We show that UT family members are expressed in the thymus of the gray short-tailed opossum and in other immune tissues of several Australian marsupials. Structural homology modelling shows that the proteins encoded by this family are predicted to have an open, though short, antigen-binding groove. We have identified a novel sub-family of putatively non-classical MHC class I genes that are specific to marsupials and monotremes. This family was present in the ancestral mammal and is found in extant marsupials and monotremes, but has been lost from the eutherian lineage. The function of this family is as yet unknown, however, their predicted structure may be consistent with presentation of antigens to T-cells.

Insights into multimodal imaging classification of ADHD

PubMed Central

Colby, John B.; Rudie, Jeffrey D.; Brown, Jesse A.; Douglas, Pamela K.; Cohen, Mark S.; Shehzad, Zarrar

2012-01-01

Attention deficit hyperactivity disorder (ADHD) currently is diagnosed in children by clinicians via subjective ADHD-specific behavioral instruments and by reports from the parents and teachers. Considering its high prevalence and large economic and societal costs, a quantitative tool that aids in diagnosis by characterizing underlying neurobiology would be extremely valuable. This provided motivation for the ADHD-200 machine learning (ML) competition, a multisite collaborative effort to investigate imaging classifiers for ADHD. Here we present our ML approach, which used structural and functional magnetic resonance imaging data, combined with demographic information, to predict diagnostic status of individuals with ADHD from typically developing (TD) children across eight different research sites. Structural features included quantitative metrics from 113 cortical and non-cortical regions. Functional features included Pearson correlation functional connectivity matrices, nodal and global graph theoretical measures, nodal power spectra, voxelwise global connectivity, and voxelwise regional homogeneity. We performed feature ranking for each site and modality using the multiple support vector machine recursive feature elimination (SVM-RFE) algorithm, and feature subset selection by optimizing the expected generalization performance of a radial basis function kernel SVM (RBF-SVM) trained across a range of the top features. Site-specific RBF-SVMs using these optimal feature sets from each imaging modality were used to predict the class labels of an independent hold-out test set. A voting approach was used to combine these multiple predictions and assign final class labels. With this methodology we were able to predict diagnosis of ADHD with 55% accuracy (versus a 39% chance level in this sample), 33% sensitivity, and 80% specificity. This approach also allowed us to evaluate predictive structural and functional features giving insight into abnormal brain circuitry in ADHD. PMID:22912605

Structure-based prediction and identification of 4-epimerization activity of phosphate sugars in class II aldolases.

PubMed

Lee, Seon-Hwa; Hong, Seung-Hye; An, Jung-Ung; Kim, Kyoung-Rok; Kim, Dong-Eun; Kang, Lin-Woo; Oh, Deok-Kun

2017-05-16

Sugar 4-epimerization reactions are important for the production of rare sugars and their derivatives, which have various potential industrial applications. For example, the production of tagatose, a functional sweetener, from fructose by sugar 4-epimerization is currently constrained because a fructose 4-epimerase does not exist in nature. We found that class II D-fructose-1,6-bisphosphate aldolase (FbaA) catalyzed the 4-epimerization of D-fructose-6-phosphate (F6P) to D-tagatose-6-phosphate (T6P) based on the prediction via structural comparisons with epimerase and molecular docking and the identification of the condensed products of C3 sugars. In vivo, the 4-epimerization activity of FbaA is normally repressed. This can be explained by our results showing the catalytic efficiency of D-fructose-6-phosphate kinase for F6P phosphorylation was significantly higher than that of FbaA for F6P epimerization. Here, we identified the epimerization reactions and the responsible catalytic residues through observation of the reactions of FbaA and L-rhamnulose-1-phosphate aldolases (RhaD) variants with substituted catalytic residues using different substrates. Moreover, we obtained detailed potential epimerization reaction mechanism of FbaA and a general epimerization mechanism of the class II aldolases L-fuculose-1-phosphate aldolase, RhaD, and FbaA. Thus, class II aldolases can be used as 4-epimerases for the stereo-selective synthesis of valuable carbohydrates.

Validity and validation of expert (Q)SAR systems.

PubMed

Hulzebos, E; Sijm, D; Traas, T; Posthumus, R; Maslankiewicz, L

2005-08-01

At a recent workshop in Setubal (Portugal) principles were drafted to assess the suitability of (quantitative) structure-activity relationships ((Q)SARs) for assessing the hazards and risks of chemicals. In the present study we applied some of the Setubal principles to test the validity of three (Q)SAR expert systems and validate the results. These principles include a mechanistic basis, the availability of a training set and validation. ECOSAR, BIOWIN and DEREK for Windows have a mechanistic or empirical basis. ECOSAR has a training set for each QSAR. For half of the structural fragments the number of chemicals in the training set is >4. Based on structural fragments and log Kow, ECOSAR uses linear regression to predict ecotoxicity. Validating ECOSAR for three 'valid' classes results in predictivity of > or = 64%. BIOWIN uses (non-)linear regressions to predict the probability of biodegradability based on fragments and molecular weight. It has a large training set and predicts non-ready biodegradability well. DEREK for Windows predictions are supported by a mechanistic rationale and literature references. The structural alerts in this program have been developed with a training set of positive and negative toxicity data. However, to support the prediction only a limited number of chemicals in the training set is presented to the user. DEREK for Windows predicts effects by 'if-then' reasoning. The program predicts best for mutagenicity and carcinogenicity. Each structural fragment in ECOSAR and DEREK for Windows needs to be evaluated and validated separately.

Modeling adaptive kernels from probabilistic phylogenetic trees.

PubMed

Nicotra, Luca; Micheli, Alessio

2009-01-01

Modeling phylogenetic interactions is an open issue in many computational biology problems. In the context of gene function prediction we introduce a class of kernels for structured data leveraging on a hierarchical probabilistic modeling of phylogeny among species. We derive three kernels belonging to this setting: a sufficient statistics kernel, a Fisher kernel, and a probability product kernel. The new kernels are used in the context of support vector machine learning. The kernels adaptivity is obtained through the estimation of the parameters of a tree structured model of evolution using as observed data phylogenetic profiles encoding the presence or absence of specific genes in a set of fully sequenced genomes. We report results obtained in the prediction of the functional class of the proteins of the budding yeast Saccharomyces cerevisae which favorably compare to a standard vector based kernel and to a non-adaptive tree kernel function. A further comparative analysis is performed in order to assess the impact of the different components of the proposed approach. We show that the key features of the proposed kernels are the adaptivity to the input domain and the ability to deal with structured data interpreted through a graphical model representation.

Internal and external relationships of the Cnidaria: implications of primary and predicted secondary structure of the 5'-end of the 23S-like rDNA.

PubMed Central

Odorico, D M; Miller, D J

1997-01-01

Since both internal (class-level) and external relationships of the Cnidaria remain unclear on the basis of analyses of 18S and (partial) 16S rDNA sequence data, we examined the informativeness of the 5'-end of the 23S-like rDNA. Here we describe analyses of both primary and predicted secondary structure data for this region from the ctenophore Bolinopsis sp., the placozoan Trichoplax adhaerens, the sponge Hymeniacidon heliophila, and representatives of all four cnidarian classes. Primary sequence analyses clearly resolved the Cnidaria from other lower Metazoa, supported sister group relationships between the Scyphozoa and Cubozoa and between the Ctenophora and the Placozoa, and confirmed the basal status of the Anthozoa within the Cnidaria. Additionally, in the ctenophore, placozoan and sponge, non-canonical base pairing is required to maintain the secondary structure of the B12 region, whereas amongst the Cnidaria this is not the case. Although the phylogenetic significance of this molecular character is unclear, our analyses do not support the close relationship between Cnidaria and Placozoa suggested by previous studies. PMID:9061962

Prediction of reacting atoms for the major biotransformation reactions of organic xenobiotics.

PubMed

Rudik, Anastasia V; Dmitriev, Alexander V; Lagunin, Alexey A; Filimonov, Dmitry A; Poroikov, Vladimir V

2016-01-01

The knowledge of drug metabolite structures is essential at the early stage of drug discovery to understand the potential liabilities and risks connected with biotransformation. The determination of the site of a molecule at which a particular metabolic reaction occurs could be used as a starting point for metabolite identification. The prediction of the site of metabolism does not always correspond to the particular atom that is modified by the enzyme but rather is often associated with a group of atoms. To overcome this problem, we propose to operate with the term "reacting atom", corresponding to a single atom in the substrate that is modified during the biotransformation reaction. The prediction of the reacting atom(s) in a molecule for the major classes of biotransformation reactions is necessary to generate drug metabolites. Substrates of the major human cytochromes P450 and UDP-glucuronosyltransferases from the Biovia Metabolite database were divided into nine groups according to their reaction classes, which are aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation. Each training set consists of positive and negative examples of structures with one labelled atom. In the positive examples, the labelled atom is the reacting atom of a particular reaction that changed adjacency. Negative examples represent non-reacting atoms of a particular reaction. We used Labelled Multilevel Neighbourhoods of Atoms descriptors for the designation of reacting atoms. A Bayesian-like algorithm was applied to estimate the structure-activity relationships. The average invariant accuracy of prediction obtained in leave-one-out and 20-fold cross-validation procedures for five human isoforms of cytochrome P450 and all isoforms of UDP-glucuronosyltransferase varies from 0.86 to 0.99 (0.96 on average). We report that reacting atoms may be predicted with reasonable accuracy for the major classes of metabolic reactions-aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation. The proposed method is implemented as a freely available web service at http://www.way2drug.com/RA and may be used for the prediction of the most probable biotransformation reaction(s) and the appropriate reacting atoms in drug-like compounds.Graphical abstract.

Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification.

PubMed

Andreatta, Massimo; Karosiene, Edita; Rasmussen, Michael; Stryhn, Anette; Buus, Søren; Nielsen, Morten

2015-11-01

A key event in the generation of a cellular response against malicious organisms through the endocytic pathway is binding of peptidic antigens by major histocompatibility complex class II (MHC class II) molecules. The bound peptide is then presented on the cell surface where it can be recognized by T helper lymphocytes. NetMHCIIpan is a state-of-the-art method for the quantitative prediction of peptide binding to any human or mouse MHC class II molecule of known sequence. In this paper, we describe an updated version of the method with improved peptide binding register identification. Binding register prediction is concerned with determining the minimal core region of nine residues directly in contact with the MHC binding cleft, a crucial piece of information both for the identification and design of CD4(+) T cell antigens. When applied to a set of 51 crystal structures of peptide-MHC complexes with known binding registers, the new method NetMHCIIpan-3.1 significantly outperformed the earlier 3.0 version. We illustrate the impact of accurate binding core identification for the interpretation of T cell cross-reactivity using tetramer double staining with a CMV epitope and its variants mapped to the epitope binding core. NetMHCIIpan is publicly available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.1 .

Analysis of a novel class of predictive microbial growth models and application to coculture growth.

PubMed

Poschet, F; Vereecken, K M; Geeraerd, A H; Nicolaï, B M; Van Impe, J F

2005-04-15

In this paper, a novel class of microbial growth models is analysed. In contrast with the currently used logistic type models (e.g., the model of Baranyi and Roberts [Baranyi, J., Roberts, T.A., 1994. A dynamic approach to predicting bacterial growth in food. International Journal of Food Microbiology 23, 277-294]), the novel model class, presented in Van Impe et al. (Van Impe, J.F., Poschet, F., Geeraerd, A.H., Vereecken, K.M., 2004. Towards a novel class of predictive microbial growth models. International Journal of Food Microbiology, this issue), explicitly incorporates nutrient exhaustion and/or metabolic waste product effects inducing stationary phase behaviour. As such, these novel model types can be extended in a natural way towards microbial interactions in cocultures and microbial growth in structured foods. Two illustrative case studies of the novel model types are thoroughly analysed and compared to the widely used model of Baranyi and Roberts. In a first case study, the stationary phase is assumed to be solely resulting from toxic product inhibition and is described as a function of the pH-evolution. In the second case study, substrate exhaustion is the sole cause of the stationary phase. Finally, a more complex case study of a so-called P-model is presented, dealing with a coculture inhibition of Listeria innocua mediated by lactic acid production of Lactococcus lactis.

Opening the World of Mathematics: The Daily Math Discussion

ERIC Educational Resources Information Center

Donoahue, Zoe

2016-01-01

During the author's everyday math discussions with her class, young children talk about mathematical ideas, theories, and concepts within a predictable structure. These discussions include many concepts from--and beyond--the first-grade math curriculum, and their depth and complexity build throughout the school year. Concepts and skills include…

Multi-class Mode of Action Classification of Toxic Compounds Using Logic Based Kernel Methods.

PubMed

Lodhi, Huma; Muggleton, Stephen; Sternberg, Mike J E

2010-09-17

Toxicity prediction is essential for drug design and development of effective therapeutics. In this paper we present an in silico strategy, to identify the mode of action of toxic compounds, that is based on the use of a novel logic based kernel method. The technique uses support vector machines in conjunction with the kernels constructed from first order rules induced by an Inductive Logic Programming system. It constructs multi-class models by using a divide and conquer reduction strategy that splits multi-classes into binary groups and solves each individual problem recursively hence generating an underlying decision list structure. In order to evaluate the effectiveness of the approach for chemoinformatics problems like predictive toxicology, we apply it to toxicity classification in aquatic systems. The method is used to identify and classify 442 compounds with respect to the mode of action. The experimental results show that the technique successfully classifies toxic compounds and can be useful in assessing environmental risks. Experimental comparison of the performance of the proposed multi-class scheme with the standard multi-class Inductive Logic Programming algorithm and multi-class Support Vector Machine yields statistically significant results and demonstrates the potential power and benefits of the approach in identifying compounds of various toxic mechanisms. Copyright © 2010 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Towards A Predictive First Principles Understanding Of Molecular Adsorption On Graphene

DTIC Science & Technology

2016-10-05

used and developed state-of-the-art quantum mechanical methods to make accurate predictions about the interaction strength and adsorption structure...density functional theory, ab initio methods 16.  SECURITY CLASSIFICATION OF: 17.  LIMITATION OF ABSTRACT SAR 18.  NUMBER OF PAGES   11   19a.  NAME OF...important physical properties for a whole class of systems with weak non-covalent interactions, for example those involving the binding between water

Natural Selection on Female Life-History Traits in Relation to Socio-Economic Class in Pre-Industrial Human Populations

PubMed Central

Pettay, Jenni E.; Helle, Samuli; Jokela, Jukka; Lummaa, Virpi

2007-01-01

Life-history theory predicts that resource scarcity constrains individual optimal reproductive strategies and shapes the evolution of life-history traits. In species where the inherited structure of social class may lead to consistent resource differences among family lines, between-class variation in resource availability should select for divergence in optimal reproductive strategies. Evaluating this prediction requires information on the phenotypic selection and quantitative genetics of life-history trait variation in relation to individual lifetime access to resources. Here, we show using path analysis how resource availability, measured as the wealth class of the family, affected the opportunity and intensity of phenotypic selection on the key life-history traits of women living in pre-industrial Finland during the 1800s and 1900s. We found the highest opportunity for total selection and the strongest selection on earlier age at first reproduction in women of the poorest wealth class, whereas selection favoured older age at reproductive cessation in mothers of the wealthier classes. We also found clear differences in female life-history traits across wealth classes: the poorest women had the lowest age-specific survival throughout their lives, they started reproduction later, delivered fewer offspring during their lifetime, ceased reproduction younger, had poorer offspring survival to adulthood and, hence, had lower fitness compared to the wealthier women. Our results show that the amount of wealth affected the selection pressure on female life-history in a pre-industrial human population. PMID:17622351

What do children know and understand about universal gravitation? Structural and developmental aspects.

PubMed

Frappart, Sören; Raijmakers, Maartje; Frède, Valérie

2014-04-01

Children's understanding of universal gravitation starts at an early age but changes until adulthood, which makes it an interesting topic for studying the development and structure of knowledge. Children's understanding of gravitation was tested for a variety of contexts and across a wide age range (5-18 years, N=144). We analyzed children's predictions and justifications for the trajectory of a stone dropped on the earth, on the moon, in a spaceship orbiting the earth, on a planet with air, on a planet with no air, and in a lift (i.e., an elevator) in free fall. Results showed that performances were related to the context and to the children's age. U-shaped developmental curves were identified for predictions for three contexts. These curves could be explained by analyzing the structure of the children's knowledge using latent class analysis. We identified three coherent patterns of predictions that were related to specific justifications. With age, children produced more scientific predictions. Children's cognitive structures, as reflected in their predictions of dropped stone trajectories, seem to be coherently built given that there were only a limited number of prediction patterns. Even by Grade 12, students had not achieved a scientific understanding of universal gravitation. Copyright © 2013 Elsevier Inc. All rights reserved.

Posttraumatic Stress Disorder: Diagnostic Data Analysis by Data Mining Methodology

PubMed Central

Marinić, Igor; Supek, Fran; Kovačić, Zrnka; Rukavina, Lea; Jendričko, Tihana; Kozarić-Kovačić, Dragica

2007-01-01

Aim To use data mining methods in assessing diagnostic symptoms in posttraumatic stress disorder (PTSD) Methods The study included 102 inpatients: 51 with a diagnosis of PTSD and 51 with psychiatric diagnoses other than PTSD. Several models for predicting diagnosis were built using the random forest classifier, one of the intelligent data analysis methods. The first prediction model was based on a structured psychiatric interview, the second on psychiatric scales (Clinician-administered PTSD Scale – CAPS, Positive and Negative Syndrome Scale – PANSS, Hamilton Anxiety Scale – HAMA, and Hamilton Depression Scale – HAMD), and the third on combined data from both sources. Additional models placing more weight on one of the classes (PTSD or non-PTSD) were trained, and prototypes representing subgroups in the classes constructed. Results The first model was the most relevant for distinguishing PTSD diagnosis from comorbid diagnoses such as neurotic, stress-related, and somatoform disorders. The second model pointed out the scores obtained on the Clinician-administered PTSD Scale (CAPS) and additional Positive and Negative Syndrome Scale (PANSS) scales, together with comorbid diagnoses of neurotic, stress-related, and somatoform disorders as most relevant. In the third model, psychiatric scales and the same group of comorbid diagnoses were found to be most relevant. Specialized models placing more weight on either the PTSD or non-PTSD class were able to better predict their targeted diagnoses at some expense of overall accuracy. Class subgroup prototypes mainly differed in values achieved on psychiatric scales and frequency of comorbid diagnoses. Conclusion Our work demonstrated the applicability of data mining methods for the analysis of structured psychiatric data for PTSD. In all models, the group of comorbid diagnoses, including neurotic, stress-related, and somatoform disorders, surfaced as important. The important attributes of the data, based on the structured psychiatric interview, were the current symptoms and conditions such as presence and degree of disability, hospitalizations, and duration of military service during the war, while CAPS total scores, symptoms of increased arousal, and PANSS additional criteria scores were indicated as relevant from the psychiatric symptom scales. PMID:17436383

QSAR Study and Molecular Design of Open-Chain Enaminones as Anticonvulsant Agents

PubMed Central

Garro Martinez, Juan C.; Duchowicz, Pablo R.; Estrada, Mario R.; Zamarbide, Graciela N.; Castro, Eduardo A.

2011-01-01

Present work employs the QSAR formalism to predict the ED50 anticonvulsant activity of ringed-enaminones, in order to apply these relationships for the prediction of unknown open-chain compounds containing the same types of functional groups in their molecular structure. Two different modeling approaches are applied with the purpose of comparing the consistency of our results: (a) the search of molecular descriptors via multivariable linear regressions; and (b) the calculation of flexible descriptors with the CORAL (CORrelation And Logic) program. Among the results found, we propose some potent candidate open-chain enaminones having ED50 values lower than 10 mg·kg−1 for corresponding pharmacological studies. These compounds are classified as Class 1 and Class 2 according to the Anticonvulsant Selection Project. PMID:22272137

Rational design of class I MHC ligands

NASA Astrophysics Data System (ADS)

Rognan, D.; Scapozza, L.; Folkers, G.; Daser, Angelika

1995-04-01

From the knowledge of the three-dimensional structure of a class I MHC protein, several non natural peptides were designed in order to either optimize the interactions of one secondary anchor amino acid with its HLA binding pocket or to substitute the non interacting part with spacer residues. All peptides were synthesized and tested for binding to the class I MHC protein in an in vitro reconstitution assay. As predicted, the non natural peptides present an enhanced binding to the HLA-B27 molecule with respect to their natural parent peptides. This study constitutes the first step towards the rational design of non peptidic MHC ligands that should be very promising tools for the selective immunotherapy of autoimmune diseases.

Two classes of ODE models with switch-like behavior

PubMed Central

Just, Winfried; Korb, Mason; Elbert, Ben; Young, Todd

2013-01-01

In cases where the same real-world system can be modeled both by an ODE system ⅅ and a Boolean system 𝔹, it is of interest to identify conditions under which the two systems will be consistent, that is, will make qualitatively equivalent predictions. In this note we introduce two broad classes of relatively simple models that provide a convenient framework for studying such questions. In contrast to the widely known class of Glass networks, the right-hand sides of our ODEs are Lipschitz-continuous. We prove that if 𝔹 has certain structures, consistency between ⅅ and 𝔹 is implied by sufficient separation of time scales in one class of our models. Namely, if the trajectories of 𝔹 are “one-stepping” then we prove a strong form of consistency and if 𝔹 has a certain monotonicity property then there is a weaker consistency between ⅅ and 𝔹. These results appear to point to more general structure properties that favor consistency between ODE and Boolean models. PMID:24244061

Transporters associated with antigen processing (TAP) in sea bass (Dicentrarchus labrax, L.): molecular cloning and characterization of TAP1 and TAP2.

PubMed

Pinto, Rute D; Pereira, Pedro J B; dos Santos, Nuno M S

2011-11-01

The transporters associated with antigen processing (TAP), play an important role in the MHC class I antigen presentation pathway. In this work, sea bass (Dicentrarchus labrax) TAP1 and TAP2 genes and transcripts were isolated and characterized. Only the TAP2 gene is structurally similar to its human orthologue. As other TAP molecules, sea bass TAP1 and TAP2 are formed by one N-terminal accessory domain, one core membrane-spanning domain and one canonical C-terminal nucleotide-binding domain. Homology modelling of the sea bass TAP dimer predicts that its quaternary structure is in accordance with that of other ABC transporters. Phylogenetic analysis segregates sea bass TAP1 and TAP2 into each subfamily cluster of transporters, placing them in the fish class and suggesting that the basic structure of these transport-associated proteins is evolutionarily conserved. Furthermore, the present data provides information that will enable more studies on the class I antigen presentation pathway in this important fish species. Copyright © 2011 Elsevier Ltd. All rights reserved.

Avibactam and Class C β-Lactamases: Mechanism of Inhibition, Conservation of the Binding Pocket, and Implications for Resistance

PubMed Central

Johnstone, M. R.; Ross, P. L.; McLaughlin, R. E.; Olivier, N. B.

2014-01-01

Avibactam is a novel non-β-lactam β-lactamase inhibitor that inhibits a wide range of β-lactamases. These include class A, class C, and some class D enzymes, which erode the activity of β-lactam drugs in multidrug-resistant pathogens like Pseudomonas aeruginosa and Enterobacteriaceae spp. Avibactam is currently in clinical development in combination with the β-lactam antibiotics ceftazidime, ceftaroline fosamil, and aztreonam. Avibactam has the potential to be the first β-lactamase inhibitor that might provide activity against class C-mediated resistance, which represents a growing concern in both hospital- and community-acquired infections. Avibactam has an unusual mechanism of action: it is a covalent inhibitor that acts via ring opening, but in contrast to other currently used β-lactamase inhibitors, this reaction is reversible. Here, we present a high-resolution structure of avibactam bound to a class C β-lactamase, AmpC, from P. aeruginosa that provided insight into the mechanism of both acylation and recyclization in this enzyme class and highlighted the differences observed between class A and class C inhibition. Furthermore, variants resistant to avibactam that identified the residues important for inhibition were isolated. Finally, the structural information was used to predict effective inhibition by sequence analysis and functional studies of class C β-lactamases from a large and diverse set of contemporary clinical isolates (P. aeruginosa and several Enterobacteriaceae spp.) obtained from recent infections to understand any preexisting variability in the binding pocket that might affect inhibition by avibactam. PMID:25022578

Structure-Based Sequence Alignment of the Transmembrane Domains of All Human GPCRs: Phylogenetic, Structural and Functional Implications

PubMed Central

Cvicek, Vaclav; Goddard, William A.; Abrol, Ravinder

2016-01-01

The understanding of G-protein coupled receptors (GPCRs) is undergoing a revolution due to increased information about their signaling and the experimental determination of structures for more than 25 receptors. The availability of at least one receptor structure for each of the GPCR classes, well separated in sequence space, enables an integrated superfamily-wide analysis to identify signatures involving the role of conserved residues, conserved contacts, and downstream signaling in the context of receptor structures. In this study, we align the transmembrane (TM) domains of all experimental GPCR structures to maximize the conserved inter-helical contacts. The resulting superfamily-wide GpcR Sequence-Structure (GRoSS) alignment of the TM domains for all human GPCR sequences is sufficient to generate a phylogenetic tree that correctly distinguishes all different GPCR classes, suggesting that the class-level differences in the GPCR superfamily are encoded at least partly in the TM domains. The inter-helical contacts conserved across all GPCR classes describe the evolutionarily conserved GPCR structural fold. The corresponding structural alignment of the inactive and active conformations, available for a few GPCRs, identifies activation hot-spot residues in the TM domains that get rewired upon activation. Many GPCR mutations, known to alter receptor signaling and cause disease, are located at these conserved contact and activation hot-spot residue positions. The GRoSS alignment places the chemosensory receptor subfamilies for bitter taste (TAS2R) and pheromones (Vomeronasal, VN1R) in the rhodopsin family, known to contain the chemosensory olfactory receptor subfamily. The GRoSS alignment also enables the quantification of the structural variability in the TM regions of experimental structures, useful for homology modeling and structure prediction of receptors. Furthermore, this alignment identifies structurally and functionally important residues in all human GPCRs. These residues can be used to make testable hypotheses about the structural basis of receptor function and about the molecular basis of disease-associated single nucleotide polymorphisms. PMID:27028541

Molecular Phylogeny and Predicted 3D Structure of Plant beta-D-N-Acetylhexosaminidase

PubMed Central

Hossain, Md. Anowar

2014-01-01

beta-D-N-Acetylhexosaminidase, a family 20 glycosyl hydrolase, catalyzes the removal of β-1,4-linked N-acetylhexosamine residues from oligosaccharides and their conjugates. We constructed phylogenetic tree of β-hexosaminidases to analyze the evolutionary history and predicted functions of plant hexosaminidases. Phylogenetic analysis reveals the complex history of evolution of plant β-hexosaminidase that can be described by gene duplication events. The 3D structure of tomato β-hexosaminidase (β-Hex-Sl) was predicted by homology modeling using 1now as a template. Structural conformity studies of the best fit model showed that more than 98% of the residues lie inside the favoured and allowed regions where only 0.9% lie in the unfavourable region. Predicted 3D structure contains 531 amino acids residues with glycosyl hydrolase20b domain-I and glycosyl hydrolase20 superfamily domain-II including the (β/α)8 barrel in the central part. The α and β contents of the modeled structure were found to be 33.3% and 12.2%, respectively. Eleven amino acids were found to be involved in ligand-binding site; Asp(330) and Glu(331) could play important roles in enzyme-catalyzed reactions. The predicted model provides a structural framework that can act as a guide to develop a hypothesis for β-Hex-Sl mutagenesis experiments for exploring the functions of this class of enzymes in plant kingdom. PMID:25165734

The three latent classes of adolescent delinquency and the risk factors for membership in each class.

PubMed

Hasking, Penelope Anne; Scheier, Lawrence M; Abdallah, Arbi Ben

2011-01-01

This study used latent class analysis to examine subpopulation membership based on self-reports of delinquent behaviors obtained from Australian youth. Three discrete identifiable classes were derived based on 51 indicators of physical violence, property damage, minor infractions, drug use, and social delinquency. One class of youth engaged in primarily rule breaking and norm violations including underage alcohol use, typical of this age period. A second class was more actively delinquent emphasizing drug use, trespassing, and various forms of disobedience. A third class of highly delinquent youth differed from their counterparts by endorsing drug use, thievery that involved stealing money, goods, and cars, property damage, gambling, precocious sexual experiences, involvement with pornographic materials, and fighting. Multinomial logistic regression predicting class membership indicated highly delinquent youth were more likely to be older males, use venting coping strategies, and be fun or novelty seeking compared with rule breakers. Findings are discussed in terms of refining current taxonomic arguments regarding the structure of delinquency and implications for prevention of early-stage antisocial behavior. © 2010 Wiley-Liss, Inc.

Social class disparities in health and education: reducing inequality by applying a sociocultural self model of behavior.

PubMed

Stephens, Nicole M; Markus, Hazel Rose; Fryberg, Stephanie A

2012-10-01

The literature on social class disparities in health and education contains 2 underlying, yet often opposed, models of behavior: the individual model and the structural model. These models refer to largely unacknowledged assumptions about the sources of human behavior that are foundational to research and interventions. Our review and theoretical integration proposes that, in contrast to how the 2 models are typically represented, they are not opposed, but instead they are complementary sets of understandings that inform and extend each other. Further, we elaborate the theoretical rationale and predictions for a third model: the sociocultural self model of behavior. This model incorporates and extends key tenets of the individual and structural models. First, the sociocultural self model conceptualizes individual characteristics (e.g., skills) and structural conditions (e.g., access to resources) as interdependent forces that mutually constitute each other and that are best understood together. Second, the sociocultural self model recognizes that both individual characteristics and structural conditions indirectly influence behavior through the selves that emerge in the situation. These selves are malleable psychological states that are a product of the ongoing mutual constitution of individuals and structures and serve to guide people's behavior by systematically shaping how people construe situations. The theoretical foundation of the sociocultural self model lays the groundwork for a more complete understanding of behavior and provides new tools for developing interventions that will reduce social class disparities in health and education. The model predicts that intervention efforts will be more effective at producing sustained behavior change when (a) current selves are congruent, rather than incongruent, with the desired behavior and (b) individual characteristics and structural conditions provide ongoing support for the selves that are necessary to support the desired behavior. PsycINFO Database Record (c) 2012 APA, all rights reserved.

Major Histocompatibility Complex (MHC) Class I and MHC Class II Proteins: Conformational Plasticity in Antigen Presentation

PubMed Central

Wieczorek, Marek; Abualrous, Esam T.; Sticht, Jana; Álvaro-Benito, Miguel; Stolzenberg, Sebastian; Noé, Frank; Freund, Christian

2017-01-01

Antigen presentation by major histocompatibility complex (MHC) proteins is essential for adaptive immunity. Prior to presentation, peptides need to be generated from proteins that are either produced by the cell’s own translational machinery or that are funneled into the endo-lysosomal vesicular system. The prolonged interaction between a T cell receptor and specific pMHC complexes, after an extensive search process in secondary lymphatic organs, eventually triggers T cells to proliferate and to mount a specific cellular immune response. Once processed, the peptide repertoire presented by MHC proteins largely depends on structural features of the binding groove of each particular MHC allelic variant. Additionally, two peptide editors—tapasin for class I and HLA-DM for class II—contribute to the shaping of the presented peptidome by favoring the binding of high-affinity antigens. Although there is a vast amount of biochemical and structural information, the mechanism of the catalyzed peptide exchange for MHC class I and class II proteins still remains controversial, and it is not well understood why certain MHC allelic variants are more susceptible to peptide editing than others. Recent studies predict a high impact of protein intermediate states on MHC allele-specific peptide presentation, which implies a profound influence of MHC dynamics on the phenomenon of immunodominance and the development of autoimmune diseases. Here, we review the recent literature that describe MHC class I and II dynamics from a theoretical and experimental point of view and we highlight the similarities between MHC class I and class II dynamics despite the distinct functions they fulfill in adaptive immunity. PMID:28367149

Comparing five modelling techniques for predicting forest characteristics

Treesearch

Gretchen G. Moisen; Tracey S. Frescino

2002-01-01

Broad-scale maps of forest characteristics are needed throughout the United States for a wide variety of forest land management applications. Inexpensive maps can be produced by modelling forest class and structure variables collected in nationwide forest inventories as functions of satellite-based information. But little work has been directed at comparing modelling...

Non-linear quantitative structure-activity relationship for adenine derivatives as competitive inhibitors of adenosine deaminase

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

Sadat Hayatshahi, Sayyed Hamed; Abdolmaleki, Parviz; Safarian, Shahrokh

2005-12-16

Logistic regression and artificial neural networks have been developed as two non-linear models to establish quantitative structure-activity relationships between structural descriptors and biochemical activity of adenosine based competitive inhibitors, toward adenosine deaminase. The training set included 24 compounds with known k {sub i} values. The models were trained to solve two-class problems. Unlike the previous work in which multiple linear regression was used, the highest of positive charge on the molecules was recognized to be in close relation with their inhibition activity, while the electric charge on atom N1 of adenosine was found to be a poor descriptor. Consequently, themore » previously developed equation was improved and the newly formed one could predict the class of 91.66% of compounds correctly. Also optimized 2-3-1 and 3-4-1 neural networks could increase this rate to 95.83%.« less

[Influence of autonomy support, social goals and relatedness on amotivation in physical education classes].

PubMed

Moreno Murcia, Juan A; Parra Rojas, Nicolás; González-Cutre Coll, David

2008-11-01

The purpose of this study was to analyze some factors that influence amotivation in physical education classes. A sample of 399 students, of ages 14 to 16 years, was used. They completed the Perceived Autonomy Support Scale in Exercise Settings (PASSES), the Social Goal Scale-Physical Education (SGS-PE), the factor of the Basic Psychological Needs in Exercise Scale (BPNES) adapted to physical education and the factor of the Perceived Locus of Causality Scale (PLOC). The psychometric properties of the PASSES were analyzed, as this scale had not been validated to the Spanish context. In this analysis, the scale showed appropriate validity and reliability. The results of the structural equation model indicated that social responsibility and social relationship goals positively predicted perception of relatedness, whereas the context of autonomy support did not significantly predict it. In turn, perception of relatedness negatively predicted amotivation. The findings are discussed with regard to enhancing students' positive motivation.

Prediction of future uniform milk prices in Florida federal milk marketing order 6 from milk futures markets.

PubMed

De Vries, A; Feleke, S

2008-12-01

This study assessed the accuracy of 3 methods that predict the uniform milk price in Federal Milk Marketing Order 6 (Florida). Predictions were made for 1 to 12 mo into the future. Data were from January 2003 to May 2007. The CURRENT method assumed that future uniform milk prices were equal to the last announced uniform milk price. The F+BASIS and F+UTIL methods were based on the milk futures markets because the futures prices reflect the market's expectation of the class III and class IV cash prices that are announced monthly by USDA. The F+BASIS method added an exponentially weighted moving average of the difference between the class III cash price and the historical uniform milk price (also known as basis) to the class III futures price. The F+UTIL method used the class III and class IV futures prices, the most recently announced butter price, and historical utilizations to predict the skim milk prices, butterfat prices, and utilizations in all 4 classes. Predictions of future utilizations were made with a Holt-Winters smoothing method. Federal Milk Marketing Order 6 had high class I utilization (85 +/- 4.8%). Mean and standard deviation of the class III and class IV cash prices were $13.39 +/- 2.40/cwt (1 cwt = 45.36 kg) and $12.06 +/- 1.80/cwt, respectively. The actual uniform price in Tampa, Florida, was $16.62 +/- 2.16/cwt. The basis was $3.23 +/- 1.23/cwt. The F+BASIS and F+UTIL predictions were generally too low during the period considered because the class III cash prices were greater than the corresponding class III futures prices. For the 1- to 6-mo-ahead predictions, the root of the mean squared prediction errors from the F+BASIS method were $1.12, $1.20, $1.55, $1.91, $2.16, and $2.34/cwt, respectively. The root of the mean squared prediction errors ranged from $2.50 to $2.73/cwt for predictions up to 12 mo ahead. Results from the F+UTIL method were similar. The accuracies of the F+BASIS and F+UTIL methods for all 12 fore-cast horizons were not significantly different. Application of the modified Mariano-Diebold tests showed that no method included all the information contained in the other methods. In conclusion, both F+BASIS and F+UTIL methods tended to more accurately predict the future uniform milk prices than the CURRENT method, but prediction errors could be substantial even a few months into the future. The majority of the prediction error was caused by the inefficiency of the futures markets to predict the class III cash prices.

StrBioLib: a Java library for development of custom computational structural biology applications.

PubMed

Chandonia, John-Marc

2007-08-01

StrBioLib is a library of Java classes useful for developing software for computational structural biology research. StrBioLib contains classes to represent and manipulate protein structures, biopolymer sequences, sets of biopolymer sequences, and alignments between biopolymers based on either sequence or structure. Interfaces are provided to interact with commonly used bioinformatics applications, including (psi)-blast, modeller, muscle and Primer3, and tools are provided to read and write many file formats used to represent bioinformatic data. The library includes a general-purpose neural network object with multiple training algorithms, the Hooke and Jeeves non-linear optimization algorithm, and tools for efficient C-style string parsing and formatting. StrBioLib is the basis for the Pred2ary secondary structure prediction program, is used to build the astral compendium for sequence and structure analysis, and has been extensively tested through use in many smaller projects. Examples and documentation are available at the site below. StrBioLib may be obtained under the terms of the GNU LGPL license from http://strbio.sourceforge.net/

A Mixtures-of-Trees Framework for Multi-Label Classification

PubMed Central

Hong, Charmgil; Batal, Iyad; Hauskrecht, Milos

2015-01-01

We propose a new probabilistic approach for multi-label classification that aims to represent the class posterior distribution P(Y|X). Our approach uses a mixture of tree-structured Bayesian networks, which can leverage the computational advantages of conditional tree-structured models and the abilities of mixtures to compensate for tree-structured restrictions. We develop algorithms for learning the model from data and for performing multi-label predictions using the learned model. Experiments on multiple datasets demonstrate that our approach outperforms several state-of-the-art multi-label classification methods. PMID:25927011

Social class, power, and selfishness: when and why upper and lower class individuals behave unethically.

PubMed

Dubois, David; Rucker, Derek D; Galinsky, Adam D

2015-03-01

Are the rich more unethical than the poor? To answer this question, the current research introduces a key conceptual distinction between selfish and unethical behavior. Based on this distinction, the current article offers 2 novel findings that illuminate the relationship between social class and unethical behavior. First, the effects of social class on unethical behavior are not invariant; rather, the effects of social class are moderated by whether unethical behavior benefits the self or others. Replicating past work, social class positively predicted unethical behavior; however, this relationship was only observed when that behavior was self-beneficial. When unethical behavior was performed to benefit others, social class negatively predicted unethical behavior; lower class individuals were more likely than upper class individuals to engage in unethical behavior. Overall, social class predicts people's tendency to behave selfishly, rather than predicting unethical behavior per se. Second, individuals' sense of power drove the effects of social class on unethical behavior. Evidence for this relationship was provided in three forms. First, income, but not education level, predicted unethical behavior. Second, feelings of power mediated the effect of social class on unethical behavior, but feelings of status did not. Third, two distinct manipulations of power produced the same moderation by self-versus-other beneficiary as was found with social class. The current theoretical framework and data both synthesize and help to explain a range of findings in the social class and power literatures. PsycINFO Database Record (c) 2015 APA, all rights reserved.

Characterization of Non-coding DNA Satellites Associated with Sweepoviruses (Genus Begomovirus, Geminiviridae) – Definition of a Distinct Class of Begomovirus-Associated Satellites

PubMed Central

Lozano, Gloria; Trenado, Helena P.; Fiallo-Olivé, Elvira; Chirinos, Dorys; Geraud-Pouey, Francis; Briddon, Rob W.; Navas-Castillo, Jesús

2016-01-01

Begomoviruses (family Geminiviridae) are whitefly-transmitted, plant-infecting single-stranded DNA viruses that cause crop losses throughout the warmer parts of the World. Sweepoviruses are a phylogenetically distinct group of begomoviruses that infect plants of the family Convolvulaceae, including sweet potato (Ipomoea batatas). Two classes of subviral molecules are often associated with begomoviruses, particularly in the Old World; the betasatellites and the alphasatellites. An analysis of sweet potato and Ipomoea indica samples from Spain and Merremia dissecta samples from Venezuela identified small non-coding subviral molecules in association with several distinct sweepoviruses. The sequences of 18 clones were obtained and found to be structurally similar to tomato leaf curl virus-satellite (ToLCV-sat, the first DNA satellite identified in association with a begomovirus), with a region with significant sequence identity to the conserved region of betasatellites, an A-rich sequence, a predicted stem–loop structure containing the nonanucleotide TAATATTAC, and a second predicted stem–loop. These sweepovirus-associated satellites join an increasing number of ToLCV-sat-like non-coding satellites identified recently. Although sharing some features with betasatellites, evidence is provided to suggest that the ToLCV-sat-like satellites are distinct from betasatellites and should be considered a separate class of satellites, for which the collective name deltasatellites is proposed. PMID:26925037

A novel TctA citrate transporter from an activated sludge metagenome: structural and mechanistic predictions for the TTT family.

PubMed

Batista-García, Ramón Alberto; Sánchez-Reyes, Ayixon; Millán-Pacheco, César; González-Zuñiga, Víctor Manuel; Juárez, Soledad; Folch-Mallol, Jorge Luis; Pastor, Nina

2014-09-01

We isolated a putative citrate transporter of the tripartite tricarboxylate transporter (TTT) class from a metagenomic library of activated sludge from a sewage treatment plant. The transporter, dubbed TctA_ar, shares ∼50% sequence identity with TctA of Comamonas testosteroni (TctA_ct) and other β-Proteobacteria, and contains two 20-amino acid repeat signature sequences, considered a hallmark of this particular transporter class. The structures for both TctA_ar and TctA_ct were modeled with I-TASSER and two possible structures for this transporter family were proposed. Docking assays with citrate resulted in the corresponding sets of proposed critical residues for function. These models suggest functions for the 20-amino acid repeats in the context of the two different architectures. This constitutes the first attempt at structure modeling of the TTT family, to the best of our knowledge, and could aid functional understanding of this little-studied family. © 2014 Wiley Periodicals, Inc.

Probing binding hot spots at protein-RNA recognition sites.

PubMed

Barik, Amita; Nithin, Chandran; Karampudi, Naga Bhushana Rao; Mukherjee, Sunandan; Bahadur, Ranjit Prasad

2016-01-29

We use evolutionary conservation derived from structure alignment of polypeptide sequences along with structural and physicochemical attributes of protein-RNA interfaces to probe the binding hot spots at protein-RNA recognition sites. We find that the degree of conservation varies across the RNA binding proteins; some evolve rapidly compared to others. Additionally, irrespective of the structural class of the complexes, residues at the RNA binding sites are evolutionary better conserved than those at the solvent exposed surfaces. For recognitions involving duplex RNA, residues interacting with the major groove are better conserved than those interacting with the minor groove. We identify multi-interface residues participating simultaneously in protein-protein and protein-RNA interfaces in complexes where more than one polypeptide is involved in RNA recognition, and show that they are better conserved compared to any other RNA binding residues. We find that the residues at water preservation site are better conserved than those at hydrated or at dehydrated sites. Finally, we develop a Random Forests model using structural and physicochemical attributes for predicting binding hot spots. The model accurately predicts 80% of the instances of experimental ΔΔG values in a particular class, and provides a stepping-stone towards the engineering of protein-RNA recognition sites with desired affinity. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

The unfoldomics decade: an update on intrinsically disordered proteins.

PubMed

Dunker, A Keith; Oldfield, Christopher J; Meng, Jingwei; Romero, Pedro; Yang, Jack Y; Chen, Jessica Walton; Vacic, Vladimir; Obradovic, Zoran; Uversky, Vladimir N

2008-09-16

Our first predictor of protein disorder was published just over a decade ago in the Proceedings of the IEEE International Conference on Neural Networks (Romero P, Obradovic Z, Kissinger C, Villafranca JE, Dunker AK (1997) Identifying disordered regions in proteins from amino acid sequence. Proceedings of the IEEE International Conference on Neural Networks, 1: 90-95). By now more than twenty other laboratory groups have joined the efforts to improve the prediction of protein disorder. While the various prediction methodologies used for protein intrinsic disorder resemble those methodologies used for secondary structure prediction, the two types of structures are entirely different. For example, the two structural classes have very different dynamic properties, with the irregular secondary structure class being much less mobile than the disorder class. The prediction of secondary structure has been useful. On the other hand, the prediction of intrinsic disorder has been revolutionary, leading to major modifications of the more than 100 year-old views relating protein structure and function. Experimentalists have been providing evidence over many decades that some proteins lack fixed structure or are disordered (or unfolded) under physiological conditions. In addition, experimentalists are also showing that, for many proteins, their functions depend on the unstructured rather than structured state; such results are in marked contrast to the greater than hundred year old views such as the lock and key hypothesis. Despite extensive data on many important examples, including disease-associated proteins, the importance of disorder for protein function has been largely ignored. Indeed, to our knowledge, current biochemistry books don't present even one acknowledged example of a disorder-dependent function, even though some reports of disorder-dependent functions are more than 50 years old. The results from genome-wide predictions of intrinsic disorder and the results from other bioinformatics studies of intrinsic disorder are demanding attention for these proteins. Disorder prediction has been important for showing that the relatively few experimentally characterized examples are members of a very large collection of related disordered proteins that are wide-spread over all three domains of life. Many significant biological functions are now known to depend directly on, or are importantly associated with, the unfolded or partially folded state. Here our goal is to review the key discoveries and to weave these discoveries together to support novel approaches for understanding sequence-function relationships. Intrinsically disordered protein is common across the three domains of life, but especially common among the eukaryotic proteomes. Signaling sequences and sites of posttranslational modifications are frequently, or very likely most often, located within regions of intrinsic disorder. Disorder-to-order transitions are coupled with the adoption of different structures with different partners. Also, the flexibility of intrinsic disorder helps different disordered regions to bind to a common binding site on a common partner. Such capacity for binding diversity plays important roles in both protein-protein interaction networks and likely also in gene regulation networks. Such disorder-based signaling is further modulated in multicellular eukaryotes by alternative splicing, for which such splicing events map to regions of disorder much more often than to regions of structure. Associating alternative splicing with disorder rather than structure alleviates theoretical and experimentally observed problems associated with the folding of different length, isomeric amino acid sequences. The combination of disorder and alternative splicing is proposed to provide a mechanism for easily "trying out" different signaling pathways, thereby providing the mechanism for generating signaling diversity and enabling the evolution of cell differentiation and multicellularity. Finally, several recent small molecules of interest as potential drugs have been shown to act by blocking protein-protein interactions based on intrinsic disorder of one of the partners. Study of these examples has led to a new approach for drug discovery, and bioinformatics analysis of the human proteome suggests that various disease-associated proteins are very rich in such disorder-based drug discovery targets.

Using latent class analysis to model prescription medications in the measurement of falling among a community elderly population

PubMed Central

2013-01-01

Background Falls among the elderly are a major public health concern. Therefore, the possibility of a modeling technique which could better estimate fall probability is both timely and needed. Using biomedical, pharmacological and demographic variables as predictors, latent class analysis (LCA) is demonstrated as a tool for the prediction of falls among community dwelling elderly. Methods Using a retrospective data-set a two-step LCA modeling approach was employed. First, we looked for the optimal number of latent classes for the seven medical indicators, along with the patients’ prescription medication and three covariates (age, gender, and number of medications). Second, the appropriate latent class structure, with the covariates, were modeled on the distal outcome (fall/no fall). The default estimator was maximum likelihood with robust standard errors. The Pearson chi-square, likelihood ratio chi-square, BIC, Lo-Mendell-Rubin Adjusted Likelihood Ratio test and the bootstrap likelihood ratio test were used for model comparisons. Results A review of the model fit indices with covariates shows that a six-class solution was preferred. The predictive probability for latent classes ranged from 84% to 97%. Entropy, a measure of classification accuracy, was good at 90%. Specific prescription medications were found to strongly influence group membership. Conclusions In conclusion the LCA method was effective at finding relevant subgroups within a heterogenous at-risk population for falling. This study demonstrated that LCA offers researchers a valuable tool to model medical data. PMID:23705639

In silico designing of power conversion efficient organic lead dyes for solar cells using todays innovative approaches to assure renewable energy for future

NASA Astrophysics Data System (ADS)

Kar, Supratik; Roy, Juganta K.; Leszczynski, Jerzy

2017-06-01

Advances in solar cell technology require designing of new organic dye sensitizers for dye-sensitized solar cells with high power conversion efficiency to circumvent the disadvantages of silicon-based solar cells. In silico studies including quantitative structure-property relationship analysis combined with quantum chemical analysis were employed to understand the primary electron transfer mechanism and photo-physical properties of 273 arylamine organic dyes from 11 diverse chemical families explicit to iodine electrolyte. The direct quantitative structure-property relationship models enable identification of the essential electronic and structural attributes necessary for quantifying the molecular prerequisites of 11 classes of arylamine organic dyes, responsible for high power conversion efficiency of dye-sensitized solar cells. Tetrahydroquinoline, N,N'-dialkylaniline and indoline have been least explored classes under arylamine organic dyes for dye-sensitized solar cells. Therefore, the identified properties from the corresponding quantitative structure-property relationship models of the mentioned classes were employed in designing of "lead dyes". Followed by, a series of electrochemical and photo-physical parameters were computed for designed dyes to check the required variables for electron flow of dye-sensitized solar cells. The combined computational techniques yielded seven promising lead dyes each for all three chemical classes considered. Significant (130, 183, and 46%) increment in predicted %power conversion efficiency was observed comparing with the existing dye with highest experimental %power conversion efficiency value for tetrahydroquinoline, N,N'-dialkylaniline and indoline, respectively maintaining required electrochemical parameters.

A class-based link prediction using Distance Dependent Chinese Restaurant Process

NASA Astrophysics Data System (ADS)

Andalib, Azam; Babamir, Seyed Morteza

2016-08-01

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

Community detection in complex networks using link prediction

NASA Astrophysics Data System (ADS)

Cheng, Hui-Min; Ning, Yi-Zi; Yin, Zhao; Yan, Chao; Liu, Xin; Zhang, Zhong-Yuan

2018-01-01

Community detection and link prediction are both of great significance in network analysis, which provide very valuable insights into topological structures of the network from different perspectives. In this paper, we propose a novel community detection algorithm with inclusion of link prediction, motivated by the question whether link prediction can be devoted to improving the accuracy of community partition. For link prediction, we propose two novel indices to compute the similarity between each pair of nodes, one of which aims to add missing links, and the other tries to remove spurious edges. Extensive experiments are conducted on benchmark data sets, and the results of our proposed algorithm are compared with two classes of baselines. In conclusion, our proposed algorithm is competitive, revealing that link prediction does improve the precision of community detection.

Fishing and temperature effects on the size structure of exploited fish stocks.

PubMed

Tu, Chen-Yi; Chen, Kuan-Ting; Hsieh, Chih-Hao

2018-05-08

Size structure of fish stock plays an important role in maintaining sustainability of the population. Size distribution of an exploited stock is predicted to shift toward small individuals caused by size-selective fishing and/or warming; however, their relative contribution remains relatively unexplored. In addition, existing analyses on size structure have focused on univariate size-based indicators (SBIs), such as mean length, evenness of size classes, or the upper 95-percentile of the length frequency distribution; these approaches may not capture full information of size structure. To bridge the gap, we used the variation partitioning approach to examine how the size structure (composition of size classes) responded to fishing, warming and the interaction. We analyzed 28 exploited stocks in the West US, Alaska and North Sea. Our result shows fishing has the most prominent effect on the size structure of the exploited stocks. In addition, the fish stocks experienced higher variability in fishing is more responsive to the temperature effect in their size structure, suggesting that fishing may elevate the sensitivity of exploited stocks in responding to environmental effects. The variation partitioning approach provides complementary information to univariate SBIs in analyzing size structure.

A heating mechanism for the chromospheres of M dwarf stars

NASA Technical Reports Server (NTRS)

Giampapa, M. S.; Golub, L.; Rosner, R.; Vaiana, G.; Linsky, J. L.; Worden, S. P.

1981-01-01

The atmospheric structure of the dwarf M-stars which is especially important to the general field of stellar chromospheres and coronae was investigated. The M-dwarf stars constitute a class of objects for which the discrepancy between the predictions of the acoustic wave chromospheric/coronal heating hypothesis and the observations is most vivid. It is assumed that they represent a class of stars where alternative atmospheric heating mechanisms, presumably magnetically related, are most clearly manifested. Ascertainment of the validity of a hypothesis to account for the origin of the chromospheric and transition region line emission in M-dwarf stars is proposed.

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.

A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks.

PubMed

Mei, Suyu; Zhu, Hao

2015-01-26

Protein-protein interaction (PPI) prediction is generally treated as a problem of binary classification wherein negative data sampling is still an open problem to be addressed. The commonly used random sampling is prone to yield less representative negative data with considerable false negatives. Meanwhile rational constraints are seldom exerted on model selection to reduce the risk of false positive predictions for most of the existing computational methods. In this work, we propose a novel negative data sampling method based on one-class SVM (support vector machine, SVM) to predict proteome-wide protein interactions between HTLV retrovirus and Homo sapiens, wherein one-class SVM is used to choose reliable and representative negative data, and two-class SVM is used to yield proteome-wide outcomes as predictive feedback for rational model selection. Computational results suggest that one-class SVM is more suited to be used as negative data sampling method than two-class PPI predictor, and the predictive feedback constrained model selection helps to yield a rational predictive model that reduces the risk of false positive predictions. Some predictions have been validated by the recent literature. Lastly, gene ontology based clustering of the predicted PPI networks is conducted to provide valuable cues for the pathogenesis of HTLV retrovirus.

Inhibition of the Hantavirus Fusion Process by Predicted Domain III and Stem Peptides from Glycoprotein Gc.

PubMed

Barriga, Gonzalo P; Villalón-Letelier, Fernando; Márquez, Chantal L; Bignon, Eduardo A; Acuña, Rodrigo; Ross, Breyan H; Monasterio, Octavio; Mardones, Gonzalo A; Vidal, Simon E; Tischler, Nicole D

2016-07-01

Hantaviruses can cause hantavirus pulmonary syndrome or hemorrhagic fever with renal syndrome in humans. To enter cells, hantaviruses fuse their envelope membrane with host cell membranes. Previously, we have shown that the Gc envelope glycoprotein is the viral fusion protein sharing characteristics with class II fusion proteins. The ectodomain of class II fusion proteins is composed of three domains connected by a stem region to a transmembrane anchor in the viral envelope. These fusion proteins can be inhibited through exogenous fusion protein fragments spanning domain III (DIII) and the stem region. Such fragments are thought to interact with the core of the fusion protein trimer during the transition from its pre-fusion to its post-fusion conformation. Based on our previous homology model structure for Gc from Andes hantavirus (ANDV), here we predicted and generated recombinant DIII and stem peptides to test whether these fragments inhibit hantavirus membrane fusion and cell entry. Recombinant ANDV DIII was soluble, presented disulfide bridges and beta-sheet secondary structure, supporting the in silico model. Using DIII and the C-terminal part of the stem region, the infection of cells by ANDV was blocked up to 60% when fusion of ANDV occurred within the endosomal route, and up to 95% when fusion occurred with the plasma membrane. Furthermore, the fragments impaired ANDV glycoprotein-mediated cell-cell fusion, and cross-inhibited the fusion mediated by the glycoproteins from Puumala virus (PUUV). The Gc fragments interfered in ANDV cell entry by preventing membrane hemifusion and pore formation, retaining Gc in a non-resistant homotrimer stage, as described for DIII and stem peptide inhibitors of class II fusion proteins. Collectively, our results demonstrate that hantavirus Gc shares not only structural, but also mechanistic similarity with class II viral fusion proteins, and will hopefully help in developing novel therapeutic strategies against hantaviruses.

Sensitivity of Numerical Simulations of a Mesoscale Convective System to Ice Hydrometeors in Bulk Microphysical Parameterization

NASA Astrophysics Data System (ADS)

Pu, Zhaoxia; Lin, Chao; Dong, Xiquan; Krueger, Steven K.

2018-01-01

Mesoscale convective systems (MCSs) and their associated cloud properties are the important factors that influence the aviation activities, yet they present a forecasting challenge in numerical weather prediction. In this study, the sensitivity of numerical simulations of an MCS over the US Southern Great Plains to ice hydrometeors in bulk microphysics (MP) schemes has been investigated using the Weather Research and Forecasting (WRF) model. It is found that the simulated structure, life cycle, cloud coverage, and precipitation of the convective system as well as its associated cold pools are sensitive to three selected MP schemes, namely, the WRF single-moment 6-class (WSM6), WRF double-moment 6-class (WDM6, with the double-moment treatment of warm-rain only), and Morrison double-moment (MORR, with the double-moment representation of both warm-rain and ice) schemes. Compared with observations, the WRF simulation with WSM6 only produces a less organized convection structure with a short lifetime, while WDM6 can produce the structure and length of the MCS very well. Both simulations heavily underestimate the precipitation amount, the height of the radar echo top, and stratiform cloud fractions. With MORR, the model performs well in predicting the lifetime, cloud coverage, echo top, and precipitation amount of the convection. Overall results demonstrate the importance of including double-moment representation of ice hydrometeors along with warm-rain. Additional experiments are performed to further examine the role of ice hydrometeors in numerical simulations of the MCS. Results indicate that replacing graupel with hail in the MORR scheme improves the prediction of the convective structure, especially in the convective core region.

HLaffy: estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials.

PubMed

Mukherjee, Sumanta; Bhattacharyya, Chiranjib; Chandra, Nagasuma

2016-08-01

T-cell epitopes serve as molecular keys to initiate adaptive immune responses. Identification of T-cell epitopes is also a key step in rational vaccine design. Most available methods are driven by informatics and are critically dependent on experimentally obtained training data. Analysis of a training set from Immune Epitope Database (IEDB) for several alleles indicates that the sampling of the peptide space is extremely sparse covering a tiny fraction of the possible nonamer space, and also heavily skewed, thus restricting the range of epitope prediction. We present a new epitope prediction method that has four distinct computational modules: (i) structural modelling, estimating statistical pair-potentials and constraint derivation, (ii) implicit modelling and interaction profiling, (iii) feature representation and binding affinity prediction and (iv) use of graphical models to extract peptide sequence signatures to predict epitopes for HLA class I alleles. HLaffy is a novel and efficient epitope prediction method that predicts epitopes for any Class-1 HLA allele, by estimating the binding strengths of peptide-HLA complexes which is achieved through learning pair-potentials important for peptide binding. It relies on the strength of the mechanistic understanding of peptide-HLA recognition and provides an estimate of the total ligand space for each allele. The performance of HLaffy is seen to be superior to the currently available methods. The method is made accessible through a webserver http://proline.biochem.iisc.ernet.in/HLaffy : nchandra@biochem.iisc.ernet.in Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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. 

Site-specific vibrational spectral signatures of water molecules in the magic H3O+(H2O)20 and Cs+(H2O)20 clusters

PubMed Central

Fournier, Joseph A.; Wolke, Conrad T.; Johnson, Christopher J.; Johnson, Mark A.; Heine, Nadja; Gewinner, Sandy; Schöllkopf, Wieland; Esser, Tim K.; Fagiani, Matias R.; Knorke, Harald; Asmis, Knut R.

2014-01-01

Theoretical models of proton hydration with tens of water molecules indicate that the excess proton is embedded on the surface of clathrate-like cage structures with one or two water molecules in the interior. The evidence for these structures has been indirect, however, because the experimental spectra in the critical H-bonding region of the OH stretching vibrations have been too diffuse to provide band patterns that distinguish between candidate structures predicted theoretically. Here we exploit the slow cooling afforded by cryogenic ion trapping, along with isotopic substitution, to quench water clusters attached to the H3O+ and Cs+ ions into structures that yield well-resolved vibrational bands over the entire 215- to 3,800-cm−1 range. The magic H3O+(H2O)20 cluster yields particularly clear spectral signatures that can, with the aid of ab initio predictions, be traced to specific classes of network sites in the predicted pentagonal dodecahedron H-bonded cage with the hydronium ion residing on the surface. PMID:25489068

Site-specific vibrational spectral signatures of water molecules in the magic H 3O +(H 2O) 20 and Cs +(H 2O) 20 clusters

DOE PAGES

Fournier, Joseph A.; Wolke, Conrad T.; Johnson, Christopher J.; ...

2014-12-08

Here, theoretical models of proton hydration with tens of water molecules indicate that the excess proton is embedded on the surface of clathrate-like cage structures with one or two water molecules in the interior. The evidence for these structures has been indirect, however, because the experimental spectra in the critical H-bonding region of the OH stretching vibrations have been too diffuse to provide band patterns that distinguish between candidate structures predicted theoretically. Here we exploit the slow cooling afforded by cryogenic ion trapping, along with isotopic substitution, to quench water clusters attached to the H 3O + and Cs +more » ions into structures that yield well-resolved vibrational bands over the entire 215- to 3,800-cm -1 range. The magic H 3O +(H 2O) 20 cluster yields particularly clear spectral signatures that can, with the aid of ab initio predictions, be traced to specific classes of network sites in the predicted pentagonal dodecahedron H-bonded cage with the hydronium ion residing on the surface.« less

Distribution of cavity trees in midwestern old-growth and second-growth forests

Treesearch

Zhaofei Fan; Stephen R. Shifley; Martin A. Spetich; Frank R. Thompson; David R. Larsen

2003-01-01

We used classification and regression tree analysis to determine the primary variables associated with the occurrence of cavity trees and the hierarchical structure among those variables. We applied that information to develop logistic models predicting cavity tree probability as a function of diameter, species group, and decay class. Inventories of cavity abundance in...

Distribution of cavity trees in midwesternold-growth and second-growth forests

Treesearch

Zhaofei Fan; Stephen R. Shifley; Martin A. Spetich; Frank R., III Thompson; David R. Larsen

2003-01-01

We used classification and regression tree analysis to determine the primary variables associated with the occurrence of cavity trees and the hierarchical structure among those variables. We applied that information to develop logistic models predicting cavity tree probability as a function of diameter, species group, and decay class. Inventories of cavity abundance in...

Two-Year Predictive Validity of Conduct Disorder Subtypes in Early Adolescence: A Latent Class Analysis of a Canadian Longitudinal Sample

ERIC Educational Resources Information Center

Lacourse, Eric; Baillargeon, Raymond; Dupere, Veronique; Vitaro, Frank; Romano, Elisa; Tremblay, Richard

2010-01-01

Background: Investigating the latent structure of conduct disorder (CD) can help clarify how symptoms related to aggression, property destruction, theft, and serious violations of rules cluster in individuals with this disorder. Discovering homogeneous subtypes can be useful for etiologic, treatment, and prevention purposes depending on the…

The nop gene from Phanerochaete chrysosporium encodes a peroxidase with novel structural features

Treesearch

Luis F. Larrondo; Angel Gonzalez; Tomas Perez-Acle; Dan Cullen; Rafael Vicuna

2005-01-01

Inspection of the genome of the ligninolytic basidiomycete Phanerochaete chrysosporium revealed an unusual peroxidase-like sequence. The corresponding full length cDNA was sequenced and an archetypal secretion signal predicted. The deduced mature protein (NoP, novel peroxidase) contains 295 aa residues and is therefore considerably shorter than other Class II (fungal)...

The intersection of class origin and immigration background in structuring social capital: the role of transnational ties.

PubMed

Andersson, Anton; Edling, Christofer; Rydgren, Jens

2018-03-01

The study investigates inequalities in access to social capital based on social class origin and immigration background and examines the role of transnational ties in explaining these differences. Social capital is measured with a position generator methodology that separates between national and transnational contacts in a sample of young adults in Sweden with three parental backgrounds: at least one parent born in Iran or Yugoslavia, or two Sweden-born parents. The results show that having socioeconomically advantaged parents is associated with higher levels of social capital. Children of immigrants are found to have a greater access to social capital compared to individuals with native background, and the study shows that this is related to transnational contacts, parents' education and social class in their country of origin. Children of immigrants tend to have more contacts abroad, while there is little difference in the amount of contacts living in Sweden across the three groups. It is concluded that knowledge about immigration group resources help us predict its member's social capital, but that the analysis also needs to consider how social class trajectories and migration jointly structure national and transnational contacts. © London School of Economics and Political Science 2017.

Stochasticity in materials structure, properties, and processing—A review

NASA Astrophysics Data System (ADS)

Hull, Robert; Keblinski, Pawel; Lewis, Dan; Maniatty, Antoinette; Meunier, Vincent; Oberai, Assad A.; Picu, Catalin R.; Samuel, Johnson; Shephard, Mark S.; Tomozawa, Minoru; Vashishth, Deepak; Zhang, Shengbai

2018-03-01

We review the concept of stochasticity—i.e., unpredictable or uncontrolled fluctuations in structure, chemistry, or kinetic processes—in materials. We first define six broad classes of stochasticity: equilibrium (thermodynamic) fluctuations; structural/compositional fluctuations; kinetic fluctuations; frustration and degeneracy; imprecision in measurements; and stochasticity in modeling and simulation. In this review, we focus on the first four classes that are inherent to materials phenomena. We next develop a mathematical framework for describing materials stochasticity and then show how it can be broadly applied to these four materials-related stochastic classes. In subsequent sections, we describe structural and compositional fluctuations at small length scales that modify material properties and behavior at larger length scales; systems with engineered fluctuations, concentrating primarily on composite materials; systems in which stochasticity is developed through nucleation and kinetic phenomena; and configurations in which constraints in a given system prevent it from attaining its ground state and cause it to attain several, equally likely (degenerate) states. We next describe how stochasticity in these processes results in variations in physical properties and how these variations are then accentuated by—or amplify—stochasticity in processing and manufacturing procedures. In summary, the origins of materials stochasticity, the degree to which it can be predicted and/or controlled, and the possibility of using stochastic descriptions of materials structure, properties, and processing as a new degree of freedom in materials design are described.

Order within disorder: Aggrecan chondroitin sulphate-attachment region provides new structural insights into protein sequences classified as disordered

PubMed Central

Jowitt, Thomas A; Murdoch, Alan D; Baldock, Clair; Berry, Richard; Day, Joanna M; Hardingham, Timothy E

2010-01-01

Structural investigation of proteins containing large stretches of sequences without predicted secondary structure is the focus of much increased attention. Here, we have produced an unglycosylated 30 kDa peptide from the chondroitin sulphate (CS)-attachment region of human aggrecan (CS-peptide), which was predicted to be intrinsically disordered and compared its structure with the adjacent aggrecan G3 domain. Biophysical analyses, including analytical ultracentrifugation, light scattering, and circular dichroism showed that the CS-peptide had an elongated and stiffened conformation in contrast to the globular G3 domain. The results suggested that it contained significant secondary structure, which was sensitive to urea, and we propose that the CS-peptide forms an elongated wormlike molecule based on a dynamic range of energetically equivalent secondary structures stabilized by hydrogen bonds. The dimensions of the structure predicted from small-angle X-ray scattering analysis were compatible with EM images of fully glycosylated aggrecan and a partly glycosylated aggrecan CS2-G3 construct. The semiordered structure identified in CS-peptide was not predicted by common structural algorithms and identified a potentially distinct class of semiordered structure within sequences currently identified as disordered. Sequence comparisons suggested some evidence for comparable structures in proteins encoded by other genes (PRG4, MUC5B, and CBP). The function of these semiordered sequences may serve to spatially position attached folded modules and/or to present polypeptides for modification, such as glycosylation, and to provide templates for the multiple pleiotropic interactions proposed for disordered proteins. Proteins 2010. © 2010 Wiley-Liss, Inc. PMID:20806220

Structure-based control of complex networks with nonlinear dynamics.

PubMed

Zañudo, Jorge Gomez Tejeda; Yang, Gang; Albert, Réka

2017-07-11

What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

Functional assignment to JEV proteins using SVM.

PubMed

Sahoo, Ganesh Chandra; Dikhit, Manas Ranjan; Das, Pradeep

2008-01-01

Identification of different protein functions facilitates a mechanistic understanding of Japanese encephalitis virus (JEV) infection and opens novel means for drug development. Support vector machines (SVM), useful for predicting the functional class of distantly related proteins, is employed to ascribe a possible functional class to Japanese encephalitis virus protein. Our study from SVMProt and available JE virus sequences suggests that structural and nonstructural proteins of JEV genome possibly belong to diverse protein functions, are expected to occur in the life cycle of JE virus. Protein functions common to both structural and non-structural proteins are iron-binding, metal-binding, lipid-binding, copper-binding, transmembrane, outer membrane, channels/Pores - Pore-forming toxins (proteins and peptides) group of proteins. Non-structural proteins perform functions like actin binding, zinc-binding, calcium-binding, hydrolases, Carbon-Oxygen Lyases, P-type ATPase, proteins belonging to major facilitator family (MFS), secreting main terminal branch (MTB) family, phosphotransfer-driven group translocators and ATP-binding cassette (ABC) family group of proteins. Whereas structural proteins besides belonging to same structural group of proteins (capsid, structural, envelope), they also perform functions like nuclear receptor, antibiotic resistance, RNA-binding, DNA-binding, magnesium-binding, isomerase (intra-molecular), oxidoreductase and participate in type II (general) secretory pathway (IISP).

Functional assignment to JEV proteins using SVM

PubMed Central

Sahoo, Ganesh Chandra; Dikhit, Manas Ranjan; Das, Pradeep

2008-01-01

Identification of different protein functions facilitates a mechanistic understanding of Japanese encephalitis virus (JEV) infection and opens novel means for drug development. Support vector machines (SVM), useful for predicting the functional class of distantly related proteins, is employed to ascribe a possible functional class to Japanese encephalitis virus protein. Our study from SVMProt and available JE virus sequences suggests that structural and nonstructural proteins of JEV genome possibly belong to diverse protein functions, are expected to occur in the life cycle of JE virus. Protein functions common to both structural and non-structural proteins are iron-binding, metal-binding, lipid-binding, copper-binding, transmembrane, outer membrane, channels/Pores - Pore-forming toxins (proteins and peptides) group of proteins. Non-structural proteins perform functions like actin binding, zinc-binding, calcium-binding, hydrolases, Carbon-Oxygen Lyases, P-type ATPase, proteins belonging to major facilitator family (MFS), secreting main terminal branch (MTB) family, phosphotransfer-driven group translocators and ATP-binding cassette (ABC) family group of proteins. Whereas structural proteins besides belonging to same structural group of proteins (capsid, structural, envelope), they also perform functions like nuclear receptor, antibiotic resistance, RNA-binding, DNA-binding, magnesium-binding, isomerase (intra-molecular), oxidoreductase and participate in type II (general) secretory pathway (IISP). PMID:19052658

Ford Class Aircraft Carrier: Poor Outcomes Are the Predictable Consequences of the Prevalent Acquisition Culture

DTIC Science & Technology

2015-10-01

FORD CLASS AIRCRAFT CARRIER Poor Outcomes Are the Predictable Consequences of the Prevalent Acquisition Culture...2. REPORT TYPE 3. DATES COVERED 00-00-2015 to 00-00-2015 4. TITLE AND SUBTITLE Ford Class Aircraft Carrier: Poor Outcomes Are the Predictable...This Study The Navy set ambitious goals for the Ford -class program, including an array of new technologies and design features that were intended

FDA approved drugs complexed to their targets: evaluating pose prediction accuracy of docking protocols.

PubMed

Bohari, Mohammed H; Sastry, G Narahari

2012-09-01

Efficient drug discovery programs can be designed by utilizing existing pools of knowledge from the already approved drugs. This can be achieved in one way by repositioning of drugs approved for some indications to newer indications. Complex of drug to its target gives fundamental insight into molecular recognition and a clear understanding of putative binding site. Five popular docking protocols, Glide, Gold, FlexX, Cdocker and LigandFit have been evaluated on a dataset of 199 FDA approved drug-target complexes for their accuracy in predicting the experimental pose. Performance for all the protocols is assessed at default settings, with root mean square deviation (RMSD) between the experimental ligand pose and the docked pose of less than 2.0 Å as the success criteria in predicting the pose. Glide (38.7 %) is found to be the most accurate in top ranked pose and Cdocker (58.8 %) in top RMSD pose. Ligand flexibility is a major bottleneck in failure of docking protocols to correctly predict the pose. Resolution of the crystal structure shows an inverse relationship with the performance of docking protocol. All the protocols perform optimally when a balanced type of hydrophilic and hydrophobic interaction or dominant hydrophilic interaction exists. Overall in 16 different target classes, hydrophobic interactions dominate in the binding site and maximum success is achieved for all the docking protocols in nuclear hormone receptor class while performance for the rest of the classes varied based on individual protocol.

Using psychological constructs from the MUSIC Model of Motivation to predict students' science identification and career goals: results from the U.S. and Iceland

NASA Astrophysics Data System (ADS)

Jones, Brett D.; Sahbaz, Sumeyra; Schram, Asta B.; Chittum, Jessica R.

2017-05-01

We investigated students' perceptions related to psychological constructs in their science classes and the influence of these perceptions on their science identification and science career goals. Participants included 575 middle school students from two countries (334 students in the U.S. and 241 students in Iceland). Students completed a self-report questionnaire that included items from several measures. We conducted correlational analyses, confirmatory factor analyses, and structural equation modelling to test our hypotheses. Students' class perceptions (i.e. empowerment, usefulness, success, interest, and caring) were significantly correlated with their science identification, which was correlated positively with their science career goals. Combining students' science class perceptions, science identification, and career goals into one model, we documented that the U.S. and Icelandic samples fit the data reasonably well. However, not all of the hypothesised paths were statistically significant. For example, only students' perceptions of usefulness (for the U.S. and Icelandic students) and success (for the U.S. students only) significantly predicted students' career goals in the full model. Theoretically, our findings are consistent with results from samples of university engineering students, yet different in some ways. Our results provide evidence for the theoretical relationships between students' perceptions of science classes and their career goals.

Achilles tendons from decorin- and biglycan-null mouse models have inferior mechanical and structural properties predicted by an image-based empirical damage model

PubMed Central

Gordon, J.A.; Freedman, B.R.; Zuskov, A.; Iozzo, R.V.; Birk, D.E.; Soslowsky, L.J.

2015-01-01

Achilles tendons are a common source of pain and injury, and their pathology may originate from aberrant structure function relationships. Small leucine rich proteoglycans (SLRPs) influence mechanical and structural properties in a tendon-specific manner. However, their roles in the Achilles tendon have not been defined. The objective of this study was to evaluate the mechanical and structural differences observed in mouse Achilles tendons lacking class I SLRPs; either decorin or biglycan. In addition, empirical modeling techniques based on mechanical and image-based measures were employed. Achilles tendons from decorin-null (Dcn−/−) and biglycan-null (Bgn−/−) C57BL/6 female mice (N=102) were used. Each tendon underwent a dynamic mechanical testing protocol including simultaneous polarized light image capture to evaluate both structural and mechanical properties of each Achilles tendon. An empirical damage model was adapted for application to genetic variation and for use with image based structural properties to predict tendon dynamic mechanical properties. We found that Achilles tendons lacking decorin and biglycan had inferior mechanical and structural properties that were age dependent; and that simple empirical models, based on previously described damage models, were predictive of Achilles tendon dynamic modulus in both decorin- and biglycan-null mice. PMID:25888014

Accelerated probabilistic inference of RNA structure evolution

PubMed Central

Holmes, Ian

2005-01-01

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

Achilles tendons from decorin- and biglycan-null mouse models have inferior mechanical and structural properties predicted by an image-based empirical damage model.

PubMed

Gordon, J A; Freedman, B R; Zuskov, A; Iozzo, R V; Birk, D E; Soslowsky, L J

2015-07-16

Achilles tendons are a common source of pain and injury, and their pathology may originate from aberrant structure function relationships. Small leucine rich proteoglycans (SLRPs) influence mechanical and structural properties in a tendon-specific manner. However, their roles in the Achilles tendon have not been defined. The objective of this study was to evaluate the mechanical and structural differences observed in mouse Achilles tendons lacking class I SLRPs; either decorin or biglycan. In addition, empirical modeling techniques based on mechanical and image-based measures were employed. Achilles tendons from decorin-null (Dcn(-/-)) and biglycan-null (Bgn(-/-)) C57BL/6 female mice (N=102) were used. Each tendon underwent a dynamic mechanical testing protocol including simultaneous polarized light image capture to evaluate both structural and mechanical properties of each Achilles tendon. An empirical damage model was adapted for application to genetic variation and for use with image based structural properties to predict tendon dynamic mechanical properties. We found that Achilles tendons lacking decorin and biglycan had inferior mechanical and structural properties that were age dependent; and that simple empirical models, based on previously described damage models, were predictive of Achilles tendon dynamic modulus in both decorin- and biglycan-null mice. Copyright © 2015 Elsevier Ltd. All rights reserved.

Direct Associations or Internal Transformations? Exploring the Mechanisms Underlying Sequential Learning Behavior

PubMed Central

Gureckis, Todd M.; Love, Bradley C.

2009-01-01

We evaluate two broad classes of cognitive mechanisms that might support the learning of sequential patterns. According to the first, learning is based on the gradual accumulation of direct associations between events based on simple conditioning principles. The other view describes learning as the process of inducing the transformational structure that defines the material. Each of these learning mechanisms predict differences in the rate of acquisition for differently organized sequences. Across a set of empirical studies, we compare the predictions of each class of model with the behavior of human subjects. We find that learning mechanisms based on transformations of an internal state, such as recurrent network architectures (e.g., Elman, 1990), have difficulty accounting for the pattern of human results relative to a simpler (but more limited) learning mechanism based on learning direct associations. Our results suggest new constraints on the cognitive mechanisms supporting sequential learning behavior. PMID:20396653

Recruitment dynamics in adaptive social networks

NASA Astrophysics Data System (ADS)

Shkarayev, Maxim S.; Schwartz, Ira B.; Shaw, Leah B.

2013-06-01

We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean-field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime).

Recruitment dynamics in adaptive social networks.

PubMed

Shkarayev, Maxim S; Schwartz, Ira B; Shaw, Leah B

2013-01-01

We model recruitment in adaptive social networks in the presence of birth and death processes. Recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. Only a susceptible subset of nodes can be recruited. The recruiting individuals may adapt their connections in order to improve recruitment capabilities, thus changing the network structure adaptively. We derive a mean field theory to predict the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment level, as well as on network topology. The theoretical predictions are compared with direct simulations of the full system. We identify two parameter regimes with qualitatively different bifurcation diagrams depending on whether nodes become susceptible frequently (multiple times in their lifetime) or rarely (much less than once per lifetime).

Immunoinformatic Analysis of Crimean Congo Hemorrhagic Fever Virus Glycoproteins and Epitope Prediction for Synthetic Peptide Vaccine.

PubMed

Tipu, Hamid Nawaz

2016-02-01

To determine the Crimean Congo Hemorrhagic Fever (CCHF) virus M segement glycoprotein's immunoinformatic parameters, and identify Human Leukocyte Antigen (HLA) class I binders as candidates for synthetic peptide vaccines. Cross-sectional study. Combined Military Hospital, Khuzdar Cantt, in May 2015. Data acquisition, antigenicity prediction, secondary and tertiary structure prediction, residue analysis were done using immunoinformatics tools. HLAclass I binders in glycoprotein's sequence were identified at nanomer length using NetMHC 3.4 and mapped onto tertiary structure. Docking was done for strongest binder against its corresponding allele with CABS-dock. HLAA*0101, 0201, 0301, 2402, 2601 and B*0702, 0801, 2705, 3901, 4001, 5801, 1501 were analyzed against two glycoprotein components of the virus. Atotal of 35 nanomers from GP1, and 3 from GP2 were identified. HLAB*0702 bound maximum number of peptides (6), while HLAB*4001 showed strongest binding affinity. HLAspecific glycoproteins epitope prediction can help identify synthetic peptide vaccine candidates.

Stochastic does not equal ad hoc. [theories of lunar origin

NASA Technical Reports Server (NTRS)

Hartmann, W. K.

1984-01-01

Some classes of influential events in solar system history are class-predictable but not event-predictable. Theories of lunar origin should not ignore class-predictable stochastic events. Impacts and close encounters with large objects during planet formation are class-predictable. These stochastic events, such as large impacts that triggered ejection of Earth-mantle material into a circum-Earth cloud, should not be rejected as ad hoc. A way to deal with such events scientifically is to investigate their consequences; if it can be shown that they might produce the Moon, they become viable concepts in theories of lunar origin.

33 CFR 67.25-1 - Class “B” structures.

Code of Federal Regulations, 2010 CFR

2010-07-01

... 33 Navigation and Navigable Waters 1 2010-07-01 2010-07-01 false Class âBâ structures. 67.25-1... NAVIGATION AIDS TO NAVIGATION ON ARTIFICIAL ISLANDS AND FIXED STRUCTURES Class âBâ Requirements § 67.25-1 Class “B” structures. Class “B” structures shall be the structures erected in an area where Class “B...

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

PubMed Central

2012-01-01

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

"Staying in Class so No One Can Get to Him": A Case for the Institutional Reproduction of ADHD Categories and Behaviours

ERIC Educational Resources Information Center

Exley, Beryl

2008-01-01

This paper focuses a sociological lens on what two early years Australian school boys labelled as having attention deficit hyperactivity disorder (ADHD) and an early years teacher have to say about social relations within informal play environments. The boys participated in separate semi-structured interviews where they predicted the likely…

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs

PubMed Central

2016-01-01

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages. PMID:27074285

First-principles design of nanostructured hybrid photovoltaics based on layered transition metal phosphates

DOE PAGES

Lentz, Levi C.; Kolpak, Alexie M.

2017-04-28

The performance of bulk organic and hybrid organic-inorganic heterojunction photovoltaics is often limited by high carrier recombination arising from strongly bound excitons and low carrier mobility. Structuring materials to minimize the length scales required for exciton separation and carrier collection is therefore a promising approach for improving efficiency. In this work, first-principles computations are employed to design and characterize a new class of photovoltaic materials composed of layered transition metal phosphates (TMPs) covalently bound to organic absorber molecules to form nanostructured superlattices. Using a combination of transition metal substitution and organic functionalization, the electronic structure of these materials is systematicallymore » tuned to design a new hybrid photovoltaic material predicted to exhibit very low recombination due to the presence of a local electric field and spatially isolated, high mobility, two-dimensional electron and hole conducting channels. Furthermore, this material is predicted to have a large open-circuit voltage of 1.7 V. Here, this work suggests that hybrid TMPs constitute an interesting class of materials for further investigation in the search for achieving high efficiency, high power, and low cost photo Zirconium phosphate was chosen, in part, due to previous experiment voltaics.« less

Emerging Computational Methods for the Rational Discovery of Allosteric Drugs.

PubMed

Wagner, Jeffrey R; Lee, Christopher T; Durrant, Jacob D; Malmstrom, Robert D; Feher, Victoria A; Amaro, Rommie E

2016-06-08

Allosteric drug development holds promise for delivering medicines that are more selective and less toxic than those that target orthosteric sites. To date, the discovery of allosteric binding sites and lead compounds has been mostly serendipitous, achieved through high-throughput screening. Over the past decade, structural data has become more readily available for larger protein systems and more membrane protein classes (e.g., GPCRs and ion channels), which are common allosteric drug targets. In parallel, improved simulation methods now provide better atomistic understanding of the protein dynamics and cooperative motions that are critical to allosteric mechanisms. As a result of these advances, the field of predictive allosteric drug development is now on the cusp of a new era of rational structure-based computational methods. Here, we review algorithms that predict allosteric sites based on sequence data and molecular dynamics simulations, describe tools that assess the druggability of these pockets, and discuss how Markov state models and topology analyses provide insight into the relationship between protein dynamics and allosteric drug binding. In each section, we first provide an overview of the various method classes before describing relevant algorithms and software packages.

First-principles design of nanostructured hybrid photovoltaics based on layered transition metal phosphates

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

Lentz, Levi C.; Kolpak, Alexie M.

The performance of bulk organic and hybrid organic-inorganic heterojunction photovoltaics is often limited by high carrier recombination arising from strongly bound excitons and low carrier mobility. Structuring materials to minimize the length scales required for exciton separation and carrier collection is therefore a promising approach for improving efficiency. In this work, first-principles computations are employed to design and characterize a new class of photovoltaic materials composed of layered transition metal phosphates (TMPs) covalently bound to organic absorber molecules to form nanostructured superlattices. Using a combination of transition metal substitution and organic functionalization, the electronic structure of these materials is systematicallymore » tuned to design a new hybrid photovoltaic material predicted to exhibit very low recombination due to the presence of a local electric field and spatially isolated, high mobility, two-dimensional electron and hole conducting channels. Furthermore, this material is predicted to have a large open-circuit voltage of 1.7 V. Here, this work suggests that hybrid TMPs constitute an interesting class of materials for further investigation in the search for achieving high efficiency, high power, and low cost photo Zirconium phosphate was chosen, in part, due to previous experiment voltaics.« less

Computer predictions on Rh-based double perovskites with unusual electronic and magnetic properties

NASA Astrophysics Data System (ADS)

Halder, Anita; Nafday, Dhani; Sanyal, Prabuddha; Saha-Dasgupta, Tanusri

2018-03-01

In search for new magnetic materials, we make computer prediction of structural, electronic and magnetic properties of yet-to-be synthesized Rh-based double perovskite compounds, Sr(Ca)2BRhO6 (B=Cr, Mn, Fe). We use combination of evolutionary algorithm, density functional theory, and statistical-mechanical tool for this purpose. We find that the unusual valence of Rh5+ may be stabilized in these compounds through formation of oxygen ligand hole. Interestingly, while the Cr-Rh and Mn-Rh compounds are predicted to be ferromagnetic half-metals, the Fe-Rh compounds are found to be rare examples of antiferromagnetic and metallic transition-metal oxide with three-dimensional electronic structure. The computed magnetic transition temperatures of the predicted compounds, obtained from finite temperature Monte Carlo study of the first principles-derived model Hamiltonian, are found to be reasonably high. The prediction of favorable growth condition of the compounds, reported in our study, obtained through extensive thermodynamic analysis should be useful for future synthesize of this interesting class of materials with intriguing properties.

Crystallographic and Computational Studies of a Class II MHC Complex with a Nonconforming Peptide: HLA-DRA/DRB3*0101

NASA Astrophysics Data System (ADS)

Parry, Christian S.; Gorski, Jack; Stern, Lawrence J.

2003-03-01

The stable binding of processed foreign peptide to a class II major histocompatibility (MHC) molecule and subsequent presentation to a T cell receptor is a central event in immune recognition and regulation. Polymorphic residues on the floor of the peptide binding site form pockets that anchor peptide side chains. These and other residues in the helical wall of the groove determine the specificity of each allele and define a motif. Allele specific motifs allow the prediction of epitopes from the sequence of pathogens. There are, however, known epitopes that do not satisfy these motifs: anchor motifs are not adequate for predicting epitopes as there are apparently major and minor motifs. We present crystallographic studies into the nature of the interactions that govern the binding of these so called nonconforming peptides. We would like to understand the role of the P10 pocket and find out whether the peptides that do not obey the consensus anchor motif bind in the canonical conformation observed in in prior structures of class II MHC-peptide complexes. HLA-DRB3*0101 complexed with peptide crystallized in unit cell 92.10 x 92.10 x 248.30 (90, 90, 90), P41212, and the diffraction data is reliable to 2.2ÅWe are complementing our studies with dynamical long time simulations to answer these questions, particularly the interplay of the anchor motifs in peptide binding, the range of protein and ligand conformations, and water hydration structures.

Effects of metric hierarchy and rhyme predictability on word duration in The Cat in the Hat.

PubMed

Breen, Mara

2018-05-01

Word durations convey many types of linguistic information, including intrinsic lexical features like length and frequency and contextual features like syntactic and semantic structure. The current study was designed to investigate whether hierarchical metric structure and rhyme predictability account for durational variation over and above other features in productions of a rhyming, metrically-regular children's book: The Cat in the Hat (Dr. Seuss, 1957). One-syllable word durations and inter-onset intervals were modeled as functions of segment number, lexical frequency, word class, syntactic structure, repetition, and font emphasis. Consistent with prior work, factors predicting longer word durations and inter-onset intervals included more phonemes, lower frequency, first mention, alignment with a syntactic boundary, and capitalization. A model parameter corresponding to metric grid height improved model fit of word durations and inter-onset intervals. Specifically, speakers realized five levels of metric hierarchy with inter-onset intervals such that interval duration increased linearly with increased height in the metric hierarchy. Conversely, speakers realized only three levels of metric hierarchy with word duration, demonstrating that they shortened the highly predictable rhyme resolutions. These results further understanding of the factors that affect spoken word duration, and demonstrate the myriad cues that children receive about linguistic structure from nursery rhymes. Copyright © 2018 Elsevier B.V. All rights reserved.

The development of structure-activity relationships for mitochondrial dysfunction: uncoupling of oxidative phosphorylation.

PubMed

Naven, Russell T; Swiss, Rachel; Klug-McLeod, Jacquelyn; Will, Yvonne; Greene, Nigel

2013-01-01

Mitochondrial dysfunction has been implicated as an important factor in the development of idiosyncratic organ toxicity. An ability to predict mitochondrial dysfunction early in the drug development process enables the deselection of those drug candidates with potential safety liabilities, allowing resources to be focused on those compounds with the highest chance of success to the market. A database of greater than 2000 compounds was analyzed to identify structural and physicochemical features associated with the uncoupling of oxidative phosphorylation (herein defined as an increase in basal respiration). Many toxicophores associated with potent uncoupling activity were identified, and these could be divided into two main mechanistic classes, protonophores and redox cyclers. For the protonophores, potent uncoupling activity was often promoted by high lipophilicity and apparent stabilization of the anionic charge resulting from deprotonation of the protonophore. The potency of redox cyclers did not appear to be prone to variations in lipophilicity. Only 11 toxicophores were of sufficient predictive performance that they could be incorporated into a structural-alert model. Each alert was associated with one of three confidence levels (high, medium, and low) depending upon the lipophilicity-activity profile of the structural class. The final model identified over 68% of those compounds with potent uncoupling activity and with a value for specificity above 99%. We discuss the advantages and limitations of this approach and conclude that although structural alert methodology is useful for identifying toxicophores associated with mitochondrial dysfunction, they are not a replacement for the mitochondrial dysfunction assays in early screening paradigms.

Human cell structure-driven model construction for predicting protein subcellular location from biological images.

PubMed

Shao, Wei; Liu, Mingxia; Zhang, Daoqiang

2016-01-01

The systematic study of subcellular location pattern is very important for fully characterizing the human proteome. Nowadays, with the great advances in automated microscopic imaging, accurate bioimage-based classification methods to predict protein subcellular locations are highly desired. All existing models were constructed on the independent parallel hypothesis, where the cellular component classes are positioned independently in a multi-class classification engine. The important structural information of cellular compartments is missed. To deal with this problem for developing more accurate models, we proposed a novel cell structure-driven classifier construction approach (SC-PSorter) by employing the prior biological structural information in the learning model. Specifically, the structural relationship among the cellular components is reflected by a new codeword matrix under the error correcting output coding framework. Then, we construct multiple SC-PSorter-based classifiers corresponding to the columns of the error correcting output coding codeword matrix using a multi-kernel support vector machine classification approach. Finally, we perform the classifier ensemble by combining those multiple SC-PSorter-based classifiers via majority voting. We evaluate our method on a collection of 1636 immunohistochemistry images from the Human Protein Atlas database. The experimental results show that our method achieves an overall accuracy of 89.0%, which is 6.4% higher than the state-of-the-art method. The dataset and code can be downloaded from https://github.com/shaoweinuaa/. dqzhang@nuaa.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Physical stability of drugs after storage above and below the glass transition temperature: Relationship to glass-forming ability.

PubMed

Alhalaweh, Amjad; Alzghoul, Ahmad; Mahlin, Denny; Bergström, Christel A S

2015-11-10

Amorphous materials are inherently unstable and tend to crystallize upon storage. In this study, we investigated the extent to which the physical stability and inherent crystallization tendency of drugs are related to their glass-forming ability (GFA), the glass transition temperature (Tg) and thermodynamic factors. Differential scanning calorimetry was used to produce the amorphous state of 52 drugs [18 compounds crystallized upon heating (Class II) and 34 remained in the amorphous state (Class III)] and to perform in situ storage for the amorphous material for 12h at temperatures 20°C above or below the Tg. A computational model based on the support vector machine (SVM) algorithm was developed to predict the structure-property relationships. All drugs maintained their Class when stored at 20°C below the Tg. Fourteen of the Class II compounds crystallized when stored above the Tg whereas all except one of the Class III compounds remained amorphous. These results were only related to the glass-forming ability and no relationship to e.g. thermodynamic factors was found. The experimental data were used for computational modeling and a classification model was developed that correctly predicted the physical stability above the Tg. The use of a large dataset revealed that molecular features related to aromaticity and π-π interactions reduce the inherent physical stability of amorphous drugs. Copyright © 2015 Elsevier B.V. All rights reserved.

CNNH_PSS: protein 8-class secondary structure prediction by convolutional neural network with highway.

PubMed

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

2018-05-08

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

REVIEWS OF TOPICAL PROBLEMS: Prediction and discovery of new structures in spiral galaxies

NASA Astrophysics Data System (ADS)

Fridman, Aleksei M.

2007-02-01

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

F pocket flexibility influences the tapasin dependence of two differentially disease-associated MHC Class I proteins.

PubMed

Abualrous, Esam T; Fritzsche, Susanne; Hein, Zeynep; Al-Balushi, Mohammed S; Reinink, Peter; Boyle, Louise H; Wellbrock, Ursula; Antoniou, Antony N; Springer, Sebastian

2015-04-01

The human MHC class I protein HLA-B*27:05 is statistically associated with ankylosing spondylitis, unlike HLA-B*27:09, which differs in a single amino acid in the F pocket of the peptide-binding groove. To understand how this unique amino acid difference leads to a different behavior of the proteins in the cell, we have investigated the conformational stability of both proteins using a combination of in silico and experimental approaches. Here, we show that the binding site of B*27:05 is conformationally disordered in the absence of peptide due to a charge repulsion at the bottom of the F pocket. In agreement with this, B*27:05 requires the chaperone protein tapasin to a greater extent than the conformationally stable B*27:09 in order to remain structured and to bind peptide. Taken together, our data demonstrate a method to predict tapasin dependence and physiological behavior from the sequence and crystal structure of a particular class I allotype. Also watch the Video Abstract. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Predicting hepatotoxicity using ToxCast in vitro bioactivity and ...

EPA Pesticide Factsheets

Background: The U.S. EPA ToxCastTM program is screening thousands of environmental chemicals for bioactivity using hundreds of high-throughput in vitro assays to build predictive models of toxicity. We represented chemicals based on bioactivity and chemical structure descriptors then used supervised machine learning to predict their hepatotoxic effects.Results: A set of 677 chemicals were represented by 711 in vitro bioactivity descriptors (from ToxCast assays), 4,376 chemical structure descriptors (from QikProp, OpenBabel, PADEL, and PubChem), and three hepatotoxicity categories (from animal studies). Hepatotoxicants were defined by rat liver histopathology observed after chronic chemical testing and grouped into hypertrophy (161), injury (101) and proliferative lesions (99). Classifiers were built using six machine learning algorithms: linear discriminant analysis (LDA), Naïve Bayes (NB), support vector classification (SVM), classification and regression trees (CART), k-nearest neighbors (KNN) and an ensemble of classifiers (ENSMB). Classifiers of hepatotoxicity were built using chemical structure, ToxCast bioactivity, and a hybrid representation. Predictive performance was evaluated using 10-fold cross-validation testing and in-loop, filter-based, feature subset selection. Hybrid classifiers had the best balanced accuracy for predicting hypertrophy (0.78±0.08), injury (0.73±0.10) and proliferative lesions (0.72±0.09). Though chemical and bioactivity class

Toward structure prediction of cyclic peptides.

PubMed

Yu, Hongtao; Lin, Yu-Shan

2015-02-14

Cyclic peptides are a promising class of molecules that can be used to target specific protein-protein interactions. A computational method to accurately predict their structures would substantially advance the development of cyclic peptides as modulators of protein-protein interactions. Here, we develop a computational method that integrates bias-exchange metadynamics simulations, a Boltzmann reweighting scheme, dihedral principal component analysis and a modified density peak-based cluster analysis to provide a converged structural description for cyclic peptides. Using this method, we evaluate the performance of a number of popular protein force fields on a model cyclic peptide. All the tested force fields seem to over-stabilize the α-helix and PPII/β regions in the Ramachandran plot, commonly populated by linear peptides and proteins. Our findings suggest that re-parameterization of a force field that well describes the full Ramachandran plot is necessary to accurately model cyclic peptides.

Polymorphism in magic-sized Au144(SR)60 clusters

NASA Astrophysics Data System (ADS)

Jensen, Kirsten M. Ø.; Juhas, Pavol; Tofanelli, Marcus A.; Heinecke, Christine L.; Vaughan, Gavin; Ackerson, Christopher J.; Billinge, Simon J. L.

2016-06-01

Ultra-small, magic-sized metal nanoclusters represent an important new class of materials with properties between molecules and particles. However, their small size challenges the conventional methods for structure characterization. Here we present the structure of ultra-stable Au144(SR)60 magic-sized nanoclusters obtained from atomic pair distribution function analysis of X-ray powder diffraction data. The study reveals structural polymorphism in these archetypal nanoclusters. In addition to confirming the theoretically predicted icosahedral-cored cluster, we also find samples with a truncated decahedral core structure, with some samples exhibiting a coexistence of both cluster structures. Although the clusters are monodisperse in size, structural diversity is apparent. The discovery of polymorphism may open up a new dimension in nanoscale engineering.

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

Yu, Qianlong; Blissard, Gary W.; Liu, Tong-Xian

The Autographa californica multiple nucleopolyhedrovirus GP64 is a class III viral fusion protein. Although the post-fusion structure of GP64 has been solved, its pre-fusion structure and the detailed mechanism of conformational change are unknown. In GP64, domain V is predicted to interact with two domain I segments that flank fusion loop 2. To evaluate the significance of the amino acids involved in these interactions, we examined 24 amino acid positions that represent interacting and conserved residues within domains I and V. In several cases, substitution of a single amino acid involved in a predicted interaction disrupted membrane fusion activity, butmore » no single amino acid pair appears to be absolutely required. We identified 4 critical residues in domain V (G438, W439, T452, and T456) that are important for membrane fusion, and two residues (G438 and W439) that appear to be important for formation or stability of the pre-fusion conformation of GP64. - Highlights: • The baculovirus envelope glycoprotein GP64 is a class III viral fusion protein. • The detailed mechanism of conformational change of GP64 is unknown. • We analyzed 24 positions that might stabilize the post-fusion structure of GP64. • We identified 4 residues in domain V that were critical for membrane fusion. • Two residues are critical for formation of the pre-fusion conformation of GP64.« less

A Multidimensional Item Response Model: Constrained Latent Class Analysis Using the Gibbs Sampler and Posterior Predictive Checks.

ERIC Educational Resources Information Center

Hoijtink, Herbert; Molenaar, Ivo W.

1997-01-01

This paper shows that a certain class of constrained latent class models may be interpreted as a special case of nonparametric multidimensional item response models. Parameters of this latent class model are estimated using an application of the Gibbs sampler, and model fit is investigated using posterior predictive checks. (SLD)

Cellulose synthase 'class specific regions' are intrinsically disordered and functionally undifferentiated.

PubMed

Scavuzzo-Duggan, Tess R; Chaves, Arielle M; Singh, Abhishek; Sethaphong, Latsavongsakda; Slabaugh, Erin; Yingling, Yaroslava G; Haigler, Candace H; Roberts, Alison W

2018-06-01

Cellulose synthases (CESAs) are glycosyltransferases that catalyze formation of cellulose microfibrils in plant cell walls. Seed plant CESA isoforms cluster in six phylogenetic clades, whose non-interchangeable members play distinct roles within cellulose synthesis complexes (CSCs). A 'class specific region' (CSR), with higher sequence similarity within versus between functional CESA classes, has been suggested to contribute to specific activities or interactions of different isoforms. We investigated CESA isoform specificity in the moss, Physcomitrella patens (Hedw.) B. S. G. to gain evolutionary insights into CESA structure/function relationships. Like seed plants, P. patens has oligomeric rosette-type CSCs, but the PpCESAs diverged independently and form a separate CESA clade. We showed that P. patens has two functionally distinct CESAs classes, based on the ability to complement the gametophore-negative phenotype of a ppcesa5 knockout line. Thus, non-interchangeable CESA classes evolved separately in mosses and seed plants. However, testing of chimeric moss CESA genes for complementation demonstrated that functional class-specificity is not determined by the CSR. Sequence analysis and computational modeling showed that the CSR is intrinsically disordered and contains predicted molecular recognition features, consistent with a possible role in CESA oligomerization and explaining the evolution of class-specific sequences without selection for class-specific function. © 2018 Institute of Botany, Chinese Academy of Sciences.

Nuclear charge radii: density functional theory meets Bayesian neural networks

NASA Astrophysics Data System (ADS)

Utama, R.; Chen, Wei-Chia; Piekarewicz, J.

2016-11-01

The distribution of electric charge in atomic nuclei is fundamental to our understanding of the complex nuclear dynamics and a quintessential observable to validate nuclear structure models. The aim of this study is to explore a novel approach that combines sophisticated models of nuclear structure with Bayesian neural networks (BNN) to generate predictions for the charge radii of thousands of nuclei throughout the nuclear chart. A class of relativistic energy density functionals is used to provide robust predictions for nuclear charge radii. In turn, these predictions are refined through Bayesian learning for a neural network that is trained using residuals between theoretical predictions and the experimental data. Although predictions obtained with density functional theory provide a fairly good description of experiment, our results show significant improvement (better than 40%) after BNN refinement. Moreover, these improved results for nuclear charge radii are supplemented with theoretical error bars. We have successfully demonstrated the ability of the BNN approach to significantly increase the accuracy of nuclear models in the predictions of nuclear charge radii. However, as many before us, we failed to uncover the underlying physics behind the intriguing behavior of charge radii along the calcium isotopic chain.

Prediction protein structural classes with pseudo-amino acid composition: approximate entropy and hydrophobicity pattern.

PubMed

Zhang, Tong-Liang; Ding, Yong-Sheng; Chou, Kuo-Chen

2008-01-07

Compared with the conventional amino acid (AA) composition, the pseudo-amino acid (PseAA) composition as originally introduced for protein subcellular location prediction can incorporate much more information of a protein sequence, so as to remarkably enhance the power of using a discrete model to predict various attributes of a protein. In this study, based on the concept of PseAA composition, the approximate entropy and hydrophobicity pattern of a protein sequence are used to characterize the PseAA components. Also, the immune genetic algorithm (IGA) is applied to search the optimal weight factors in generating the PseAA composition. Thus, for a given protein sequence sample, a 27-D (dimensional) PseAA composition is generated as its descriptor. The fuzzy K nearest neighbors (FKNN) classifier is adopted as the prediction engine. The results thus obtained in predicting protein structural classification are quite encouraging, indicating that the current approach may also be used to improve the prediction quality of other protein attributes, or at least can play a complimentary role to the existing methods in the relevant areas. Our algorithm is written in Matlab that is available by contacting the corresponding author.

Computational neural networks in chemistry: Model free mapping devices for predicting chemical reactivity from molecular structure

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

Elrod, D.W.

1992-01-01

Computational neural networks (CNNs) are a computational paradigm inspired by the brain's massively parallel network of highly interconnected neurons. The power of computational neural networks derives not so much from their ability to model the brain as from their ability to learn by example and to map highly complex, nonlinear functions, without the need to explicitly specify the functional relationship. Two central questions about CNNs were investigated in the context of predicting chemical reactions: (1) the mapping properties of neural networks and (2) the representation of chemical information for use in CNNs. Chemical reactivity is here considered an example ofmore » a complex, nonlinear function of molecular structure. CNN's were trained using modifications of the back propagation learning rule to map a three dimensional response surface similar to those typically observed in quantitative structure-activity and structure-property relationships. The computational neural network's mapping of the response surface was found to be robust to the effects of training sample size, noisy data and intercorrelated input variables. The investigation of chemical structure representation led to the development of a molecular structure-based connection-table representation suitable for neural network training. An extension of this work led to a BE-matrix structure representation that was found to be general for several classes of reactions. The CNN prediction of chemical reactivity and regiochemistry was investigated for electrophilic aromatic substitution reactions, Markovnikov addition to alkenes, Saytzeff elimination from haloalkanes, Diels-Alder cycloaddition, and retro Diels-Alder ring opening reactions using these connectivity-matrix derived representations. The reaction predictions made by the CNNs were more accurate than those of an expert system and were comparable to predictions made by chemists.« less

Social-class differences in self-concept clarity and their implications for well-being

PubMed Central

Na, Jinkyung; Chan, Micaela Y.; Lodi-Smith, Jennifer; Park, Denise C.

2017-01-01

A consistent/stable sense of the self is more valued in middle-class contexts than working-class contexts; hence we predicted that middle-class individuals would have higher SCC than working-class individuals. It is further expected that SCC would be more important to one’s well-being among middle-class individuals than among working-class individuals. Supporting these predictions, SCC was positively associated with higher social-class. Moreover, although SCC was associated with higher life satisfaction and better mental health, the association significantly attenuated among working-class individuals. In addition, SCC was not associated with physical health and its association with physical health did not interact with social class. PMID:27114215

Definition of Proteasomal Peptide Splicing Rules for High-Efficiency Spliced Peptide Presentation by MHC Class I Molecules

PubMed Central

Berkers, Celia R.; de Jong, Annemieke; Schuurman, Karianne G.; Linnemann, Carsten; Meiring, Hugo D.; Janssen, Lennert; Neefjes, Jacques J.; Schumacher, Ton N. M.; Rodenko, Boris

2015-01-01

Peptide splicing, in which two distant parts of a protein are excised and then ligated to form a novel peptide, can generate unique MHC class I–restricted responses. Because these peptides are not genetically encoded and the rules behind proteasomal splicing are unknown, it is difficult to predict these spliced Ags. In the current study, small libraries of short peptides were used to identify amino acid sequences that affect the efficiency of this transpeptidation process. We observed that splicing does not occur at random, neither in terms of the amino acid sequences nor through random splicing of peptides from different sources. In contrast, splicing followed distinct rules that we deduced and validated both in vitro and in cells. Peptide ligation was quantified using a model peptide and demonstrated to occur with up to 30% ligation efficiency in vitro, provided that optimal structural requirements for ligation were met by both ligating partners. In addition, many splicing products could be formed from a single protein. Our splicing rules will facilitate prediction and detection of new spliced Ags to expand the peptidome presented by MHC class I Ags. PMID:26401003

Recapitulation of Ayurveda constitution types by machine learning of phenotypic traits.

PubMed

Tiwari, Pradeep; Kutum, Rintu; Sethi, Tavpritesh; Shrivastava, Ankita; Girase, Bhushan; Aggarwal, Shilpi; Patil, Rutuja; Agarwal, Dhiraj; Gautam, Pramod; Agrawal, Anurag; Dash, Debasis; Ghosh, Saurabh; Juvekar, Sanjay; Mukerji, Mitali; Prasher, Bhavana

2017-01-01

In Ayurveda system of medicine individuals are classified into seven constitution types, "Prakriti", for assessing disease susceptibility and drug responsiveness. Prakriti evaluation involves clinical examination including questions about physiological and behavioural traits. A need was felt to develop models for accurately predicting Prakriti classes that have been shown to exhibit molecular differences. The present study was carried out on data of phenotypic attributes in 147 healthy individuals of three extreme Prakriti types, from a genetically homogeneous population of Western India. Unsupervised and supervised machine learning approaches were used to infer inherent structure of the data, and for feature selection and building classification models for Prakriti respectively. These models were validated in a North Indian population. Unsupervised clustering led to emergence of three natural clusters corresponding to three extreme Prakriti classes. The supervised modelling approaches could classify individuals, with distinct Prakriti types, in the training and validation sets. This study is the first to demonstrate that Prakriti types are distinct verifiable clusters within a multidimensional space of multiple interrelated phenotypic traits. It also provides a computational framework for predicting Prakriti classes from phenotypic attributes. This approach may be useful in precision medicine for stratification of endophenotypes in healthy and diseased populations.

Predicting activation energy of thermolysis of polynitro arenes through molecular structure.

PubMed

Keshavarz, Mohammad Hossein; Pouretedal, Hamid Reza; Shokrolahi, Arash; Zali, Abbas; Semnani, Abolfazl

2008-12-15

The paper presents a new method for activation energy or the Arrhenius parameter E(a) of the thermolysis in the condensed state for different polynitro arenes as an important class of energetic molecules. The methodology assumes that E(a) of a polynitro arene with general formula C(a)H(b)N(c)O(d) can be expressed as a function of optimized elemental composition as well as the contribution of specific molecular structural parameters. The new method can predict E(a) of the thermolysis under conditions of Soviet Manometric Method (SMM), which can be related to the other convenient methods. The new correlation has the root mean square (rms) and the average deviations of 13.79 and 11.94kJ/mol, respectively, for 20 polynitro arenes with different molecular structures. The proposed new method can also be used to predict E(a) of three polynitro arenes, i.e. 2,2',2'',4,4',4'',6,6',6''-nonanitro-1,1':3',1''-terphenyl (NONA), 3,3'-diamino-2,2',4,4',6,6'-hexanitro-1,1'-biphenyl-3,3'-diamine (DIPAM) and N,N-bis(2,4-dinitrophenyl)-2,4,6-trinitroaniline (NTFA), which have complex molecular structures.

Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers.

PubMed

Borchani, Hanen; Bielza, Concha; Toro, Carlos; Larrañaga, Pedro

2013-03-01

Our aim is to use multi-dimensional Bayesian network classifiers in order to predict the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors given an input set of respective resistance mutations that an HIV patient carries. Multi-dimensional Bayesian network classifiers (MBCs) are probabilistic graphical models especially designed to solve multi-dimensional classification problems, where each input instance in the data set has to be assigned simultaneously to multiple output class variables that are not necessarily binary. In this paper, we introduce a new method, named MB-MBC, for learning MBCs from data by determining the Markov blanket around each class variable using the HITON algorithm. Our method is applied to both reverse transcriptase and protease data sets obtained from the Stanford HIV-1 database. Regarding the prediction of antiretroviral combination therapies, the experimental study shows promising results in terms of classification accuracy compared with state-of-the-art MBC learning algorithms. For reverse transcriptase inhibitors, we get 71% and 11% in mean and global accuracy, respectively; while for protease inhibitors, we get more than 84% and 31% in mean and global accuracy, respectively. In addition, the analysis of MBC graphical structures lets us gain insight into both known and novel interactions between reverse transcriptase and protease inhibitors and their respective resistance mutations. MB-MBC algorithm is a valuable tool to analyze the HIV-1 reverse transcriptase and protease inhibitors prediction problem and to discover interactions within and between these two classes of inhibitors. Copyright © 2012 Elsevier B.V. All rights reserved.

Deep Flare Net (DeFN) Model for Solar Flare Prediction

NASA Astrophysics Data System (ADS)

Nishizuka, N.; Sugiura, K.; Kubo, Y.; Den, M.; Ishii, M.

2018-05-01

We developed a solar flare prediction model using a deep neural network (DNN) named Deep Flare Net (DeFN). This model can calculate the probability of flares occurring in the following 24 hr in each active region, which is used to determine the most likely maximum classes of flares via a binary classification (e.g., ≥M class versus

Helping decision makers frame, analyze, and implement decisions

USGS Publications Warehouse

Runge, Michael C.; McDonald-Madden, Eve

2018-01-01

All decisions have the same recognizable elements. Context, objectives, alternatives, consequences, and deliberation. Decision makers and analysts familiar with these elements can quickly see the underlying structure of a decision.There are only a small number of classes of decisions. These classes differ in the cognitive and scientific challenge they present to the decision maker; the ability to recognize the class of decision leads a decision maker to tools to aid in the analysis.Sometimes we need more information, sometimes we don’t. The role of science in a decision-making process is to provide the predictions that link the alternative actions to the desired outcomes. Investing in more science is only valuable if it helps to choose a better action.Implementation. The successful integration of decision analysis into environmental decisions requires careful attention to the decision, the people, and the institutions involved.

Computational modeling of RNA 3D structures, with the aid of experimental restraints

PubMed Central

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

2014-01-01

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

Simple neural substrate predicts complex rhythmic structure in duetting birds

NASA Astrophysics Data System (ADS)

Amador, Ana; Trevisan, M. A.; Mindlin, G. B.

2005-09-01

Horneros (Furnarius Rufus) are South American birds well known for their oven-looking nests and their ability to sing in couples. Previous work has analyzed the rhythmic organization of the duets, unveiling a mathematical structure behind the songs. In this work we analyze in detail an extended database of duets. The rhythms of the songs are compatible with the dynamics presented by a wide class of dynamical systems: forced excitable systems. Compatible with this nonlinear rule, we build a biologically inspired model for how the neural and the anatomical elements may interact to produce the observed rhythmic patterns. This model allows us to synthesize songs presenting the acoustic and rhythmic features observed in real songs. We also make testable predictions in order to support our hypothesis.

Robust distributed model predictive control of linear systems with structured time-varying uncertainties

NASA Astrophysics Data System (ADS)

Zhang, Langwen; Xie, Wei; Wang, Jingcheng

2017-11-01

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

The Rodin-Ohno hypothesis that two enzyme superfamilies descended from one ancestral gene: an unlikely scenario for the origins of translation that will not be dismissed

PubMed Central

2014-01-01

Background Because amino acid activation is rate-limiting for uncatalyzed protein synthesis, it is a key puzzle in understanding the origin of the genetic code. Two unrelated classes (I and II) of contemporary aminoacyl-tRNA synthetases (aaRS) now translate the code. Observing that codons for the most highly conserved, Class I catalytic peptides, when read in the reverse direction, are very nearly anticodons for Class II defining catalytic peptides, Rodin and Ohno proposed that the two superfamilies descended from opposite strands of the same ancestral gene. This unusual hypothesis languished for a decade, perhaps because it appeared to be unfalsifiable. Results The proposed sense/antisense alignment makes important predictions. Fragments that align in antiparallel orientations, and contain the respective active sites, should catalyze the same two reactions catalyzed by contemporary synthetases. Recent experiments confirmed that prediction. Invariant cores from both classes, called Urzymes after Ur = primitive, authentic, plus enzyme and representing ~20% of the contemporary structures, can be expressed and exhibit high, proportionate rate accelerations for both amino-acid activation and tRNA acylation. A major fraction (60%) of the catalytic rate acceleration by contemporary synthetases resides in segments that align sense/antisense. Bioinformatic evidence for sense/antisense ancestry extends to codons specifying the invariant secondary and tertiary structures outside the active sites of the two synthetase classes. Peptides from a designed, 46-residue gene constrained by Rosetta to encode Class I and II ATP binding sites with fully complementary sequences both accelerate amino acid activation by ATP ~400 fold. Conclusions Biochemical and bioinformatic results substantially enhance the posterior probability that ancestors of the two synthetase classes arose from opposite strands of the same ancestral gene. The remarkable acceleration by short peptides of the rate-limiting step in uncatalyzed protein synthesis, together with the synergy of synthetase Urzymes and their cognate tRNAs, introduce a new paradigm for the origin of protein catalysts, emphasize the potential relevance of an operational RNA code embedded in the tRNA acceptor stems, and challenge the RNA-World hypothesis. Reviewers This article was reviewed by Dr. Paul Schimmel (nominated by Laura Landweber), Dr. Eugene Koonin and Professor David Ardell. PMID:24927791

Predicting tree species presence and basal area in Utah: A comparison of stochastic gradient boosting, generalized additive models, and tree-based methods

Treesearch

Gretchen G. Moisen; Elizabeth A. Freeman; Jock A. Blackard; Tracey S. Frescino; Niklaus E. Zimmermann; Thomas C. Edwards

2006-01-01

Many efforts are underway to produce broad-scale forest attribute maps by modelling forest class and structure variables collected in forest inventories as functions of satellite-based and biophysical information. Typically, variants of classification and regression trees implemented in Rulequest's© See5 and Cubist (for binary and continuous responses,...

Unsupervised classification of scattering behavior using radar polarimetry data

NASA Technical Reports Server (NTRS)

Van Zyl, Jakob J.

1989-01-01

The use of an imaging radar polarimeter data for unsupervised classification of scattering behavior is described by comparing the polarization properties of each pixel in a image to that of simple classes of scattering such as even number of reflections, odd number of reflections, and diffuse scattering. For example, when this algorithm is applied to data acquired over the San Francisco Bay area in California, it classifies scattering by the ocean as being similar to that predicted by the class of odd number of reflections, scattering by the urban area as being similar to that predicted by the class of even number of reflections, and scattering by the Golden Gate Park as being similar to that predicted by the diffuse scattering class. It also classifies the scattering by a lighthouse in the ocean and boats on the ocean surface as being similar to that predicted by the even number of reflections class, making it easy to identify these objects against the background of the surrounding ocean. The algorithm is also applied to forested areas and shows that scattering from clear-cut areas and agricultural fields is mostly similar to that predicted by the odd number of reflections class, while the scattering from tree-covered areas generally is classified as being a mixture of pixels exhibiting the characteristics of all three classes, although each pixel is identified with only a single class.

Induction of models under uncertainty

NASA Technical Reports Server (NTRS)

Cheeseman, Peter

1986-01-01

This paper outlines a procedure for performing induction under uncertainty. This procedure uses a probabilistic representation and uses Bayes' theorem to decide between alternative hypotheses (theories). This procedure is illustrated by a robot with no prior world experience performing induction on data it has gathered about the world. The particular inductive problem is the formation of class descriptions both for the tutored and untutored cases. The resulting class definitions are inherently probabilistic and so do not have any sharply defined membership criterion. This robot example raises some fundamental problems about induction; particularly, it is shown that inductively formed theories are not the best way to make predictions. Another difficulty is the need to provide prior probabilities for the set of possible theories. The main criterion for such priors is a pragmatic one aimed at keeping the theory structure as simple as possible, while still reflecting any structure discovered in the data.

Observation of unusual topological surface states in half-Heusler compounds LnPtBi (Ln=Lu, Y)

DOE PAGES

Liu, Z. K.; Yang, L. X.; Wu, S. -C.; ...

2016-09-27

Topological quantum materials represent a new class of matter with both exotic physical phenomena and novel application potentials. Many Heusler compounds, which exhibit rich emergent properties such as unusual magnetism, superconductivity and heavy fermion behaviour, have been predicted to host non-trivial topological electronic structures. The coexistence of topological order and other unusual properties makes Heusler materials ideal platform to search for new topological quantum phases (such as quantum anomalous Hall insulator and topological superconductor). By carrying out angle-resolved photoemission spectroscopy and ab initio calculations on rare-earth half-Heusler compounds LnPtBi (Ln=Lu, Y), we directly observe the unusual topological surface states onmore » these materials, establishing them as first members with non-trivial topological electronic structure in this class of materials. Moreover, as LnPtBi compounds are non-centrosymmetric superconductors, our discovery further highlights them as promising candidates of topological superconductors.« less

Observation of unusual topological surface states in half-Heusler compounds LnPtBi (Ln=Lu, Y)

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

Liu, Z. K.; Yang, L. X.; Wu, S. -C.

Topological quantum materials represent a new class of matter with both exotic physical phenomena and novel application potentials. Many Heusler compounds, which exhibit rich emergent properties such as unusual magnetism, superconductivity and heavy fermion behaviour, have been predicted to host non-trivial topological electronic structures. The coexistence of topological order and other unusual properties makes Heusler materials ideal platform to search for new topological quantum phases (such as quantum anomalous Hall insulator and topological superconductor). By carrying out angle-resolved photoemission spectroscopy and ab initio calculations on rare-earth half-Heusler compounds LnPtBi (Ln=Lu, Y), we directly observe the unusual topological surface states onmore » these materials, establishing them as first members with non-trivial topological electronic structure in this class of materials. Moreover, as LnPtBi compounds are non-centrosymmetric superconductors, our discovery further highlights them as promising candidates of topological superconductors.« less

TIM Barrel Protein Structure Classification Using Alignment Approach and Best Hit Strategy

NASA Astrophysics Data System (ADS)

Chu, Jia-Han; Lin, Chun Yuan; Chang, Cheng-Wen; Lee, Chihan; Yang, Yuh-Shyong; Tang, Chuan Yi

2007-11-01

The classification of protein structures is essential for their function determination in bioinformatics. It has been estimated that around 10% of all known enzymes have TIM barrel domains from the Structural Classification of Proteins (SCOP) database. With its high sequence variation and diverse functionalities, TIM barrel protein becomes to be an attractive target for protein engineering and for the evolution study. Hence, in this paper, an alignment approach with the best hit strategy is proposed to classify the TIM barrel protein structure in terms of superfamily and family levels in the SCOP. This work is also used to do the classification for class level in the Enzyme nomenclature (ENZYME) database. Two testing data sets, TIM40D and TIM95D, both are used to evaluate this approach. The resulting classification has an overall prediction accuracy rate of 90.3% for the superfamily level in the SCOP, 89.5% for the family level in the SCOP and 70.1% for the class level in the ENZYME. These results demonstrate that the alignment approach with the best hit strategy is a simple and viable method for the TIM barrel protein structure classification, even only has the amino acid sequences information.

Structural classification of proteins using texture descriptors extracted from the cellular automata image.

PubMed

Kavianpour, Hamidreza; Vasighi, Mahdi

2017-02-01

Nowadays, having knowledge about cellular attributes of proteins has an important role in pharmacy, medical science and molecular biology. These attributes are closely correlated with the function and three-dimensional structure of proteins. Knowledge of protein structural class is used by various methods for better understanding the protein functionality and folding patterns. Computational methods and intelligence systems can have an important role in performing structural classification of proteins. Most of protein sequences are saved in databanks as characters and strings and a numerical representation is essential for applying machine learning methods. In this work, a binary representation of protein sequences is introduced based on reduced amino acids alphabets according to surrounding hydrophobicity index. Many important features which are hidden in these long binary sequences can be clearly displayed through their cellular automata images. The extracted features from these images are used to build a classification model by support vector machine. Comparing to previous studies on the several benchmark datasets, the promising classification rates obtained by tenfold cross-validation imply that the current approach can help in revealing some inherent features deeply hidden in protein sequences and improve the quality of predicting protein structural class.

Unified constitutive models for high-temperature structural applications

NASA Technical Reports Server (NTRS)

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

1988-01-01

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

Dynamic and fluid-structure interaction simulations of bioprosthetic heart valves using parametric design with T-splines and Fung-type material models

NASA Astrophysics Data System (ADS)

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

2015-06-01

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

Classification of Structural Coal-Controlling Styles and Analysis on Structural Coal-Controlling Actions

NASA Astrophysics Data System (ADS)

Zhan, Wen-feng

2017-11-01

Tectonism was the primary geologic factors for controlling the formation, deformation, and occurrence of coal measures. As the core of a new round of prediction and evaluation on the coalfield resource potential, the effect of coal-controlling structure was further strengthened and deepened in related researches. By systematically combing the tectonic coal-controlling effect and structure, this study determined the geodynamical classification basis for coal-controlling structures. According to the systematic analysis and summary on the related research results, the coal-controlling structure was categorized into extensional structure, compressive structure, shearing and rotational structure, inverted structure, as well as the sliding structure, syndepositional structure with coalfield structure characteristics. In accordance with the structure combination and distribution characteristics, the six major classes were further classified into 32 subclasses. Moreover, corresponding mode maps were drawn to discuss the basic characteristics and effect of the coal-controlling structures.

Advances in dental materials.

PubMed

Vaderhobli, Ram M

2011-07-01

The use of materials to rehabilitate tooth structures is constantly changing. Over the past decade, newer material processing techniques and technologies have significantly improved the dependability and predictability of dental material for clinicians. The greatest obstacle, however, is in choosing the right combination for continued success. Finding predictable approaches for successful restorative procedures has been the goal of clinical and material scientists. This article provides a broad perspective on the advances made in various classes of dental restorative materials in terms of their functionality with respect to pit and fissure sealants, glass ionomers, and dental composites. Copyright © 2011 Elsevier Inc. All rights reserved.

Structural and Evolutionary Aspects of Antenna Chromophore Usage by Class II Photolyases*

PubMed Central

Kiontke, Stephan; Gnau, Petra; Haselsberger, Reinhard; Batschauer, Alfred; Essen, Lars-Oliver

2014-01-01

Light-harvesting and resonance energy transfer to the catalytic FAD cofactor are key roles for the antenna chromophores of light-driven DNA photolyases, which remove UV-induced DNA lesions. So far, five chemically diverse chromophores have been described for several photolyases and related cryptochromes, but no correlation between phylogeny and used antenna has been found. Despite a common protein topology, structural analysis of the distantly related class II photolyase from the archaeon Methanosarcina mazei (MmCPDII) as well as plantal orthologues indicated several differences in terms of DNA and FAD binding and electron transfer pathways. For MmCPDII we identify 8-hydroxydeazaflavin (8-HDF) as cognate antenna by in vitro and in vivo reconstitution, whereas the higher plant class II photolyase from Arabidopsis thaliana fails to bind any of the known chromophores. According to the 1.9 Å structure of the MmCPDII·8-HDF complex, its antenna binding site differs from other members of the photolyase-cryptochrome superfamily by an antenna loop that changes its conformation by 12 Å upon 8-HDF binding. Additionally, so-called N- and C-motifs contribute as conserved elements to the binding of deprotonated 8-HDF and allow predicting 8-HDF binding for most of the class II photolyases in the whole phylome. The 8-HDF antenna is used throughout the viridiplantae ranging from green microalgae to bryophyta and pteridophyta, i.e. mosses and ferns, but interestingly not in higher plants. Overall, we suggest that 8-hydroxydeazaflavin is a crucial factor for the survival of most higher eukaryotes which depend on class II photolyases to struggle with the genotoxic effects of solar UV exposure. PMID:24849603

A tripartite taxonomy of character: Evidence for intrapersonal, interpersonal, and intellectual competencies in children

PubMed Central

Park, Daeun; Tsukayama, Eli; Goodwin, Geoffrey P.; Patrick, Sarah; Duckworth, Angela L.

2017-01-01

Other than cognitive ability, what competencies should schools promote in children? How are they organized, and to what extent do they predict consequential outcomes? Separate theoretical traditions have suggested interpersonal, intrapersonal, and intellectual dimensions, reflecting how children relate to other people, manage their own goals and impulses, and engage with ideas, respectively. However, very little work has examined character empirically. In the current investigation, we partnered with middle schools that had previously identified character strengths relevant in their communities. Across three longitudinal, prospective studies, we examined the factor structure of character, associations with intelligence and Big Five personality traits, and predictive validity for consequential outcomes like peer relations, class participation, and report card grades. In Study 1, teachers rated their students on behaviors exemplifying character strengths as they played out in students’ daily lives. Exploratory factor analyses yielded a three-factor structure consisting of interpersonal (interpersonal self-control, gratitude, social intelligence), intellectual (zest, curiosity), and intrapersonal (academic self-control, grit) factors of character. In Study 2, children rated their own behavior and completed a test of cognitive ability. Confirmatory factor analyses supported the same three-factor structure, and these factors were only weakly associated with cognitive ability. In Study 3, teachers provided character ratings; in parallel, students completed measures of character as well as Big Five personality factors. As expected, intellectual, interpersonal, and intrapersonal character factors related to Big Five openness to experience, agreeableness, and conscientiousness, respectively. Across studies, positive peer relations were most consistently predicted by interpersonal character, class participation by intellectual character, and report card grades by intrapersonal character. Collectively, our findings support a tripartite taxonomy of character in the school context. PMID:29051684

A tripartite taxonomy of character: Evidence for intrapersonal, interpersonal, and intellectual competencies in children.

PubMed

Park, Daeun; Tsukayama, Eli; Goodwin, Geoffrey P; Patrick, Sarah; Duckworth, Angela L

2017-01-01

Other than cognitive ability, what competencies should schools promote in children? How are they organized, and to what extent do they predict consequential outcomes? Separate theoretical traditions have suggested interpersonal, intrapersonal, and intellectual dimensions, reflecting how children relate to other people, manage their own goals and impulses, and engage with ideas, respectively. However, very little work has examined character empirically. In the current investigation, we partnered with middle schools that had previously identified character strengths relevant in their communities. Across three longitudinal, prospective studies, we examined the factor structure of character, associations with intelligence and Big Five personality traits, and predictive validity for consequential outcomes like peer relations, class participation, and report card grades. In Study 1, teachers rated their students on behaviors exemplifying character strengths as they played out in students' daily lives. Exploratory factor analyses yielded a three-factor structure consisting of interpersonal (interpersonal self-control, gratitude, social intelligence), intellectual (zest, curiosity), and intrapersonal (academic self-control, grit) factors of character. In Study 2, children rated their own behavior and completed a test of cognitive ability. Confirmatory factor analyses supported the same three-factor structure, and these factors were only weakly associated with cognitive ability. In Study 3, teachers provided character ratings; in parallel, students completed measures of character as well as Big Five personality factors. As expected, intellectual, interpersonal, and intrapersonal character factors related to Big Five openness to experience, agreeableness, and conscientiousness, respectively. Across studies, positive peer relations were most consistently predicted by interpersonal character, class participation by intellectual character, and report card grades by intrapersonal character. Collectively, our findings support a tripartite taxonomy of character in the school context.

Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding.

PubMed

Tenzer, S; Peters, B; Bulik, S; Schoor, O; Lemmel, C; Schatz, M M; Kloetzel, P-M; Rammensee, H-G; Schild, H; Holzhütter, H-G

2005-05-01

Epitopes presented by major histocompatibility complex (MHC) class I molecules are selected by a multi-step process. Here we present the first computational prediction of this process based on in vitro experiments characterizing proteasomal cleavage, transport by the transporter associated with antigen processing (TAP) and MHC class I binding. Our novel prediction method for proteasomal cleavages outperforms existing methods when tested on in vitro cleavage data. The analysis of our predictions for a new dataset consisting of 390 endogenously processed MHC class I ligands from cells with known proteasome composition shows that the immunological advantage of switching from constitutive to immunoproteasomes is mainly to suppress the creation of peptides in the cytosol that TAP cannot transport. Furthermore, we show that proteasomes are unlikely to generate MHC class I ligands with a C-terminal lysine residue, suggesting processing of these ligands by a different protease that may be tripeptidyl-peptidase II (TPPII).

Delivery style moderates study habits in an online nutrition class.

PubMed

Connors, Priscilla

2013-03-01

To report how the design of an online class affected student ability to stay on task, find critical resources, and communicate with the instructor via e-mail. Audiorecorded focus group meetings at a United States university featured a structured approach to discussions among undergraduate students enrolled in an Internet nutrition class. Meeting transcripts were read and reread by a trained investigator, who coded concepts until themes coalesced, which were authenticated by college students taking online classes. Three themes emerged that described factors moderating study habits in an Internet nutrition course: keeping up, e-mail fatigue, and wayfinding. A well-designed online course plans for productive study habits by posting a schedule of events and maintaining a predictable pattern, supporting navigation that stimulates exploration and return visits to critical information, and constructing e-mail messages that convey a concise message and maximize "open and read." Copyright © 2013 Society for Nutrition Education and Behavior. Published by Elsevier Inc. All rights reserved.

Irritability Trajectories, Cortical Thickness, and Clinical Outcomes in a Sample Enriched for Preschool Depression.

PubMed

Pagliaccio, David; Pine, Daniel S; Barch, Deanna M; Luby, Joan L; Leibenluft, Ellen

2018-05-01

Cross-sectional, longitudinal, and genetic associations exist between irritability and depression. Prior studies have examined developmental trajectories of irritability, clinical outcomes, and associations with child and familial depression. However, studies have not integrated neurobiological measures. The present study examined developmental trajectories of irritability, clinical outcomes, and cortical structure among preschoolers oversampled for depressive symptoms. Beginning at 3 to 5 years old, a sample of 271 children enriched for early depressive symptoms were assessed longitudinally by clinical interview. Latent class mixture models identified trajectories of irritability severity. Risk factors, clinical outcomes, and cortical thickness were compared across trajectory classes. Cortical thickness measures were extracted from 3 waves of magnetic resonance imaging at 7 to 12 years of age. Three trajectory classes were identified among these youth: 53.50% of children exhibited elevated irritability during preschool that decreased longitudinally, 30.26% exhibited consistently low irritability, and 16.24% exhibited consistently elevated irritability. Compared with other classes, the elevated irritability class exhibited higher rates of maternal depression, early life adversity, later psychiatric diagnoses, and functional impairment. Further, elevated baseline irritability predicted later depression beyond adversity and personal and maternal depression history. The elevated irritability class exhibited a thicker cortex in the left superior frontal and temporal gyri and the right inferior parietal lobule. Irritability manifested with specific developmental trajectories in this sample enriched for early depression. Persistently elevated irritability predicted poor psychiatric outcomes, higher risk for later depression, and decreased overall function later in development. Greater frontal, temporal, and parietal cortical thickness also was found, providing neural correlates of this risk trajectory. Copyright © 2018 American Academy of Child and Adolescent Psychiatry. All rights reserved.

Machine learning for predicting soil classes in three semi-arid landscapes

USGS Publications Warehouse

Brungard, Colby W.; Boettinger, Janis L.; Duniway, Michael C.; Wills, Skye A.; Edwards, Thomas C.

2015-01-01

Mapping the spatial distribution of soil taxonomic classes is important for informing soil use and management decisions. Digital soil mapping (DSM) can quantitatively predict the spatial distribution of soil taxonomic classes. Key components of DSM are the method and the set of environmental covariates used to predict soil classes. Machine learning is a general term for a broad set of statistical modeling techniques. Many different machine learning models have been applied in the literature and there are different approaches for selecting covariates for DSM. However, there is little guidance as to which, if any, machine learning model and covariate set might be optimal for predicting soil classes across different landscapes. Our objective was to compare multiple machine learning models and covariate sets for predicting soil taxonomic classes at three geographically distinct areas in the semi-arid western United States of America (southern New Mexico, southwestern Utah, and northeastern Wyoming). All three areas were the focus of digital soil mapping studies. Sampling sites at each study area were selected using conditioned Latin hypercube sampling (cLHS). We compared models that had been used in other DSM studies, including clustering algorithms, discriminant analysis, multinomial logistic regression, neural networks, tree based methods, and support vector machine classifiers. Tested machine learning models were divided into three groups based on model complexity: simple, moderate, and complex. We also compared environmental covariates derived from digital elevation models and Landsat imagery that were divided into three different sets: 1) covariates selected a priori by soil scientists familiar with each area and used as input into cLHS, 2) the covariates in set 1 plus 113 additional covariates, and 3) covariates selected using recursive feature elimination. Overall, complex models were consistently more accurate than simple or moderately complex models. Random forests (RF) using covariates selected via recursive feature elimination was consistently the most accurate, or was among the most accurate, classifiers between study areas and between covariate sets within each study area. We recommend that for soil taxonomic class prediction, complex models and covariates selected by recursive feature elimination be used. Overall classification accuracy in each study area was largely dependent upon the number of soil taxonomic classes and the frequency distribution of pedon observations between taxonomic classes. Individual subgroup class accuracy was generally dependent upon the number of soil pedon observations in each taxonomic class. The number of soil classes is related to the inherent variability of a given area. The imbalance of soil pedon observations between classes is likely related to cLHS. Imbalanced frequency distributions of soil pedon observations between classes must be addressed to improve model accuracy. Solutions include increasing the number of soil pedon observations in classes with few observations or decreasing the number of classes. Spatial predictions using the most accurate models generally agree with expected soil–landscape relationships. Spatial prediction uncertainty was lowest in areas of relatively low relief for each study area.

Social-class differences in self-concept clarity and their implications for well-being.

PubMed

Na, Jinkyung; Chan, Micaela Y; Lodi-Smith, Jennifer; Park, Denise C

2018-06-01

A consistent/stable sense of the self is more valued in middle-class contexts than working-class contexts; hence, we predicted that middle-class individuals would have higher self-concept clarity than working-class individuals. It is further expected that self-concept clarity would be more important to one's well-being among middle-class individuals than among working-class individuals. Supporting these predictions, self-concept clarity was positively associated with higher social class. Moreover, although self-concept clarity was associated with higher life satisfaction and better mental health, the association significantly attenuated among working-class individuals. In addition, self-concept clarity was not associated with physical health and its association with physical health did not interact with social class.

The structure-AChE inhibitory activity relationships study in a series of pyridazine analogues.

PubMed

Saracoglu, M; Kandemirli, F

2009-07-01

The structure-activity relationships (SAR) are investigated by means of the Electronic-Topological Method (ETM) followed by the Neural Networks application (ETM-NN) for a class of anti-cholinesterase inhibitors (AChE, 53 molecules) being pyridazine derivatives. AChE activities of the series were measured in IC(50) units, and relative to the activity levels, the series was partitioned into classes of active and inactive compounds. Based on pharmacophores and antipharmacophores calculated by the ETM-software as sub-matrices containing important spatial and electronic characteristics, a system for the activity prognostication is developed. Input data for the ETM were taken as the results of conformational and quantum-mechanics calculations. To predict the activity, we used one of the most well known neural networks, namely, the feed-forward neural networks (FFNNs) trained with the back propagation algorithm. The supervised learning was performed using a variant of FFNN known as the Associative Neural Networks (ASNN). The result of the testing revealed that the high ETM's ability of predicting both activity and inactivity of potential AChE inhibitors. Analysis of HOMOs for the compounds containing Ph1 and APh1 has shown that atoms with the highest values of the atomic orbital coefficients are mainly those atoms that enter into the pharmacophores. Thus, the set of pharmacophores and antipharmacophores found as the result of this study forms a basis for a system of the anti-cholinesterase activity prediction.

NAViGaTing the Micronome – Using Multiple MicroRNA Prediction Databases to Identify Signalling Pathway-Associated MicroRNAs

PubMed Central

Shirdel, Elize A.; Xie, Wing; Mak, Tak W.; Jurisica, Igor

2011-01-01

Background MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome – referred to as the micronome – to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal — mirDIP (http://ophid.utoronto.ca/mirDIP). Results mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05), suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001), to be more studied (p<0.0002), and to have higher degree in the KEGG cancer pathway (p<0.0001), compared to intra-pathway microRNAs. Conclusions Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level. PMID:21364759

Benchmark data sets for structure-based computational target prediction.

PubMed

Schomburg, Karen T; Rarey, Matthias

2014-08-25

Structure-based computational target prediction methods identify potential targets for a bioactive compound. Methods based on protein-ligand docking so far face many challenges, where the greatest probably is the ranking of true targets in a large data set of protein structures. Currently, no standard data sets for evaluation exist, rendering comparison and demonstration of improvements of methods cumbersome. Therefore, we propose two data sets and evaluation strategies for a meaningful evaluation of new target prediction methods, i.e., a small data set consisting of three target classes for detailed proof-of-concept and selectivity studies and a large data set consisting of 7992 protein structures and 72 drug-like ligands allowing statistical evaluation with performance metrics on a drug-like chemical space. Both data sets are built from openly available resources, and any information needed to perform the described experiments is reported. We describe the composition of the data sets, the setup of screening experiments, and the evaluation strategy. Performance metrics capable to measure the early recognition of enrichments like AUC, BEDROC, and NSLR are proposed. We apply a sequence-based target prediction method to the large data set to analyze its content of nontrivial evaluation cases. The proposed data sets are used for method evaluation of our new inverse screening method iRAISE. The small data set reveals the method's capability and limitations to selectively distinguish between rather similar protein structures. The large data set simulates real target identification scenarios. iRAISE achieves in 55% excellent or good enrichment a median AUC of 0.67 and RMSDs below 2.0 Å for 74% and was able to predict the first true target in 59 out of 72 cases in the top 2% of the protein data set of about 8000 structures.

A Hydrogen-Bonded Polar Network in the Core of the Glucagon-Like Peptide-1 Receptor Is a Fulcrum for Biased Agonism: Lessons from Class B Crystal Structures.

PubMed

Wootten, Denise; Reynolds, Christopher A; Koole, Cassandra; Smith, Kevin J; Mobarec, Juan C; Simms, John; Quon, Tezz; Coudrat, Thomas; Furness, Sebastian G B; Miller, Laurence J; Christopoulos, Arthur; Sexton, Patrick M

2016-03-01

The glucagon-like peptide 1 (GLP-1) receptor is a class B G protein-coupled receptor (GPCR) that is a key target for treatments for type II diabetes and obesity. This receptor, like other class B GPCRs, displays biased agonism, though the physiologic significance of this is yet to be elucidated. Previous work has implicated R2.60(190), N3.43(240), Q7.49(394), and H6.52(363) as key residues involved in peptide-mediated biased agonism, with R2.60(190), N3.43(240), and Q7.49(394) predicted to form a polar interaction network. In this study, we used novel insight gained from recent crystal structures of the transmembrane domains of the glucagon and corticotropin releasing factor 1 (CRF1) receptors to develop improved models of the GLP-1 receptor that predict additional key molecular interactions with these amino acids. We have introduced E6.53(364)A, N3.43(240)Q, Q7.49(394)N, and N3.43(240)Q/Q7.49(394)N mutations to probe the role of predicted H-bonding and charge-charge interactions in driving cAMP, calcium, or extracellular signal-regulated kinase (ERK) signaling. A polar interaction between E6.53(364) and R2.60(190) was predicted to be important for GLP-1- and exendin-4-, but not oxyntomodulin-mediated cAMP formation and also ERK1/2 phosphorylation. In contrast, Q7.49(394), but not R2.60(190)/E6.53(364) was critical for calcium mobilization for all three peptides. Mutation of N3.43(240) and Q7.49(394) had differential effects on individual peptides, providing evidence for molecular differences in activation transition. Collectively, this work expands our understanding of peptide-mediated signaling from the GLP-1 receptor and the key role that the central polar network plays in these events. Copyright © 2016 by The American Society for Pharmacology and Experimental Therapeutics.

Polymorphism in magic-sized Au144(SR)60 clusters

DOE PAGES

Jensen, Kirsten M. O.; Juhas, Pavol; Tofanelli, Marcus A.; ...

2016-06-14

Ultra-small, magic-sized metal nanoclusters represent an important new class of materials with properties between molecules and particles. However, their small size challenges the conventional methods for structure characterization. We present the structure of ultra-stable Au144(SR)60 magic-sized nanoclusters obtained from atomic pair distribution function analysis of X-ray powder diffraction data. Our study reveals structural polymorphism in these archetypal nanoclusters. Additionally, in order to confirm the theoretically predicted icosahedral-cored cluster, we also find samples with a truncated decahedral core structure, with some samples exhibiting a coexistence of both cluster structures. Although the clusters are monodisperse in size, structural diversity is apparent. Finally,more » the discovery of polymorphism may open up a new dimension in nanoscale engineering.« less

Using Factor Mixture Models to Evaluate the Type A/B Classification of Alcohol Use Disorders in a Heterogeneous Treatment Sample

PubMed Central

Hildebrandt, Tom; Epstein, Elizabeth E.; Sysko, Robyn; Bux, Donald A.

2017-01-01

Background The type A/B classification model for alcohol use disorders (AUDs) has received considerable empirical support. However, few studies examine the underlying latent structure of this subtyping model, which has been challenged as a dichotomization of a single drinking severity dimension. Type B, relative to type A, alcoholics represent those with early age of onset, greater familial risk, and worse outcomes from alcohol use. Method We examined the latent structure of the type A/B model using categorical, dimensional, and factor mixture models in a mixed gender community treatment-seeking sample of adults with an AUD. Results Factor analytic models identified 2-factors (drinking severity/externalizing psychopathology and internalizing psychopathology) underlying the type A/B indicators. A factor mixture model with 2-dimensions and 3-classes emerged as the best overall fitting model. The classes reflected a type A class and two type B classes (B1 and B2) that differed on the respective level of drinking severity/externalizing pathology and internalizing pathology. Type B1 had a greater prevalence of women and more internalizing pathology and B2 had a greater prevalence of men and more drinking severity/externalizing pathology. The 2-factor, 3-class model also exhibited predictive validity by explaining significant variance in 12-month drinking and drug use outcomes. Conclusions The model identified in the current study may provide a basis for examining different sources of heterogeneity in the course and outcome of AUDs. PMID:28247423

The structure of paranoia in the general population.

PubMed

Bebbington, Paul E; McBride, Orla; Steel, Craig; Kuipers, Elizabeth; Radovanovic, Mirjana; Brugha, Traolach; Jenkins, Rachel; Meltzer, Howard I; Freeman, Daniel

2013-06-01

Psychotic phenomena appear to form a continuum with normal experience and beliefs, and may build on common emotional interpersonal concerns. We tested predictions that paranoid ideation is exponentially distributed and hierarchically arranged in the general population, and that persecutory ideas build on more common cognitions of mistrust, interpersonal sensitivity and ideas of reference. Items were chosen from the Structured Clinical Interview for DSM-IV Axis II Disorders (SCID-II) questionnaire and the Psychosis Screening Questionnaire in the second British National Survey of Psychiatric Morbidity (n = 8580), to test a putative hierarchy of paranoid development using confirmatory factor analysis, latent class analysis and factor mixture modelling analysis. Different types of paranoid ideation ranged in frequency from less than 2% to nearly 30%. Total scores on these items followed an almost perfect exponential distribution (r = 0.99). Our four a priori first-order factors were corroborated (interpersonal sensitivity; mistrust; ideas of reference; ideas of persecution). These mapped onto four classes of individual respondents: a rare, severe, persecutory class with high endorsement of all item factors, including persecutory ideation; a quasi-normal class with infrequent endorsement of interpersonal sensitivity, mistrust and ideas of reference, and no ideas of persecution; and two intermediate classes, characterised respectively by relatively high endorsement of items relating to mistrust and to ideas of reference. The paranoia continuum has implications for the aetiology, mechanisms and treatment of psychotic disorders, while confirming the lack of a clear distinction from normal experiences and processes.

Exploring the potential of 3D Zernike descriptors and SVM for protein-protein interface prediction.

PubMed

Daberdaku, Sebastian; Ferrari, Carlo

2018-02-06

The correct determination of protein-protein interaction interfaces is important for understanding disease mechanisms and for rational drug design. To date, several computational methods for the prediction of protein interfaces have been developed, but the interface prediction problem is still not fully understood. Experimental evidence suggests that the location of binding sites is imprinted in the protein structure, but there are major differences among the interfaces of the various protein types: the characterising properties can vary a lot depending on the interaction type and function. The selection of an optimal set of features characterising the protein interface and the development of an effective method to represent and capture the complex protein recognition patterns are of paramount importance for this task. In this work we investigate the potential of a novel local surface descriptor based on 3D Zernike moments for the interface prediction task. Descriptors invariant to roto-translations are extracted from circular patches of the protein surface enriched with physico-chemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. Support Vector Machines are used as a classifier to distinguish interface local surface patches from non-interface ones. The proposed method was validated on 16 classes of proteins extracted from the Protein-Protein Docking Benchmark 5.0 and compared to other state-of-the-art protein interface predictors (SPPIDER, PrISE and NPS-HomPPI). The 3D Zernike descriptors are able to capture the similarity among patterns of physico-chemical and biochemical properties mapped on the protein surface arising from the various spatial arrangements of the underlying residues, and their usage can be easily extended to other sets of amino acid properties. The results suggest that the choice of a proper set of features characterising the protein interface is crucial for the interface prediction task, and that optimality strongly depends on the class of proteins whose interface we want to characterise. We postulate that different protein classes should be treated separately and that it is necessary to identify an optimal set of features for each protein class.

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

Non-B DB: a database of predicted non-B DNA-forming motifs in mammalian genomes.

PubMed

Cer, Regina Z; Bruce, Kevin H; Mudunuri, Uma S; Yi, Ming; Volfovsky, Natalia; Luke, Brian T; Bacolla, Albino; Collins, Jack R; Stephens, Robert M

2011-01-01

Although the capability of DNA to form a variety of non-canonical (non-B) structures has long been recognized, the overall significance of these alternate conformations in biology has only recently become accepted en masse. In order to provide access to genome-wide locations of these classes of predicted structures, we have developed non-B DB, a database integrating annotations and analysis of non-B DNA-forming sequence motifs. The database provides the most complete list of alternative DNA structure predictions available, including Z-DNA motifs, quadruplex-forming motifs, inverted repeats, mirror repeats and direct repeats and their associated subsets of cruciforms, triplex and slipped structures, respectively. The database also contains motifs predicted to form static DNA bends, short tandem repeats and homo(purine•pyrimidine) tracts that have been associated with disease. The database has been built using the latest releases of the human, chimp, dog, macaque and mouse genomes, so that the results can be compared directly with other data sources. In order to make the data interpretable in a genomic context, features such as genes, single-nucleotide polymorphisms and repetitive elements (SINE, LINE, etc.) have also been incorporated. The database is accessed through query pages that produce results with links to the UCSC browser and a GBrowse-based genomic viewer. It is freely accessible at http://nonb.abcc.ncifcrf.gov.

Recruitment dynamics in adaptive social networks

NASA Astrophysics Data System (ADS)

Shkarayev, Maxim; Shaw, Leah; Schwartz, Ira

2011-03-01

We model recruitment in social networks in the presence of birth and death processes. The recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. The recruiting nodes may adapt their connections in order to improve recruitment capabilities, thus changing the network structure. We develop a mean-field theory describing the system dynamics. Using mean-field theory we characterize the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment dynamics, as well as on network topology. The theoretical predictions are confirmed by the direct simulations of the full system.

Structure Prediction and Analysis of DNA Transposon and LINE Retrotransposon Proteins*

PubMed Central

Abrusán, György; Zhang, Yang; Szilágyi, András

2013-01-01

Despite the considerable amount of research on transposable elements, no large-scale structural analyses of the TE proteome have been performed so far. We predicted the structures of hundreds of proteins from a representative set of DNA and LINE transposable elements and used the obtained structural data to provide the first general structural characterization of TE proteins and to estimate the frequency of TE domestication and horizontal transfer events. We show that 1) ORF1 and Gag proteins of retrotransposons contain high amounts of structural disorder; thus, despite their very low conservation, the presence of disordered regions and probably their chaperone function is conserved. 2) The distribution of SCOP classes in DNA transposons and LINEs indicates that the proteins of DNA transposons are more ancient, containing folds that already existed when the first cellular organisms appeared. 3) DNA transposon proteins have lower contact order than randomly selected reference proteins, indicating rapid folding, most likely to avoid protein aggregation. 4) Structure-based searches for TE homologs indicate that the overall frequency of TE domestication events is low, whereas we found a relatively high number of cases where horizontal transfer, frequently involving parasites, is the most likely explanation for the observed homology. PMID:23530042

Increased Course Structure Improves Performance in Introductory Biology

PubMed Central

Freeman, Scott; Haak, David; Wenderoth, Mary Pat

2011-01-01

We tested the hypothesis that highly structured course designs, which implement reading quizzes and/or extensive in-class active-learning activities and weekly practice exams, can lower failure rates in an introductory biology course for majors, compared with low-structure course designs that are based on lecturing and a few high-risk assessments. We controlled for 1) instructor effects by analyzing data from quarters when the same instructor taught the course, 2) exam equivalence with new assessments called the Weighted Bloom's Index and Predicted Exam Score, and 3) student equivalence using a regression-based Predicted Grade. We also tested the hypothesis that points from reading quizzes, clicker questions, and other “practice” assessments in highly structured courses inflate grades and confound comparisons with low-structure course designs. We found no evidence that points from active-learning exercises inflate grades or reduce the impact of exams on final grades. When we controlled for variation in student ability, failure rates were lower in a moderately structured course design and were dramatically lower in a highly structured course design. This result supports the hypothesis that active-learning exercises can make students more skilled learners and help bridge the gap between poorly prepared students and their better-prepared peers. PMID:21633066

Increased course structure improves performance in introductory biology.

PubMed

Freeman, Scott; Haak, David; Wenderoth, Mary Pat

2011-01-01

We tested the hypothesis that highly structured course designs, which implement reading quizzes and/or extensive in-class active-learning activities and weekly practice exams, can lower failure rates in an introductory biology course for majors, compared with low-structure course designs that are based on lecturing and a few high-risk assessments. We controlled for 1) instructor effects by analyzing data from quarters when the same instructor taught the course, 2) exam equivalence with new assessments called the Weighted Bloom's Index and Predicted Exam Score, and 3) student equivalence using a regression-based Predicted Grade. We also tested the hypothesis that points from reading quizzes, clicker questions, and other "practice" assessments in highly structured courses inflate grades and confound comparisons with low-structure course designs. We found no evidence that points from active-learning exercises inflate grades or reduce the impact of exams on final grades. When we controlled for variation in student ability, failure rates were lower in a moderately structured course design and were dramatically lower in a highly structured course design. This result supports the hypothesis that active-learning exercises can make students more skilled learners and help bridge the gap between poorly prepared students and their better-prepared peers.

Joint use of over- and under-sampling techniques and cross-validation for the development and assessment of prediction models.

PubMed

Blagus, Rok; Lusa, Lara

2015-11-04

Prediction models are used in clinical research to develop rules that can be used to accurately predict the outcome of the patients based on some of their characteristics. They represent a valuable tool in the decision making process of clinicians and health policy makers, as they enable them to estimate the probability that patients have or will develop a disease, will respond to a treatment, or that their disease will recur. The interest devoted to prediction models in the biomedical community has been growing in the last few years. Often the data used to develop the prediction models are class-imbalanced as only few patients experience the event (and therefore belong to minority class). Prediction models developed using class-imbalanced data tend to achieve sub-optimal predictive accuracy in the minority class. This problem can be diminished by using sampling techniques aimed at balancing the class distribution. These techniques include under- and oversampling, where a fraction of the majority class samples are retained in the analysis or new samples from the minority class are generated. The correct assessment of how the prediction model is likely to perform on independent data is of crucial importance; in the absence of an independent data set, cross-validation is normally used. While the importance of correct cross-validation is well documented in the biomedical literature, the challenges posed by the joint use of sampling techniques and cross-validation have not been addressed. We show that care must be taken to ensure that cross-validation is performed correctly on sampled data, and that the risk of overestimating the predictive accuracy is greater when oversampling techniques are used. Examples based on the re-analysis of real datasets and simulation studies are provided. We identify some results from the biomedical literature where the incorrect cross-validation was performed, where we expect that the performance of oversampling techniques was heavily overestimated.

Homology modelling of frequent HLA class-II alleles: A perspective to improve prediction of HLA binding peptide and understand the HLA associated disease susceptibility.

PubMed

Kashyap, Manju; Farooq, Umar; Jaiswal, Varun

2016-10-01

Human leukocyte antigen (HLA) plays significant role via the regulation of immune system and contribute in the progression and protection of many diseases. HLA molecules bind and present peptides to T- cell receptors which generate the immune response. HLA peptide interaction and molecular function of HLA molecule is the key to predict peptide binding and understanding its role in different diseases. The availability of accurate three dimensional (3D) structures is the initial step towards this direction. In the present work, homology modelling of important and frequent HLA-DRB1 alleles (07:01, 11:01 and 09:01) was done and acceptable models were generated. These modelled alleles were further refined and cross validated by using several methods including Ramachandran plot, Z-score, ERRAT analysis and root mean square deviation (RMSD) calculations. It is known that numbers of allelic variants are related to the susceptibility or protection of various infectious diseases. Difference in amino acid sequences and structures of alleles were also studied to understand the association of HLA with disease susceptibility and protection. Susceptible alleles showed more amino acid variations than protective alleles in three selected diseases caused by different pathogens. Amino acid variations at binding site were found to be more than other part of alleles. RMSD values were also higher at variable positions within binding site. Higher RMSD values indicate that mutations occurring at peptide binding site alter protein structure more than rest of the protein. Hence, these findings and modelled structures can be used to design HLA-DRB1 binding peptides to overcome low prediction accuracy of HLA class II binding peptides. Furthermore, it may help to understand the allele specific molecular mechanisms involved in susceptibility/resistance against pathogenic diseases. Copyright © 2016 Elsevier B.V. All rights reserved.

Template CoMFA Generates Single 3D-QSAR Models that, for Twelve of Twelve Biological Targets, Predict All ChEMBL-Tabulated Affinities

PubMed Central

Cramer, Richard D.

2015-01-01

The possible applicability of the new template CoMFA methodology to the prediction of unknown biological affinities was explored. For twelve selected targets, all ChEMBL binding affinities were used as training and/or prediction sets, making these 3D-QSAR models the most structurally diverse and among the largest ever. For six of the targets, X-ray crystallographic structures provided the aligned templates required as input (BACE, cdk1, chk2, carbonic anhydrase-II, factor Xa, PTP1B). For all targets including the other six (hERG, cyp3A4 binding, endocrine receptor, COX2, D2, and GABAa), six modeling protocols applied to only three familiar ligands provided six alternate sets of aligned templates. The statistical qualities of the six or seven models thus resulting for each individual target were remarkably similar. Also, perhaps unexpectedly, the standard deviations of the errors of cross-validation predictions accompanying model derivations were indistinguishable from the standard deviations of the errors of truly prospective predictions. These standard deviations of prediction ranged from 0.70 to 1.14 log units and averaged 0.89 (8x in concentration units) over the twelve targets, representing an average reduction of almost 50% in uncertainty, compared to the null hypothesis of “predicting” an unknown affinity to be the average of known affinities. These errors of prediction are similar to those from Tanimoto coefficients of fragment occurrence frequencies, the predominant approach to side effect prediction, which template CoMFA can augment by identifying additional active structural classes, by improving Tanimoto-only predictions, by yielding quantitative predictions of potency, and by providing interpretable guidance for avoiding or enhancing any specific target response. PMID:26065424

Discriminative motif discovery via simulated evolution and random under-sampling.

PubMed

Song, Tao; Gu, Hong

2014-01-01

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

A GRAPH PARTITIONING APPROACH TO PREDICTING PATTERNS IN LATERAL INHIBITION SYSTEMS

PubMed Central

RUFINO FERREIRA, ANA S.; ARCAK, MURAT

2017-01-01

We analyze spatial patterns on networks of cells where adjacent cells inhibit each other through contact signaling. We represent the network as a graph where each vertex represents the dynamics of identical individual cells and where graph edges represent cell-to-cell signaling. To predict steady-state patterns we find equitable partitions of the graph vertices and assign them into disjoint classes. We then use results from monotone systems theory to prove the existence of patterns that are structured in such a way that all the cells in the same class have the same final fate. To study the stability properties of these patterns, we rely on the graph partition to perform a block decomposition of the system. Then, to guarantee stability, we provide a small-gain type criterion that depends on the input-output properties of each cell in the reduced system. Finally, we discuss pattern formation in stochastic models. With the help of a modal decomposition we show that noise can enhance the parameter region where patterning occurs. PMID:29225552

QSAR Analysis of 2-Amino or 2-Methyl-1-Substituted Benzimidazoles Against Pseudomonas aeruginosa

PubMed Central

Podunavac-Kuzmanović, Sanja O.; Cvetković, Dragoljub D.; Barna, Dijana J.

2009-01-01

A set of benzimidazole derivatives were tested for their inhibitory activities against the Gram-negative bacterium Pseudomonas aeruginosa and minimum inhibitory concentrations were determined for all the compounds. Quantitative structure activity relationship (QSAR) analysis was applied to fourteen of the abovementioned derivatives using a combination of various physicochemical, steric, electronic, and structural molecular descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the antibacterial activity of the benzimidazole derivatives. The stepwise regression method was used to derive the most significant models as a calibration model for predicting the inhibitory activity of this class of molecules. The best QSAR models were further validated by a leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. To confirm the predictive power of the models, an external set of molecules was used. High agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the derived QSAR models. PMID:19468332

Identification of Specific DNA Binding Residues in the TCP Family of Transcription Factors in Arabidopsis[W

PubMed Central

Aggarwal, Pooja; Das Gupta, Mainak; Joseph, Agnel Praveen; Chatterjee, Nirmalya; Srinivasan, N.; Nath, Utpal

2010-01-01

The TCP transcription factors control multiple developmental traits in diverse plant species. Members of this family share an ∼60-residue-long TCP domain that binds to DNA. The TCP domain is predicted to form a basic helix-loop-helix (bHLH) structure but shares little sequence similarity with canonical bHLH domain. This classifies the TCP domain as a novel class of DNA binding domain specific to the plant kingdom. Little is known about how the TCP domain interacts with its target DNA. We report biochemical characterization and DNA binding properties of a TCP member in Arabidopsis thaliana, TCP4. We have shown that the 58-residue domain of TCP4 is essential and sufficient for binding to DNA and possesses DNA binding parameters comparable to canonical bHLH proteins. Using a yeast-based random mutagenesis screen and site-directed mutants, we identified the residues important for DNA binding and dimer formation. Mutants defective in binding and dimerization failed to rescue the phenotype of an Arabidopsis line lacking the endogenous TCP4 activity. By combining structure prediction, functional characterization of the mutants, and molecular modeling, we suggest a possible DNA binding mechanism for this class of transcription factors. PMID:20363772

BlockLogo: visualization of peptide and sequence motif conservation

PubMed Central

Olsen, Lars Rønn; Kudahl, Ulrich Johan; Simon, Christian; Sun, Jing; Schönbach, Christian; Reinherz, Ellis L.; Zhang, Guang Lan; Brusic, Vladimir

2013-01-01

BlockLogo is a web-server application for visualization of protein and nucleotide fragments, continuous protein sequence motifs, and discontinuous sequence motifs using calculation of block entropy from multiple sequence alignments. The user input consists of a multiple sequence alignment, selection of motif positions, type of sequence, and output format definition. The output has BlockLogo along with the sequence logo, and a table of motif frequencies. We deployed BlockLogo as an online application and have demonstrated its utility through examples that show visualization of T-cell epitopes and B-cell epitopes (both continuous and discontinuous). Our additional example shows a visualization and analysis of structural motifs that determine specificity of peptide binding to HLA-DR molecules. The BlockLogo server also employs selected experimentally validated prediction algorithms to enable on-the-fly prediction of MHC binding affinity to 15 common HLA class I and class II alleles as well as visual analysis of discontinuous epitopes from multiple sequence alignments. It enables the visualization and analysis of structural and functional motifs that are usually described as regular expressions. It provides a compact view of discontinuous motifs composed of distant positions within biological sequences. BlockLogo is available at: http://research4.dfci.harvard.edu/cvc/blocklogo/ and http://methilab.bu.edu/blocklogo/ PMID:24001880

A Critical Review of Mode of Action (MOA) Assignment ...

EPA Pesticide Factsheets

There are various structure-based classification schemes to categorize chemicals based on mode of action (MOA) which have been applied for both eco and human health toxicology. With increasing calls to assess thousands of chemicals, some of which have little available information other than structure, clear understanding how each of these MOA schemes was devised, what information they are based on, and the limitations of each approach is critical. Several groups are developing low-tier methods to more easily classify or assess chemicals, using approaches such as the ecological threshold of concern (eco-TTC) and chemical-activity. Evaluation of these approaches and determination of their domain of applicability is partly dependent on the MOA classification that is used. The most commonly used MOA classification schemes for ecotoxicology include Verhaar and Russom (included in ASTER), both of which are used to predict acute aquatic toxicity MOA. Verhaar is a QSAR-based system that classifies chemicals into one of 4 classes, with a 5th class specified for those chemicals that are not classified in the other 4. ASTER/Russom includes 8 classifications: narcotics (3 groups), oxidative phosphorylation uncouplers, respiratory inhibitors, electrophiles/proelectrophiles, AChE inhibitors, or CNS seizure agents. Other methodologies include TEST (Toxicity Estimation Software Tool), a computational chemistry-based application that allows prediction to one of 5 broad MOA

Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology.

PubMed

Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil

2014-09-07

Due to the central roles of lipid binding proteins (LBPs) in many biological processes, sequence based identification of LBPs is of great interest. The major challenge is that LBPs are diverse in sequence, structure, and function which results in low accuracy of sequence homology based methods. Therefore, there is a need for developing alternative functional prediction methods irrespective of sequence similarity. To identify LBPs from non-LBPs, the performances of support vector machine (SVM) and neural network were compared in this study. Comprehensive protein features and various techniques were employed to create datasets. Five-fold cross-validation (CV) and independent evaluation (IE) tests were used to assess the validity of the two methods. The results indicated that SVM outperforms neural network. SVM achieved 89.28% (CV) and 89.55% (IE) overall accuracy in identification of LBPs from non-LBPs and 92.06% (CV) and 92.90% (IE) (in average) for classification of different LBPs classes. Increasing the number and the range of extracted protein features as well as optimization of the SVM parameters significantly increased the efficiency of LBPs class prediction in comparison to the only previous report in this field. Altogether, the results showed that the SVM algorithm can be run on broad, computationally calculated protein features and offers a promising tool in detection of LBPs classes. The proposed approach has the potential to integrate and improve the common sequence alignment based methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

NASA Astrophysics Data System (ADS)

Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo

2015-11-01

Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug-target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively implement neighbourhood-based link prediction entirely in the bipartite domain.

Discrete structural features among interface residue-level classes.

PubMed

Sowmya, Gopichandran; Ranganathan, Shoba

2015-01-01

Protein-protein interaction (PPI) is essential for molecular functions in biological cells. Investigation on protein interfaces of known complexes is an important step towards deciphering the driving forces of PPIs. Each PPI complex is specific, sensitive and selective to binding. Therefore, we have estimated the relative difference in percentage of polar residues between surface and the interface for each complex in a non-redundant heterodimer dataset of 278 complexes to understand the predominant forces driving binding. Our analysis showed ~60% of protein complexes with surface polarity greater than interface polarity (designated as class A). However, a considerable number of complexes (~40%) have interface polarity greater than surface polarity, (designated as class B), with a significantly different p-value of 1.66E-45 from class A. Comprehensive analyses of protein complexes show that interface features such as interface area, interface polarity abundance, solvation free energy gain upon interface formation, binding energy and the percentage of interface charged residue abundance distinguish among class A and class B complexes, while electrostatic visualization maps also help differentiate interface classes among complexes. Class A complexes are classical with abundant non-polar interactions at the interface; however class B complexes have abundant polar interactions at the interface, similar to protein surface characteristics. Five physicochemical interface features analyzed from the protein heterodimer dataset are discriminatory among the interface residue-level classes. These novel observations find application in developing residue-level models for protein-protein binding prediction, protein-protein docking studies and interface inhibitor design as drugs.

Discrete structural features among interface residue-level classes

PubMed Central

2015-01-01

Background Protein-protein interaction (PPI) is essential for molecular functions in biological cells. Investigation on protein interfaces of known complexes is an important step towards deciphering the driving forces of PPIs. Each PPI complex is specific, sensitive and selective to binding. Therefore, we have estimated the relative difference in percentage of polar residues between surface and the interface for each complex in a non-redundant heterodimer dataset of 278 complexes to understand the predominant forces driving binding. Results Our analysis showed ~60% of protein complexes with surface polarity greater than interface polarity (designated as class A). However, a considerable number of complexes (~40%) have interface polarity greater than surface polarity, (designated as class B), with a significantly different p-value of 1.66E-45 from class A. Comprehensive analyses of protein complexes show that interface features such as interface area, interface polarity abundance, solvation free energy gain upon interface formation, binding energy and the percentage of interface charged residue abundance distinguish among class A and class B complexes, while electrostatic visualization maps also help differentiate interface classes among complexes. Conclusions Class A complexes are classical with abundant non-polar interactions at the interface; however class B complexes have abundant polar interactions at the interface, similar to protein surface characteristics. Five physicochemical interface features analyzed from the protein heterodimer dataset are discriminatory among the interface residue-level classes. These novel observations find application in developing residue-level models for protein-protein binding prediction, protein-protein docking studies and interface inhibitor design as drugs. PMID:26679043

Phi Class of Glutathione S-transferase Gene Superfamily Widely Exists in Nonplant Taxonomic Groups.

PubMed

Munyampundu, Jean-Pierre; Xu, You-Ping; Cai, Xin-Zhong

2016-01-01

Glutathione S-transferases (GSTs) constitute a superfamily of enzymes involved in detoxification of noxious compounds and protection against oxidative damage. GST class Phi (GSTF), one of the important classes of plant GSTs, has long been considered as plant specific but was recently found in basidiomycete fungi. However, the range of nonplant taxonomic groups containing GSTFs remains unknown. In this study, the distribution and phylogenetic relationships of nonplant GSTFs were investigated. We identified GSTFs in ascomycete fungi, myxobacteria, and protists Naegleria gruberi and Aureococcus anophagefferens. GSTF occurrence in these bacteria and protists correlated with their genome sizes and habitats. While this link was missing across ascomycetes, the distribution and abundance of GSTFs among ascomycete genomes could be associated with their lifestyles to some extent. Sequence comparison, gene structure, and phylogenetic analyses indicated divergence among nonplant GSTFs, suggesting polyphyletic origins during evolution. Furthermore, in silico prediction of functional partners suggested functional diversification among nonplant GSTFs.

Spectral simplicity of apparent complexity. I. The nondiagonalizable metadynamics of prediction

NASA Astrophysics Data System (ADS)

Riechers, Paul M.; Crutchfield, James P.

2018-03-01

Virtually all questions that one can ask about the behavioral and structural complexity of a stochastic process reduce to a linear algebraic framing of a time evolution governed by an appropriate hidden-Markov process generator. Each type of question—correlation, predictability, predictive cost, observer synchronization, and the like—induces a distinct generator class. Answers are then functions of the class-appropriate transition dynamic. Unfortunately, these dynamics are generically nonnormal, nondiagonalizable, singular, and so on. Tractably analyzing these dynamics relies on adapting the recently introduced meromorphic functional calculus, which specifies the spectral decomposition of functions of nondiagonalizable linear operators, even when the function poles and zeros coincide with the operator's spectrum. Along the way, we establish special properties of the spectral projection operators that demonstrate how they capture the organization of subprocesses within a complex system. Circumventing the spurious infinities of alternative calculi, this leads in the sequel, Part II [P. M. Riechers and J. P. Crutchfield, Chaos 28, 033116 (2018)], to the first closed-form expressions for complexity measures, couched either in terms of the Drazin inverse (negative-one power of a singular operator) or the eigenvalues and projection operators of the appropriate transition dynamic.

Parameter Calibration and Numerical Analysis of Twin Shallow Tunnels

NASA Astrophysics Data System (ADS)

Paternesi, Alessandra; Schweiger, Helmut F.; Scarpelli, Giuseppe

2017-05-01

Prediction of displacements and lining stresses in underground openings represents a challenging task. The main reason is primarily related to the complexity of this ground-structure interaction problem and secondly to the difficulties in obtaining a reliable geotechnical characterisation of the soil or the rock. In any case, especially when class A predictions fail in forecasting the system behaviour, performing class B or C predictions, which rely on a higher level of knowledge of the surrounding ground, can represent a useful resource for identifying and reducing model deficiencies. The case study presented in this paper deals with the construction works of twin-tube shallow tunnels excavated in a stiff and fine-grained deposit. The work initially focuses on the ground parameter calibration against experimental data, which together with the choice of an appropriate constitutive model plays a major role in the assessment of tunnelling-induced deformations. Since two-dimensional analyses imply initial assumptions to take into account the effect of the 3D excavation, three-dimensional finite element analyses were preferred. Comparisons between monitoring data and results of numerical simulations are provided. The available field data include displacements and deformation measurements regarding both the ground and tunnel lining.

Prediction of Protein Configurational Entropy (Popcoen).

PubMed

Goethe, Martin; Gleixner, Jan; Fita, Ignacio; Rubi, J Miguel

2018-03-13

A knowledge-based method for configurational entropy prediction of proteins is presented; this methodology is extremely fast, compared to previous approaches, because it does not involve any type of configurational sampling. Instead, the configurational entropy of a query fold is estimated by evaluating an artificial neural network, which was trained on molecular-dynamics simulations of ∼1000 proteins. The predicted entropy can be incorporated into a large class of protein software based on cost-function minimization/evaluation, in which configurational entropy is currently neglected for performance reasons. Software of this type is used for all major protein tasks such as structure predictions, proteins design, NMR and X-ray refinement, docking, and mutation effect predictions. Integrating the predicted entropy can yield a significant accuracy increase as we show exemplarily for native-state identification with the prominent protein software FoldX. The method has been termed Popcoen for Prediction of Protein Configurational Entropy. An implementation is freely available at http://fmc.ub.edu/popcoen/ .

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

PubMed

Ganesan, K; Parthasarathy, S

2011-12-01

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

Molecular Characterization, Gene Evolution, and Expression Analysis of the Fructose-1, 6-bisphosphate Aldolase (FBA) Gene Family in Wheat (Triticum aestivum L.)

PubMed Central

Lv, Geng-Yin; Guo, Xiao-Guang; Xie, Li-Ping; Xie, Chang-Gen; Zhang, Xiao-Hong; Yang, Yuan; Xiao, Lei; Tang, Yu-Ying; Pan, Xing-Lai; Guo, Ai-Guang; Xu, Hong

2017-01-01

Fructose-1, 6-bisphosphate aldolase (FBA) is a key plant enzyme that is involved in glycolysis, gluconeogenesis, and the Calvin cycle. It plays significant roles in biotic and abiotic stress responses, as well as in regulating growth and development processes. In the present paper, 21 genes encoding TaFBA isoenzymes were identified, characterized, and categorized into three groups: class I chloroplast/plastid FBA (CpFBA), class I cytosol FBA (cFBA), and class II chloroplast/plastid FBA. By using a prediction online database and genomic PCR analysis of Chinese Spring nulli-tetrasomic lines, we have confirmed the chromosomal location of these genes in 12 chromosomes of four homologous groups. Sequence and genomic structure analysis revealed the high identity of the allelic TaFBA genes and the origin of different TaFBA genes. Numerous putative environment stimulus-responsive cis-elements have been identified in 1,500-bp regions of TaFBA gene promoters, of which the most abundant are the light-regulated elements (LREs). Phylogenetic reconstruction using the deduced protein sequence of 245 FBA genes indicated an independent evolutionary pathway for the class I and class II groups. Although, earlier studies have indicated that class II FBA only occurs in prokaryote and fungi, our results have demonstrated that a few class II CpFBAs exist in wheat and other closely related species. Class I TaFBA was predicted to be tetramers and class II to be dimers. Gene expression analysis based on microarray and transcriptome databases suggested the distinct role of TaFBAs in different tissues and developmental stages. The TaFBA 4–9 genes were highly expressed in leaves and might play important roles in wheat development. The differential expression patterns of the TaFBA genes in light/dark and a few abiotic stress conditions were also analyzed. The results suggested that LRE cis-elements of TaFBA gene promoters were not directly related to light responses. Most TaFBA genes had higher expression levels in the roots than in the shoots when under various stresses. Class I cytosol TaFBA genes, particularly TaFBA10/12/18 and TaFBA13/16, and three class II TaFBA genes are involved in responses to various abiotic stresses. Class I CpFBA genes in wheat are apparently sensitive to different stress conditions. PMID:28659962

Classes and continua of hippocampal CA1 inhibitory neurons revealed by single-cell transcriptomics.

PubMed

Harris, Kenneth D; Hochgerner, Hannah; Skene, Nathan G; Magno, Lorenza; Katona, Linda; Bengtsson Gonzales, Carolina; Somogyi, Peter; Kessaris, Nicoletta; Linnarsson, Sten; Hjerling-Leffler, Jens

2018-06-18

Understanding any brain circuit will require a categorization of its constituent neurons. In hippocampal area CA1, at least 23 classes of GABAergic neuron have been proposed to date. However, this list may be incomplete; additionally, it is unclear whether discrete classes are sufficient to describe the diversity of cortical inhibitory neurons or whether continuous modes of variability are also required. We studied the transcriptomes of 3,663 CA1 inhibitory cells, revealing 10 major GABAergic groups that divided into 49 fine-scale clusters. All previously described and several novel cell classes were identified, with three previously described classes unexpectedly found to be identical. A division into discrete classes, however, was not sufficient to describe the diversity of these cells, as continuous variation also occurred between and within classes. Latent factor analysis revealed that a single continuous variable could predict the expression levels of several genes, which correlated similarly with it across multiple cell types. Analysis of the genes correlating with this variable suggested it reflects a range from metabolically highly active faster-spiking cells that proximally target pyramidal cells to slower-spiking cells targeting distal dendrites or interneurons. These results elucidate the complexity of inhibitory neurons in one of the simplest cortical structures and show that characterizing these cells requires continuous modes of variation as well as discrete cell classes.

Interior noise control prediction study for high-speed propeller-driven aircraft

NASA Technical Reports Server (NTRS)

Rennison, D. C.; Wilby, J. F.; Marsh, A. H.; Wilby, E. G.

1979-01-01

An analytical model was developed to predict the noise levels inside propeller-driven aircraft during cruise at M = 0.8. The model was applied to three study aircraft with fuselages of different size (wide body, narrow body and small diameter) in order to determine the noise reductions required to achieve the goal of an A-weighted sound level which does not exceed 80 dB. The model was then used to determine noise control methods which could achieve the required noise reductions. Two classes of noise control treatments were investigated: add-on treatments which can be added to existing structures, and advanced concepts which would require changes to the fuselage primary structure. Only one treatment, a double wall with limp panel, provided the required noise reductions. Weight penalties associated with the treatment were estimated for the three study aircraft.

Trajectories of Substance Use Disorders in Youth: Identifying and Predicting Group Memberships

ERIC Educational Resources Information Center

Lee, Chih-Yuan S.; Winters, Ken C.; Wall, Melanie M.

2010-01-01

This study used latent class regression to identify latent trajectory classes based on individuals' diagnostic course of substance use disorders (SUDs) from late adolescence to early adulthood as well as to examine whether several psychosocial risk factors predicted the trajectory class membership. The study sample consisted of 310 individuals…

Protein disorder in the human diseasome: unfoldomics of human genetic diseases

PubMed Central

Midic, Uros; Oldfield, Christopher J; Dunker, A Keith; Obradovic, Zoran; Uversky, Vladimir N

2009-01-01

Background Intrinsically disordered proteins lack stable structure under physiological conditions, yet carry out many crucial biological functions, especially functions associated with regulation, recognition, signaling and control. Recently, human genetic diseases and related genes were organized into a bipartite graph (Goh KI, Cusick ME, Valle D, Childs B, Vidal M, et al. (2007) The human disease network. Proc Natl Acad Sci U S A 104: 8685–8690). This diseasome network revealed several significant features such as the common genetic origin of many diseases. Methods and findings We analyzed the abundance of intrinsic disorder in these diseasome network proteins by means of several prediction algorithms, and we analyzed the functional repertoires of these proteins based on prior studies relating disorder to function. Our analyses revealed that (i) Intrinsic disorder is common in proteins associated with many human genetic diseases; (ii) Different disease classes vary in the IDP contents of their associated proteins; (iii) Molecular recognition features, which are relatively short loosely structured protein regions within mostly disordered sequences and which gain structure upon binding to partners, are common in the diseasome, and their abundance correlates with the intrinsic disorder level; (iv) Some disease classes have a significant fraction of genes affected by alternative splicing, and the alternatively spliced regions in the corresponding proteins are predicted to be highly disordered; and (v) Correlations were found among the various diseasome graph-related properties and intrinsic disorder. Conclusion These observations provide the basis for the construction of the human-genetic-disease-associated unfoldome. PMID:19594871

Multi-Cohort Stand Structural Classification: Ground- and LiDAR-based Approaches for Boreal Mixedwood and Black Spruce Forest Types of Northeastern Ontario

NASA Astrophysics Data System (ADS)

Kuttner, Benjamin George

Natural fire return intervals are relatively long in eastern Canadian boreal forests and often allow for the development of stands with multiple, successive cohorts of trees. Multi-cohort forest management (MCM) provides a strategy to maintain such multi-cohort stands that focuses on three broad phases of increasingly complex, post-fire stand development, termed "cohorts", and recommends different silvicultural approaches be applied to emulate different cohort types. Previous research on structural cohort typing has relied upon primarily subjective classification methods; in this thesis, I develop more comprehensive and objective methods for three common boreal mixedwood and black spruce forest types in northeastern Ontario. Additionally, I examine relationships between cohort types and stand age, productivity, and disturbance history and the utility of airborne LiDAR to retrieve ground-based classifications and to extend structural cohort typing from plot- to stand-levels. In both mixedwood and black spruce forest types, stand age and age-related deadwood features varied systematically with cohort classes in support of an age-based interpretation of increasing cohort complexity. However, correlations of stand age with cohort classes were surprisingly weak. Differences in site productivity had a significant effect on the accrual of increasingly complex multi-cohort stand structure in both forest types, especially in black spruce stands. The effects of past harvesting in predictive models of class membership were only significant when considered in isolation of age. As an age-emulation strategy, the three cohort model appeared to be poorly suited to black spruce forests where the accrual of structural complexity appeared to be more a function of site productivity than age. Airborne LiDAR data appear to be particularly useful in recovering plot-based cohort types and extending them to the stand-level. The main gradients of structural variability detected using LiDAR were similar between boreal mixedwood and black spruce forest types; the best LiDAR-based models of cohort type relied upon combinations of tree size, size heterogeneity, and tree density related variables. The methods described here to measure, classify, and predict cohort-related structural complexity assist in translating the conceptual three cohort model to a more precise, measurement-based management system. In addition, the approaches presented here to measure and classify stand structural complexity promise to significantly enhance the detail of structural information in operational forest inventories in support of a wide array of forest management and conservation applications.

A novel framework for molecular characterization of atmospherically relevant organic compounds based on collision cross section and mass-to-charge ratio

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

Zhang, Xuan; Krechmer, Jordan E.; Groessl, Michael

Here, a new metric is introduced for representing the molecular signature of atmospherically relevant organic compounds, the collision cross section (Ω), a quantity that is related to the structure and geometry of molecules and is derived from ion mobility measurements. By combination with the mass-to-charge ratio ( m/z), a two-dimensional Ω – m/z space is developed to facilitate the comprehensive investigation of the complex organic mixtures. A unique distribution pattern of chemical classes, characterized by functional groups including amine, alcohol, carbonyl, carboxylic acid, ester, and organic sulfate, is developed on the 2-D Ω – m/z space. Species of the samemore » chemical class, despite variations in the molecular structures, tend to situate as a narrow band on the space and follow a trend line. Reactions involving changes in functionalization and fragmentation can be represented by the directionalities along or across these trend lines, thus allowing for the interpretation of atmospheric transformation mechanisms of organic species. The characteristics of trend lines for a variety of functionalities that are commonly present in the atmosphere can be predicted by the core model simulations, which provide a useful tool to identify the chemical class to which an unknown species belongs on the Ω – m/z space. Within the band produced by each chemical class on the space, molecular structural assignment can be achieved by utilizing collision-induced dissociation as well as by comparing the measured collision cross sections in the context of those obtained via molecular dynamics simulations.« less

A novel framework for molecular characterization of atmospherically relevant organic compounds based on collision cross section and mass-to-charge ratio

DOE PAGES

Zhang, Xuan; Krechmer, Jordan E.; Groessl, Michael; ...

2016-10-19

Here, a new metric is introduced for representing the molecular signature of atmospherically relevant organic compounds, the collision cross section (Ω), a quantity that is related to the structure and geometry of molecules and is derived from ion mobility measurements. By combination with the mass-to-charge ratio ( m/z), a two-dimensional Ω – m/z space is developed to facilitate the comprehensive investigation of the complex organic mixtures. A unique distribution pattern of chemical classes, characterized by functional groups including amine, alcohol, carbonyl, carboxylic acid, ester, and organic sulfate, is developed on the 2-D Ω – m/z space. Species of the samemore » chemical class, despite variations in the molecular structures, tend to situate as a narrow band on the space and follow a trend line. Reactions involving changes in functionalization and fragmentation can be represented by the directionalities along or across these trend lines, thus allowing for the interpretation of atmospheric transformation mechanisms of organic species. The characteristics of trend lines for a variety of functionalities that are commonly present in the atmosphere can be predicted by the core model simulations, which provide a useful tool to identify the chemical class to which an unknown species belongs on the Ω – m/z space. Within the band produced by each chemical class on the space, molecular structural assignment can be achieved by utilizing collision-induced dissociation as well as by comparing the measured collision cross sections in the context of those obtained via molecular dynamics simulations.« less

Crystal and Solution Structures of a Prokaryotic M16B Peptidase: an Open and Shut Case

PubMed Central

Aleshin, Alexander E.; Gramatikova, Svetlana; Hura, Gregory L.; Bobkov, Andrey; Strongin, Alex Y.; Stec, Boguslaw; Tainer, John A.; Liddington, Robert C.; Smith, Jeffrey W.

2013-01-01

SUMMARY The M16 family of zinc peptidases comprises a pair of homologous domains that form two halves of a ‘‘clam-shell’’ surrounding the active site. The M16A and M16C subfamilies form one class (‘‘peptidasomes’’): they degrade 30–70 residue peptides, and adopt both open and closed conformations. The eukaryotic M16B subfamily forms a second class (‘‘processing proteases’’): they adopt a single partly-open conformation that enables them to cleave signal sequences from larger proteins. Here, we report the solution and crystal structures of a prokaryotic M16B peptidase, and demonstrate that it has features of both classes: thus, it forms stable ‘‘open’’ homodimers in solution that resemble the processing proteases; but the clam-shell closes upon binding substrate, a feature of the M16A/C peptidasomes. Moreover, clam-shell closure is required for proteolytic activity. We predict that other prokaryotic M16B family members will form dimeric peptidasomes, and propose a model for the evolution of the M16 family. PMID:19913481

Random waves in the brain: Symmetries and defect generation in the visual cortex

NASA Astrophysics Data System (ADS)

Schnabel, M.; Kaschube, M.; Löwel, S.; Wolf, F.

2007-06-01

How orientation maps in the visual cortex of the brain develop is a matter of long standing debate. Experimental and theoretical evidence suggests that their development represents an activity-dependent self-organization process. Theoretical analysis [1] exploring this hypothesis predicted that maps at an early developmental stage are realizations of Gaussian random fields exhibiting a rigorous lower bound for their densities of topological defects, called pinwheels. As a consequence, lower pinwheel densities, if observed in adult animals, are predicted to develop through the motion and annihilation of pinwheel pairs. Despite of being valid for a large class of developmental models this result depends on the symmetries of the models and thus of the predicted random field ensembles. In [1] invariance of the orientation map's statistical properties under independent space rotations and orientation shifts was assumed. However, full rotation symmetry appears to be broken by interactions of cortical neurons, e.g. selective couplings between groups of neurons with collinear orientation preferences [2]. A recently proposed new symmetry, called shift-twist symmetry [3], stating that spatial rotations have to occur together with orientation shifts in order to be an appropriate symmetry transformation, is more consistent with this organization. Here we generalize our random field approach to this important symmetry class. We propose a new class of shift-twist symmetric Gaussian random fields and derive the general correlation functions of this ensemble. It turns out that despite strong effects of the shift-twist symmetry on the structure of the correlation functions and on the map layout the lower bound on the pinwheel densities remains unaffected, predicting pinwheel annihilation in systems with low pinwheel densities.

Combining electronic structure and many-body theory with large databases: A method for predicting the nature of 4 f states in Ce compounds

NASA Astrophysics Data System (ADS)

Herper, H. C.; Ahmed, T.; Wills, J. M.; Di Marco, I.; Björkman, T.; Iuşan, D.; Balatsky, A. V.; Eriksson, O.

2017-08-01

Recent progress in materials informatics has opened up the possibility of a new approach to accessing properties of materials in which one assays the aggregate properties of a large set of materials within the same class in addition to a detailed investigation of each compound in that class. Here we present a large scale investigation of electronic properties and correlated magnetism in Ce-based compounds accompanied by a systematic study of the electronic structure and 4 f -hybridization function of a large body of Ce compounds. We systematically study the electronic structure and 4 f -hybridization function of a large body of Ce compounds with the goal of elucidating the nature of the 4 f states and their interrelation with the measured Kondo energy in these compounds. The hybridization function has been analyzed for more than 350 data sets (being part of the IMS database) of cubic Ce compounds using electronic structure theory that relies on a full-potential approach. We demonstrate that the strength of the hybridization function, evaluated in this way, allows us to draw precise conclusions about the degree of localization of the 4 f states in these compounds. The theoretical results are entirely consistent with all experimental information, relevant to the degree of 4 f localization for all investigated materials. Furthermore, a more detailed analysis of the electronic structure and the hybridization function allows us to make precise statements about Kondo correlations in these systems. The calculated hybridization functions, together with the corresponding density of states, reproduce the expected exponential behavior of the observed Kondo temperatures and prove a consistent trend in real materials. This trend allows us to predict which systems may be correctly identified as Kondo systems. A strong anticorrelation between the size of the hybridization function and the volume of the systems has been observed. The information entropy for this set of systems is about 0.42. Our approach demonstrates the predictive power of materials informatics when a large number of materials is used to establish significant trends. This predictive power can be used to design new materials with desired properties. The applicability of this approach for other correlated electron systems is discussed.

Preferences for Early Intervention Mental Health Services: A Discrete-Choice Conjoint Experiment.

PubMed

Becker, Mackenzie P E; Christensen, Bruce K; Cunningham, Charles E; Furimsky, Ivana; Rimas, Heather; Wilson, Fiona; Jeffs, Lisa; Bieling, Peter J; Madsen, Victoria; Chen, Yvonne Y S; Mielko, Stephanie; Zipursky, Robert B

2016-02-01

Early intervention services (EISs) for mental illness may improve outcomes, although treatment engagement is often a problem. Incorporating patients' preferences in the design of interventions improves engagement. A discrete-choice conjoint experiment was conducted in Canada to identify EIS attributes that encourage treatment initiation. Sixteen four-level attributes were formalized into a conjoint survey, completed by patients, family members, and mental health professionals (N=562). Participants were asked which EIS option people with mental illness would contact. Latent-class analysis identified respondent classes characterized by shared preferences. Randomized first-choice simulations predicted which hypothetical options, based on attributes, would result in maximum utilization. Participants in the conventional-service class (N=241, 43%) predicted that individuals would contact traditional services (for example, hospital location and staffed by psychologists or psychiatrists). Membership was associated with being a patient or family member and being male. Participants in the convenient-service class (N=321, 57%) predicted that people would contact services promoting easy access (for example, self-referral and access from home). Membership was associated with being a professional. Both classes predicted that people would contact services that included short wait times, direct contact with professionals, patient autonomy, and psychological treatment information. The convenient-service class predicted that people would use an e-health model, whereas the conventional-service class predicted that people would use a primary care or clinic-hospital model. Provision of a range of services may maximize EIS use. Professionals may be more apt to adopt EISs in line with their beliefs regarding patient preferences. Considering several perspectives is important for service design.

Aerodynamic-structural study of canard wing, dual wing, and conventional wing systems for general aviation applications

NASA Technical Reports Server (NTRS)

Selberg, B. P.; Cronin, D. L.

1985-01-01

An analytical aerodynamic-structural airplane configuration study was conducted to assess performance gains achievable through advanced design concepts. The mission specification was for 350 mph, range of 1500 st. mi., at altitudes between 30,000 and 40,000 ft. Two payload classes were studied - 1200 lb (6 passengers) and 2400 lb (12 passengers). The configurations analyzed included canard wings, closely coupled dual wings, swept forward - swept rearward wings, joined wings, and conventional wing tail arrangements. The results illustrate substantial performance gains possible with the dual wing configuration. These gains result from weight savings due to predicted structural efficiencies. The need for further studies of structural efficiencies for the various advanced configurations was highlighted.

Dynamic and fluid–structure interaction simulations of bioprosthetic heart valves using parametric design with T-splines and Fung-type material models

PubMed Central

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

2015-01-01

This paper builds on a recently developed immersogeometric fluid–structure interaction (FSI) methodology for bioprosthetic heart valve (BHV) modeling and simulation. It enhances the proposed framework in the areas of geometry design and constitutive modeling. With these enhancements, BHV FSI simulations may be performed with greater levels of automation, robustness and physical realism. In addition, the paper presents a comparison between FSI analysis and standalone structural dynamics simulation driven by prescribed transvalvular pressure, the latter being a more common modeling choice for this class of problems. The FSI computation achieved better physiological realism in predicting the valve leaflet deformation than its standalone structural dynamics counterpart. PMID:26392645

How does symmetry impact the flexibility of proteins?

PubMed

Schulze, Bernd; Sljoka, Adnan; Whiteley, Walter

2014-02-13

It is well known that (i) the flexibility and rigidity of proteins are central to their function, (ii) a number of oligomers with several copies of individual protein chains assemble with symmetry in the native state and (iii) added symmetry sometimes leads to added flexibility in structures. We observe that the most common symmetry classes of protein oligomers are also the symmetry classes that lead to increased flexibility in certain three-dimensional structures-and investigate the possible significance of this coincidence. This builds on the well-developed theory of generic rigidity of body-bar frameworks, which permits an analysis of the rigidity and flexibility of molecular structures such as proteins via fast combinatorial algorithms. In particular, we outline some very simple counting rules and possible algorithmic extensions that allow us to predict continuous symmetry-preserving motions in body-bar frameworks that possess non-trivial point-group symmetry. For simplicity, we focus on dimers, which typically assemble with twofold rotational axes, and often have allosteric function that requires motions to link distant sites on the two protein chains.

Analysis of spatial distribution of land cover maps accuracy

NASA Astrophysics Data System (ADS)

Khatami, R.; Mountrakis, G.; Stehman, S. V.

2017-12-01

Land cover maps have become one of the most important products of remote sensing science. However, classification errors will exist in any classified map and affect the reliability of subsequent map usage. Moreover, classification accuracy often varies over different regions of a classified map. These variations of accuracy will affect the reliability of subsequent analyses of different regions based on the classified maps. The traditional approach of map accuracy assessment based on an error matrix does not capture the spatial variation in classification accuracy. Here, per-pixel accuracy prediction methods are proposed based on interpolating accuracy values from a test sample to produce wall-to-wall accuracy maps. Different accuracy prediction methods were developed based on four factors: predictive domain (spatial versus spectral), interpolation function (constant, linear, Gaussian, and logistic), incorporation of class information (interpolating each class separately versus grouping them together), and sample size. Incorporation of spectral domain as explanatory feature spaces of classification accuracy interpolation was done for the first time in this research. Performance of the prediction methods was evaluated using 26 test blocks, with 10 km × 10 km dimensions, dispersed throughout the United States. The performance of the predictions was evaluated using the area under the curve (AUC) of the receiver operating characteristic. Relative to existing accuracy prediction methods, our proposed methods resulted in improvements of AUC of 0.15 or greater. Evaluation of the four factors comprising the accuracy prediction methods demonstrated that: i) interpolations should be done separately for each class instead of grouping all classes together; ii) if an all-classes approach is used, the spectral domain will result in substantially greater AUC than the spatial domain; iii) for the smaller sample size and per-class predictions, the spectral and spatial domain yielded similar AUC; iv) for the larger sample size (i.e., very dense spatial sample) and per-class predictions, the spatial domain yielded larger AUC; v) increasing the sample size improved accuracy predictions with a greater benefit accruing to the spatial domain; and vi) the function used for interpolation had the smallest effect on AUC.

Active site nanospace of aminoacyl tRNA synthetase: difference between the class I and class II synthetases.

PubMed

Dutta, Saheb; Choudhury, Kaberi; Banik, Sindrila Dutta; Nandi, Nilashis

2014-03-01

The present work is aimed at understanding the origin of the difference in the molecular organization of the active site nanospaces of the class I and class II aminoacyl tRNA synthetases (aaRSs) which are tunnel-like structures. The active site encloses the cognate amino acid (AA) and the adenosine triphosphate (ATP) to carry out aminoacylation reaction. Comparison of the structures of the active site of the class I and class II (aaRSs) shows that the nanodimensional tunnels are curved in opposite directions in the two classes. We investigated the origin of this difference using quantum mechanical computation of electrostatic potential (ESP) of substrates, surrounding residues and ions, using Atoms in Molecule (AIM) Theory and charge population analysis. We show that the difference is principally due to the variation in the spatial charge distribution of ATP in the two classes which correspond to extended and bent conformations of ATP. The present computation shows that the most feasible pathway for nucleophilic attack to alphaP is oppositely directed for class I and class II aaRSs. The available crystal structures show that the cognate AA is indeed located along the channel favorable for nucleophilic attack as predicted by the ESP analysis. It is also shown that the direction of the channel changes its orientation when the orientation of ATP is changed from extended to a bent like structure. We further used the AIM theory to confirm the direction of the approach of AA in each case and the results corroborate the results from the ESP analysis. The opposite curvatures of the active site nanospaces in class I and class II aaRSs are related with the influence of the charge distributions of the extended and bent conformations of ATP, respectively. The results of the computation of electrostatic potential by successive addition of active site residues show that their roles on the reaction are similar in both classes despite the difference in the organization of the active sites of class I and class II aaRSs. The difference in mechanism in two classes as pointed out in recent study (S. Dutta Banik and N. Nandi, J. Biomol. Struct. Dyn. 30, 701 (2012)) is related with the fact that the relative arrangement of the ATP with respect to the AA is opposite in class I and class II aaRSs as explained in the present work. The charge population difference between the reacting centers (which are the alphaP atom of ATP (q(p)) and the attacking oxygen atom of carboxylic acid group (q(o)), respectively) denoted by delta(q), is a measure of the propensity of nucleophilic attack. The population analysis of the substrate AA shows that a non-negligible difference exists between the attacking oxygens of AA in class I (syn) and in class II (anti) which is one reason for the lower value of delta(q) in class II relative to class I. The population analysis of the AA, ATP, Mg+2 ions and active site residues shows that the difference in delta(q) values of the two classes is substantially reduced. When ions and residues are considered. Thus, the bent state of ATP, Mg+2 ions and active site residues complements it cognate AA to carry out the nucleophilic reaction in class I as efficiently as occurs in class I (with the extended state of ATP, single Mg+2 ion and active site residues). This could be one reason for the two different conformations of ATP in the two classes. The mutual arrangements of AA and ATP in each aaRS are guided by the spatial charge distribution of ATP (extended and bent). The present work shows that the construction of nanospace complements the arrangement of the substrate (AA and ATP).

Student perception of group dynamics predicts individual performance: Comfort and equity matter

PubMed Central

Theobald, Elli J.; Eddy, Sarah L.; Grunspan, Daniel Z.; Wiggins, Benjamin L.

2017-01-01

Active learning in college classes and participation in the workforce frequently hinge on small group work. However, group dynamics vary, ranging from equitable collaboration to dysfunctional groups dominated by one individual. To explore how group dynamics impact student learning, we asked students in a large-enrollment university biology class to self-report their experience during in-class group work. Specifically, we asked students whether there was a friend in their group, whether they were comfortable in their group, and whether someone dominated their group. Surveys were administered after students participated in two different types of intentionally constructed group activities: 1) a loosely-structured activity wherein students worked together for an entire class period (termed the ‘single-group’ activity), or 2) a highly-structured ‘jigsaw’ activity wherein students first independently mastered different subtopics, then formed new groups to peer-teach their respective subtopics. We measured content mastery by the change in score on identical pre-/post-tests. We then investigated whether activity type or student demographics predicted the likelihood of reporting working with a dominator, being comfortable in their group, or working with a friend. We found that students who more strongly agreed that they worked with a dominator were 17.8% less likely to answer an additional question correct on the 8-question post-test. Similarly, when students were comfortable in their group, content mastery increased by 27.5%. Working with a friend was the single biggest predictor of student comfort, although working with a friend did not impact performance. Finally, we found that students were 67% less likely to agree that someone dominated their group during the jigsaw activities than during the single group activities. We conclude that group activities that rely on positive interdependence, and include turn-taking and have explicit prompts for students to explain their reasoning, such as our jigsaw, can help reduce the negative impact of inequitable groups. PMID:28727749

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.

Using Demographic Subgroup and Dummy Variable Equations to Predict College Freshman Grade Average.

ERIC Educational Resources Information Center

Sawyer, Richard

1986-01-01

This study was designed to determine whether adjustments for the differential prediction observed among sex, racial/ethnic, or age subgroups in one freshman class at a college could be used to improve prediction accuracy for these subgroups in future freshman classes. (Author/LMO)

A Matter of Classes: Stratifying Health Care Populations to Produce Better Estimates of Inpatient Costs

PubMed Central

Rein, David B

2005-01-01

Objective To stratify traditional risk-adjustment models by health severity classes in a way that is empirically based, is accessible to policy makers, and improves predictions of inpatient costs. Data Sources Secondary data created from the administrative claims from all 829,356 children aged 21 years and under enrolled in Georgia Medicaid in 1999. Study Design A finite mixture model was used to assign child Medicaid patients to health severity classes. These class assignments were then used to stratify both portions of a traditional two-part risk-adjustment model predicting inpatient Medicaid expenditures. Traditional model results were compared with the stratified model using actuarial statistics. Principal Findings The finite mixture model identified four classes of children: a majority healthy class and three illness classes with increasing levels of severity. Stratifying the traditional two-part risk-adjustment model by health severity classes improved its R2 from 0.17 to 0.25. The majority of additional predictive power resulted from stratifying the second part of the two-part model. Further, the preference for the stratified model was unaffected by months of patient enrollment time. Conclusions Stratifying health care populations based on measures of health severity is a powerful method to achieve more accurate cost predictions. Insurers who ignore the predictive advances of sample stratification in setting risk-adjusted premiums may create strong financial incentives for adverse selection. Finite mixture models provide an empirically based, replicable methodology for stratification that should be accessible to most health care financial managers. PMID:16033501

Reef Fishes at All Trophic Levels Respond Positively to Effective Marine Protected Areas

PubMed Central

Soler, German A.; Edgar, Graham J.; Thomson, Russell J.; Kininmonth, Stuart; Campbell, Stuart J.; Dawson, Terence P.; Barrett, Neville S.; Bernard, Anthony T. F.; Galván, David E.; Willis, Trevor J.; Alexander, Timothy J.; Stuart-Smith, Rick D.

2015-01-01

Marine Protected Areas (MPAs) offer a unique opportunity to test the assumption that fishing pressure affects some trophic groups more than others. Removal of larger predators through fishing is often suggested to have positive flow-on effects for some lower trophic groups, in which case protection from fishing should result in suppression of lower trophic groups as predator populations recover. We tested this by assessing differences in the trophic structure of reef fish communities associated with 79 MPAs and open-access sites worldwide, using a standardised quantitative dataset on reef fish community structure. The biomass of all major trophic groups (higher carnivores, benthic carnivores, planktivores and herbivores) was significantly greater (by 40% - 200%) in effective no-take MPAs relative to fished open-access areas. This effect was most pronounced for individuals in large size classes, but with no size class of any trophic group showing signs of depressed biomass in MPAs, as predicted from higher predator abundance. Thus, greater biomass in effective MPAs implies that exploitation on shallow rocky and coral reefs negatively affects biomass of all fish trophic groups and size classes. These direct effects of fishing on trophic structure appear stronger than any top down effects on lower trophic levels that would be imposed by intact predator populations. We propose that exploitation affects fish assemblages at all trophic levels, and that local ecosystem function is generally modified by fishing. PMID:26461104

Rational truncation of an RNA aptamer to prostate-specific membrane antigen using computational structural modeling.

PubMed

Rockey, William M; Hernandez, Frank J; Huang, Sheng-You; Cao, Song; Howell, Craig A; Thomas, Gregory S; Liu, Xiu Ying; Lapteva, Natalia; Spencer, David M; McNamara, James O; Zou, Xiaoqin; Chen, Shi-Jie; Giangrande, Paloma H

2011-10-01

RNA aptamers represent an emerging class of pharmaceuticals with great potential for targeted cancer diagnostics and therapy. Several RNA aptamers that bind cancer cell-surface antigens with high affinity and specificity have been described. However, their clinical potential has yet to be realized. A significant obstacle to the clinical adoption of RNA aptamers is the high cost of manufacturing long RNA sequences through chemical synthesis. Therapeutic aptamers are often truncated postselection by using a trial-and-error process, which is time consuming and inefficient. Here, we used a "rational truncation" approach guided by RNA structural prediction and protein/RNA docking algorithms that enabled us to substantially truncateA9, an RNA aptamer to prostate-specific membrane antigen (PSMA),with great potential for targeted therapeutics. This truncated PSMA aptamer (A9L; 41mer) retains binding activity, functionality, and is amenable to large-scale chemical synthesis for future clinical applications. In addition, the modeled RNA tertiary structure and protein/RNA docking predictions revealed key nucleotides within the aptamer critical for binding to PSMA and inhibiting its enzymatic activity. Finally, this work highlights the utility of existing RNA structural prediction and protein docking techniques that may be generally applicable to developing RNA aptamers optimized for therapeutic use.

Bone accumulation of the Tc-99m complex of carbamyl phosphate and its analogs

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

Hosain, P.; Spencer, R.P.; Ahlquist, K.J.

1978-05-01

Carbamyl phosphate, an organic moecule containing a single phosphate group, has been used in the therapy of sickle-cell disease. Carbamyl phosphate bound Tc-99m and achieved bone uptake in mice, rabbits, and a human volunteer. By examination of the structural formula, a working hypothesis was developed that predicted that the Tc-99m complexes of the analogous compounds acetyl phosphate, propionyl phosphate, and butyryl phosphate, each carrying single phosphate and carbonyl groups, would also show bone specificity. This was confirmed experimentally. Phosphonoacetic acid is a structural analog of these compounds. The structural analysis also predicted that aminomethylphosphonic acid and phosphoenolpyruvate would not havemore » as avid bone affinity, and this was also confirmed. These compounds represent a new class of bone-seeking agents that have the common properties of a lone phosphate and a carbonyl function. Such agents may permit the synthesis of additional analogs in an effort to obtain optimal affinity in the Tc-99m complexes.« less

Structure-based control of complex networks with nonlinear dynamics

NASA Astrophysics Data System (ADS)

Zanudo, Jorge G. T.; Yang, Gang; Albert, Reka

What can we learn about controlling a system solely from its underlying network structure? Here we use a framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system towards any of its natural long term dynamic behaviors, regardless of the dynamic details and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of classical structural control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case, but not in specific model instances. This work was supported by NSF Grants PHY 1205840 and IIS 1160995. JGTZ is a recipient of a Stand Up To Cancer - The V Foundation Convergence Scholar Award.

Modeling ligand recognition at the P2Y12 receptor in light of X-ray structural information

NASA Astrophysics Data System (ADS)

Paoletta, Silvia; Sabbadin, Davide; von Kügelgen, Ivar; Hinz, Sonja; Katritch, Vsevolod; Hoffmann, Kristina; Abdelrahman, Aliaa; Straßburger, Jens; Baqi, Younis; Zhao, Qiang; Stevens, Raymond C.; Moro, Stefano; Müller, Christa E.; Jacobson, Kenneth A.

2015-08-01

The G protein-coupled P2Y12 receptor (P2Y12R) is an important antithrombotic target and of great interest for pharmaceutical discovery. Its recently solved, highly divergent crystallographic structures in complex either with nucleotides (full or partial agonist) or with a nonnucleotide antagonist raise the question of which structure is more useful to understand ligand recognition. Therefore, we performed extensive molecular modeling studies based on these structures and mutagenesis, to predict the binding modes of major classes of P2Y12R ligands previously reported. Various nucleotide derivatives docked readily to the agonist-bound P2Y12R, but uncharged nucleotide-like antagonist ticagrelor required a hybrid receptor resembling the agonist-bound P2Y12R except for the top portion of TM6. Supervised molecular dynamics (SuMD) of ticagrelor binding indicated interactions with the extracellular regions of P2Y12R, defining possible meta-binding sites. Ureas, sulfonylureas, sulfonamides, anthraquinones and glutamic acid piperazines docked readily to the antagonist-bound P2Y12R. Docking dinucleotides at both agonist- and antagonist-bound structures suggested interactions with two P2Y12R pockets. Thus, our structure-based approach consistently rationalized the main structure-activity relationships within each ligand class, giving useful information for designing improved ligands.

Issues in the deregulation of the electric industry

NASA Astrophysics Data System (ADS)

Tyler, Cleve Brent

The electric industry is undergoing a major restructuring which allows competition in the generation portion of the industry. This dissertation explores several pricing issues relevant to this restructuring. First, an extensive overview examines the industry's history, discusses major regulation theories, and relays the major issues of deregulation. Second, a literature review recounts major works in the economics literature on price discrimination, pricing efficiency, and cost estimation. Then, customer specific generation, transmission, distribution, and general and administration costs are estimated for each company. The customer classes are residential, general service, large general service, and large industrial, representing a finer division of customer classes than found in previous studies. Average prices are compiled and marginal prices are determined from a set of utility schedules. Average and marginal price/cost ratios are computed for each customer class. These ratios show that larger use customers face relative price discrimination but operate under more efficient price structures than small use consumers. Finally, issues in peak load pricing are discussed using a model which predicts inefficient capital choice by regulated utilities. Efficiency losses are estimated to be $620 million dollars a year from the lack of peak load prices under regulation. This result is based on the time-of-use pricing predictions from the Department of Energy.

Does mClass Reading 3D Predict Student Reading Proficiency on High-Stakes Assessments?

ERIC Educational Resources Information Center

Bowles, Amy S.

2015-01-01

This quantitative, correlational study investigated the relationship between the North Carolina End of Grade Assessment of Reading Comprehension (NCEOG) and mClass Reading 3D assessment in a North Carolina elementary school. It especially examined the degree to which mClass Reading 3D measures predict scores on the reading comprehension portion of…

NOAA's world-class weather and climate prediction center opens at

Science.gov Websites

StumbleUpon Digg More Destinations NOAA's world-class weather and climate prediction center opens at currents and large-scale rain and snow storms. Billions of earth observations from around the world flow operations. Investing in this center is an investment in our human capital, serving as a world class facility

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

PubMed

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

2018-06-01

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

A Nonlinear Three-Dimensional Micromechanics Model for Fiber-Reinforced Laminated Composites

DTIC Science & Technology

1993-11-01

Interfacial Properties Employed for the SCS6/Ti-15-3 Composite ......................... 150 11. Constants Employed for the LLFM Predictions of Quasi...Region m Matrix Property or Mean of the Interfacial Stress Distribution ml, m2, m3 Signifies Matrix Region n Normal to Interface r Signifies Equation...usage of the new class of titanium based com- posites in advanced aerospace structures and engines such as are targeted for the advanced tactical fighter

Prediction of adolescents doing physical activity after completing secondary education.

PubMed

Moreno-Murcia, Juan Antonio; Huéscar, Elisa; Cervelló, Eduardo

2012-03-01

The purpose of this study, based on the self-determination theory (Ryan & Deci, 2000) was to test the prediction power of student's responsibility, psychological mediators, intrinsic motivation and the importance attached to physical education in the intention to continue to practice some form of physical activity and/or sport, and the possible relationships that exist between these variables. We used a sample of 482 adolescent students in physical education classes, with a mean age of 14.3 years, which were measured for responsibility, psychological mediators, sports motivation, the importance of physical education and intention to be physically active. We completed an analysis of structural equations modelling. The results showed that the responsibility positively predicted psychological mediators, and this predicted intrinsic motivation, which positively predicted the importance students attach to physical education, and this, finally, positively predicted the intention of the student to continue doing sport. Results are discussed in relation to the promotion of student's responsibility towards a greater commitment to the practice of physical exercise.

Fuzzy association rule mining and classification for the prediction of malaria in South Korea.

PubMed

Buczak, Anna L; Baugher, Benjamin; Guven, Erhan; Ramac-Thomas, Liane C; Elbert, Yevgeniy; Babin, Steven M; Lewis, Sheri H

2015-06-18

Malaria is the world's most prevalent vector-borne disease. Accurate prediction of malaria outbreaks may lead to public health interventions that mitigate disease morbidity and mortality. We describe an application of a method for creating prediction models utilizing Fuzzy Association Rule Mining to extract relationships between epidemiological, meteorological, climatic, and socio-economic data from Korea. These relationships are in the form of rules, from which the best set of rules is automatically chosen and forms a classifier. Two classifiers have been built and their results fused to become a malaria prediction model. Future malaria cases are predicted as Low, Medium or High, where these classes are defined as a total of 0-2, 3-16, and above 17 cases, respectively, for a region in South Korea during a two-week period. Based on user recommendations, HIGH is considered an outbreak. Model accuracy is described by Positive Predictive Value (PPV), Sensitivity, and F-score for each class, computed on test data not previously used to develop the model. For predictions made 7-8 weeks in advance, model PPV and Sensitivity are 0.842 and 0.681, respectively, for the HIGH classes. The F0.5 and F3 scores (which combine PPV and Sensitivity) are 0.804 and 0.694, respectively, for the HIGH classes. The overall FARM results (as measured by F-scores) are significantly better than those obtained by Decision Tree, Random Forest, Support Vector Machine, and Holt-Winters methods for the HIGH class. For the Medium class, Random Forest and FARM obtain comparable results, with FARM being better at F0.5, and Random Forest obtaining a higher F3. A previously described method for creating disease prediction models has been modified and extended to build models for predicting malaria. In addition, some new input variables were used, including indicators of intervention measures. The South Korea malaria prediction models predict Low, Medium or High cases 7-8 weeks in the future. This paper demonstrates that our data driven approach can be used for the prediction of different diseases.

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

Dispelling the myth of "smart drugs": cannabis and alcohol use problems predict nonmedical use of prescription stimulants for studying.

PubMed

Arria, Amelia M; Wilcox, Holly C; Caldeira, Kimberly M; Vincent, Kathryn B; Garnier-Dykstra, Laura M; O'Grady, Kevin E

2013-03-01

This study tested the hypothesis that college students' substance use problems would predict increases in skipping classes and declining academic performance, and that nonmedical use of prescription stimulants (NPS) for studying would occur in association with this decline. A cohort of 984 students in the College Life Study at a large public university in the US participated in a longitudinal prospective study. Interviewers assessed NPS; Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) cannabis and alcohol use disorders; and frequency of skipping class. Semester grade point average (GPA) was obtained from the university. Control variables were race, sex, family income, high school GPA, and self-reported attention deficit hyperactivity disorder diagnosis. Longitudinal growth curve modeling of four annual data waves estimated the associations among the rates of change of cannabis use disorder, percentage of classes skipped, and semester GPA. The associations between these trajectories and NPS for studying were then evaluated. A second structural model substituted alcohol use disorder for cannabis use disorder. More than one-third (38%) reported NPS for studying at least once by Year 4. Increases in skipping class were associated with both alcohol and cannabis use disorder, which were associated with declining GPA. The hypothesized relationships between these trajectories and NPS for studying were confirmed. These longitudinal findings suggest that escalation of substance use problems during college is related to increases in skipping class and to declining academic performance. NPS for studying is associated with academic difficulties. Although additional research is needed to investigate causal pathways, these results suggest that nonmedical users of prescription stimulants could benefit from a comprehensive drug and alcohol assessment to possibly mitigate future academic declines. Copyright © 2012 Elsevier Ltd. All rights reserved.

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.

Return to Work After Lumbar Microdiscectomy - Personalizing Approach Through Predictive Modeling.

PubMed

Papić, Monika; Brdar, Sanja; Papić, Vladimir; Lončar-Turukalo, Tatjana

2016-01-01

Lumbar disc herniation (LDH) is the most common disease among working population requiring surgical intervention. This study aims to predict the return to work after operative treatment of LDH based on the observational study including 153 patients. The classification problem was approached using decision trees (DT), support vector machines (SVM) and multilayer perception (MLP) combined with RELIEF algorithm for feature selection. MLP provided best recall of 0.86 for the class of patients not returning to work, which combined with the selected features enables early identification and personalized targeted interventions towards subjects at risk of prolonged disability. The predictive modeling indicated at the most decisive risk factors in prolongation of work absence: psychosocial factors, mobility of the spine and structural changes of facet joints and professional factors including standing, sitting and microclimate.

Design and study of piracetam-like nootropics, controversial members of the problematic class of cognition-enhancing drugs.

PubMed

Gualtieri, Fulvio; Manetti, Dina; Romanelli, Maria Novella; Ghelardini, Carla

2002-01-01

Cognition enhancers are drugs able to facilitate attentional abilities and acquisition, storage and retrieval of information, and to attenuate the impairment of cognitive functions associated with head traumas, stroke, age and age-related pathologies. Development of cognition enhancers is still a difficult task because of complexity of the brain functions, poor predictivity of animal tests and lengthy and expensive clinical trials. After the early serendipitous discovery of first generation cognition enhancers, current research is based on a variety of working hypotheses, derived from the progress of knowledge in the neurobiopathology of cognitive processes. Among other classes of drugs, piracetam-like cognition enhancers (nootropics) have never reached general acceptance, in spite of their excellent tolerability and safety. In the present review, after a general discussion of the problems connected with the design and development of cognition enhancers, the class is examined in more detail. Reasons for the problems encountered by nootropics, compounds therapeutically available and those in development, their structure activity relationships and mechanisms of action are discussed. Recent developments which hopefully will lead to a revival of the class are reviewed.

Graded Structure in Sexual Definitions: Categorizations of Having "Had Sex" and Virginity Loss Among Homosexual and Heterosexual Men and Women.

PubMed

Horowitz, Ava D; Bedford, Edward

2017-08-01

Definitions of sexual behavior display a robust hierarchy of agreement regarding whether or not acts should be classed as, for example, sex or virginity loss. The current research offers a theoretical explanation for this hierarchy, proposing that sexual definitions display graded categorical structure, arising from goodness of membership judgments. Moderation of this graded structure is also predicted, with the focus here on how sexual orientation identity affects sexual definitions. A total of 300 18- to 30-year-old participants completed an online survey, rating 18 behaviors for how far each constitutes having "had sex" and virginity loss. Participants fell into one of four groups: heterosexual male or female, gay male or lesbian. The predicted ratings hierarchy emerged, in which bidirectional genital acts were rated significantly higher than unidirectional or nonpenetrative contact, which was in turn rated significantly higher than acts involving no genital contact. Moderation of graded structure was also in line with predictions. Compared to the other groups, the lesbian group significantly upgraded ratings of genital contact that was either unidirectional or nonpenetrative. There was also evidence of upgrading by the gay male sample of anal intercourse ratings. These effects are theorized to reflect group-level variation in experience, contextual perspective, and identity-management. The implications of the findings in relation to previous research are discussed. It is suggested that a graded structure approach can greatly benefit future research into sexual definitions, by permitting variable definitions to be predicted and explained, rather than merely identified.

Asymmetry in search.

PubMed

Kaindl, H; Kainz, G; Radda, K

2001-01-01

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

Evidence for the Continuous Latent Structure of Mania in the Epidemiologic Catchment Area from Multiple Latent Structure and Construct Validation Methodologies

PubMed Central

Prisciandaro, James J.; Roberts, John E.

2011-01-01

Background Although psychiatric diagnostic systems have conceptualized mania as a discrete phenomenon, appropriate latent structure investigations testing this conceptualization are lacking. In contrast to these diagnostic systems, several influential theories of mania have suggested a continuous conceptualization. The present study examined whether mania has a continuous or discrete latent structure using a comprehensive approach including taxometric, information-theoretic latent distribution modeling (ITLDM), and predictive validity methodologies in the Epidemiologic Catchment Area (ECA) study. Methods Eight dichotomous manic symptom items were submitted to a variety of latent structural analyses; including factor analyses, taxometric procedures, and ITLDM; in 10,105 ECA community participants. Additionally, a variety of continuous and discrete models of mania were compared in terms of their relative abilities to predict outcomes (i.e., health service utilization, internalizing and externalizing disorders, and suicidal behavior). Results Taxometric and ITLDM analyses consistently supported a continuous conceptualization of mania. In ITLDM analyses, a continuous model of mania demonstrated 6:52:1 odds over the best fitting latent class model of mania. Factor analyses suggested that the continuous structure of mania was best represented by a single latent factor. Predictive validity analyses demonstrated a consistent superior ability of continuous models of mania relative to discrete models. Conclusions The present study provided three independent lines of support for a continuous conceptualization of mania. The implications of a continuous model of mania are discussed. PMID:20507671

Design Principles for Covalent Organic Frameworks as Efficient Electrocatalysts in Clean Energy Conversion and Green Oxidizer Production.

PubMed

Lin, Chun-Yu; Zhang, Lipeng; Zhao, Zhenghang; Xia, Zhenhai

2017-05-01

Covalent organic frameworks (COFs), an emerging class of framework materials linked by covalent bonds, hold potential for various applications such as efficient electrocatalysts, photovoltaics, and sensors. To rationally design COF-based electrocatalysts for oxygen reduction and evolution reactions in fuel cells and metal-air batteries, activity descriptors, derived from orbital energy and bonding structures, are identified with the first-principle calculations for the COFs, which correlate COF structures with their catalytic activities. The calculations also predict that alkaline-earth metal-porphyrin COFs could catalyze the direct production of H 2 O 2 , a green oxidizer and an energy carrier. These predictions are supported by experimental data, and the design principles derived from the descriptors provide an approach for rational design of new electrocatalysts for both clean energy conversion and green oxidizer production. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

QSAR studies in substituted 1,2,3,4,6,7,12,12a-octa-hydropyrazino[2',1':6,1]pyrido[3,4-b]indoles--a potent class of neuroleptics.

PubMed

Saxena, Anil K; Ram, Siya; Saxena, Mridula; Singh, Nidhi; Prathipati, Philip; Jain, Padam C; Singh, H K; Anand, Nitya

2003-05-01

A series of nineteen substituted 1,2,3,4,6,7,12,12a-octahydropyrazino[2',1':6,1]pyrido[3, 4-b]indoles analogues of neuroleptic drug, Centbutindole have been studied using quantitative structure-activity relationship analysis. The derived models display good fits to the experimental data (r>or=0.75) having good predictive power (r(cv)>or=0.688). The best model describes a high correlation between predicted and experimental activity data (r=0.967). Statistical analysis of the equation populations indicates that hydrophobicity (as measured by pi(R), logP(o/w) and SlogP_VSA8), dipole y and structural parameters in terms of indicator variable, (In(1)) and globularity are important variables in describing the variation in the neuroleptic activity in the series.

NetCTLpan: pan-specific MHC class I pathway epitope predictions

PubMed Central

Larsen, Mette Voldby; Lundegaard, Claus; Nielsen, Morten

2010-01-01

Reliable predictions of immunogenic peptides are essential in rational vaccine design and can minimize the experimental effort needed to identify epitopes. In this work, we describe a pan-specific major histocompatibility complex (MHC) class I epitope predictor, NetCTLpan. The method integrates predictions of proteasomal cleavage, transporter associated with antigen processing (TAP) transport efficiency, and MHC class I binding affinity into a MHC class I pathway likelihood score and is an improved and extended version of NetCTL. The NetCTLpan method performs predictions for all MHC class I molecules with known protein sequence and allows predictions for 8-, 9-, 10-, and 11-mer peptides. In order to meet the need for a low false positive rate, the method is optimized to achieve high specificity. The method was trained and validated on large datasets of experimentally identified MHC class I ligands and cytotoxic T lymphocyte (CTL) epitopes. It has been reported that MHC molecules are differentially dependent on TAP transport and proteasomal cleavage. Here, we did not find any consistent signs of such MHC dependencies, and the NetCTLpan method is implemented with fixed weights for proteasomal cleavage and TAP transport for all MHC molecules. The predictive performance of the NetCTLpan method was shown to outperform other state-of-the-art CTL epitope prediction methods. Our results further confirm the importance of using full-type human leukocyte antigen restriction information when identifying MHC class I epitopes. Using the NetCTLpan method, the experimental effort to identify 90% of new epitopes can be reduced by 15% and 40%, respectively, when compared to the NetMHCpan and NetCTL methods. The method and benchmark datasets are available at http://www.cbs.dtu.dk/services/NetCTLpan/. Electronic supplementary material The online version of this article (doi:10.1007/s00251-010-0441-4) contains supplementary material, which is available to authorized users. PMID:20379710

Community violence, protective factors, and adolescent mental health: a profile analysis.

PubMed

Copeland-Linder, Nikeea; Lambert, Sharon F; Ialongo, Nicholas S

2010-01-01

This study examined interrelationships among community violence exposure, protective factors, and mental health in a sample of urban, predominantly African American adolescents (N = 504). Latent Profile Analysis was conducted to identify profiles of adolescents based on a combination of community violence exposure, self-worth, parental monitoring, and parental involvement and to examine whether these profiles differentially predict adolescents' depressive symptoms and aggressive behavior. Three classes were identified-a vulnerable class, a moderate risk/medium protection class, and a moderate risk/high protection class. The classes differentially predicted depressive symptoms but not aggressive behavior for boys and girls. The class with the highest community violence exposure also had the lowest self-worth.

A Latent Class Analysis of Maternal Responsiveness and Autonomy-Granting in Early Adolescence: Prediction to Later Adolescent Sexual Risk-Taking

PubMed Central

Lanza, H. Isabella; Huang, David Y. C.; Murphy, Debra A.; Hser, Yih-Ing

2013-01-01

The present study sought to extend empirical inquiry related to the role of parenting on adolescent sexual risk-taking by using latent class analysis (LCA) to identify patterns of adolescent-reported mother responsiveness and autonomy-granting in early adolescence and examine associations with sexual risk-taking in mid- and late-adolescence. Utilizing a sample of 12- to 14-year-old adolescents (N = 4,743) from the 1997 National Longitudinal Survey of Youth (NLSY97), results identified a four-class model of maternal responsiveness and autonomy-granting: low responsiveness/high autonomy-granting, moderate responsiveness/moderate autonomy-granting, high responsiveness/low autonomy-granting, high responsiveness/moderate autonomy-granting. Membership in the low responsiveness/high autonomy-granting class predicted greater sexual risk-taking in mid- and late-adolescence compared to all other classes, and membership in the high responsiveness/ moderate autonomy-granting class predicted lower sexual risk-taking. Gender and ethnic differences in responsiveness and autonomy-granting class membership were also found, potentially informing gender and ethnic disparities of adolescent sexual risk-taking. PMID:23828712

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

PubMed

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

2008-01-01

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

Temperature-driven regime shifts in the dynamics of size-structured populations.

PubMed

Ohlberger, Jan; Edeline, Eric; Vøllestad, Leif Asbjørn; Stenseth, Nils C; Claessen, David

2011-02-01

Global warming impacts virtually all biota and ecosystems. Many of these impacts are mediated through direct effects of temperature on individual vital rates. Yet how this translates from the individual to the population level is still poorly understood, hampering the assessment of global warming impacts on population structure and dynamics. Here, we study the effects of temperature on intraspecific competition and cannibalism and the population dynamical consequences in a size-structured fish population. We use a physiologically structured consumer-resource model in which we explicitly model the temperature dependencies of the consumer vital rates and the resource population growth rate. Our model predicts that increased temperature decreases resource density despite higher resource growth rates, reflecting stronger intraspecific competition among consumers. At a critical temperature, the consumer population dynamics destabilize and shift from a stable equilibrium to competition-driven generation cycles that are dominated by recruits. As a consequence, maximum age decreases and the proportion of younger and smaller-sized fish increases. These model predictions support the hypothesis of decreasing mean body sizes due to increased temperatures. We conclude that in size-structured fish populations, global warming may increase competition, favor smaller size classes, and induce regime shifts that destabilize population and community dynamics.

Structural habitat predicts functional dispersal habitat of a large carnivore: how leopards change spots.

PubMed

Fattebert, Julien; Robinson, Hugh S; Balme, Guy; Slotow, Rob; Hunter, Luke

2015-10-01

Natal dispersal promotes inter-population linkage, and is key to spatial distribution of populations. Degradation of suitable landscape structures beyond the specific threshold of an individual's ability to disperse can therefore lead to disruption of functional landscape connectivity and impact metapopulation function. Because it ignores behavioral responses of individuals, structural connectivity is easier to assess than functional connectivity and is often used as a surrogate for landscape connectivity modeling. However using structural resource selection models as surrogate for modeling functional connectivity through dispersal could be erroneous. We tested how well a second-order resource selection function (RSF) models (structural connectivity), based on GPS telemetry data from resident adult leopard (Panthera pardus L.), could predict subadult habitat use during dispersal (functional connectivity). We created eight non-exclusive subsets of the subadult data based on differing definitions of dispersal to assess the predictive ability of our adult-based RSF model extrapolated over a broader landscape. Dispersing leopards used habitats in accordance with adult selection patterns, regardless of the definition of dispersal considered. We demonstrate that, for a wide-ranging apex carnivore, functional connectivity through natal dispersal corresponds to structural connectivity as modeled by a second-order RSF. Mapping of the adult-based habitat classes provides direct visualization of the potential linkages between populations, without the need to model paths between a priori starting and destination points. The use of such landscape scale RSFs may provide insight into predicting suitable dispersal habitat peninsulas in human-dominated landscapes where mitigation of human-wildlife conflict should be focused. We recommend the use of second-order RSFs for landscape conservation planning and propose a similar approach to the conservation of other wide-ranging large carnivore species where landscape-scale resource selection data already exist.

47 CFR 74.793 - Digital low power TV and TV translator station protection of broadcast stations.

Code of Federal Regulations, 2012 CFR

2012-10-01

... this section, interference prediction analysis is based on the interference thresholds (D/U signal.... Predictions of interference to co-channel DTV broadcast, digital Class A TV, digital LPTV and digital TV....” Predictions of interference to co-channel TV broadcast, Class A TV, LPTV and TV translator stations will be...

47 CFR 74.793 - Digital low power TV and TV translator station protection of broadcast stations.

Code of Federal Regulations, 2014 CFR

2014-10-01

... this section, interference prediction analysis is based on the interference thresholds (D/U signal.... Predictions of interference to co-channel DTV broadcast, digital Class A TV, digital LPTV and digital TV....” Predictions of interference to co-channel TV broadcast, Class A TV, LPTV and TV translator stations will be...

47 CFR 74.793 - Digital low power TV and TV translator station protection of broadcast stations.

Code of Federal Regulations, 2013 CFR

2013-10-01

... this section, interference prediction analysis is based on the interference thresholds (D/U signal.... Predictions of interference to co-channel DTV broadcast, digital Class A TV, digital LPTV and digital TV....” Predictions of interference to co-channel TV broadcast, Class A TV, LPTV and TV translator stations will be...

47 CFR 74.793 - Digital low power TV and TV translator station protection of broadcast stations.

Code of Federal Regulations, 2011 CFR

2011-10-01

... this section, interference prediction analysis is based on the interference thresholds (D/U signal.... Predictions of interference to co-channel DTV broadcast, digital Class A TV, digital LPTV and digital TV....” Predictions of interference to co-channel TV broadcast, Class A TV, LPTV and TV translator stations will be...

The nodulation factor hydrolase of Medicago truncatula: characterization of an enzyme specifically cleaving rhizobial nodulation signals.

PubMed

Tian, Ye; Liu, Wei; Cai, Jie; Zhang, Lan-Yue; Wong, Kam-Bo; Feddermann, Nadja; Boller, Thomas; Xie, Zhi-Ping; Staehelin, Christian

2013-11-01

Nodule formation induced by nitrogen-fixing rhizobia depends on bacterial nodulation factors (NFs), modified chitin oligosaccharides with a fatty acid moiety. Certain NFs can be cleaved and inactivated by plant chitinases. However, the most abundant NF of Sinorhizobium meliloti, an O-acetylated and sulfated tetramer, is resistant to hydrolysis by all plant chitinases tested so far. Nevertheless, this NF is rapidly degraded in the host rhizosphere. Here, we identify and characterize MtNFH1 (for Medicago truncatula Nod factor hydrolase 1), a legume enzyme structurally related to defense-related class V chitinases (glycoside hydrolase family 18). MtNFH1 lacks chitinase activity but efficiently hydrolyzes all tested NFs of S. meliloti. The enzyme shows a high cleavage preference, releasing exclusively lipodisaccharides from NFs. Substrate specificity and kinetic properties of MtNFH1 were compared with those of class V chitinases from Arabidopsis (Arabidopsis thaliana) and tobacco (Nicotiana tabacum), which cannot hydrolyze tetrameric NFs of S. meliloti. The Michaelis-Menten constants of MtNFH1 for NFs are in the micromolar concentration range, whereas nonmodified chitin oligosaccharides represent neither substrates nor inhibitors for MtNFH1. The three-dimensional structure of MtNFH1 was modeled on the basis of the known structure of class V chitinases. Docking simulation of NFs to MtNFH1 predicted a distinct binding cleft for the fatty acid moiety, which is absent in the class V chitinases. Point mutation analysis confirmed the modeled NF-MtNFH1 interaction. Silencing of MtNFH1 by RNA interference resulted in reduced NF degradation in the rhizosphere of M. truncatula. In conclusion, we have found a novel legume hydrolase that specifically inactivates NFs.

Statistical Evaluation of the Rodin–Ohno Hypothesis: Sense/Antisense Coding of Ancestral Class I and II Aminoacyl-tRNA Synthetases

PubMed Central

Chandrasekaran, Srinivas Niranj; Yardimci, Galip Gürkan; Erdogan, Ozgün; Roach, Jeffrey; Carter, Charles W.

2013-01-01

We tested the idea that ancestral class I and II aminoacyl-tRNA synthetases arose on opposite strands of the same gene. We assembled excerpted 94-residue Urgenes for class I tryptophanyl-tRNA synthetase (TrpRS) and class II Histidyl-tRNA synthetase (HisRS) from a diverse group of species, by identifying and catenating three blocks coding for secondary structures that position the most highly conserved, active-site residues. The codon middle-base pairing frequency was 0.35 ± 0.0002 in all-by-all sense/antisense alignments for 211 TrpRS and 207 HisRS sequences, compared with frequencies between 0.22 ± 0.0009 and 0.27 ± 0.0005 for eight different representations of the null hypothesis. Clustering algorithms demonstrate further that profiles of middle-base pairing in the synthetase antisense alignments are correlated along the sequences from one species-pair to another, whereas this is not the case for similar operations on sets representing the null hypothesis. Most probable reconstructed sequences for ancestral nodes of maximum likelihood trees show that middle-base pairing frequency increases to approximately 0.42 ± 0.002 as bacterial trees approach their roots; ancestral nodes from trees including archaeal sequences show a less pronounced increase. Thus, contemporary and reconstructed sequences all validate important bioinformatic predictions based on descent from opposite strands of the same ancestral gene. They further provide novel evidence for the hypothesis that bacteria lie closer than archaea to the origin of translation. Moreover, the inverse polarity of genetic coding, together with a priori α-helix propensities suggest that in-frame coding on opposite strands leads to similar secondary structures with opposite polarity, as observed in TrpRS and HisRS crystal structures. PMID:23576570

The Nodulation Factor Hydrolase of Medicago truncatula: Characterization of an Enzyme Specifically Cleaving Rhizobial Nodulation Signals1[W][OPEN

PubMed Central

Tian, Ye; Liu, Wei; Cai, Jie; Zhang, Lan-Yue; Wong, Kam-Bo; Feddermann, Nadja; Boller, Thomas; Xie, Zhi-Ping; Staehelin, Christian

2013-01-01

Nodule formation induced by nitrogen-fixing rhizobia depends on bacterial nodulation factors (NFs), modified chitin oligosaccharides with a fatty acid moiety. Certain NFs can be cleaved and inactivated by plant chitinases. However, the most abundant NF of Sinorhizobium meliloti, an O-acetylated and sulfated tetramer, is resistant to hydrolysis by all plant chitinases tested so far. Nevertheless, this NF is rapidly degraded in the host rhizosphere. Here, we identify and characterize MtNFH1 (for Medicago truncatula Nod factor hydrolase 1), a legume enzyme structurally related to defense-related class V chitinases (glycoside hydrolase family 18). MtNFH1 lacks chitinase activity but efficiently hydrolyzes all tested NFs of S. meliloti. The enzyme shows a high cleavage preference, releasing exclusively lipodisaccharides from NFs. Substrate specificity and kinetic properties of MtNFH1 were compared with those of class V chitinases from Arabidopsis (Arabidopsis thaliana) and tobacco (Nicotiana tabacum), which cannot hydrolyze tetrameric NFs of S. meliloti. The Michaelis-Menten constants of MtNFH1 for NFs are in the micromolar concentration range, whereas nonmodified chitin oligosaccharides represent neither substrates nor inhibitors for MtNFH1. The three-dimensional structure of MtNFH1 was modeled on the basis of the known structure of class V chitinases. Docking simulation of NFs to MtNFH1 predicted a distinct binding cleft for the fatty acid moiety, which is absent in the class V chitinases. Point mutation analysis confirmed the modeled NF-MtNFH1 interaction. Silencing of MtNFH1 by RNA interference resulted in reduced NF degradation in the rhizosphere of M. truncatula. In conclusion, we have found a novel legume hydrolase that specifically inactivates NFs. PMID:24082029

Transmembrane protein topology prediction using support vector machines.

PubMed

Nugent, Timothy; Jones, David T

2009-05-26

Alpha-helical transmembrane (TM) proteins are involved in a wide range of important biological processes such as cell signaling, transport of membrane-impermeable molecules, cell-cell communication, cell recognition and cell adhesion. Many are also prime drug targets, and it has been estimated that more than half of all drugs currently on the market target membrane proteins. However, due to the experimental difficulties involved in obtaining high quality crystals, this class of protein is severely under-represented in structural databases. In the absence of structural data, sequence-based prediction methods allow TM protein topology to be investigated. We present a support vector machine-based (SVM) TM protein topology predictor that integrates both signal peptide and re-entrant helix prediction, benchmarked with full cross-validation on a novel data set of 131 sequences with known crystal structures. The method achieves topology prediction accuracy of 89%, while signal peptides and re-entrant helices are predicted with 93% and 44% accuracy respectively. An additional SVM trained to discriminate between globular and TM proteins detected zero false positives, with a low false negative rate of 0.4%. We present the results of applying these tools to a number of complete genomes. Source code, data sets and a web server are freely available from http://bioinf.cs.ucl.ac.uk/psipred/. The high accuracy of TM topology prediction which includes detection of both signal peptides and re-entrant helices, combined with the ability to effectively discriminate between TM and globular proteins, make this method ideally suited to whole genome annotation of alpha-helical transmembrane proteins.

Prediction of Protein-Protein Interaction Sites with Machine-Learning-Based Data-Cleaning and Post-Filtering Procedures.

PubMed

Liu, Guang-Hui; Shen, Hong-Bin; Yu, Dong-Jun

2016-04-01

Accurately predicting protein-protein interaction sites (PPIs) is currently a hot topic because it has been demonstrated to be very useful for understanding disease mechanisms and designing drugs. Machine-learning-based computational approaches have been broadly utilized and demonstrated to be useful for PPI prediction. However, directly applying traditional machine learning algorithms, which often assume that samples in different classes are balanced, often leads to poor performance because of the severe class imbalance that exists in the PPI prediction problem. In this study, we propose a novel method for improving PPI prediction performance by relieving the severity of class imbalance using a data-cleaning procedure and reducing predicted false positives with a post-filtering procedure: First, a machine-learning-based data-cleaning procedure is applied to remove those marginal targets, which may potentially have a negative effect on training a model with a clear classification boundary, from the majority samples to relieve the severity of class imbalance in the original training dataset; then, a prediction model is trained on the cleaned dataset; finally, an effective post-filtering procedure is further used to reduce potential false positive predictions. Stringent cross-validation and independent validation tests on benchmark datasets demonstrated the efficacy of the proposed method, which exhibits highly competitive performance compared with existing state-of-the-art sequence-based PPIs predictors and should supplement existing PPI prediction methods.

Self-organizing ontology of biochemically relevant small molecules

PubMed Central

2012-01-01

Background The advent of high-throughput experimentation in biochemistry has led to the generation of vast amounts of chemical data, necessitating the development of novel analysis, characterization, and cataloguing techniques and tools. Recently, a movement to publically release such data has advanced biochemical structure-activity relationship research, while providing new challenges, the biggest being the curation, annotation, and classification of this information to facilitate useful biochemical pattern analysis. Unfortunately, the human resources currently employed by the organizations supporting these efforts (e.g. ChEBI) are expanding linearly, while new useful scientific information is being released in a seemingly exponential fashion. Compounding this, currently existing chemical classification and annotation systems are not amenable to automated classification, formal and transparent chemical class definition axiomatization, facile class redefinition, or novel class integration, thus further limiting chemical ontology growth by necessitating human involvement in curation. Clearly, there is a need for the automation of this process, especially for novel chemical entities of biological interest. Results To address this, we present a formal framework based on Semantic Web technologies for the automatic design of chemical ontology which can be used for automated classification of novel entities. We demonstrate the automatic self-assembly of a structure-based chemical ontology based on 60 MeSH and 40 ChEBI chemical classes. This ontology is then used to classify 200 compounds with an accuracy of 92.7%. We extend these structure-based classes with molecular feature information and demonstrate the utility of our framework for classification of functionally relevant chemicals. Finally, we discuss an iterative approach that we envision for future biochemical ontology development. Conclusions We conclude that the proposed methodology can ease the burden of chemical data annotators and dramatically increase their productivity. We anticipate that the use of formal logic in our proposed framework will make chemical classification criteria more transparent to humans and machines alike and will thus facilitate predictive and integrative bioactivity model development. PMID:22221313

Factor Structure of the Air Force Officer Qualifying Test Form S: Analysis and Comparison with Previous Forms

DTIC Science & Technology

2008-10-01

training grades, and class rank (Carretta, in press; Carretta & Ree, 2003; Olea & Ree, 1994), and several non-aviation officer jobs (Arth, 1986...series of papers, Ree and colleagues ( Olea & Ree, 1994; Ree, Carretta, & Teachout, 1996; Ree, & Earles, 1991; Ree, Earles, & Teachout, 1994) showed...Report No. 4. Washington, DC: U.S. Government Printing Office. Olea , M. M., Ree, M. J. (1994). Predicting pilot and navigator criteria: Not much

D3R Grand Challenge 2: blind prediction of protein-ligand poses, affinity rankings, and relative binding free energies

NASA Astrophysics Data System (ADS)

Gaieb, Zied; Liu, Shuai; Gathiaka, Symon; Chiu, Michael; Yang, Huanwang; Shao, Chenghua; Feher, Victoria A.; Walters, W. Patrick; Kuhn, Bernd; Rudolph, Markus G.; Burley, Stephen K.; Gilson, Michael K.; Amaro, Rommie E.

2018-01-01

The Drug Design Data Resource (D3R) ran Grand Challenge 2 (GC2) from September 2016 through February 2017. This challenge was based on a dataset of structures and affinities for the nuclear receptor farnesoid X receptor (FXR), contributed by F. Hoffmann-La Roche. The dataset contained 102 IC50 values, spanning six orders of magnitude, and 36 high-resolution co-crystal structures with representatives of four major ligand classes. Strong global participation was evident, with 49 participants submitting 262 prediction submission packages in total. Procedurally, GC2 mimicked Grand Challenge 2015 (GC2015), with a Stage 1 subchallenge testing ligand pose prediction methods and ranking and scoring methods, and a Stage 2 subchallenge testing only ligand ranking and scoring methods after the release of all blinded co-crystal structures. Two smaller curated sets of 18 and 15 ligands were developed to test alchemical free energy methods. This overview summarizes all aspects of GC2, including the dataset details, challenge procedures, and participant results. We also consider implications for progress in the field, while highlighting methodological areas that merit continued development. Similar to GC2015, the outcome of GC2 underscores the pressing need for methods development in pose prediction, particularly for ligand scaffolds not currently represented in the Protein Data Bank (http://www.pdb.org), and in affinity ranking and scoring of bound ligands.

Target specific proteochemometric model development for BACE1 - protein flexibility and structural water are critical in virtual screening.

PubMed

Manoharan, Prabu; Chennoju, Kiranmai; Ghoshal, Nanda

2015-07-01

BACE1 is an attractive target in Alzheimer's disease (AD) treatment. A rational drug design effort for the inhibition of BACE1 is actively pursued by researchers in both academic and pharmaceutical industries. This continued effort led to the steady accumulation of BACE1 crystal structures, co-complexed with different classes of inhibitors. This wealth of information is used in this study to develop target specific proteochemometric models and these models are exploited for predicting the prospective BACE1 inhibitors. The models developed in this study have performed excellently in predicting the computationally generated poses, separately obtained from single and ensemble docking approaches. The simple protein-ligand contact (SPLC) model outperforms other sophisticated high end models, in virtual screening performance, developed during this study. In an attempt to account for BACE1 protein active site flexibility information in predictive models, we included the change in the area of solvent accessible surface and the change in the volume of solvent accessible surface in our models. The ensemble and single receptor docking results obtained from this study indicate that the structural water mediated interactions improve the virtual screening results. Also, these waters are essential for recapitulating bioactive conformation during docking study. The proteochemometric models developed in this study can be used for the prediction of BACE1 inhibitors, during the early stage of AD drug discovery.

Structural Analysis: Folds Classification of metasedimentary rock in the Peninsular Malaysia

NASA Astrophysics Data System (ADS)

Shamsuddin, A.

2017-10-01

Understanding shear zone characteristics of deformation are a crucial part in the oil and gas industry as it might increase the knowledge of the fracture characteristics and lead to the prediction of the location of fracture zones or fracture swarms. This zone might give high influence on reservoir performance. There are four general types of shear zones which are brittle, ductile, semibrittle and brittle-ductile transition zones. The objective of this study is to study and observe the structural geometry of the shear zones and its implication as there is a lack of understanding, especially in the subsurface area because of the limitation of seismic resolution. A field study was conducted on the metasedimentary rocks (shear zone) which are exposed along the coastal part of the Peninsular Malaysia as this type of rock resembles the types of rock in the subsurface. The analysis in this area shows three main types of rock which are non-foliated metaquartzite and foliated rock which can be divided into slate and phyllite. Two different fold classification can be determined in this study. Layer 1 with phyllite as the main type of rock can be classified in class 1C and layer 2 with slate as the main type of rock can be classified in class 1A. This study will benefit in predicting the characteristics of the fracture and fracture zones.

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.

The Impact Of Middle Class Consumption On Democratization In Northeast Asia

DTIC Science & Technology

2016-03-01

middle-class Koreans. This consumption disparity caused the structurally disadvantaged working-class Koreans to join national protests that ultimately...inequality and a mobility-restraining household registration system. There exists a key political tension around structurally disadvantaged Chinese migrant...lower middle-class Koreans. This consumption disparity caused the structurally disadvantaged working-class Koreans to join national protests that

Druggability of methyl-lysine binding sites

NASA Astrophysics Data System (ADS)

Santiago, C.; Nguyen, K.; Schapira, M.

2011-12-01

Structural modules that specifically recognize—or read—methylated or acetylated lysine residues on histone peptides are important components of chromatin-mediated signaling and epigenetic regulation of gene expression. Deregulation of epigenetic mechanisms is associated with disease conditions, and antagonists of acetyl-lysine binding bromodomains are efficacious in animal models of cancer and inflammation, but little is known regarding the druggability of methyl-lysine binding modules. We conducted a systematic structural analysis of readers of methyl marks and derived a predictive druggability landscape of methyl-lysine binding modules. We show that these target classes are generally less druggable than bromodomains, but that some proteins stand as notable exceptions.

From sequence to enzyme mechanism using multi-label machine learning.

PubMed

De Ferrari, Luna; Mitchell, John B O

2014-05-19

In this work we predict enzyme function at the level of chemical mechanism, providing a finer granularity of annotation than traditional Enzyme Commission (EC) classes. Hence we can predict not only whether a putative enzyme in a newly sequenced organism has the potential to perform a certain reaction, but how the reaction is performed, using which cofactors and with susceptibility to which drugs or inhibitors, details with important consequences for drug and enzyme design. Work that predicts enzyme catalytic activity based on 3D protein structure features limits the prediction of mechanism to proteins already having either a solved structure or a close relative suitable for homology modelling. In this study, we evaluate whether sequence identity, InterPro or Catalytic Site Atlas sequence signatures provide enough information for bulk prediction of enzyme mechanism. By splitting MACiE (Mechanism, Annotation and Classification in Enzymes database) mechanism labels to a finer granularity, which includes the role of the protein chain in the overall enzyme complex, the method can predict at 96% accuracy (and 96% micro-averaged precision, 99.9% macro-averaged recall) the MACiE mechanism definitions of 248 proteins available in the MACiE, EzCatDb (Database of Enzyme Catalytic Mechanisms) and SFLD (Structure Function Linkage Database) databases using an off-the-shelf K-Nearest Neighbours multi-label algorithm. We find that InterPro signatures are critical for accurate prediction of enzyme mechanism. We also find that incorporating Catalytic Site Atlas attributes does not seem to provide additional accuracy. The software code (ml2db), data and results are available online at http://sourceforge.net/projects/ml2db/ and as supplementary files.

Structure and Function of Iron-Loaded Synthetic Melanin

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

Li, Yiwen; Xie, Yijun; Wang, Zhao

We describe a synthetic method for increasing and controlling the iron loading of synthetic melanin nanoparticles and use the resulting materials to perform a systematic quantitative investigation on their structure- property relationship. A comprehensive analysis by magnetometry, electron paramagnetic resonance, and nuclear magnetic relaxation dispersion reveals the complexities of their magnetic behavior and how these intraparticle magnetic interactions manifest in useful material properties such as their performance as MRI contrast agents. This analysis allows predictions of the optimal iron loading through a quantitative modeling of antiferromagnetic coupling that arises from proximal iron ions. This study provides a detailed understanding ofmore » this complex class of synthetic biomaterials and gives insight into interactions and structures prevalent in naturally occurring melanins.« less

Identification of a new protein in the centrosome-like "atractophore" of Trichomonas vaginalis.

PubMed

Bricheux, Geneviève; Coffe, Gérard; Brugerolle, Guy

2007-06-01

The human parasite Trichomonas vaginalis has specific structural bodies, atractophores, associated at one end to the kinetosomes and at the other to the spindle during division. A monoclonal antibody specific for a component of this structure was obtained. It recognizes a protein with a predicted molecular mass of 477 kDa. Sequence analysis of this protein shows that P477 belongs to the family of large coiled-coil proteins, sharing a highly versatile protein folding motif adaptable to many biological functions. P477-might act as an anchor to localize cellular activities and components to the golgi centrosomal region. It may represent a new class of structural proteins, since similar proteins were found in many protozoans.

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

PubMed

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

2011-06-01

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

Method for determining optimal supercell representation of interfaces

NASA Astrophysics Data System (ADS)

Stradi, Daniele; Jelver, Line; Smidstrup, Søren; Stokbro, Kurt

2017-05-01

The geometry and structure of an interface ultimately determines the behavior of devices at the nanoscale. We present a generic method to determine the possible lattice matches between two arbitrary surfaces and to calculate the strain of the corresponding matched interface. We apply this method to explore two relevant classes of interfaces for which accurate structural measurements of the interface are available: (i) the interface between pentacene crystals and the (1 1 1) surface of gold, and (ii) the interface between the semiconductor indium-arsenide and aluminum. For both systems, we demonstrate that the presented method predicts interface geometries in good agreement with those measured experimentally, which present nontrivial matching characteristics and would be difficult to guess without relying on automated structure-searching methods.

Neural networks for learning and prediction with applications to remote sensing and speech perception

NASA Astrophysics Data System (ADS)

Gjaja, Marin N.

1997-11-01

Neural networks for supervised and unsupervised learning are developed and applied to problems in remote sensing, continuous map learning, and speech perception. Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART networks synthesize fuzzy logic and neural networks, and supervised ARTMAP networks incorporate ART modules for prediction and classification. New ART and ARTMAP methods resulting from analyses of data structure, parameter specification, and category selection are developed. Architectural modifications providing flexibility for a variety of applications are also introduced and explored. A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on fuzzy ARTMAP, is developed. System capabilities are tested on a challenging remote sensing problem, prediction of vegetation classes in the Cleveland National Forest from spectral and terrain features. After training at the pixel level, performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, back propagation neural networks, and K-nearest neighbor algorithms. Best performance is obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. This work forms the foundation for additional studies exploring fuzzy ARTMAP's capability to estimate class mixture composition for non-homogeneous sites. Exploratory simulations apply ARTMAP to the problem of learning continuous multidimensional mappings. A novel system architecture retains basic ARTMAP properties of incremental and fast learning in an on-line setting while adding components to solve this class of problems. The perceptual magnet effect is a language-specific phenomenon arising early in infant speech development that is characterized by a warping of speech sound perception. An unsupervised neural network model is proposed that embodies two principal hypotheses supported by experimental data--that sensory experience guides language-specific development of an auditory neural map and that a population vector can predict psychological phenomena based on map cell activities. Model simulations show how a nonuniform distribution of map cell firing preferences can develop from language-specific input and give rise to the magnet effect.

Predicting the performance of fingerprint similarity searching.

PubMed

Vogt, Martin; Bajorath, Jürgen

2011-01-01

Fingerprints are bit string representations of molecular structure that typically encode structural fragments, topological features, or pharmacophore patterns. Various fingerprint designs are utilized in virtual screening and their search performance essentially depends on three parameters: the nature of the fingerprint, the active compounds serving as reference molecules, and the composition of the screening database. It is of considerable interest and practical relevance to predict the performance of fingerprint similarity searching. A quantitative assessment of the potential that a fingerprint search might successfully retrieve active compounds, if available in the screening database, would substantially help to select the type of fingerprint most suitable for a given search problem. The method presented herein utilizes concepts from information theory to relate the fingerprint feature distributions of reference compounds to screening libraries. If these feature distributions do not sufficiently differ, active database compounds that are similar to reference molecules cannot be retrieved because they disappear in the "background." By quantifying the difference in feature distribution using the Kullback-Leibler divergence and relating the divergence to compound recovery rates obtained for different benchmark classes, fingerprint search performance can be quantitatively predicted.

3x2 Classroom Goal Structures, Motivational Regulations, Self-Concept, and Affectivity in Secondary School.

PubMed

Méndez-Giménez, Antonio; Cecchini-Estrada, José-Antonio; Fernández-Río, Javier; Prieto Saborit, José Antonio; Méndez-Alonso, David

2017-09-20

The main objective was to analyze relationships and predictive patterns between 3x2 classroom goal structures (CGS), and motivational regulations, dimensions of self-concept, and affectivity in the context of secondary education. A sample of 1,347 secondary school students (56.6% young men, 43.4% young women) from 10 different provinces of Spain agreed to participate (M age = 13.43, SD = 1.05). Hierarchical regression analyses indicated the self-approach CGS was the most adaptive within the spectrum of self-determination, followed by the task-approach CGS. The other-approach CGS had an ambivalent influence on motivation. Task-approach and self-approach CGS predicted academic self-concept (p < .01; p < .001, respectively; R 2 = .134), and both along with other-approach CGS (negatively) predicted family self-concept (p < .05; p < .001; p < .01, respectively; R 2 = .064). Physical self-concept was predicted by the task-approach and other-approach CGS's (p < .05; p < .001, respectively; R 2 = .078). Finally, positive affect was predicted by all three approach-oriented CGS's (p < .001; R 2 = .137), whereas negative affect was predicted by other-approach (positively) and self-approach (negatively) CGS (p < .001; p < .05, respectively; R 2 = .028). These results expand the 3x2 achievement goal framework to include environmental factors, and reiterate that teachers should focus on raising levels of self- and task-based goals for students in their classes.

Semiempirical studies of atomic structure. Progress report, 1 July 1984-1 January 1985

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

Curtis, L.J.

1985-01-01

Through the acquisition and systematization of empirical data, remarkably precise methods for predicting excitation energies, transition wavelengths, transition probabilities, level lifetimes, ionization potentials, core polarizabilities, and core penetrabilities have been and are being developed and applied. Although the data base for heavy, highly ionized atoms is still sparse, much new information has become available since this program was begun in 1980. The purpose of the project is to perform needed measurements and to utilize the available data through parametrized extrapolations and interpolations along isoelectronic, homologous, and Rydberg sequences to provide predictions for large classes of quantities with a precision thatmore » is sharpened by subsequent measurements.« less

Sparse Zero-Sum Games as Stable Functional Feature Selection

PubMed Central

Sokolovska, Nataliya; Teytaud, Olivier; Rizkalla, Salwa; Clément, Karine; Zucker, Jean-Daniel

2015-01-01

In large-scale systems biology applications, features are structured in hidden functional categories whose predictive power is identical. Feature selection, therefore, can lead not only to a problem with a reduced dimensionality, but also reveal some knowledge on functional classes of variables. In this contribution, we propose a framework based on a sparse zero-sum game which performs a stable functional feature selection. In particular, the approach is based on feature subsets ranking by a thresholding stochastic bandit. We provide a theoretical analysis of the introduced algorithm. We illustrate by experiments on both synthetic and real complex data that the proposed method is competitive from the predictive and stability viewpoints. PMID:26325268

Family nonuniversal Z' models with protected flavor-changing interactions

NASA Astrophysics Data System (ADS)

Celis, Alejandro; Fuentes-Martín, Javier; Jung, Martin; Serôdio, Hugo

2015-07-01

We define a new class of Z' models with neutral flavor-changing interactions at tree level in the down-quark sector. They are related in an exact way to elements of the quark mixing matrix due to an underlying flavored U(1)' gauge symmetry, rendering these models particularly predictive. The same symmetry implies lepton-flavor nonuniversal couplings, fully determined by the gauge structure of the model. Our models allow us to address presently observed deviations from the standard model and specific correlations among the new physics contributions to the Wilson coefficients C9,10' ℓ can be tested in b →s ℓ+ℓ- transitions. We furthermore predict lepton-universality violations in Z' decays, testable at the LHC.

Mechanistic Insight into Bunyavirus-Induced Membrane Fusion from Structure-Function Analyses of the Hantavirus Envelope Glycoprotein Gc

PubMed Central

Stettner, Eva; Jeffers, Scott Allen; Pérez-Vargas, Jimena; Pehau-Arnaudet, Gerard; Tortorici, M. Alejandra; Jestin, Jean-Luc; England, Patrick; Tischler, Nicole D.; Rey, Félix A.

2016-01-01

Hantaviruses are zoonotic viruses transmitted to humans by persistently infected rodents, giving rise to serious outbreaks of hemorrhagic fever with renal syndrome (HFRS) or of hantavirus pulmonary syndrome (HPS), depending on the virus, which are associated with high case fatality rates. There is only limited knowledge about the organization of the viral particles and in particular, about the hantavirus membrane fusion glycoprotein Gc, the function of which is essential for virus entry. We describe here the X-ray structures of Gc from Hantaan virus, the type species hantavirus and responsible for HFRS, both in its neutral pH, monomeric pre-fusion conformation, and in its acidic pH, trimeric post-fusion form. The structures confirm the prediction that Gc is a class II fusion protein, containing the characteristic β-sheet rich domains termed I, II and III as initially identified in the fusion proteins of arboviruses such as alpha- and flaviviruses. The structures also show a number of features of Gc that are distinct from arbovirus class II proteins. In particular, hantavirus Gc inserts residues from three different loops into the target membrane to drive fusion, as confirmed functionally by structure-guided mutagenesis on the HPS-inducing Andes virus, instead of having a single “fusion loop”. We further show that the membrane interacting region of Gc becomes structured only at acidic pH via a set of polar and electrostatic interactions. Furthermore, the structure reveals that hantavirus Gc has an additional N-terminal “tail” that is crucial in stabilizing the post-fusion trimer, accompanying the swapping of domain III in the quaternary arrangement of the trimer as compared to the standard class II fusion proteins. The mechanistic understandings derived from these data are likely to provide a unique handle for devising treatments against these human pathogens. PMID:27783711

Mechanistic Insight into Bunyavirus-Induced Membrane Fusion from Structure-Function Analyses of the Hantavirus Envelope Glycoprotein Gc.

PubMed

Guardado-Calvo, Pablo; Bignon, Eduardo A; Stettner, Eva; Jeffers, Scott Allen; Pérez-Vargas, Jimena; Pehau-Arnaudet, Gerard; Tortorici, M Alejandra; Jestin, Jean-Luc; England, Patrick; Tischler, Nicole D; Rey, Félix A

2016-10-01

Hantaviruses are zoonotic viruses transmitted to humans by persistently infected rodents, giving rise to serious outbreaks of hemorrhagic fever with renal syndrome (HFRS) or of hantavirus pulmonary syndrome (HPS), depending on the virus, which are associated with high case fatality rates. There is only limited knowledge about the organization of the viral particles and in particular, about the hantavirus membrane fusion glycoprotein Gc, the function of which is essential for virus entry. We describe here the X-ray structures of Gc from Hantaan virus, the type species hantavirus and responsible for HFRS, both in its neutral pH, monomeric pre-fusion conformation, and in its acidic pH, trimeric post-fusion form. The structures confirm the prediction that Gc is a class II fusion protein, containing the characteristic β-sheet rich domains termed I, II and III as initially identified in the fusion proteins of arboviruses such as alpha- and flaviviruses. The structures also show a number of features of Gc that are distinct from arbovirus class II proteins. In particular, hantavirus Gc inserts residues from three different loops into the target membrane to drive fusion, as confirmed functionally by structure-guided mutagenesis on the HPS-inducing Andes virus, instead of having a single "fusion loop". We further show that the membrane interacting region of Gc becomes structured only at acidic pH via a set of polar and electrostatic interactions. Furthermore, the structure reveals that hantavirus Gc has an additional N-terminal "tail" that is crucial in stabilizing the post-fusion trimer, accompanying the swapping of domain III in the quaternary arrangement of the trimer as compared to the standard class II fusion proteins. The mechanistic understandings derived from these data are likely to provide a unique handle for devising treatments against these human pathogens.

Use of the Biopharmaceutics Drug Disposition Classification System (BDDCS) to Help Predict the Occurrence of Idiosyncratic Cutaneous Adverse Drug Reactions Associated with Antiepileptic Drug Usage.

PubMed

Chan, Rosa; Wei, Chun-Yu; Chen, Yuan-Tsong; Benet, Leslie Z

2016-05-01

Cutaneous adverse reactions (CARs) from antiepileptic drugs (AEDs) are common, ranging from mild to life-threatening, including Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN). The identification of subjects carrying the HLA-B*15:02, an inherited allelic variant of the HLA-B gene, and the avoidance of carbamazepine (CBZ) therapy in these subjects are strongly associated with a decrease in the incidence of carbamazepine-induced SJS/TEN. In spite of the strong genetic associations, the initiation of hypersensitivity for AEDs is still not very well characterized. Predicting the potential for other AEDs to cause adverse reactions will be undoubtedly beneficial to avoid CARs, which is the focus of this report. Here, we explore the use of the Biopharmaceutics Drug Disposition Classification System (BDDCS) to distinguish AEDs associated with and without CARs by examining the binding relationship of AEDs to HLA-B*15:02 and data from extensive reviews of medical records. We also evaluate the lack of benefit from a Hong Kong population policy on the effects of screening for HLA-B*15:02 and previous incorrect structure-activity hypotheses. Our analysis concludes that BDDCS class 2 AEDs are more prone to cause adverse cutaneous reactions than certain BDDCS class 1 AEDs and that BDDCS Class 3 drugs have the lowest levels of cutaneous adverse reactions. We propose that BDDCS Class 3 AEDs should be preferentially used for patients with Asian backgrounds (i.e., Han Chinese, Thai, and Malaysian populations) if possible and in patients predisposed to skin rashes.

Flexibility Correlation between Active Site Regions Is Conserved across Four AmpC β-Lactamase Enzymes.

PubMed

Brown, Jenna R; Livesay, Dennis R

2015-01-01

β-lactamases are bacterial enzymes that confer resistance to β-lactam antibiotics, such as penicillins and cephalosporins. There are four classes of β-lactamase enzymes, each with characteristic sequence and structure properties. Enzymes from class A are the most common and have been well characterized across the family; however, less is known about how physicochemical properties vary across the C and D families. In this report, we compare the dynamical properties of four AmpC (class C) β-lactamases using our distance constraint model (DCM). The DCM reliably predicts thermodynamic and mechanical properties in an integrated way. As a consequence, quantitative stability/flexibility relationships (QSFR) can be determined and compared across the whole family. The DCM calculates a large number of QSFR metrics. Perhaps the most useful is the flexibility index (FI), which quantifies flexibility along the enzyme backbone. As typically observed in other systems, FI is well conserved across the four AmpC enzymes. Cooperativity correlation (CC), which quantifies intramolecular couplings within structure, is rarely conserved across protein families; however, it is in AmpC. In particular, the bulk of each structure is composed of a large rigid cluster, punctuated by three flexibly correlated regions located at the active site. These regions include several catalytic residues and the Ω-loop. This evolutionary conservation combined with active their site location strongly suggests that these coupled dynamical modes are important for proper functioning of the enzyme.

Flexibility Correlation between Active Site Regions Is Conserved across Four AmpC β-Lactamase Enzymes

PubMed Central

Brown, Jenna R.; Livesay, Dennis R.

2015-01-01

β-lactamases are bacterial enzymes that confer resistance to β-lactam antibiotics, such as penicillins and cephalosporins. There are four classes of β-lactamase enzymes, each with characteristic sequence and structure properties. Enzymes from class A are the most common and have been well characterized across the family; however, less is known about how physicochemical properties vary across the C and D families. In this report, we compare the dynamical properties of four AmpC (class C) β-lactamases using our distance constraint model (DCM). The DCM reliably predicts thermodynamic and mechanical properties in an integrated way. As a consequence, quantitative stability/flexibility relationships (QSFR) can be determined and compared across the whole family. The DCM calculates a large number of QSFR metrics. Perhaps the most useful is the flexibility index (FI), which quantifies flexibility along the enzyme backbone. As typically observed in other systems, FI is well conserved across the four AmpC enzymes. Cooperativity correlation (CC), which quantifies intramolecular couplings within structure, is rarely conserved across protein families; however, it is in AmpC. In particular, the bulk of each structure is composed of a large rigid cluster, punctuated by three flexibly correlated regions located at the active site. These regions include several catalytic residues and the Ω-loop. This evolutionary conservation combined with active their site location strongly suggests that these coupled dynamical modes are important for proper functioning of the enzyme. PMID:26018804

Relationship between global structural parameters and Enzyme Commission hierarchy: implications for function prediction.

PubMed

Boareto, Marcelo; Yamagishi, Michel E B; Caticha, Nestor; Leite, Vitor B P

2012-10-01

In protein databases there is a substantial number of proteins structurally determined but without function annotation. Understanding the relationship between function and structure can be useful to predict function on a large scale. We have analyzed the similarities in global physicochemical parameters for a set of enzymes which were classified according to the four Enzyme Commission (EC) hierarchical levels. Using relevance theory we introduced a distance between proteins in the space of physicochemical characteristics. This was done by minimizing a cost function of the metric tensor built to reflect the EC classification system. Using an unsupervised clustering method on a set of 1025 enzymes, we obtained no relevant clustering formation compatible with EC classification. The distance distributions between enzymes from the same EC group and from different EC groups were compared by histograms. Such analysis was also performed using sequence alignment similarity as a distance. Our results suggest that global structure parameters are not sufficient to segregate enzymes according to EC hierarchy. This indicates that features essential for function are rather local than global. Consequently, methods for predicting function based on global attributes should not obtain high accuracy in main EC classes prediction without relying on similarities between enzymes from training and validation datasets. Furthermore, these results are consistent with a substantial number of studies suggesting that function evolves fundamentally by recruitment, i.e., a same protein motif or fold can be used to perform different enzymatic functions and a few specific amino acids (AAs) are actually responsible for enzyme activity. These essential amino acids should belong to active sites and an effective method for predicting function should be able to recognize them. Copyright © 2012 Elsevier Ltd. All rights reserved.

Phi Class of Glutathione S-transferase Gene Superfamily Widely Exists in Nonplant Taxonomic Groups

PubMed Central

Munyampundu, Jean-Pierre; Xu, You-Ping; Cai, Xin-Zhong

2016-01-01

Glutathione S-transferases (GSTs) constitute a superfamily of enzymes involved in detoxification of noxious compounds and protection against oxidative damage. GST class Phi (GSTF), one of the important classes of plant GSTs, has long been considered as plant specific but was recently found in basidiomycete fungi. However, the range of nonplant taxonomic groups containing GSTFs remains unknown. In this study, the distribution and phylogenetic relationships of nonplant GSTFs were investigated. We identified GSTFs in ascomycete fungi, myxobacteria, and protists Naegleria gruberi and Aureococcus anophagefferens. GSTF occurrence in these bacteria and protists correlated with their genome sizes and habitats. While this link was missing across ascomycetes, the distribution and abundance of GSTFs among ascomycete genomes could be associated with their lifestyles to some extent. Sequence comparison, gene structure, and phylogenetic analyses indicated divergence among nonplant GSTFs, suggesting polyphyletic origins during evolution. Furthermore, in silico prediction of functional partners suggested functional diversification among nonplant GSTFs. PMID:26884677

First-Principles Prediction of New Electrides with Nontrivial Band Topology Based on One-Dimensional Building Blocks

NASA Astrophysics Data System (ADS)

Park, Changwon; Kim, Sung Wng; Yoon, Mina

2018-01-01

We introduce a new class of electrides with nontrivial band topology by coupling materials database searches and first-principles-calculations-based analysis. Cs3O and Ba3N are for the first time identified as a new class of electrides, consisting of one-dimensional (1D) nanorod building blocks. Their crystal structures mimic β -TiCl3 with the position of anions and cations exchanged. Unlike the weakly coupled nanorods of β -TiCl3 , Cs3O and Ba3N retain 1D anionic electrons along the hollow interrod sites; additionally, a strong interrod interaction in C3O and Ba3N induces band inversion in a 2D superatomic triangular lattice, resulting in Dirac-node lines. The new class of electrides can serve as a prototype for new electrides with a large cavity space that can be utilized for various applications such as gas storage, ion transport, and metal intercalation.

Synergy between NMR measurements and MD simulations of protein/RNA complexes: application to the RRMs, the most common RNA recognition motifs

PubMed Central

Krepl, Miroslav; Cléry, Antoine; Blatter, Markus; Allain, Frederic H.T.; Sponer, Jiri

2016-01-01

RNA recognition motif (RRM) proteins represent an abundant class of proteins playing key roles in RNA biology. We present a joint atomistic molecular dynamics (MD) and experimental study of two RRM-containing proteins bound with their single-stranded target RNAs, namely the Fox-1 and SRSF1 complexes. The simulations are used in conjunction with NMR spectroscopy to interpret and expand the available structural data. We accumulate more than 50 μs of simulations and show that the MD method is robust enough to reliably describe the structural dynamics of the RRM–RNA complexes. The simulations predict unanticipated specific participation of Arg142 at the protein–RNA interface of the SRFS1 complex, which is subsequently confirmed by NMR and ITC measurements. Several segments of the protein–RNA interface may involve competition between dynamical local substates rather than firmly formed interactions, which is indirectly consistent with the primary NMR data. We demonstrate that the simulations can be used to interpret the NMR atomistic models and can provide qualified predictions. Finally, we propose a protocol for ‘MD-adapted structure ensemble’ as a way to integrate the simulation predictions and expand upon the deposited NMR structures. Unbiased μs-scale atomistic MD could become a technique routinely complementing the NMR measurements of protein–RNA complexes. PMID:27193998

Crystal structures of penicillin-binding protein 3 (PBP3) from methicillin-resistant Staphylococcus aureus in the apo and cefotaxime-bound forms.

PubMed

Yoshida, Hisashi; Kawai, Fumihiro; Obayashi, Eiji; Akashi, Satoko; Roper, David I; Tame, Jeremy R H; Park, Sam-Yong

2012-10-26

Staphylococcus aureus is a widespread Gram-positive opportunistic pathogen, and a methicillin-resistant form (MRSA) is particularly difficult to treat clinically. We have solved two crystal structures of penicillin-binding protein (PBP) 3 (PBP3) from MRSA, the apo form and a complex with the β-lactam antibiotic cefotaxime, and used electrospray mass spectrometry to measure its sensitivity to a variety of penicillin derivatives. PBP3 is a class B PBP, possessing an N-terminal non-penicillin-binding domain, sometimes called a dimerization domain, and a C-terminal transpeptidase domain. The model shows a different orientation of its two domains compared to earlier models of other class B PBPs and a novel, larger N-domain. Consistent with the nomenclature of "dimerization domain", the N-terminal region forms an apparently tight interaction with a neighboring molecule related by a 2-fold symmetry axis in the crystal structure. This dimer form is predicted to be highly stable in solution by the PISA server, but mass spectrometry and analytical ultracentrifugation provide unequivocal evidence that the protein is a monomer in solution. Copyright © 2012 Elsevier Ltd. All rights reserved.

On the geometrically nonlinear elastic response of class θ = 1 tensegrity prisms

NASA Astrophysics Data System (ADS)

Mascolo, Ida; Amendola, Ada; Zuccaro, Giulio; Feo, Luciano; Fraternali, Fernando

2018-03-01

The present work studies the geometrically nonlinear response of class ϑ=1 tensegrity prisms modeled as a collection of elastic springs reacting in tension (strings or cables) or compression (bars), under uniform uniaxial loading. The incremental equilibrium equations of the structure are numerically solved through a path-following procedure, with the aim of modeling the mechanical behavior of the structure in the large displacement regime. Several numerical results are presented with reference to a variety of physical models, which use two different materials for the cables and the bars, and show different aspect ratios associated with either 'standard' or 'expanded' configurations. An experimental validation of the predicted constitutive response is conducted with reference to a 'thick' and a 'slender' model, observing rather good theory vs. experiment matching. The given numerical and experimental results highlight that the elastic response of the examined structures may switch from stiffening to softening, depending on the geometry of the system, the magnitude of the external load, and the applied prestress. The outcomes of the current study confirm previous literature results on the elastic response of minimal tensegrity prisms, and pave the way to the use of tensegrity systems as nonlinear spring units forming tunable mechanical metamaterials.

Ferroelectricity in corundum derivatives

NASA Astrophysics Data System (ADS)

Ye, Meng; Vanderbilt, David

2016-04-01

The search for new ferroelectric (FE) materials holds promise for broadening our understanding of FE mechanisms and extending the range of application of FE materials. Here we investigate a class of A B O3 and A2B B'O6 materials that can be derived from the X2O3 corundum structure by mixing two or three ordered cations on the X site. Most such corundum derivatives have a polar structure, but it is unclear whether the polarization is reversible, which is a requirement for a FE material. In this paper, we propose a method to study the FE reversal path of materials in the corundum derivative family. We first categorize the corundum derivatives into four classes and show that only two of these allow for the possibility of FE reversal. We then calculate the energy profile and energy barrier of the FE reversal path using first-principles density functional methods with a structural constraint. Furthermore, we identify several empirical measures that can provide a rule of thumb for estimating the energy barriers. Finally, the conditions under which the magnetic ordering is compatible with ferroelectricity are determined. These results lead us to predict several potentially new FE materials.

From template to image: reconstructing fingerprints from minutiae points.

PubMed

Ross, Arun; Shah, Jidnya; Jain, Anil K

2007-04-01

Most fingerprint-based biometric systems store the minutiae template of a user in the database. It has been traditionally assumed that the minutiae template of a user does not reveal any information about the original fingerprint. In this paper, we challenge this notion and show that three levels of information about the parent fingerprint can be elicited from the minutiae template alone, viz., 1) the orientation field information, 2) the class or type information, and 3) the friction ridge structure. The orientation estimation algorithm determines the direction of local ridges using the evidence of minutiae triplets. The estimated orientation field, along with the given minutiae distribution, is then used to predict the class of the fingerprint. Finally, the ridge structure of the parent fingerprint is generated using streamlines that are based on the estimated orientation field. Line Integral Convolution is used to impart texture to the ensuing ridges, resulting in a ridge map resembling the parent fingerprint. The salient feature of this noniterative method to generate ridges is its ability to preserve the minutiae at specified locations in the reconstructed ridge map. Experiments using a commercial fingerprint matcher suggest that the reconstructed ridge structure bears close resemblance to the parent fingerprint.

Phylogenetic analysis of eIF4E-family members

PubMed Central

Joshi, Bhavesh; Lee, Kibwe; Maeder, Dennis L; Jagus, Rosemary

2005-01-01

Background Translation initiation in eukaryotes involves the recruitment of mRNA to the ribosome which is controlled by the translation factor eIF4E. eIF4E binds to the 5'-m7Gppp cap-structure of mRNA. Three dimensional structures of eIF4Es bound to cap-analogues resemble 'cupped-hands' in which the cap-structure is sandwiched between two conserved Trp residues (Trp-56 and Trp-102 of H. sapiens eIF4E). A third conserved Trp residue (Trp-166 of H. sapiens eIF4E) recognizes the 7-methyl moiety of the cap-structure. Assessment of GenBank NR and dbEST databases reveals that many organisms encode a number of proteins with homology to eIF4E. Little is understood about the relationships of these structurally related proteins to each other. Results By combining sequence data deposited in the Genbank databases, we have identified sequences encoding 411 eIF4E-family members from 230 species. These sequences have been deposited into an internet-accessible database designed for sequence comparisons of eIF4E-family members. Most members can be grouped into one of three classes. Class I members carry Trp residues equivalent to Trp-43 and Trp-56 of H. sapiens eIF4E and appear to be present in all eukaryotes. Class II members, possess Trp→Tyr/Phe/Leu and Trp→Tyr/Phe substitutions relative to Trp-43 and Trp-56 of H. sapiens eIF4E, and can be identified in Metazoa, Viridiplantae, and Fungi. Class III members possess a Trp residue equivalent to Trp-43 of H. sapiens eIF4E but carry a Trp→Cys/Tyr substitution relative to Trp-56 of H. sapiens eIF4E, and can be identified in Coelomata and Cnidaria. Some eIF4E-family members from Protista show extension or compaction relative to prototypical eIF4E-family members. Conclusion The expansion of sequenced cDNAs and genomic DNAs from all eukaryotic kingdoms has revealed a variety of proteins related in structure to eIF4E. Evolutionarily it seems that a single early eIF4E gene has undergone multiple gene duplications generating multiple structural classes, such that it is no longer possible to predict function from the primary amino acid sequence of an eIF4E-family member. The variety of eIF4E-family members provides a source of alternatives on the eIF4E structural theme that will benefit structure/function analyses and therapeutic drug design. PMID:16191198

Spatial and temporal predictions of agricultural land prices using DSM techniques.

NASA Astrophysics Data System (ADS)

Carré, F.; Grandgirard, D.; Diafas, I.; Reuter, H. I.; Julien, V.; Lemercier, B.

2009-04-01

Agricultural land prices highly impacts land accessibility to farmers and by consequence the evolution of agricultural landscapes (crop changes, land conversion to urban infrastructures…) which can turn to irreversible soil degradation. The economic value of agricultural land has been studied spatially, in every one of the 374 French Agricultural Counties, and temporally- from 1995 to 2007, by using data of the SAFER Institute. To this aim, agricultural land price was considered as a digital soil property. The spatial and temporal predictions were done using Digital Soil Mapping techniques combined with tools mainly used for studying temporal financial behaviors. For making both predictions, a first classification of the Agricultural Counties was done for the 1995-2006 periods (2007 was excluded and served as the date of prediction) using a fuzzy k-means clustering. The Agricultural Counties were then aggregated according to land price at the different times. The clustering allows for characterizing the counties by their memberships to each class centroid. The memberships were used for the spatial prediction, whereas the centroids were used for the temporal prediction. For the spatial prediction, from the 374 Agricultural counties, three fourths were used for modeling and one fourth for validating. Random sampling was done by class to ensure that all classes are represented by at least one county in the modeling and validation datasets. The prediction was done for each class by testing the relationships between the memberships and the following factors: (i) soil variable (organic matter from the French BDAT database), (ii) soil covariates (land use classes from CORINE LANDCOVER, bioclimatic zones from the WorldClim Database, landform attributes and landform classes from the SRTM, major roads and hydrographic densities from EUROSTAT, average field sizes estimated by automatic classification of remote sensed images) and (iii) socio-economic factors (population density, gross domestic product and its combination with the population density obtained from EUROSTAT). Linear (Generalized Linear Models) and non-linear models (neural network) were used for building the relationships. For the validation, the relationships were applied to the validation datasets. The RMSE and the coefficient of determination (from a linear regression) between predicted and actual memberships, and the contingency table between the predicted and actual allocation classes were used as validation criteria. The temporal prediction was done on the year 2007 from the centroid land prices characterizing the 1995-2006 period. For each class, the land prices of the time-series 1995-2006 were modeled using an Auto-Regressive Moving Average approach. For the validation, the models were applied to the year 2007. The RMSE between predicted and actual prices is used as the validation criteria. We then discussed the methods and the results of the spatial and temporal validation. Based on this methodology, an extrapolation will be tested on another European country with land price market similar to France (to be determined).

Prediction of the binding affinities of peptides to class II MHC using a regularized thermodynamic model

PubMed Central

2010-01-01

Background The binding of peptide fragments of extracellular peptides to class II MHC is a crucial event in the adaptive immune response. Each MHC allotype generally binds a distinct subset of peptides and the enormous number of possible peptide epitopes prevents their complete experimental characterization. Computational methods can utilize the limited experimental data to predict the binding affinities of peptides to class II MHC. Results We have developed the Regularized Thermodynamic Average, or RTA, method for predicting the affinities of peptides binding to class II MHC. RTA accounts for all possible peptide binding conformations using a thermodynamic average and includes a parameter constraint for regularization to improve accuracy on novel data. RTA was shown to achieve higher accuracy, as measured by AUC, than SMM-align on the same data for all 17 MHC allotypes examined. RTA also gave the highest accuracy on all but three allotypes when compared with results from 9 different prediction methods applied to the same data. In addition, the method correctly predicted the peptide binding register of 17 out of 18 peptide-MHC complexes. Finally, we found that suboptimal peptide binding registers, which are often ignored in other prediction methods, made significant contributions of at least 50% of the total binding energy for approximately 20% of the peptides. Conclusions The RTA method accurately predicts peptide binding affinities to class II MHC and accounts for multiple peptide binding registers while reducing overfitting through regularization. The method has potential applications in vaccine design and in understanding autoimmune disorders. A web server implementing the RTA prediction method is available at http://bordnerlab.org/RTA/. PMID:20089173

Dance Class Structure Affects Youth Physical Activity and Sedentary Behavior: A Study of Seven Dance Types

ERIC Educational Resources Information Center

Lopez Castillo, Maria A.; Carlson, Jordan A.; Cain, Kelli L.; Bonilla, Edith A.; Chuang, Emmeline; Elder, John P.; Sallis, James F.

2015-01-01

Purpose: The study aims were to determine: (a) how class structure varies by dance type, (b) how moderate-to-vigorous physical activity (MVPA) and sedentary behavior vary by dance class segments, and (c) how class structure relates to total MVPA in dance classes. Method: Participants were 291 boys and girls ages 5 to 18 years old enrolled in 58…

Ontology for Structural Geology

NASA Astrophysics Data System (ADS)

Zhong, J.; McGuinness, D. L.; Antonellini, M.; Aydin, A.

2005-12-01

We present our comprehensive process-based ontology for Structural Geology. This ontology covers major domain concepts, especially those related to geological structure type, properties of these structures, their deformation mechanisms, and the factors that control which deformation mechanisms may operate under certain conditions. The structure class in our ontology extends the planetary structure class of the SWEET ontology by providing additional information required for use in the structural geology domain. The classification followed the architectures of structures, such as structure element, set, zone, and pattern. Our deformation mechanism class does not have a corresponding class in SWEET. In our ontology, it has two subclasses, Macro- and Micro- mechanisms. The property class and the factor class are both subclasses of the physical property class of SWEET. Relationships among those concepts are also included in our ontology. For example, the class structure element has properties associated with the deformation mechanisms, descriptive properties such as geometry and morphology, and physical properties of rocks such as strength, compressibility, seismic velocity, porosity, and permeability. The subject matter expertise was provided by domain experts. Additionally, we surveyed text books and journal articles with the goal of evaluating the completeness and correctness of the domain terms and we used logical reasoners and validators to eliminate logical problems. We propose that our ontology provides a reusable extension to the SWEET ontology that may be of value to scientists and lay people interested in structural geology issues. We have also implemented prototype services that utilize this ontology for search.

Class-Size Effects in Secondary School

ERIC Educational Resources Information Center

Krassel, Karl Fritjof; Heinesen, Eskil

2014-01-01

We analyze class-size effects on academic achievement in secondary school in Denmark exploiting an institutional setting where pupils cannot predict class size prior to enrollment, and where post-enrollment responses aimed at affecting realized class size are unlikely. We identify class-size effects combining a regression discontinuity design with…

Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds.

PubMed

Cannon, Edward O; Amini, Ata; Bender, Andreas; Sternberg, Michael J E; Muggleton, Stephen H; Glen, Robert C; Mitchell, John B O

2007-05-01

We investigate the classification performance of circular fingerprints in combination with the Naive Bayes Classifier (MP2D), Inductive Logic Programming (ILP) and Support Vector Inductive Logic Programming (SVILP) on a standard molecular benchmark dataset comprising 11 activity classes and about 102,000 structures. The Naive Bayes Classifier treats features independently while ILP combines structural fragments, and then creates new features with higher predictive power. SVILP is a very recently presented method which adds a support vector machine after common ILP procedures. The performance of the methods is evaluated via a number of statistical measures, namely recall, specificity, precision, F-measure, Matthews Correlation Coefficient, area under the Receiver Operating Characteristic (ROC) curve and enrichment factor (EF). According to the F-measure, which takes both recall and precision into account, SVILP is for seven out of the 11 classes the superior method. The results show that the Bayes Classifier gives the best recall performance for eight of the 11 targets, but has a much lower precision, specificity and F-measure. The SVILP model on the other hand has the highest recall for only three of the 11 classes, but generally far superior specificity and precision. To evaluate the statistical significance of the SVILP superiority, we employ McNemar's test which shows that SVILP performs significantly (p < 5%) better than both other methods for six out of 11 activity classes, while being superior with less significance for three of the remaining classes. While previously the Bayes Classifier was shown to perform very well in molecular classification studies, these results suggest that SVILP is able to extract additional knowledge from the data, thus improving classification results further.

Segments and segmental properties in cross-language perception: Korean perception of English obstruents in various prosodic locations

NASA Astrophysics Data System (ADS)

de Jong, Kenneth; Silbert, Noah; Park, Hanyong

2004-05-01

Experimental models of cross-language perception and second-language acquisition (such as PAM and SLM) typically treat language differences in terms of whether the two languages share phonological segmental categories. Linguistic models, by contrast, generally examine properties which cross classify segments, such as features, rules, or prosodic constraints. Such models predict that perceptual patterns found for one segment will generalize to other segments of the same class. This paper presents perceptual identifications of Korean listeners to a set of voiced and voiceless English stops and fricatives in various prosodic locations to determine the extent to which such generality occurs. Results show some class-general effects; for example, voicing identification patterns generalize from stops, which occur in Korean, to nonsibilant fricatives, which are new to Korean listeners. However, when identification is poor, there are clear differences between segments within the same class. For example, in identifying stops and fricatives, both point of articulation and prosodic position bias perceptions; coronals are more often labeled fricatives, and syllable initial obstruents are more often labeled stops. These results suggest that class-general perceptual patterns are not a simple consequence of the structure of the perceptual system, but need to be acquired by factoring out within-class differences.

Improving the Representation of Snow Crystal Properties Within a Single-Moment Microphysics Scheme

NASA Technical Reports Server (NTRS)

Molthan, Andrew L.; Petersen, Walter A.; Case, Jonathan L.; Dembek, S. R.

2010-01-01

As computational resources continue their expansion, weather forecast models are transitioning to the use of parameterizations that predict the evolution of hydrometeors and their microphysical processes, rather than estimating the bulk effects of clouds and precipitation that occur on a sub-grid scale. These parameterizations are referred to as single-moment, bulk water microphysics schemes, as they predict the total water mass among hydrometeors in a limited number of classes. Although the development of single moment microphysics schemes have often been driven by the need to predict the structure of convective storms, they may also provide value in predicting accumulations of snowfall. Predicting the accumulation of snowfall presents unique challenges to forecasters and microphysics schemes. In cases where surface temperatures are near freezing, accumulated depth often depends upon the snowfall rate and the ability to overcome an initial warm layer. Precipitation efficiency relates to the dominant ice crystal habit, as dendrites and plates have relatively large surface areas for the accretion of cloud water and ice, but are only favored within a narrow range of ice supersaturation and temperature. Forecast models and their parameterizations must accurately represent the characteristics of snow crystal populations, such as their size distribution, bulk density and fall speed. These properties relate to the vertical distribution of ice within simulated clouds, the temperature profile through latent heat release, and the eventual precipitation rate measured at the surface. The NASA Goddard, single-moment microphysics scheme is available to the operational forecast community as an option within the Weather Research and Forecasting (WRF) model. The NASA Goddard scheme predicts the occurrence of up to six classes of water mass: vapor, cloud ice, cloud water, rain, snow and either graupel or hail.

Manufacturing Challenges and Benefits when Scaling the HIAD Stacked-Torus Aeroshell to a 15m Class System

NASA Technical Reports Server (NTRS)

Cheatwood, F. McNeil; Swanson, Gregory T.; Johnson, R. Keith; Hughes, Stephen; Calomino, Anthony; Gilles, Brian; Anderson, Paul; Bond, Bruce

2016-01-01

Over a decade of work has been conducted in the development of NASA's Hypersonic Inflatable Aerodynamic Decelerator (HIAD) deployable aeroshell technology. This effort has included multiple ground test campaigns and flight tests culminating in the HIAD project's second generation (Gen-2) aeroshell system. The HIAD project team has developed, fabricated, and tested stacked-torus inflatable structures (IS) with flexible thermal protection systems (F-TPS) ranging in diameters from 3-6m, with cone angles of 60 and 70 deg. To meet NASA and commercial near term objectives, the HIAD team must scale the current technology up to 12-15m in diameter. The HIAD project's experience in scaling the technology has reached a critical juncture. Growing from a 6m to a 15m class system will introduce many new structural and logistical challenges to an already complicated manufacturing process. Although the general architecture and key aspects of the HIAD design scale well to larger vehicles, details of the technology will need to be reevaluated and possibly redesigned for use in a 15m-class HIAD system. These include: layout and size of the structural webbing that transfers load throughout the IS, inflatable gas barrier design, torus diameter and braid construction, internal pressure and inflation line routing, adhesives used for coating and bonding, and F-TPS gore design and seam fabrication. The logistics of fabricating and testing the IS and the F-TPS also become more challenging with increased scale. Compared to the 6m aeroshell (the largest HIAD built to date), a 12m aeroshell has four times the cross-sectional area, and a 15m one has over six times the area. This means that fabrication and test procedures will need to be reexamined to account for the sheer size and weight of the aeroshell components. This will affect a variety of steps in the manufacturing process, such as: stacking the tori during assembly, stitching the structural webbing, initial inflation of tori, and stitching of F-TPS gores. Additionally, new approaches and hardware will be required for handling and ground testing of both individual tori and the fully assembled HIADs. There are also noteworthy benefits of scaling up the HIAD aeroshell to 15m-class system. Two complications in working with handmade textiles structures are the non-linearity of the materials and the role of human accuracy during fabrication. Larger, more capable, HIAD structures should see much larger operational loads, potentially bringing the structural response of the materials out of the non-linear regime and into the preferred linear response range. Also, making the reasonable assumption that the magnitude of fabrication accuracy remains constant as the structures grow, the relative effect of fabrication errors should decrease as a percentage of the textile component size. Combined, these two effects improve the predictive capability and the uniformity of the structural response for a 12-15m class HIAD. In this paper, the challenges and associated mitigation plans related to scaling up the HIAD stacked-torus aeroshell to a 15m class system will be discussed. In addition, the benefits of enlarging the structure will be further explored.

Mapping Vegetation Structure in Kakadu National Park: An AIRSAR and GIS Application in Conservation

NASA Technical Reports Server (NTRS)

Imhoff, Marc L.; Sisk, Thomas D.; Hampton, Haydee; Milne, Anthony K.

1999-01-01

Airborne Synthetic Aperture Radar (AIRSAR) data were used to map vegetation structure in Kakadu National Park Australia as part of the PACRIM project. SAR data were co-registered with Landsat TM, aerial photos, and map data in a geographic information system for a small test area consisting of mangrove, floodplain grasslands, lowland tropical evergreen forest and upland mixed deciduous and evergreen tropical forest near the South Alligator River. Landsat (Thematic Mapper) TM very clearly showed the floristic composition and burn scars from the previous years fires and the AIRSAR data provided a profile of vegetation structure. Extensive field data on vegetation species composition and structure were collected across a series of transects in cooperation with a survey of avifauna in an effort to link the habitat edge structure with bird species responses. A test site was found that contained two types of habitat edges: 1) A structure specific edge - characterized by the appearance of a very strong structural change in the forest canopy occurring in the absence of a substantial turnover in floristics. 2) Floristic edge - a sharp transition in vegetation genetic composition with a mixed set of structural changes. Specific polarization combinations were selected that were highly correlated to a set of desired structural parameters found in the field data. Classification routines were employed to group radar pixels into 3 structural classes based on: the Surface Area to Volume ratio (SA/V) of the stems, the SA/V of the branches, and the leaf area index of the canopy. Separate canopy structure maps were then entered into the GIS and bird responses were observed relative to the classes and their boundaries. Follow-on work will consist of extending this approach to neighboring areas, generating structure maps, predicting bird responses across the edges, and make accuracy assessments.

Spatio-temporal evolutions of non-orthogonal equatorial wave modes derived from observations

NASA Astrophysics Data System (ADS)

Barton, Cory

Equatorial waves have been studied extensively due to their importance to the tropical climate and weather systems. Historically, their activity is diagnosed mainly in the wavenumber-frequency domain. Recently, many studies have projected observational data onto parabolic cylinder functions (PCFs), which represent the meridional structure of individual wave modes, to attain time-dependent spatial wave structures. The non-orthogonality of wave modes has yet posed a problem when attempting to separate data into wave fields where the waves project onto the same structure functions. We propose the development and application of a new methodology for equatorial wave expansion of instantaneous flows using the full equatorial wave spectrum. By creating a mapping from the meridional structure function amplitudes to the equatorial wave class amplitudes, we are able to diagnose instantaneous wave fields and determine their evolution. Because all meridional modes are shared by some subset of the wave classes, we require constraints on the wave class amplitudes to yield a closed system with a unique solution for all waves' spatial structures, including IG waves. A synthetic field is analyzed using this method to determine its accuracy for data of a single vertical mode. The wave class spectra diagnosed using this method successfully match the correct dispersion curves even if the incorrect depth is chosen for the spatial decomposition. In the case of more than one depth scale, waves with varying equivalent depth may be similarly identified using the dispersion curves. The primary vertical mode is the 200 m equivalent depth mode, which is that of the peak projection response. A distinct spectral power peak along the Kelvin wave dispersion curve for this value validates our choice of equivalent depth, although the possibility of depth varying with time and height is explored. The wave class spectra diagnosed assuming this depth scale mostly match their expected dispersion curves, showing that this method successfully partitions the wave spectra by calculating wave amplitudes in physical space. This is particularly striking because the time evolution, and therefore the frequency characteristics, is determined simply by a timeseries of independently-diagnosed instantaneous horizontal fields. We use the wave fields diagnosed by this method to study wave evolution in the context of the stratospheric QBO of zonal wind, confirming the continuous evolution of the selection mechanism for equatorial waves in the middle atmosphere. The amplitude cycle synchronized with the background zonal wind as predicted by QBO theory is present in the wave class fields even though the dynamics are not forced by the method itself. We have additionally identified a time-evolution of the zonal wavenumber spectrum responsible for the amplitude variability in physical space. Similar to the temporal characteristics, the vertical structures are also the result of a simple height cross-section through multiple independently-diagnosed levels.

Linking dynamics of the inhibitory network to the input structure

PubMed Central

Komarov, Maxim

2017-01-01

Networks of inhibitory interneurons are found in many distinct classes of biological systems. Inhibitory interneurons govern the dynamics of principal cells and are likely to be critically involved in the coding of information. In this theoretical study, we describe the dynamics of a generic inhibitory network in terms of low-dimensional, simplified rate models. We study the relationship between the structure of external input applied to the network and the patterns of activity arising in response to that stimulation. We found that even a minimal inhibitory network can generate a great diversity of spatio-temporal patterning including complex bursting regimes with non-trivial ratios of burst firing. Despite the complexity of these dynamics, the network’s response patterns can be predicted from the rankings of the magnitudes of external inputs to the inhibitory neurons. This type of invariant dynamics is robust to noise and stable in densely connected networks with strong inhibitory coupling. Our study predicts that the response dynamics generated by an inhibitory network may provide critical insights about the temporal structure of the sensory input it receives. PMID:27650865

Application of artificial neural networks to the design optimization of aerospace structural components

NASA Technical Reports Server (NTRS)

Berke, Laszlo; Patnaik, Surya N.; Murthy, Pappu L. N.

1993-01-01

The application of artificial neural networks to capture structural design expertise is demonstrated. The principal advantage of a trained neural network is that it requires trivial computational effort to produce an acceptable new design. For the class of problems addressed, the development of a conventional expert system would be extremely difficult. In the present effort, a structural optimization code with multiple nonlinear programming algorithms and an artificial neural network code NETS were used. A set of optimum designs for a ring and two aircraft wings for static and dynamic constraints were generated by using the optimization codes. The optimum design data were processed to obtain input and output pairs, which were used to develop a trained artificial neural network with the code NETS. Optimum designs for new design conditions were predicted by using the trained network. Neural net prediction of optimum designs was found to be satisfactory for most of the output design parameters. However, results from the present study indicate that caution must be exercised to ensure that all design variables are within selected error bounds.

Optimum Design of Aerospace Structural Components Using Neural Networks

NASA Technical Reports Server (NTRS)

Berke, L.; Patnaik, S. N.; Murthy, P. L. N.

1993-01-01

The application of artificial neural networks to capture structural design expertise is demonstrated. The principal advantage of a trained neural network is that it requires a trivial computational effort to produce an acceptable new design. For the class of problems addressed, the development of a conventional expert system would be extremely difficult. In the present effort, a structural optimization code with multiple nonlinear programming algorithms and an artificial neural network code NETS were used. A set of optimum designs for a ring and two aircraft wings for static and dynamic constraints were generated using the optimization codes. The optimum design data were processed to obtain input and output pairs, which were used to develop a trained artificial neural network using the code NETS. Optimum designs for new design conditions were predicted using the trained network. Neural net prediction of optimum designs was found to be satisfactory for the majority of the output design parameters. However, results from the present study indicate that caution must be exercised to ensure that all design variables are within selected error bounds.

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

PubMed

Guo, Dong; IJzerman, Adriaan P

2018-01-01

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

Low Energy Nuclear Structure Modeling: Can It Be Improved?

NASA Astrophysics Data System (ADS)

Stone, Jirina R.

Since the discovery of the atomic nucleus in 1911 generations of physicists have devoted enormous effort to understand low energy nuclear structure. Properties of nuclei in their ground state, including mass, binding energy and shape, provide vital input to many areas of sub-atomic physics as well as astrophysics and cosmology. Low energy excited states are equally important for understanding nuclear dynamics. Yet, no consensus exists as to what is the best path to a theory which would not only consistently reproduce a wide variety of experimental data but also have enough predictive power to yield credible predictions in areas where data are still missing. In this contribution some of the main obstacles preventing building such a theory are discussed. These include modification of the free nucleon-nucleon force in the nuclear environment and effects of the sub-nucleon (quark) structure of the nucleon. Selected classes of nuclear models, mean-field, shell and ab-initio models are briefly outlined. Finally, suggestions are made for, at least partial, progress that can be achieved with the quark-meson coupling model, as reported in recent publication [1].

Synthesis, activity and pharmacophore development for isatin-β-thiosemicarbazones with selective activity towards multidrug resistant cellsa

PubMed Central

Hall, Matthew D.; Salam, Noeris K.; Hellawell, Jennifer L.; Fales, Henry M.; Kensler, Caroline B.; Ludwig, Joseph A.; Szakacs, Gergely; Hibbs, David E.; Gottesman, Michael M.

2009-01-01

We have recently identified a new class of compounds that selectively kill cells that express P-glycoprotein (P-gp, MDR1), the ATPase efflux pump that confers multidrug resistance on cancer cells. Several isatin-β-thiosemicarbazones from our initial study have been validated, and a range of analogs synthesized and tested. A number demonstrated improved MDR1-selective activity over the lead, NSC73306 (1). Pharmacophores for cytotoxicity and MDR1-selectivity were generated to delineate the structural features required for activity. The MDR1-selective pharmacophore highlights the importance of aromatic/hydrophobic features at the N4 position of the thiosemicarbazone, and the reliance on the isatin moiety as key bioisosteric contributors. Additionally, a quantitative structure-activity relationship (QSAR) model that yielded a cross-validated correlation coefficient of 0.85 effectively predicts the cytotoxicty of untested thiosemicarbazones. Together, the models serve as effective approaches for predicting structures with MDR1-selective activity, and aid in directing the search for the mechanism of action of 1. PMID:19397322

Analysis of intelligent hinged shell structures: deployable deformation and shape memory effect

NASA Astrophysics Data System (ADS)

Shi, Guang-Hui; Yang, Qing-Sheng; He, X. Q.

2013-12-01

Shape memory polymers (SMPs) are a class of intelligent materials with the ability to recover their initial shape from a temporarily fixable state when subjected to external stimuli. In this work, the thermo-mechanical behavior of a deployable SMP-based hinged structure is modeled by the finite element method using a 3D constitutive model with shape memory effect. The influences of hinge structure parameters on the nonlinear loading process are investigated. The total shape memory of the processes the hinged structure goes through, including loading at high temperature, decreasing temperature with load carrying, unloading at low temperature and recovering the initial shape with increasing temperature, are illustrated. Numerical results show that the present constitutive theory and the finite element method can effectively predict the complicated thermo-mechanical deformation behavior and shape memory effect of SMP-based hinged shell structures.

Mechanical low-frequency filter via modes separation in 3D periodic structures

NASA Astrophysics Data System (ADS)

D'Alessandro, L.; Belloni, E.; Ardito, R.; Braghin, F.; Corigliano, A.

2017-12-01

This work presents a strategy to design three-dimensional elastic periodic structures endowed with complete bandgaps, the first of which is ultra-wide, where the top limits of the first two bandgaps are overstepped in terms of wave transmission in the finite structure. Thus, subsequent bandgaps are merged, approaching the behaviour of a three-dimensional low-pass mechanical filter. This result relies on a proper organization of the modal characteristics, and it is validated by performing numerical and analytical calculations over the unit cell. A prototype of the analysed layout, made of Nylon by means of additive manufacturing, is experimentally tested to assess the transmission spectrum of the finite structure, obtaining good agreement with numerical predictions. The presented strategy paves the way for the development of a class of periodic structures to be used in robust and reliable wave attenuation over a wide frequency band.

Computational design of water-soluble α-helical barrels.

PubMed

Thomson, Andrew R; Wood, Christopher W; Burton, Antony J; Bartlett, Gail J; Sessions, Richard B; Brady, R Leo; Woolfson, Derek N

2014-10-24

The design of protein sequences that fold into prescribed de novo structures is challenging. General solutions to this problem require geometric descriptions of protein folds and methods to fit sequences to these. The α-helical coiled coils present a promising class of protein for this and offer considerable scope for exploring hitherto unseen structures. For α-helical barrels, which have more than four helices and accessible central channels, many of the possible structures remain unobserved. Here, we combine geometrical considerations, knowledge-based scoring, and atomistic modeling to facilitate the design of new channel-containing α-helical barrels. X-ray crystal structures of the resulting designs match predicted in silico models. Furthermore, the observed channels are chemically defined and have diameters related to oligomer state, which present routes to design protein function. Copyright © 2014, American Association for the Advancement of Science.

Highly sensitive detection of individual HEAT and ARM repeats with HHpred and COACH.

PubMed

Kippert, Fred; Gerloff, Dietlind L

2009-09-24

HEAT and ARM repeats occur in a large number of eukaryotic proteins. As these repeats are often highly diverged, the prediction of HEAT or ARM domains can be challenging. Except for the most clear-cut cases, identification at the individual repeat level is indispensable, in particular for determining domain boundaries. However, methods using single sequence queries do not have the sensitivity required to deal with more divergent repeats and, when applied to proteins with known structures, in some cases failed to detect a single repeat. Testing algorithms which use multiple sequence alignments as queries, we found two of them, HHpred and COACH, to detect HEAT and ARM repeats with greatly enhanced sensitivity. Calibration against experimentally determined structures suggests the use of three score classes with increasing confidence in the prediction, and prediction thresholds for each method. When we applied a new protocol using both HHpred and COACH to these structures, it detected 82% of HEAT repeats and 90% of ARM repeats, with the minimum for a given protein of 57% for HEAT repeats and 60% for ARM repeats. Application to bona fide HEAT and ARM proteins or domains indicated that similar numbers can be expected for the full complement of HEAT/ARM proteins. A systematic screen of the Protein Data Bank for false positive hits revealed their number to be low, in particular for ARM repeats. Double false positive hits for a given protein were rare for HEAT and not at all observed for ARM repeats. In combination with fold prediction and consistency checking (multiple sequence alignments, secondary structure prediction, and position analysis), repeat prediction with the new HHpred/COACH protocol dramatically improves prediction in the twilight zone of fold prediction methods, as well as the delineation of HEAT/ARM domain boundaries. A protocol is presented for the identification of individual HEAT or ARM repeats which is straightforward to implement. It provides high sensitivity at a low false positive rate and will therefore greatly enhance the accuracy of predictions of HEAT and ARM domains.

Highly Sensitive Detection of Individual HEAT and ARM Repeats with HHpred and COACH

PubMed Central

Kippert, Fred; Gerloff, Dietlind L.

2009-01-01

Background HEAT and ARM repeats occur in a large number of eukaryotic proteins. As these repeats are often highly diverged, the prediction of HEAT or ARM domains can be challenging. Except for the most clear-cut cases, identification at the individual repeat level is indispensable, in particular for determining domain boundaries. However, methods using single sequence queries do not have the sensitivity required to deal with more divergent repeats and, when applied to proteins with known structures, in some cases failed to detect a single repeat. Methodology and Principal Findings Testing algorithms which use multiple sequence alignments as queries, we found two of them, HHpred and COACH, to detect HEAT and ARM repeats with greatly enhanced sensitivity. Calibration against experimentally determined structures suggests the use of three score classes with increasing confidence in the prediction, and prediction thresholds for each method. When we applied a new protocol using both HHpred and COACH to these structures, it detected 82% of HEAT repeats and 90% of ARM repeats, with the minimum for a given protein of 57% for HEAT repeats and 60% for ARM repeats. Application to bona fide HEAT and ARM proteins or domains indicated that similar numbers can be expected for the full complement of HEAT/ARM proteins. A systematic screen of the Protein Data Bank for false positive hits revealed their number to be low, in particular for ARM repeats. Double false positive hits for a given protein were rare for HEAT and not at all observed for ARM repeats. In combination with fold prediction and consistency checking (multiple sequence alignments, secondary structure prediction, and position analysis), repeat prediction with the new HHpred/COACH protocol dramatically improves prediction in the twilight zone of fold prediction methods, as well as the delineation of HEAT/ARM domain boundaries. Significance A protocol is presented for the identification of individual HEAT or ARM repeats which is straightforward to implement. It provides high sensitivity at a low false positive rate and will therefore greatly enhance the accuracy of predictions of HEAT and ARM domains. PMID:19777061

Short communication: cheminformatics analysis to identify predictors of antiviral drug penetration into the female genital tract.

PubMed

Thompson, Corbin G; Sedykh, Alexander; Nicol, Melanie R; Muratov, Eugene; Fourches, Denis; Tropsha, Alexander; Kashuba, Angela D M

2014-11-01

The exposure of oral antiretroviral (ARV) drugs in the female genital tract (FGT) is variable and almost unpredictable. Identifying an efficient method to find compounds with high tissue penetration would streamline the development of regimens for both HIV preexposure prophylaxis and viral reservoir targeting. Here we describe the cheminformatics investigation of diverse drugs with known FGT penetration using cluster analysis and quantitative structure-activity relationships (QSAR) modeling. A literature search over the 1950-2012 period identified 58 compounds (including 21 ARVs and representing 13 drug classes) associated with their actual concentration data for cervical or vaginal tissue, or cervicovaginal fluid. Cluster analysis revealed significant trends in the penetrative ability for certain chemotypes. QSAR models to predict genital tract concentrations normalized to blood plasma concentrations were developed with two machine learning techniques utilizing drugs' molecular descriptors and pharmacokinetic parameters as inputs. The QSAR model with the highest predictive accuracy had R(2)test=0.47. High volume of distribution, high MRP1 substrate probability, and low MRP4 substrate probability were associated with FGT concentrations ≥1.5-fold plasma concentrations. However, due to the limited FGT data available, prediction performances of all models were low. Despite this limitation, we were able to support our findings by correctly predicting the penetration class of rilpivirine and dolutegravir. With more data to enrich the models, we believe these methods could potentially enhance the current approach of clinical testing.

Hierarchical zeolites from class F coal fly ash

NASA Astrophysics Data System (ADS)

Chitta, Pallavi

Fly ash, a coal combustion byproduct is classified as types class C and class F. Class C fly ash is traditionally recycled for concrete applications and Class F fly ash often disposed in landfills. Class F poses an environmental hazard due to disposal and leaching of heavy metals into ground water and is important to be recycled in order to mitigate the environmental challenges. A major recycling option is to reuse the fly ash as a low-cost raw material for the production of crystalline zeolites, which serve as catalysts, detergents and adsorbents in the chemical industry. Most of the prior literature of fly ash conversion to zeolites does not focus on creating high zeolite surface area zeolites specifically with hierarchical pore structure, which are very important properties in developing a heterogeneous catalyst for catalysis applications. This research work aids in the development of an economical process for the synthesis of high surface area hierarchical zeolites from class F coal fly ash. In this work, synthesis of zeolites from fly ash using classic hydrothermal treatment approach and fusion pretreatment approach were examined. The fusion pretreatment method led to higher extent of dissolution of silica from quartz and mullite phases, which in turn led to higher surface area and pore size of the zeolite. A qualitative kinetic model developed here attributes the difference in silica content to Si/Al ratio of the beginning fraction of fly ash. At near ambient crystallization temperatures and longer crystallization times, the zeolite formed is a hierarchical faujasite with high surface area of at least 360 m2/g. This work enables the large scale recycling of class F coal fly ash to produce zeolites and mitigate environmental concerns. Design of experiments was used to predict surface area and pore sizes of zeolites - thus obviating the need for intense experimentation. The hierarchical zeolite catalyst supports tested for CO2 conversion, yielded hydrocarbons up to C9, a performance attesting the hierarchal pore structure. The preliminary techno-economic feasibility assessment demonstrates a net energy saving of 75% and cost saving of 63% compared to the commercial zeolite manufacturing process.

Consequences of Teen Parents’ Child Care Arrangements for Mothers and Children*

PubMed Central

Mollborn, Stefanie; Blalock, Casey

2013-01-01

Using the nationally representative Early Childhood Longitudinal Study-Birth Cohort (2001 - 2006; N ≈ 7900), we examined child care arrangements among teen parents from birth through prekindergarten. Four latent classes of child care arrangements at 9, 24, and 52 months emerged: “parental care,” “center care,” “paid home-based care,” and “free kin-based care.” Disadvantaged teen-parent families were overrepresented in the “parental care” class, which was negatively associated with children’s preschool reading, math, and behavior scores and mothers’ socioeconomic and fertility outcomes compared to some nonparental care classes. Nonparental care did not predict any negative maternal or child outcomes, and different care arrangements had different benefits for mothers and children. Time spent in nonparental care and improved maternal outcomes contributed to children’s increased scores across domains. Child care classes predicted maternal outcomes similarly in teen-parent and nonteen-parent families, but the “parental care” class predicted some disproportionately negative child outcomes for teen-parent families. PMID:23729861

Preferential attachment and growth dynamics in complex systems

NASA Astrophysics Data System (ADS)

Yamasaki, Kazuko; Matia, Kaushik; Buldyrev, Sergey V.; Fu, Dongfeng; Pammolli, Fabio; Riccaboni, Massimo; Stanley, H. Eugene

2006-09-01

Complex systems can be characterized by classes of equivalency of their elements defined according to system specific rules. We propose a generalized preferential attachment model to describe the class size distribution. The model postulates preferential growth of the existing classes and the steady influx of new classes. According to the model, the distribution changes from a pure exponential form for zero influx of new classes to a power law with an exponential cut-off form when the influx of new classes is substantial. Predictions of the model are tested through the analysis of a unique industrial database, which covers both elementary units (products) and classes (markets, firms) in a given industry (pharmaceuticals), covering the entire size distribution. The model’s predictions are in good agreement with the data. The paper sheds light on the emergence of the exponent τ≈2 observed as a universal feature of many biological, social and economic problems.

Defining Social Class Across Time and Between Groups.

PubMed

Cohen, Dov; Shin, Faith; Liu, Xi; Ondish, Peter; Kraus, Michael W

2017-11-01

We examined changes over four decades and between ethnic groups in how people define their social class. Changes included the increasing importance of income, decreasing importance of occupational prestige, and the demise of the "Victorian bargain," in which poor people who subscribed to conservative sexual and religious norms could think of themselves as middle class. The period also saw changes (among Whites) and continuity (among Black Americans) in subjective status perceptions. For Whites (and particularly poor Whites), their perceptions of enhanced social class were greatly reduced. Poor Whites now view their social class as slightly but significantly lower than their poor Black and Latino counterparts. For Black respondents, a caste-like understanding of social class persisted, as they continued to view their class standing as relatively independent of their achieved education, income, and occupation. Such achievement indicators, however, predicted Black respondents' self-esteem more than they predicted self-esteem for any other group.

Computational analysis of structure-based interactions and ligand properties can predict efflux effects on antibiotics.

PubMed

Sarkar, Aurijit; Anderson, Kelcey C; Kellogg, Glen E

2012-06-01

AcrA-AcrB-TolC efflux pumps extrude drugs of multiple classes from bacterial cells and are a leading cause for antimicrobial resistance. Thus, they are of paramount interest to those engaged in antibiotic discovery. Accurate prediction of antibiotic efflux has been elusive, despite several studies aimed at this purpose. Minimum inhibitory concentration (MIC) ratios of 32 β-lactam antibiotics were collected from literature. 3-Dimensional Quantitative Structure-Activity Relationship on the β-lactam antibiotic structures revealed seemingly predictive models (q(2)=0.53), but the lack of a general superposition rule does not allow its use on antibiotics that lack the β-lactam moiety. Since MIC ratios must depend on interactions of antibiotics with lipid membranes and transport proteins during influx, capture and extrusion of antibiotics from the bacterial cell, descriptors representing these factors were calculated and used in building mathematical models that quantitatively classify antibiotics as having high/low efflux (>93% accuracy). Our models provide preliminary evidence that it is possible to predict the effects of antibiotic efflux if the passage of antibiotics into, and out of, bacterial cells is taken into account--something descriptor and field-based QSAR models cannot do. While the paucity of data in the public domain remains the limiting factor in such studies, these models show significant improvements in predictions over simple LogP-based regression models and should pave the path toward further work in this field. This method should also be extensible to other pharmacologically and biologically relevant transport proteins. Copyright © 2012 Elsevier Masson SAS. All rights reserved.

Short-term droughts forecast using Markov chain model in Victoria, Australia

NASA Astrophysics Data System (ADS)

Rahmat, Siti Nazahiyah; Jayasuriya, Niranjali; Bhuiyan, Muhammed A.

2017-07-01

A comprehensive risk management strategy for dealing with drought should include both short-term and long-term planning. The objective of this paper is to present an early warning method to forecast drought using the Standardised Precipitation Index (SPI) and a non-homogeneous Markov chain model. A model such as this is useful for short-term planning. The developed method has been used to forecast droughts at a number of meteorological monitoring stations that have been regionalised into six (6) homogenous clusters with similar drought characteristics based on SPI. The non-homogeneous Markov chain model was used to estimate drought probabilities and drought predictions up to 3 months ahead. The drought severity classes defined using the SPI were computed at a 12-month time scale. The drought probabilities and the predictions were computed for six clusters that depict similar drought characteristics in Victoria, Australia. Overall, the drought severity class predicted was quite similar for all the clusters, with the non-drought class probabilities ranging from 49 to 57 %. For all clusters, the near normal class had a probability of occurrence varying from 27 to 38 %. For the more moderate and severe classes, the probabilities ranged from 2 to 13 % and 3 to 1 %, respectively. The developed model predicted drought situations 1 month ahead reasonably well. However, 2 and 3 months ahead predictions should be used with caution until the models are developed further.

Efficient visual coding and the predictability of eye movements on natural movies.

PubMed

Vig, Eleonora; Dorr, Michael; Barth, Erhardt

2009-01-01

We deal with the analysis of eye movements made on natural movies in free-viewing conditions. Saccades are detected and used to label two classes of movie patches as attended and non-attended. Machine learning techniques are then used to determine how well the two classes can be separated, i.e., how predictable saccade targets are. Although very simple saliency measures are used and then averaged to obtain just one average value per scale, the two classes can be separated with an ROC score of around 0.7, which is higher than previously reported results. Moreover, predictability is analysed for different representations to obtain indirect evidence for the likelihood of a particular representation. It is shown that the predictability correlates with the local intrinsic dimension in a movie.

47 CFR 73.6010 - Class A TV station protected contour.

Code of Federal Regulations, 2010 CFR

2010-10-01

... 47 Telecommunication 4 2010-10-01 2010-10-01 false Class A TV station protected contour. 73.6010... RADIO BROADCAST SERVICES Class A Television Broadcast Stations § 73.6010 Class A TV station protected contour. (a) A Class A TV station will be protected from interference within the following predicted...

47 CFR 73.6010 - Class A TV station protected contour.

Code of Federal Regulations, 2013 CFR

2013-10-01

... 47 Telecommunication 4 2013-10-01 2013-10-01 false Class A TV station protected contour. 73.6010... RADIO BROADCAST SERVICES Class A Television Broadcast Stations § 73.6010 Class A TV station protected contour. (a) A Class A TV station will be protected from interference within the following predicted...

47 CFR 73.6010 - Class A TV station protected contour.

Code of Federal Regulations, 2012 CFR

2012-10-01

... 47 Telecommunication 4 2012-10-01 2012-10-01 false Class A TV station protected contour. 73.6010... RADIO BROADCAST SERVICES Class A Television Broadcast Stations § 73.6010 Class A TV station protected contour. (a) A Class A TV station will be protected from interference within the following predicted...

47 CFR 73.6010 - Class A TV station protected contour.

Code of Federal Regulations, 2014 CFR

2014-10-01

... 47 Telecommunication 4 2014-10-01 2014-10-01 false Class A TV station protected contour. 73.6010... RADIO BROADCAST SERVICES Class A Television Broadcast Stations § 73.6010 Class A TV station protected contour. (a) A Class A TV station will be protected from interference within the following predicted...

47 CFR 73.6010 - Class A TV station protected contour.

Code of Federal Regulations, 2011 CFR

2011-10-01

... 47 Telecommunication 4 2011-10-01 2011-10-01 false Class A TV station protected contour. 73.6010... RADIO BROADCAST SERVICES Class A Television Broadcast Stations § 73.6010 Class A TV station protected contour. (a) A Class A TV station will be protected from interference within the following predicted...

Latent profile analyses of posttraumatic stress disorder, depression and generalized anxiety disorder symptoms in trauma-exposed soldiers.

PubMed

Contractor, Ateka A; Elhai, Jon D; Fine, Thomas H; Tamburrino, Marijo B; Cohen, Gregory; Shirley, Edwin; Chan, Philip K; Liberzon, Israel; Galea, Sandro; Calabrese, Joseph R

2015-09-01

Posttraumatic stress disorder (PTSD) is comorbid with major depressive disorder (MDD; Kessler et al., 1995) and generalized anxiety disorder (GAD; Brown et al., 2001). We aimed to (1) assess discrete patterns of post-trauma PTSD-depression-GAD symptoms using latent profile analyses (LPAs), and (2) assess covariates (gender, income, education, age) in defining the best fitting class solution. The PTSD Checklist (assessing PTSD symptoms), GAD-7 scale (assessing GAD symptoms), and Patient Health Questionnaire-9 (assessing depression) were administered to 1266 trauma-exposed Ohio National Guard soldiers. Results indicated three discrete subgroups based on symptom patterns with mild (class 1), moderate (class 2) and severe (class 3) levels of symptomatology. Classes differed in symptom severity rather than symptom type. Income and education significantly predicted class 1 versus class 3 membership, and class 2 versus class 3. In conclusion, there is heterogeneity regarding severity of PTSD-depression-GAD symptomatology among trauma-exposed soldiers, with income and education predictive of class membership. Copyright © 2015 Elsevier Ltd. All rights reserved.

Defining and Predicting Patterns of Early Response in a Web-Based Intervention for Depression

PubMed Central

Arndt, Alice; Rubel, Julian; Berger, Thomas; Schröder, Johanna; Späth, Christina; Meyer, Björn; Greiner, Wolfgang; Gräfe, Viola; Hautzinger, Martin; Fuhr, Kristina; Rose, Matthias; Nolte, Sandra; Löwe, Bernd; Hohagen, Fritz; Klein, Jan Philipp; Moritz, Steffen

2017-01-01

Background Web-based interventions for individuals with depressive disorders have been a recent focus of research and may be an effective adjunct to face-to-face psychotherapy or pharmacological treatment. Objective The aim of our study was to examine the early change patterns in Web-based interventions to identify differential effects. Methods We applied piecewise growth mixture modeling (PGMM) to identify different latent classes of early change in individuals with mild-to-moderate depression (n=409) who underwent a CBT-based web intervention for depression. Results Overall, three latent classes were identified (N=409): Two early response classes (n=158, n=185) and one early deterioration class (n=66). Latent classes differed in terms of outcome (P<.001) and adherence (P=.03) in regard to the number of modules (number of modules with a duration of at least 10 minutes) and the number of assessments (P<.001), but not in regard to the overall amount of time using the system. Class membership significantly improved outcome prediction by 24.8% over patient intake characteristics (P<.001) and significantly added to the prediction of adherence (P=.04). Conclusions These findings suggest that in Web-based interventions outcome and adherence can be predicted by patterns of early change, which can inform treatment decisions and potentially help optimize the allocation of scarce clinical resources. PMID:28600278

LYRA, a webserver for lymphocyte receptor structural modeling.

PubMed

Klausen, Michael Schantz; Anderson, Mads Valdemar; Jespersen, Martin Closter; Nielsen, Morten; Marcatili, Paolo

2015-07-01

The accurate structural modeling of B- and T-cell receptors is fundamental to gain a detailed insight in the mechanisms underlying immunity and in developing new drugs and therapies. The LYRA (LYmphocyte Receptor Automated modeling) web server (http://www.cbs.dtu.dk/services/LYRA/) implements a complete and automated method for building of B- and T-cell receptor structural models starting from their amino acid sequence alone. The webserver is freely available and easy to use for non-specialists. Upon submission, LYRA automatically generates alignments using ad hoc profiles, predicts the structural class of each hypervariable loop, selects the best templates in an automatic fashion, and provides within minutes a complete 3D model that can be downloaded or inspected online. Experienced users can manually select or exclude template structures according to case specific information. LYRA is based on the canonical structure method, that in the last 30 years has been successfully used to generate antibody models of high accuracy, and in our benchmarks this approach proves to achieve similarly good results on TCR modeling, with a benchmarked average RMSD accuracy of 1.29 and 1.48 Å for B- and T-cell receptors, respectively. To the best of our knowledge, LYRA is the first automated server for the prediction of TCR structure. © The Author(s) 2015. Published by Oxford University Press on behalf of Nucleic Acids Research.

Classifying environmental pollutants: Part 3. External validation of the classification system.

PubMed

Verhaar, H J; Solbé, J; Speksnijder, J; van Leeuwen, C J; Hermens, J L

2000-04-01

In order to validate a classification system for the prediction of the toxic effect concentrations of organic environmental pollutants to fish, all available fish acute toxicity data were retrieved from the ECETOC database, a database of quality-evaluated aquatic toxicity measurements created and maintained by the European Centre for the Ecotoxicology and Toxicology of Chemicals. The individual chemicals for which these data were available were classified according to the rulebase under consideration and predictions of effect concentrations or ranges of possible effect concentrations were generated. These predictions were compared to the actual toxicity data retrieved from the database. The results of this comparison show that generally, the classification system provides adequate predictions of either the aquatic toxicity (class 1) or the possible range of toxicity (other classes) of organic compounds. A slight underestimation of effect concentrations occurs for some highly water soluble, reactive chemicals with low log K(ow) values. On the other end of the scale, some compounds that are classified as belonging to a relatively toxic class appear to belong to the so-called baseline toxicity compounds. For some of these, additional classification rules are proposed. Furthermore, some groups of compounds cannot be classified, although they should be amenable to predictions. For these compounds additional research as to class membership and associated prediction rules is proposed.

Phase stabilities of pyrite-related MTCh compounds (M=Ni, Pd, Pt; T=Si, Ge, Sn, Pb; Ch=S, Se, Te): A systematic DFT study

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

Bachhuber, Frederik; School of Chemical Sciences, University of Auckland, Private Bag 92019, Auckland; Krach, Alexander

2015-03-15

Pyrite-type and related systems appear for a wide range of binary and ternary combinations of transition metals and main group elements that form Zintl type dumbbell anion units. Those representatives with 20 valence electrons exhibit an extraordinary structural flexibility and interesting properties as low-gap semiconductors or thermoelectric and electrode materials. This work is devoted to the systematic exploration of novel compounds within the class of MTCh compounds (M=Ni, Pd, Pt; T=Si, Ge, Sn, Pb; Ch=S, Se, Te) by means of density functional calculations. Their preferred structures are predicted from an extended scheme of colored pyrites and marcasites. To determine theirmore » stabilities, competing binary MT{sub 2} and MCh{sub 2} boundary phases are taken into account as well as ternary M{sub 3}T{sub 2}Ch{sub 2} and M{sub 2}T{sub 3}Ch{sub 3} systems. Recently established stability diagrams are presented to account for MTCh ordering phenomena with a focus on a not-yet-reported ordering variant of the NiAs{sub 2} type. Due to the good agreement with experimental data available for several PtTCh systems, the predictions for the residual systems are considered sufficiently accurate. - Graphical abstract: Compositional and structural stability of MTCh compounds is investigated from first principle calculations. A conceptional approach is presented to study and predict novel stable and metastable compounds and structures of low gap semiconductors with TCh dumbbell units that are isoelectronic and structurally related to pyrite (FeS{sub 2}). - Highlights: • Study of compositional stability of MTCh vs. M{sub 3}T{sub 2}Ch{sub 2} and M{sub 2}T{sub 3}Ch{sub 3} compounds. • Study of structural stability of known and novel MTCh compounds. • Prediction of novel stable and metastable structures and compounds isoelectronic to pyrite, FeS{sub 2}.« less

Maximizing lipocalin prediction through balanced and diversified training set and decision fusion.

PubMed

Nath, Abhigyan; Subbiah, Karthikeyan

2015-12-01

Lipocalins are short in sequence length and perform several important biological functions. These proteins are having less than 20% sequence similarity among paralogs. Experimentally identifying them is an expensive and time consuming process. The computational methods based on the sequence similarity for allocating putative members to this family are also far elusive due to the low sequence similarity existing among the members of this family. Consequently, the machine learning methods become a viable alternative for their prediction by using the underlying sequence/structurally derived features as the input. Ideally, any machine learning based prediction method must be trained with all possible variations in the input feature vector (all the sub-class input patterns) to achieve perfect learning. A near perfect learning can be achieved by training the model with diverse types of input instances belonging to the different regions of the entire input space. Furthermore, the prediction performance can be improved through balancing the training set as the imbalanced data sets will tend to produce the prediction bias towards majority class and its sub-classes. This paper is aimed to achieve (i) the high generalization ability without any classification bias through the diversified and balanced training sets as well as (ii) enhanced the prediction accuracy by combining the results of individual classifiers with an appropriate fusion scheme. Instead of creating the training set randomly, we have first used the unsupervised Kmeans clustering algorithm to create diversified clusters of input patterns and created the diversified and balanced training set by selecting an equal number of patterns from each of these clusters. Finally, probability based classifier fusion scheme was applied on boosted random forest algorithm (which produced greater sensitivity) and K nearest neighbour algorithm (which produced greater specificity) to achieve the enhanced predictive performance than that of individual base classifiers. The performance of the learned models trained on Kmeans preprocessed training set is far better than the randomly generated training sets. The proposed method achieved a sensitivity of 90.6%, specificity of 91.4% and accuracy of 91.0% on the first test set and sensitivity of 92.9%, specificity of 96.2% and accuracy of 94.7% on the second blind test set. These results have established that diversifying training set improves the performance of predictive models through superior generalization ability and balancing the training set improves prediction accuracy. For smaller data sets, unsupervised Kmeans based sampling can be an effective technique to increase generalization than that of the usual random splitting method. Copyright © 2015 Elsevier Ltd. All rights reserved.

A comprehensive analysis of the thermodynamic events involved in ligand receptor binding using CoRIA and its variants

NASA Astrophysics Data System (ADS)

Verma, Jitender; Khedkar, Vijay M.; Prabhu, Arati S.; Khedkar, Santosh A.; Malde, Alpeshkumar K.; Coutinho, Evans C.

2008-02-01

Quantitative Structure-Activity Relationships (QSAR) are being used since decades for prediction of biological activity, lead optimization, classification, identification and explanation of the mechanisms of drug action, and prediction of novel structural leads in drug discovery. Though the technique has lived up to its expectations in many aspects, much work still needs to be done in relation to problems related to the rational design of peptides. Peptides are the drugs of choice in many situations, however, designing them rationally is a complicated task and the complexity increases with the length of their sequence. In order to deal with the problem of peptide optimization, one of our recently developed QSAR formalisms CoRIA (Comparative Residue Interaction Analysis) is being expanded and modified as: reverse-CoRIA ( rCoRIA) and mixed-CoRIA ( mCoRIA) approaches. In these methodologies, the peptide is fragmented into individual units and the interaction energies (van der Waals, Coulombic and hydrophobic) of each amino acid in the peptide with the receptor as a whole ( rCoRIA) and with individual active site residues in the receptor ( mCoRIA) are calculated, which along with other thermodynamic descriptors, are used as independent variables that are correlated to the biological activity by chemometric methods. As a test case, the three CoRIA methodologies have been validated on a dataset of diverse nonamer peptides that bind to the Class I major histocompatibility complex molecule HLA-A*0201, and for which some structure activity relationships have already been reported. The different models developed, and validated both internally as well as externally, were found to be robust with statistically significant values of r 2 (correlation coefficient) and r 2 pred (predictive r 2). These models were able to identify all the structure activity relationships known for this class of peptides, as well uncover some new relationships. This means that these methodologies will perform well for other peptide datasets too. The major advantage of these approaches is that they explicitly utilize the 3D structures of small molecules or peptides as well as their macromolecular targets, to extract position-specific information about important interactions between the ligand and receptor, which can assist the medicinal and computational chemists in designing new molecules, and biologists in studying the influence of mutations in the target receptor on ligand binding.

Prediction of incidence and stability of alcohol use disorders by latent internalizing psychopathology risk profiles in adolescence and young adulthood.

PubMed

Behrendt, Silke; Bühringer, Gerhard; Höfler, Michael; Lieb, Roselind; Beesdo-Baum, Katja

2017-10-01

Comorbid internalizing mental disorders in alcohol use disorders (AUD) can be understood as putative independent risk factors for AUD or as expressions of underlying shared psychopathology vulnerabilities. However, it remains unclear whether: 1) specific latent internalizing psychopathology risk-profiles predict AUD-incidence and 2) specific latent internalizing comorbidity-profiles in AUD predict AUD-stability. To investigate baseline latent internalizing psychopathology risk profiles as predictors of subsequent AUD-incidence and -stability in adolescents and young adults. Data from the prospective-longitudinal EDSP study (baseline age 14-24 years) were used. The study-design included up to three follow-up assessments in up to ten years. DSM-IV mental disorders were assessed with the DIA-X/M-CIDI. To investigate risk-profiles and their associations with AUD-outcomes, latent class analysis with auxiliary outcome variables was applied. AUD-incidence: a 4-class model (N=1683) was identified (classes: normative-male [45.9%], normative-female [44.2%], internalizing [5.3%], nicotine dependence [4.5%]). Compared to the normative-female class, all other classes were associated with a higher risk of subsequent incident alcohol dependence (p<0.05). AUD-stability: a 3-class model (N=1940) was identified with only one class (11.6%) with high probabilities for baseline AUD. This class was further characterized by elevated substance use disorder (SUD) probabilities and predicted any subsequent AUD (OR 8.5, 95% CI 5.4-13.3). An internalizing vulnerability may constitute a pathway to AUD incidence in adolescence and young adulthood. In contrast, no indication for a role of internalizing comorbidity profiles in AUD-stability was found, which may indicate a limited importance of such profiles - in contrast to SUD-related profiles - in AUD stability. Copyright © 2017 Elsevier B.V. All rights reserved.

HLA class I expression predicts prognosis and therapeutic benefits from tyrosine kinase inhibitors in metastatic renal-cell carcinoma patients.

PubMed

Wang, Jiajun; Liu, Li; Qu, Yang; Xi, Wei; Xia, Yu; Bai, Qi; Xiong, Ying; Long, Qilai; Xu, Jiejie; Guo, Jianming

2018-01-01

Classical HLA class I antigen is highly involved in antigen presentation and adaptive immune response against tumor. In this study, we explored its predictive value for treatment response and survival in metastatic renal-cell carcinoma (mRCC) patients. A TKI cohort of 111 mRCC patients treated with sunitinib or sorafenib and a non-TKI cohort of 160 mRCC patients treated with interleukin-2 or interferon-α-based immunotherapy at a single institution were retrospectively enrolled. HLA class I expression and cytotoxic T lymphocyte (CTL) density was assessed by immunohistochemistry on tissue microarrays. Association between HLA class I and CTL was also assessed in the TCGA KIRC cohort. In the TKI cohort, down-regulated HLA class I was associated with lower objective response rate of TKI therapy (P = 0.004), shorter overall survival (OS) (P = 0.001), and shorter progression free survival (PFS) (P < 0.001). Multivariate Cox regression model defined HLA expression as an independent prognostic factor for both OS [hazard ratio 1.687 (95% CI 1.045-2.724), P = 0.032] and PFS [hazard ratio 2.139 (95% CI 1.376-3.326), P = 0.001]. In the non-TKI cohort, HLA class I was not significantly associated with survival. HLA class I expression was associated with CTL infiltration and function, and its prognostic value was more predominant in CTL high-density tumors (P < 0.001) rather than CTL low-density tumors (P = 0.294). Classical HLA class I expression can serve as a potential predictive biomarker for TKI therapy in mRCC patients. Its predictive value was restricted in CTL high-density tumors. However, further external validations and functional investigations are still required.

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.

Structural (Performance) class Potential for North America

Treesearch

Eric Jones; David E. Kretschmann; Kevin Cheung

2014-01-01

Structural class systems are species-independent product classification systems for structural timber. They are used throughout the world to reduce the number of species and grade choices that face the designer of wood construction projects. Structural class systems offer an opportunity to simplify timber specification in North America and to encourage more effective...

Predicting functional decline and survival in amyotrophic lateral sclerosis.

PubMed

Ong, Mei-Lyn; Tan, Pei Fang; Holbrook, Joanna D

2017-01-01

Better predictors of amyotrophic lateral sclerosis disease course could enable smaller and more targeted clinical trials. Partially to address this aim, the Prize for Life foundation collected de-identified records from amyotrophic lateral sclerosis sufferers who participated in clinical trials of investigational drugs and made them available to researchers in the PRO-ACT database. In this study, time series data from PRO-ACT subjects were fitted to exponential models. Binary classes for decline in the total score of amyotrophic lateral sclerosis functional rating scale revised (ALSFRS-R) (fast/slow progression) and survival (high/low death risk) were derived. Data was segregated into training and test sets via cross validation. Learning algorithms were applied to the demographic, clinical and laboratory parameters in the training set to predict ALSFRS-R decline and the derived fast/slow progression and high/low death risk categories. The performance of predictive models was assessed by cross-validation in the test set using Receiver Operator Curves and root mean squared errors. A model created using a boosting algorithm containing the decline in four parameters (weight, alkaline phosphatase, albumin and creatine kinase) post baseline, was able to predict functional decline class (fast or slow) with fair accuracy (AUC = 0.82). However similar approaches to build a predictive model for decline class by baseline subject characteristics were not successful. In contrast, baseline values of total bilirubin, gamma glutamyltransferase, urine specific gravity and ALSFRS-R item score-climbing stairs were sufficient to predict survival class. Using combinations of small numbers of variables it was possible to predict classes of functional decline and survival across the 1-2 year timeframe available in PRO-ACT. These findings may have utility for design of future ALS clinical trials.

Around the macrolide - Impact of 3D structure of macrocycles on lipophilicity and cellular accumulation.

PubMed

Koštrun, Sanja; Munic Kos, Vesna; Matanović Škugor, Maja; Palej Jakopović, Ivana; Malnar, Ivica; Dragojević, Snježana; Ralić, Jovica; Alihodžić, Sulejman

2017-06-16

The aim of this study was to investigate lipophilicity and cellular accumulation of rationally designed azithromycin and clarithromycin derivatives at the molecular level. The effect of substitution site and substituent properties on a global physico-chemical profile and cellular accumulation of investigated compounds was studied using calculated structural parameters as well as experimentally determined lipophilicity. In silico models based on the 3D structure of molecules were generated to investigate conformational effect on studied properties and to enable prediction of lipophilicity and cellular accumulation for this class of molecules based on non-empirical parameters. The applicability of developed models was explored on a validation and test sets and compared with previously developed empirical models. Copyright © 2017 Elsevier Masson SAS. All rights reserved.

On the versatility of electronic structures in polymethine dyes

NASA Astrophysics Data System (ADS)

Pascal, Simon; Haefele, Alexandre; Monnereau, Cyrille; Charaf-Eddin, Azzam; Jacquemin, Denis; Le Guennic, Boris; Maury, Olivier; Andraud, Chantal

2014-10-01

This article provides an overview of the photophysical behavior diversity of polymethine chromophores which are ubiquitous in biological imaging and material sciences. One major challenge in this class of chromophore is to correlate the chemical structure to the observed optical properties, especially when symmetry-breaking phenomena occur. With the constant concern for rationalization of their spectroscopy, we propose an extended classification of polymethine dyes based on their ground state electronic configuration using three limit forms namely: cyanine, dipole and bis-dipole. The chemical modifications of the dye and the influence of exogenous parameters can promote dramatic spectroscopic changes that can be correlated to significant electronic reorganization between the three-abovementioned forms. The deep understanding of such phenomena should allow to identify, predict and take advantage of the versatile electronic structure of polymethines.

Structural Analysis of PTM Hotspots (SAPH-ire) – A Quantitative Informatics Method Enabling the Discovery of Novel Regulatory Elements in Protein Families*

PubMed Central

Dewhurst, Henry M.; Choudhury, Shilpa; Torres, Matthew P.

2015-01-01

Predicting the biological function potential of post-translational modifications (PTMs) is becoming increasingly important in light of the exponential increase in available PTM data from high-throughput proteomics. We developed structural analysis of PTM hotspots (SAPH-ire)—a quantitative PTM ranking method that integrates experimental PTM observations, sequence conservation, protein structure, and interaction data to allow rank order comparisons within or between protein families. Here, we applied SAPH-ire to the study of PTMs in diverse G protein families, a conserved and ubiquitous class of proteins essential for maintenance of intracellular structure (tubulins) and signal transduction (large and small Ras-like G proteins). A total of 1728 experimentally verified PTMs from eight unique G protein families were clustered into 451 unique hotspots, 51 of which have a known and cited biological function or response. Using customized software, the hotspots were analyzed in the context of 598 unique protein structures. By comparing distributions of hotspots with known versus unknown function, we show that SAPH-ire analysis is predictive for PTM biological function. Notably, SAPH-ire revealed high-ranking hotspots for which a functional impact has not yet been determined, including phosphorylation hotspots in the N-terminal tails of G protein gamma subunits—conserved protein structures never before reported as regulators of G protein coupled receptor signaling. To validate this prediction we used the yeast model system for G protein coupled receptor signaling, revealing that gamma subunit–N-terminal tail phosphorylation is activated in response to G protein coupled receptor stimulation and regulates protein stability in vivo. These results demonstrate the utility of integrating protein structural and sequence features into PTM prioritization schemes that can improve the analysis and functional power of modification-specific proteomics data. PMID:26070665

FoldGPCR: structure prediction protocol for the transmembrane domain of G protein-coupled receptors from class A.

PubMed

Michino, Mayako; Chen, Jianhan; Stevens, Raymond C; Brooks, Charles L

2010-08-01

Building reliable structural models of G protein-coupled receptors (GPCRs) is a difficult task because of the paucity of suitable templates, low sequence identity, and the wide variety of ligand specificities within the superfamily. Template-based modeling is known to be the most successful method for protein structure prediction. However, refinement of homology models within 1-3 A C alpha RMSD of the native structure remains a major challenge. Here, we address this problem by developing a novel protocol (foldGPCR) for modeling the transmembrane (TM) region of GPCRs in complex with a ligand, aimed to accurately model the structural divergence between the template and target in the TM helices. The protocol is based on predicted conserved inter-residue contacts between the template and target, and exploits an all-atom implicit membrane force field. The placement of the ligand in the binding pocket is guided by biochemical data. The foldGPCR protocol is implemented by a stepwise hierarchical approach, in which the TM helical bundle and the ligand are assembled by simulated annealing trials in the first step, and the receptor-ligand complex is refined with replica exchange sampling in the second step. The protocol is applied to model the human beta(2)-adrenergic receptor (beta(2)AR) bound to carazolol, using contacts derived from the template structure of bovine rhodopsin. Comparison with the X-ray crystal structure of the beta(2)AR shows that our protocol is particularly successful in accurately capturing helix backbone irregularities and helix-helix packing interactions that distinguish rhodopsin from beta(2)AR. (c) 2010 Wiley-Liss, Inc.

Prediction and explanation in the multiverse

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

Garriga, J.; Vilenkin, A.

2008-02-15

Probabilities in the multiverse can be calculated by assuming that we are typical representatives in a given reference class. But is this class well defined? What should be included in the ensemble in which we are supposed to be typical? There is a widespread belief that this question is inherently vague, and that there are various possible choices for the types of reference objects which should be counted in. Here we argue that the 'ideal' reference class (for the purpose of making predictions) can be defined unambiguously in a rather precise way, as the set of all observers with identicalmore » information content. When the observers in a given class perform an experiment, the class branches into subclasses who learn different information from the outcome of that experiment. The probabilities for the different outcomes are defined as the relative numbers of observers in each subclass. For practical purposes, wider reference classes can be used, where we trace over all information which is uncorrelated to the outcome of the experiment, or whose correlation with it is beyond our current understanding. We argue that, once we have gathered all practically available evidence, the optimal strategy for making predictions is to consider ourselves typical in any reference class we belong to, unless we have evidence to the contrary. In the latter case, the class must be correspondingly narrowed.« less

Profiling Physical Activity, Diet, Screen and Sleep Habits in Portuguese Children

PubMed Central

Pereira, Sara; Katzmarzyk, Peter T.; Gomes, Thayse Natacha; Borges, Alessandra; Santos, Daniel; Souza, Michele; dos Santos, Fernanda K.; Chaves, Raquel N.; Champagne, Catherine M.; Barreira, Tiago V.; Maia, José A.R.

2015-01-01

Obesity in children is partly due to unhealthy lifestyle behaviours, e.g., sedentary activity and poor dietary choices. This trend has been seen globally. To determine the extent of these behaviours in a Portuguese population of children, 686 children 9.5 to 10.5 years of age were studied. Our aims were to: (1) describe profiles of children’s lifestyle behaviours; (2) identify behaviour pattern classes; and (3) estimate combined effects of individual/socio-demographic characteristics in predicting class membership. Physical activity and sleep time were estimated by 24-h accelerometry. Nutritional habits, screen time and socio-demographics were obtained. Latent Class Analysis was used to determine unhealthy lifestyle behaviours. Logistic regression analysis predicted class membership. About 78% of children had three or more unhealthy lifestyle behaviours, while 0.2% presented no risk. Two classes were identified: Class 1-Sedentary, poorer diet quality; and Class 2-Insufficiently active, better diet quality, 35% and 65% of the population, respectively. More mature children (Odds Ratio (OR) = 6.75; 95%CI = 4.74–10.41), and boys (OR = 3.06; 95% CI = 1.98–4.72) were more likely to be overweight/obese. However, those belonging to Class 2 were less likely to be overweight/obese (OR = 0.60; 95% CI = 0.43–0.84). Maternal education level and household income did not significantly predict weight status (p ≥ 0.05). PMID:26043034

An Evolution-Based Approach to De Novo Protein Design and Case Study on Mycobacterium tuberculosis

PubMed Central

Brender, Jeffrey R.; Czajka, Jeff; Marsh, David; Gray, Felicia; Cierpicki, Tomasz; Zhang, Yang

2013-01-01

Computational protein design is a reverse procedure of protein folding and structure prediction, where constructing structures from evolutionarily related proteins has been demonstrated to be the most reliable method for protein 3-dimensional structure prediction. Following this spirit, we developed a novel method to design new protein sequences based on evolutionarily related protein families. For a given target structure, a set of proteins having similar fold are identified from the PDB library by structural alignments. A structural profile is then constructed from the protein templates and used to guide the conformational search of amino acid sequence space, where physicochemical packing is accommodated by single-sequence based solvation, torsion angle, and secondary structure predictions. The method was tested on a computational folding experiment based on a large set of 87 protein structures covering different fold classes, which showed that the evolution-based design significantly enhances the foldability and biological functionality of the designed sequences compared to the traditional physics-based force field methods. Without using homologous proteins, the designed sequences can be folded with an average root-mean-square-deviation of 2.1 Å to the target. As a case study, the method is extended to redesign all 243 structurally resolved proteins in the pathogenic bacteria Mycobacterium tuberculosis, which is the second leading cause of death from infectious disease. On a smaller scale, five sequences were randomly selected from the design pool and subjected to experimental validation. The results showed that all the designed proteins are soluble with distinct secondary structure and three have well ordered tertiary structure, as demonstrated by circular dichroism and NMR spectroscopy. Together, these results demonstrate a new avenue in computational protein design that uses knowledge of evolutionary conservation from protein structural families to engineer new protein molecules of improved fold stability and biological functionality. PMID:24204234

Gender and the Construction of Class.

ERIC Educational Resources Information Center

Acker, Joan

Only by recognizing that class is not gender neutral can the processes of class formation and reproduction be understood. Class is defined as a process in which human beings take an active part, rather than a structure of categories into which individuals may be inserted. Gender organizes or structures class in many different ways. For example,…

Hierarchical Ensemble Methods for Protein Function Prediction

PubMed Central

2014-01-01

Protein function prediction is a complex multiclass multilabel classification problem, characterized by multiple issues such as the incompleteness of the available annotations, the integration of multiple sources of high dimensional biomolecular data, the unbalance of several functional classes, and the difficulty of univocally determining negative examples. Moreover, the hierarchical relationships between functional classes that characterize both the Gene Ontology and FunCat taxonomies motivate the development of hierarchy-aware prediction methods that showed significantly better performances than hierarchical-unaware “flat” prediction methods. In this paper, we provide a comprehensive review of hierarchical methods for protein function prediction based on ensembles of learning machines. According to this general approach, a separate learning machine is trained to learn a specific functional term and then the resulting predictions are assembled in a “consensus” ensemble decision, taking into account the hierarchical relationships between classes. The main hierarchical ensemble methods proposed in the literature are discussed in the context of existing computational methods for protein function prediction, highlighting their characteristics, advantages, and limitations. Open problems of this exciting research area of computational biology are finally considered, outlining novel perspectives for future research. PMID:25937954

Predicting allergic contact dermatitis: a hierarchical structure activity relationship (SAR) approach to chemical classification using topological and quantum chemical descriptors

NASA Astrophysics Data System (ADS)

Basak, Subhash C.; Mills, Denise; Hawkins, Douglas M.

2008-06-01

A hierarchical classification study was carried out based on a set of 70 chemicals—35 which produce allergic contact dermatitis (ACD) and 35 which do not. This approach was implemented using a regular ridge regression computer code, followed by conversion of regression output to binary data values. The hierarchical descriptor classes used in the modeling include topostructural (TS), topochemical (TC), and quantum chemical (QC), all of which are based solely on chemical structure. The concordance, sensitivity, and specificity are reported. The model based on the TC descriptors was found to be the best, while the TS model was extremely poor.

Tunable nano Peltier cooling device from geometric effects using a single graphene nanoribbon

NASA Astrophysics Data System (ADS)

Li, Wan-Ju; Yao, Dao-Xin; Carlson, E. W.

2014-08-01

Based on the phenomenon of curvature-induced doping in graphene we propose a class of Peltier cooling devices, produced by geometrical effects, without gating. We show how a graphene nanoribbon laid on an array of curved nano cylinders can be used to create a targeted and tunable cooling device. Using two different approaches, the Nonequilibrium Green's Function (NEGF) method and experimental inputs, we predict that the cooling power of such a device can approach the order of kW/cm2, on par with the best known techniques using standard superlattice structures. The structure proposed here helps pave the way toward designing graphene electronics which use geometry rather than gating to control devices.

Anisotropies of the cosmic microwave background in nonstandard cold dark matter models

NASA Technical Reports Server (NTRS)

Vittorio, Nicola; Silk, Joseph

1992-01-01

Small angular scale cosmic microwave anisotropies in flat, vacuum-dominated, cold dark matter cosmological models which fit large-scale structure observations and are consistent with a high value for the Hubble constant are reexamined. New predictions for CDM models in which the large-scale power is boosted via a high baryon content and low H(0) are presented. Both classes of models are consistent with current limits: an improvement in sensitivity by a factor of about 3 for experiments which probe angular scales between 7 arcmin and 1 deg is required, in the absence of very early reionization, to test boosted CDM models for large-scale structure formation.

A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules

PubMed Central

Batal, Iyad; Cooper, Gregory; Hauskrecht, Milos

2015-01-01

Rule mining is an important class of data mining methods for discovering interesting patterns in data. The success of a rule mining method heavily depends on the evaluation function that is used to assess the quality of the rules. In this work, we propose a new rule evaluation score - the Predictive and Non-Spurious Rules (PNSR) score. This score relies on Bayesian inference to evaluate the quality of the rules and considers the structure of the rules to filter out spurious rules. We present an efficient algorithm for finding rules with high PNSR scores. The experiments demonstrate that our method is able to cover and explain the data with a much smaller rule set than existing methods. PMID:25938136

A Bayesian Scoring Technique for Mining Predictive and Non-Spurious Rules.

PubMed

Batal, Iyad; Cooper, Gregory; Hauskrecht, Milos

Rule mining is an important class of data mining methods for discovering interesting patterns in data. The success of a rule mining method heavily depends on the evaluation function that is used to assess the quality of the rules. In this work, we propose a new rule evaluation score - the Predictive and Non-Spurious Rules (PNSR) score. This score relies on Bayesian inference to evaluate the quality of the rules and considers the structure of the rules to filter out spurious rules. We present an efficient algorithm for finding rules with high PNSR scores. The experiments demonstrate that our method is able to cover and explain the data with a much smaller rule set than existing methods.

An assessment of the microgravity and acoustic environments in Space Station Freedom using VAPEPS

NASA Technical Reports Server (NTRS)

Bergen, Thomas F.; Scharton, Terry D.; Badilla, Gloria A.

1992-01-01

The Vibroacoustic Payload Environment Prediction System (VAPEPS) was used to predict the stationary on-orbit environments in one of the Space Station Freedom modules. The model of the module included the outer structure, equipment and payload racks, avionics, and cabin air and duct systems. Acoustic and vibratory outputs of various source classes were derived and input to the model. Initial results of analyses, performed in one-third octave frequency bands from 10 to 10,000 Hz, show that both the microgravity and acoustic environments will be exceeded in some one-third octave bands with the current SSF design. Further analyses indicate that interior acoustic level requirements will be exceeded even if the microgravity requirements are met.

Electron delocalization and charge mobility as a function of reduction in a metal-organic framework.

PubMed

Aubrey, Michael L; Wiers, Brian M; Andrews, Sean C; Sakurai, Tsuneaki; Reyes-Lillo, Sebastian E; Hamed, Samia M; Yu, Chung-Jui; Darago, Lucy E; Mason, Jarad A; Baeg, Jin-Ook; Grandjean, Fernande; Long, Gary J; Seki, Shu; Neaton, Jeffrey B; Yang, Peidong; Long, Jeffrey R

2018-06-04

Conductive metal-organic frameworks are an emerging class of three-dimensional architectures with degrees of modularity, synthetic flexibility and structural predictability that are unprecedented in other porous materials. However, engendering long-range charge delocalization and establishing synthetic strategies that are broadly applicable to the diverse range of structures encountered for this class of materials remain challenging. Here, we report the synthesis of K x Fe 2 (BDP) 3 (0 ≤ x ≤ 2; BDP 2-  = 1,4-benzenedipyrazolate), which exhibits full charge delocalization within the parent framework and charge mobilities comparable to technologically relevant polymers and ceramics. Through a battery of spectroscopic methods, computational techniques and single-microcrystal field-effect transistor measurements, we demonstrate that fractional reduction of Fe 2 (BDP) 3 results in a metal-organic framework that displays a nearly 10,000-fold enhancement in conductivity along a single crystallographic axis. The attainment of such properties in a K x Fe 2 (BDP) 3 field-effect transistor represents the realization of a general synthetic strategy for the creation of new porous conductor-based devices.

How does symmetry impact the flexibility of proteins?

PubMed Central

Schulze, Bernd; Sljoka, Adnan; Whiteley, Walter

2014-01-01

It is well known that (i) the flexibility and rigidity of proteins are central to their function, (ii) a number of oligomers with several copies of individual protein chains assemble with symmetry in the native state and (iii) added symmetry sometimes leads to added flexibility in structures. We observe that the most common symmetry classes of protein oligomers are also the symmetry classes that lead to increased flexibility in certain three-dimensional structures—and investigate the possible significance of this coincidence. This builds on the well-developed theory of generic rigidity of body–bar frameworks, which permits an analysis of the rigidity and flexibility of molecular structures such as proteins via fast combinatorial algorithms. In particular, we outline some very simple counting rules and possible algorithmic extensions that allow us to predict continuous symmetry-preserving motions in body–bar frameworks that possess non-trivial point-group symmetry. For simplicity, we focus on dimers, which typically assemble with twofold rotational axes, and often have allosteric function that requires motions to link distant sites on the two protein chains. PMID:24379431

Identification and characterization of novel benzil (diphenylethane-1,2-dione) analogues as inhibitors of mammalian carboxylesterases.

PubMed

Wadkins, Randy M; Hyatt, Janice L; Wei, Xin; Yoon, Kyoung Jin P; Wierdl, Monika; Edwards, Carol C; Morton, Christopher L; Obenauer, John C; Damodaran, Komath; Beroza, Paul; Danks, Mary K; Potter, Philip M

2005-04-21

Carboxylesterases (CE) are ubiquitous enzymes responsible for the metabolism of xenobiotics. Because the structural and amino acid homology among esterases of different classes, the identification of selective inhibitors of these proteins has proved problematic. Using Telik's target-related affinity profiling (TRAP) technology, we have identified a class of compounds based on benzil (1,2-diphenylethane-1,2-dione) that are potent CE inhibitors, with K(i) values in the low nanomolar range. Benzil and 30 analogues demonstrated selective inhibition of CEs, with no inhibitory activity toward human acetylcholinesterase or butyrylcholinesterase. Analysis of structurally related compounds indicated that the ethane-1,2-dione moiety was essential for enzyme inhibition and that potency was dependent on the presence of, and substitution within, the benzene ring. 3D-QSAR analyses of these benzil analogues for three different mammalian CEs demonstrated excellent correlations of observed versus predicted K(i) (r(2) > 0.91), with cross-validation coefficients (q(2)) of 0.9. Overall, these results suggest that selective inhibitors of CEs with potential for use in clinical applications can be designed.

Composite Structural Analysis of Flat-Back Shaped Blade for Multi-MW Class Wind Turbine

NASA Astrophysics Data System (ADS)

Kim, Soo-Hyun; Bang, Hyung-Joon; Shin, Hyung-Ki; Jang, Moon-Seok

2014-06-01

This paper provides an overview of failure mode estimation based on 3D structural finite element (FE) analysis of the flat-back shaped wind turbine blade. Buckling stability, fiber failure (FF), and inter-fiber failure (IFF) analyses were performed to account for delamination or matrix failure of composite materials and to predict the realistic behavior of the entire blade region. Puck's fracture criteria were used for IFF evaluation. Blade design loads applicable to multi-megawatt (MW) wind turbine systems were calculated according to the Germanischer Lloyd (GL) guideline and the International Electrotechnical Commission (IEC) 61400-1 standard, under Class IIA wind conditions. After the post-processing of final load results, a number of principal load cases were selected and converted into applied forces at the each section along the blade's radius of the FE model. Nonlinear static analyses were performed for laminate failure, FF, and IFF check. For buckling stability, linear eigenvalue analysis was performed. As a result, we were able to estimate the failure mode and locate the major weak point.

Buckled two-dimensional Xene sheets.

PubMed

Molle, Alessandro; Goldberger, Joshua; Houssa, Michel; Xu, Yong; Zhang, Shou-Cheng; Akinwande, Deji

2017-02-01

Silicene, germanene and stanene are part of a monoelemental class of two-dimensional (2D) crystals termed 2D-Xenes (X = Si, Ge, Sn and so on) which, together with their ligand-functionalized derivatives referred to as Xanes, are comprised of group IVA atoms arranged in a honeycomb lattice - similar to graphene but with varying degrees of buckling. Their electronic structure ranges from trivial insulators, to semiconductors with tunable gaps, to semi-metallic, depending on the substrate, chemical functionalization and strain. More than a dozen different topological insulator states are predicted to emerge, including the quantum spin Hall state at room temperature, which, if realized, would enable new classes of nanoelectronic and spintronic devices, such as the topological field-effect transistor. The electronic structure can be tuned, for example, by changing the group IVA element, the degree of spin-orbit coupling, the functionalization chemistry or the substrate, making the 2D-Xene systems promising multifunctional 2D materials for nanotechnology. This Perspective highlights the current state of the art and future opportunities in the manipulation and stability of these materials, their functions and applications, and novel device concepts.

Linear regression models for solvent accessibility prediction in proteins.

PubMed

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

2005-04-01

The relative solvent accessibility (RSA) of an amino acid residue in a protein structure is a real number that represents the solvent exposed surface area of this residue in relative terms. The problem of predicting the RSA from the primary amino acid sequence can therefore be cast as a regression problem. Nevertheless, RSA prediction has so far typically been cast as a classification problem. Consequently, various machine learning techniques have been used within the classification framework to predict whether a given amino acid exceeds some (arbitrary) RSA threshold and would thus be predicted to be "exposed," as opposed to "buried." We have recently developed novel methods for RSA prediction using nonlinear regression techniques which provide accurate estimates of the real-valued RSA and outperform classification-based approaches with respect to commonly used two-class projections. However, while their performance seems to provide a significant improvement over previously published approaches, these Neural Network (NN) based methods are computationally expensive to train and involve several thousand parameters. In this work, we develop alternative regression models for RSA prediction which are computationally much less expensive, involve orders-of-magnitude fewer parameters, and are still competitive in terms of prediction quality. In particular, we investigate several regression models for RSA prediction using linear L1-support vector regression (SVR) approaches as well as standard linear least squares (LS) regression. Using rigorously derived validation sets of protein structures and extensive cross-validation analysis, we compare the performance of the SVR with that of LS regression and NN-based methods. In particular, we show that the flexibility of the SVR (as encoded by metaparameters such as the error insensitivity and the error penalization terms) can be very beneficial to optimize the prediction accuracy for buried residues. We conclude that the simple and computationally much more efficient linear SVR performs comparably to nonlinear models and thus can be used in order to facilitate further attempts to design more accurate RSA prediction methods, with applications to fold recognition and de novo protein structure prediction methods.

Inflammatory Biomarkers Predict Heart Failure Severity and Prognosis in Patients With Heart Failure With Preserved Ejection Fraction: A Holistic Proteomic Approach.

PubMed

Hage, Camilla; Michaëlsson, Erik; Linde, Cecilia; Donal, Erwan; Daubert, Jean-Claude; Gan, Li-Ming; Lund, Lars H

2017-02-01

Underlying mechanisms in heart failure (HF) with preserved ejection fraction remain unknown. We investigated cardiovascular plasma biomarkers in HF with preserved ejection fraction and their correlation to diastolic dysfunction, functional class, pathophysiological processes, and prognosis. In 86 stable patients with HF and EF ≥45% in the Karolinska Rennes (KaRen) biomarker substudy, biomarkers were quantified by a multiplex immunoassay. Orthogonal projection to latent structures by partial least square analysis was performed on 87 biomarkers and 240 clinical variables, ranking biomarkers associated with New York Heart Association (NYHA) Functional class and the composite outcome (all-cause mortality and HF hospitalization). Biomarkers significantly correlated with outcome were analyzed by multivariable Cox regression and correlations with echocardiographic measurements performed. The orthogonal partial least square outcome-predicting biomarker pattern was run against the Ingenuity Pathway Analysis (IPA) database, containing annotated data from the public domain. The orthogonal partial least square analyses identified 32 biomarkers correlated with NYHA class and 28 predicting outcomes. Among outcome-predicting biomarkers, growth/differentiation factor-15 was the strongest and an additional 7 were also significant in Cox regression analyses when adjusted for age, sex, and N-terminal probrain natriuretic peptide: adrenomedullin (hazard ratio per log increase 2.53), agouti-related protein; (1.48), chitinase-3-like protein 1 (1.35), C-C motif chemokine 20 (1.35), fatty acid-binding protein (1.33), tumor necrosis factor receptor 1 (2.29), and TNF-related apoptosis-inducing ligand (0.34). Twenty-three of them correlated with diastolic dysfunction (E/e') and 5 with left atrial volume index. The IPA suggested that increased inflammation, immune activation with decreased necrosis and apoptosis preceded poor outcome. In HF with preserved ejection fraction, novel biomarkers of inflammation predict HF severity and prognosis that may complement or even outperform traditional markers, such as N-terminal probrain natriuretic peptide. These findings lend support to a hypothesis implicating global systemic inflammation in HF with preserved ejection fraction. URL: http://www.clinicaltrials.gov; Unique identifier: NCT00774709. © 2017 American Heart Association, Inc.

Verification of a 2 kWe Closed-Brayton-Cycle Power Conversion System Mechanical Dynamics Model

NASA Technical Reports Server (NTRS)

Ludwiczak, Damian R.; Le, Dzu K.; McNelis, Anne M.; Yu, Albert C.; Samorezov, Sergey; Hervol, Dave S.

2005-01-01

Vibration test data from an operating 2 kWe closed-Brayton-cycle (CBC) power conversion system (PCS) located at the NASA Glenn Research Center was used for a comparison with a dynamic disturbance model of the same unit. This effort was performed to show that a dynamic disturbance model of a CBC PCS can be developed that can accurately predict the torque and vibration disturbance fields of such class of rotating machinery. The ability to accurately predict these disturbance fields is required before such hardware can be confidently integrated onto a spacecraft mission. Accurate predictions of CBC disturbance fields will be used for spacecraft control/structure interaction analyses and for understanding the vibration disturbances affecting the scientific instrumentation onboard. This paper discusses how test cell data measurements for the 2 kWe CBC PCS were obtained, the development of a dynamic disturbance model used to predict the transient torque and steady state vibration fields of the same unit, and a comparison of the two sets of data.

Bio-activity of aminosulfonyl ureas in the light of nucleic acid bases and DNA base pair interaction.

PubMed

Mondal Roy, Sutapa

2018-08-01

The quantum chemical descriptors based on density functional theory (DFT) are applied to predict the biological activity (log IC 50 ) of one class of acyl-CoA: cholesterol O-acyltransferase (ACAT) inhibitors, viz. aminosulfonyl ureas. ACAT are very effective agents for reduction of triglyceride and cholesterol levels in human body. Successful two parameter quantitative structure-activity relationship (QSAR) models are developed with a combination of relevant global and local DFT based descriptors for prediction of biological activity of aminosulfonyl ureas. The global descriptors, electron affinity of the ACAT inhibitors (EA) and/or charge transfer (ΔN) between inhibitors and model biosystems (NA bases and DNA base pairs) along with the local group atomic charge on sulfonyl moiety (∑Q Sul ) of the inhibitors reveals more than 90% efficacy of the selected descriptors for predicting the experimental log (IC 50 ) values. Copyright © 2018 Elsevier Ltd. All rights reserved.

The genetic landscape of a physical interaction

PubMed Central

Diss, Guillaume

2018-01-01

A key question in human genetics and evolutionary biology is how mutations in different genes combine to alter phenotypes. Efforts to systematically map genetic interactions have mostly made use of gene deletions. However, most genetic variation consists of point mutations of diverse and difficult to predict effects. Here, by developing a new sequencing-based protein interaction assay – deepPCA – we quantified the effects of >120,000 pairs of point mutations on the formation of the AP-1 transcription factor complex between the products of the FOS and JUN proto-oncogenes. Genetic interactions are abundant both in cis (within one protein) and trans (between the two molecules) and consist of two classes – interactions driven by thermodynamics that can be predicted using a three-parameter global model, and structural interactions between proximally located residues. These results reveal how physical interactions generate quantitatively predictable genetic interactions. PMID:29638215

Using landscape limnology to classify freshwater ecosystems for multi-ecosystem management and conservation

USGS Publications Warehouse

Soranno, Patricia A.; Cheruvelil, Kendra Spence; Webster, Katherine E.; Bremigan, Mary T.; Wagner, Tyler; Stow, Craig A.

2010-01-01

Governmental entities are responsible for managing and conserving large numbers of lake, river, and wetland ecosystems that can be addressed only rarely on a case-by-case basis. We present a system for predictive classification modeling, grounded in the theoretical foundation of landscape limnology, that creates a tractable number of ecosystem classes to which management actions may be tailored. We demonstrate our system by applying two types of predictive classification modeling approaches to develop nutrient criteria for eutrophication management in 1998 north temperate lakes. Our predictive classification system promotes the effective management of multiple ecosystems across broad geographic scales by explicitly connecting management and conservation goals to the classification modeling approach, considering multiple spatial scales as drivers of ecosystem dynamics, and acknowledging the hierarchical structure of freshwater ecosystems. Such a system is critical for adaptive management of complex mosaics of freshwater ecosystems and for balancing competing needs for ecosystem services in a changing world.

Seeing Eye to Eye: Predicting Teacher-Student Agreement on Classroom Social Networks

PubMed Central

Neal, Jennifer Watling; Cappella, Elise; Wagner, Caroline; Atkins, Marc S.

2010-01-01

This study examines the association between classroom characteristics and teacher-student agreement in perceptions of students’ classroom peer networks. Social network, peer nomination, and observational data were collected from a sample of second through fourth grade teachers (N=33) and students (N=669) in 33 classrooms across five high poverty urban schools. Results demonstrate that variation in teacher-student agreement on the structure of students’ peer networks can be explained, in part, by developmental factors and classroom characteristics. Developmental increases in network density partially mediated the positive relationship between grade level and teacher-student agreement. Larger class sizes and higher levels of normative aggressive behavior resulted in lower levels of teacher-student agreement. Teachers’ levels of classroom organization had mixed influences, with behavior management negatively predicting agreement, and productivity positively predicting agreement. These results underscore the importance of the classroom context in shaping teacher and student perceptions of peer networks. PMID:21666768

Administering Spatial and Cognitive Instruments In-class and On-line: Are These Equivalent?

NASA Astrophysics Data System (ADS)

Williamson, Kenneth C.; Williamson, Vickie M.; Hinze, Scott R.

2017-02-01

Standardized, well-established paper-and-pencil tests, which measure spatial abilities or which measure reasoning abilities, have long been found to be predictive of success in the STEM (science, technology, engineering, and mathematics) fields. Instructors can use these tests for prediction of success and to inform instruction. A comparative administration of spatial visualization and cognitive reasoning tests, between in-class (proctored paper and pencil) and on-line (unproctored Internet) ( N = 457), was used to investigate and to determine whether the differing instrument formats yielded equal measures of spatial ability and reasoning ability in large first-semester general chemistry sections. Although some gender differences were found, findings suggest that some differences across administration formats, but that on-line administration had similar properties of predicting chemistry performance as the in-class version. Therefore, on-line administration is a viable option for instructors to consider especially when dealing with large classes.

Parent and Peer Links to Trajectories of Anxious Withdrawal From Grades 5 to 8

PubMed Central

Booth-LaForce, Cathryn; Oh, Wonjung; Kennedy, Amy E.; Rubin, Kenneth H.; Rose-Krasnor, Linda; Laursen, Brett

2013-01-01

Individual differences in trajectories of anxious withdrawal were examined from Grades 5 to 8 across the transition to middle school in a community sample (N = 283), using General Growth Mixture Modeling. Three distinct pathways of anxious withdrawal were identified: low-stable (78%), high-decreasing (12%), and high-increasing (10%). In Grade 6, relative to the low-stable class, greater peer exclusion and more free time spent with mother predicted membership in the high-decreasing class; higher peer exclusion predicted membership in the high-increasing class. Within the high-increasing class, the growth of anxious withdrawal was predicted by lower parental autonomy-granting, less free time with mother, both nurturing and restrictive parenting, and greater peer exclusion. Results highlight the role of both parent–child relationship and peer difficulties in increasing the adjustment risk among youth who are anxiously withdrawn prior to the middle-school transition. PMID:22417188

Discovery of functional elements in 12 Drosophila genomes using evolutionary signatures

PubMed Central

Stark, Alexander; Lin, Michael F.; Kheradpour, Pouya; Pedersen, Jakob S.; Parts, Leopold; Carlson, Joseph W.; Crosby, Madeline A.; Rasmussen, Matthew D.; Roy, Sushmita; Deoras, Ameya N.; Ruby, J. Graham; Brennecke, Julius; Hodges, Emily; Hinrichs, Angie S.; Caspi, Anat; Paten, Benedict; Park, Seung-Won; Han, Mira V.; Maeder, Morgan L.; Polansky, Benjamin J.; Robson, Bryanne E.; Aerts, Stein; van Helden, Jacques; Hassan, Bassem; Gilbert, Donald G.; Eastman, Deborah A.; Rice, Michael; Weir, Michael; Hahn, Matthew W.; Park, Yongkyu; Dewey, Colin N.; Pachter, Lior; Kent, W. James; Haussler, David; Lai, Eric C.; Bartel, David P.; Hannon, Gregory J.; Kaufman, Thomas C.; Eisen, Michael B.; Clark, Andrew G.; Smith, Douglas; Celniker, Susan E.; Gelbart, William M.; Kellis, Manolis

2008-01-01

Sequencing of multiple related species followed by comparative genomics analysis constitutes a powerful approach for the systematic understanding of any genome. Here, we use the genomes of 12 Drosophila species for the de novo discovery of functional elements in the fly. Each type of functional element shows characteristic patterns of change, or ‘evolutionary signatures’, dictated by its precise selective constraints. Such signatures enable recognition of new protein-coding genes and exons, spurious and incorrect gene annotations, and numerous unusual gene structures, including abundant stop-codon readthrough. Similarly, we predict non-protein-coding RNA genes and structures, and new microRNA (miRNA) genes. We provide evidence of miRNA processing and functionality from both hairpin arms and both DNA strands. We identify several classes of pre- and post-transcriptional regulatory motifs, and predict individual motif instances with high confidence. We also study how discovery power scales with the divergence and number of species compared, and we provide general guidelines for comparative studies. PMID:17994088

Cometary compact H II regions are stellar-wind bow shocks

NASA Technical Reports Server (NTRS)

Van Buren, Dave; Mac Low, Mordecai-Mark; Wood, Douglas O. S.; Churchwell, ED

1990-01-01

Comet-shaped H II regions, like G34.3 + 0.2, are easily explained as bow shocks created by wind-blowing massive stars moving supersonically through molecular clouds. The required velocities of the stars through dense clumps are less than about 10 km/s, comparable to the velocity dispersion of stars in OB associations. An analytic model of bow shocks matches the gross characteristics seen in the radio continuum and the velocity structure inferred from hydrogen recombination and molecular line observations. The champagne flow model cannot account for these structures. VLBI observations of masers associated with the shells of cometary compact H II regions should reveal tailward proper motions predominantly parallel to the shell, rather than perpendicular. It is predicted that over a decade baseline, high signal-to-noise VLA observations of this class of objects will show headward pattern motion in the direction of the symmetry axis, but not expansion. Finally, shock-generated and coronal infrared lines are also predicted.

The first stable lower fullerene: C{sub 36}

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

Piskoti, C.; Zettl, A.

1998-08-01

A new pure carbon material, presumably composed of thirty six carbon atom molecules, has been synthesized and isolated in milligram quantities. It appears as though these molecules have a closed cage structure making them the smallest member of a new class of molecules known as fullerenes, most notably of which is the soccer ball shaped C{sub 60}. However, unlike other known fullerenes, any closed, fullerene-like C{sub 36} cage will necessarily contain fused pentagon rings. Therefore, this molecule apparently violates the isolated pentagon rule, a criterion which requires isolated pentagons for stability in fullerene molecules. Striking parallels between this problem andmore » the synthesis of other fused five member fused ring systems will be discussed. Also, it will be shown that certain biological structures known as clathrin behave in a manner which gives excellent predictions about fullerenes and nanotubes. These predictions help to explain the presence of abundant quantities of C{sub 36} in arced graphite soot. {copyright} {ital 1998 American Institute of Physics.}« less

Support Vector Machine-based classification of protein folds using the structural properties of amino acid residues and amino acid residue pairs.

PubMed

Shamim, Mohammad Tabrez Anwar; Anwaruddin, Mohammad; Nagarajaram, H A

2007-12-15

Fold recognition is a key step in the protein structure discovery process, especially when traditional sequence comparison methods fail to yield convincing structural homologies. Although many methods have been developed for protein fold recognition, their accuracies remain low. This can be attributed to insufficient exploitation of fold discriminatory features. We have developed a new method for protein fold recognition using structural information of amino acid residues and amino acid residue pairs. Since protein fold recognition can be treated as a protein fold classification problem, we have developed a Support Vector Machine (SVM) based classifier approach that uses secondary structural state and solvent accessibility state frequencies of amino acids and amino acid pairs as feature vectors. Among the individual properties examined secondary structural state frequencies of amino acids gave an overall accuracy of 65.2% for fold discrimination, which is better than the accuracy by any method reported so far in the literature. Combination of secondary structural state frequencies with solvent accessibility state frequencies of amino acids and amino acid pairs further improved the fold discrimination accuracy to more than 70%, which is approximately 8% higher than the best available method. In this study we have also tested, for the first time, an all-together multi-class method known as Crammer and Singer method for protein fold classification. Our studies reveal that the three multi-class classification methods, namely one versus all, one versus one and Crammer and Singer method, yield similar predictions. Dataset and stand-alone program are available upon request.

Noncanonical expression of a murine cytomegalovirus early protein CD8 T-cell epitope as an immediate early epitope based on transcription from an upstream gene.

PubMed

Fink, Annette; Büttner, Julia K; Thomas, Doris; Holtappels, Rafaela; Reddehase, Matthias J; Lemmermann, Niels A W

2014-02-14

Viral CD8 T-cell epitopes, represented by viral peptides bound to major histocompatibility complex class-I (MHC-I) glycoproteins, are often identified by "reverse immunology", a strategy not requiring biochemical and structural knowledge of the actual viral protein from which they are derived by antigen processing. Instead, bioinformatic algorithms predicting the probability of C-terminal cleavage in the proteasome, as well as binding affinity to the presenting MHC-I molecules, are applied to amino acid sequences deduced from predicted open reading frames (ORFs) based on the genomic sequence. If the protein corresponding to an antigenic ORF is known, it is usually inferred that the kinetic class of the protein also defines the phase in the viral replicative cycle during which the respective antigenic peptide is presented for recognition by CD8 T cells. We have previously identified a nonapeptide from the predicted ORFm164 of murine cytomegalovirus that is presented by the MHC-I allomorph H-2 Dd and that is immunodominant in BALB/c (H-2d haplotype) mice. Surprisingly, although the ORFm164 protein gp36.5 is expressed as an Early (E) phase protein, the m164 epitope is presented already during the Immediate Early (IE) phase, based on the expression of an upstream mRNA starting within ORFm167 and encompassing ORFm164.

Ligand Recognition by A-Class Eph Receptors: Crystal Structures of the EphA2 Ligand-Binding Domain and the EphA2/ephrin-A1 Complex

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

Himanen, J.; Goldgur, Y; Miao, H

2009-01-01

Ephrin (Eph) receptor tyrosine kinases fall into two subclasses (A and B) according to preferences for their ephrin ligands. All published structural studies of Eph receptor/ephrin complexes involve B-class receptors. Here, we present the crystal structures of an A-class complex between EphA2 and ephrin-A1 and of unbound EphA2. Although these structures are similar overall to their B-class counterparts, they reveal important differences that define subclass specificity. The structures suggest that the A-class Eph receptor/ephrin interactions involve smaller rearrangements in the interacting partners, better described by a 'lock-and-key'-type binding mechanism, in contrast to the 'induced fit' mechanism defining the B-class molecules.more » This model is supported by structure-based mutagenesis and by differential requirements for ligand oligomerization by the two subclasses in cell-based Eph receptor activation assays. Finally, the structure of the unligated receptor reveals a homodimer assembly that might represent EphA2-specific homotypic cell adhesion interactions.« less

Biomass Estimation for some Shrubs from Northeastern Minnesota

Treesearch

David F. Grigal; Lewis F. Ohmann

1977-01-01

Biomass prediction equations were developed for 23 northeastern Minnesota shrub species. The allowmetric function was used to predict leaf, current annual woody twig, stem, and total woody biomass (dry grass), using stem diameter class estimated to the nearest 0.25 cm class at 15 cm above ground level as the independent variable.

Predicting College Students' Intergroup Friendships across Race/Ethnicity, Religion, Sexual Orientation, and Social Class

ERIC Educational Resources Information Center

Goldstein, Susan B.

2013-01-01

This study seeks to expand the literature on predicting friendship diversity beyond race/ethnicity to include religion, social class, and sexual orientation. Survey packets elicited information regarding up to four close friendships developed during college. Additional measures assessed pre-college friendship diversity, participation in college…

On-Line Learning and the Implications for School Design

ERIC Educational Resources Information Center

Stack, Greg

2011-01-01

"Disrupting Class," published in 2008, is the story of how disruptive innovation, innovation that changes the business model organizations, will fundamentally change the American school system. The book's most startling prediction is that half of all high school classes will be on-line by 2019. In considering these predictions, the author began to…

Predicting Satisfaction in Physical Education from Motivational Climate and Self-Determined Motivation

ERIC Educational Resources Information Center

Baena-Extremera, Antonio; Gómez-López, Manuel; Granero-Gallegos, Antonio; Ortiz-Camacho, Maria del Mar

2015-01-01

The purpose of this research study was to determine to what extent the motivational climate perceived by students in Physical Education (PE) classes predicts self-determined motivation, and satisfaction with physical education classes. Questionnaires were administered to 758 high school students aged 13-18 years. We used the Spanish versions of…

A first estimate of the structure and density of the populations of pet cats and dogs across Great Britain

PubMed Central

Fouracre, David; Smith, Graham C.

2017-01-01

Policy development, implementation, and effective contingency response rely on a strong evidence base to ensure success and cost-effectiveness. Where this includes preventing the establishment or spread of zoonotic or veterinary diseases infecting companion cats and dogs, descriptions of the structure and density of the populations of these pets are useful. Similarly, such descriptions may help in supporting diverse fields of study such as; evidence-based veterinary practice, veterinary epidemiology, public health and ecology. As well as maps of where pets are, estimates of how many may rarely, or never, be seen by veterinarians and might not be appropriately managed in the event of a disease outbreak are also important. Unfortunately both sources of evidence are absent from the scientific and regulatory literatures. We make this first estimate of the structure and density of pet populations by using the most recent national population estimates of cats and dogs across Great Britain and subdividing these spatially, and categorically across ownership classes. For the spatial model we used the location and size of veterinary practises across GB to predict the local density of pets, using client travel time to define catchments around practises, and combined this with residential address data to estimate the rate of ownership. For the estimates of pets which may provoke problems in managing a veterinary or zoonotic disease we reviewed the literature and defined a comprehensive suite of ownership classes for cats and dogs, collated estimates of the sub-populations for each ownership class as well as their rates of interaction and produced a coherent scaled description of the structure of the national population. The predicted density of pets varied substantially, with the lowest densities in rural areas, and the highest in the centres of large cities where each species could exceed 2500 animals.km-2. Conversely, the number of pets per household showed the opposite relationship. Both qualitative and quantitative validation support key assumptions in the model structure and suggest the model is useful at predicting the populations of cats at geographical scales important for decision-making, although it also indicates where further research may improve model performance. In the event of an animal health crisis, it appears that almost all dogs could be brought under control rapidly. For cats, a substantial and unknown number might never be bought under control and would be less likely to receive veterinary support to facilitate surveillance and disease management; we estimate this to be at least 1.5 million cats. In addition, the lack of spare capacity to care for unowned cats in welfare organisations suggests that any increase in their rate of acquisition of cats, or any decrease in the rate of re-homing might provoke problems during a period of crisis. PMID:28403172

The Comparative Effects of Prediction/Discussion-Based Learning Cycle, Conceptual Change Text, and Traditional Instructions on Student Understanding of Genetics

NASA Astrophysics Data System (ADS)

Yilmaz, Diba; Tekkaya, Ceren; Sungur, Semra

2011-03-01

The present study examined the comparative effects of a prediction/discussion-based learning cycle, conceptual change text (CCT), and traditional instructions on students' understanding of genetics concepts. A quasi-experimental research design of the pre-test-post-test non-equivalent control group was adopted. The three intact classes, taught by the same science teacher, were randomly assigned as prediction/discussion-based learning cycle class (N = 30), CCT class (N = 25), and traditional class (N = 26). Participants completed the genetics concept test as pre-test, post-test, and delayed post-test to examine the effects of instructional strategies on their genetics understanding and retention. While the dependent variable of this study was students' understanding of genetics, the independent variables were time (Time 1, Time 2, and Time 3) and mode of instruction. The mixed between-within subjects analysis of variance revealed that students in both prediction/discussion-based learning cycle and CCT groups understood the genetics concepts and retained their knowledge significantly better than students in the traditional instruction group.

Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method

PubMed Central

Nielsen, Morten; Lundegaard, Claus; Lund, Ole

2007-01-01

Background Antigen presenting cells (APCs) sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II) molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC) and three mouse H2-IA alleles. Results The predictive performance of the SMM-align method was demonstrated to be superior to that of the Gibbs sampler, TEPITOPE, SVRMHC, and MHCpred methods. Cross validation between peptide data set obtained from different sources demonstrated that direct incorporation of peptide length potentially results in over-fitting of the binding prediction method. Focusing on amino terminal peptide flanking residues (PFR), we demonstrate a consistent gain in predictive performance by favoring binding registers with a minimum PFR length of two amino acids. Visualizing the binding motif as obtained by the SMM-align and TEPITOPE methods highlights a series of fundamental discrepancies between the two predicted motifs. For the DRB1*1302 allele for instance, the TEPITOPE method favors basic amino acids at most anchor positions, whereas the SMM-align method identifies a preference for hydrophobic or neutral amino acids at the anchors. Conclusion The SMM-align method was shown to outperform other state of the art MHC class II prediction methods. The method predicts quantitative peptide:MHC binding affinity values, making it ideally suited for rational epitope discovery. The method has been trained and evaluated on the, to our knowledge, largest benchmark data set publicly available and covers the nine HLA-DR supertypes suggested as well as three mouse H2-IA allele. Both the peptide benchmark data set, and SMM-align prediction method (NetMHCII) are made publicly available. PMID:17608956

Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method.

PubMed

Nielsen, Morten; Lundegaard, Claus; Lund, Ole

2007-07-04

Antigen presenting cells (APCs) sample the extra cellular space and present peptides from here to T helper cells, which can be activated if the peptides are of foreign origin. The peptides are presented on the surface of the cells in complex with major histocompatibility class II (MHC II) molecules. Identification of peptides that bind MHC II molecules is thus a key step in rational vaccine design and developing methods for accurate prediction of the peptide:MHC interactions play a central role in epitope discovery. The MHC class II binding groove is open at both ends making the correct alignment of a peptide in the binding groove a crucial part of identifying the core of an MHC class II binding motif. Here, we present a novel stabilization matrix alignment method, SMM-align, that allows for direct prediction of peptide:MHC binding affinities. The predictive performance of the method is validated on a large MHC class II benchmark data set covering 14 HLA-DR (human MHC) and three mouse H2-IA alleles. The predictive performance of the SMM-align method was demonstrated to be superior to that of the Gibbs sampler, TEPITOPE, SVRMHC, and MHCpred methods. Cross validation between peptide data set obtained from different sources demonstrated that direct incorporation of peptide length potentially results in over-fitting of the binding prediction method. Focusing on amino terminal peptide flanking residues (PFR), we demonstrate a consistent gain in predictive performance by favoring binding registers with a minimum PFR length of two amino acids. Visualizing the binding motif as obtained by the SMM-align and TEPITOPE methods highlights a series of fundamental discrepancies between the two predicted motifs. For the DRB1*1302 allele for instance, the TEPITOPE method favors basic amino acids at most anchor positions, whereas the SMM-align method identifies a preference for hydrophobic or neutral amino acids at the anchors. The SMM-align method was shown to outperform other state of the art MHC class II prediction methods. The method predicts quantitative peptide:MHC binding affinity values, making it ideally suited for rational epitope discovery. The method has been trained and evaluated on the, to our knowledge, largest benchmark data set publicly available and covers the nine HLA-DR supertypes suggested as well as three mouse H2-IA allele. Both the peptide benchmark data set, and SMM-align prediction method (NetMHCII) are made publicly available.

Gene function prediction based on the Gene Ontology hierarchical structure.

PubMed

Cheng, Liangxi; Lin, Hongfei; Hu, Yuncui; Wang, Jian; Yang, Zhihao

2014-01-01

The information of the Gene Ontology annotation is helpful in the explanation of life science phenomena, and can provide great support for the research of the biomedical field. The use of the Gene Ontology is gradually affecting the way people store and understand bioinformatic data. To facilitate the prediction of gene functions with the aid of text mining methods and existing resources, we transform it into a multi-label top-down classification problem and develop a method that uses the hierarchical relationships in the Gene Ontology structure to relieve the quantitative imbalance of positive and negative training samples. Meanwhile the method enhances the discriminating ability of classifiers by retaining and highlighting the key training samples. Additionally, the top-down classifier based on a tree structure takes the relationship of target classes into consideration and thus solves the incompatibility between the classification results and the Gene Ontology structure. Our experiment on the Gene Ontology annotation corpus achieves an F-value performance of 50.7% (precision: 52.7% recall: 48.9%). The experimental results demonstrate that when the size of training set is small, it can be expanded via topological propagation of associated documents between the parent and child nodes in the tree structure. The top-down classification model applies to the set of texts in an ontology structure or with a hierarchical relationship.

Spatio-temporal variation in age structure and abundance of the endangered snail kite: Pooling across regions masks a declining and aging population

USGS Publications Warehouse

Reichert, Brian E.; Kendall, William L.; Fletcher, Robert J.; Kitchens, Wiley M.

2016-01-01

While variation in age structure over time and space has long been considered important for population dynamics and conservation, reliable estimates of such spatio-temporal variation in age structure have been elusive for wild vertebrate populations. This limitation has arisen because of problems of imperfect detection, the potential for temporary emigration impacting assessments of age structure, and limited information on age. However, identifying patterns in age structure is important for making reliable predictions of both short- and long-term dynamics of populations of conservation concern. Using a multistate superpopulation estimator, we estimated region-specific abundance and age structure (the proportion of individuals within each age class) of a highly endangered population of snail kites for two separate regions in Florida over 17 years (1997–2013). We find that in the southern region of the snail kite—a region known to be critical for the long-term persistence of the species—the population has declined significantly since 1997, and during this time, it has increasingly become dominated by older snail kites (> 12 years old). In contrast, in the northern region—a region historically thought to serve primarily as drought refugia—the population has increased significantly since 2007 and age structure is more evenly distributed among age classes. Given that snail kites show senescence at approximately 13 years of age, where individuals suffer higher mortality rates and lower breeding rates, these results reveal an alarming trend for the southern region. Our work illustrates the importance of accounting for spatial structure when assessing changes in abundance and age distribution and the need for monitoring of age structure in imperiled species.

Potential high-Tc superconducting lanthanum and yttrium hydrides at high pressure

PubMed Central

Liu, Hanyu; Naumov, Ivan I.; Hoffmann, Roald; Ashcroft, N. W.; Hemley, Russell J.

2017-01-01

A systematic structure search in the La–H and Y–H systems under pressure reveals some hydrogen-rich structures with intriguing electronic properties. For example, LaH10 is found to adopt a sodalite-like face-centered cubic (fcc) structure, stable above 200 GPa, and LaH8 a C2/m space group structure. Phonon calculations indicate both are dynamically stable; electron phonon calculations coupled to Bardeen–Cooper–Schrieffer (BCS) arguments indicate they might be high-Tc superconductors. In particular, the superconducting transition temperature Tc calculated for LaH10 is 274–286 K at 210 GPa. Similar calculations for the Y–H system predict stability of the sodalite-like fcc YH10 and a Tc above room temperature, reaching 305–326 K at 250 GPa. The study suggests that dense hydrides consisting of these and related hydrogen polyhedral networks may represent new classes of potential very high-temperature superconductors. PMID:28630301

Prolonged grief and post-traumatic growth after loss: Latent class analysis.

PubMed

Zhou, Ningning; Yu, Wei; Tang, Suqin; Wang, Jianping; Killikelly, Clare

2018-06-06

Bereavement may trigger different psychological outcomes, such as prolonged grief disorder or post-traumatic growth. The relationship between these two outcomes and potential precipitators remain unknown. The current study aimed to identify classes of Chinese bereaved individuals based on prolonged grief symptoms and post-traumatic growth and to examine predictors for these classes. We used data from 273 Chinese individuals who lost a relative due to disease (92.3%), accident (4.4%) and other reasons (1.8%). Latent class analysis revealed three classes: a resilient class, a growth class, and a combined grief/growth class. A higher level of functional impairment was found for the combined grief/growth class than for the other two classes. Membership in the combined grief/growth class was significantly predicted by the younger age of the deceased and the death of a parent, child or spouse. Subjective closeness with the deceased and gender were marginally significant predictors. When the four variables were included in the multinomial regression analysis, death of a parent, child or spouse significantly predicted the membership to the combined grief/growth class. These findings provide valuable information for the development of tailored interventions that may build on the bereaved individuals' personal strengths. Copyright © 2018. Published by Elsevier B.V.

Materials prediction via classification learning

DOE PAGES

Balachandran, Prasanna V.; Theiler, James; Rondinelli, James M.; ...

2015-08-25

In the paradigm of materials informatics for accelerated materials discovery, the choice of feature set (i.e. attributes that capture aspects of structure, chemistry and/or bonding) is critical. Ideally, the feature sets should provide a simple physical basis for extracting major structural and chemical trends and furthermore, enable rapid predictions of new material chemistries. Orbital radii calculated from model pseudopotential fits to spectroscopic data are potential candidates to satisfy these conditions. Although these radii (and their linear combinations) have been utilized in the past, their functional forms are largely justified with heuristic arguments. Here we show that machine learning methods naturallymore » uncover the functional forms that mimic most frequently used features in the literature, thereby providing a mathematical basis for feature set construction without a priori assumptions. We apply these principles to study two broad materials classes: (i) wide band gap AB compounds and (ii) rare earth-main group RM intermetallics. The AB compounds serve as a prototypical example to demonstrate our approach, whereas the RM intermetallics show how these concepts can be used to rapidly design new ductile materials. In conclusion, our predictive models indicate that ScCo, ScIr, and YCd should be ductile, whereas each was previously proposed to be brittle.« less

Materials Prediction via Classification Learning

PubMed Central

Balachandran, Prasanna V.; Theiler, James; Rondinelli, James M.; Lookman, Turab

2015-01-01

In the paradigm of materials informatics for accelerated materials discovery, the choice of feature set (i.e. attributes that capture aspects of structure, chemistry and/or bonding) is critical. Ideally, the feature sets should provide a simple physical basis for extracting major structural and chemical trends and furthermore, enable rapid predictions of new material chemistries. Orbital radii calculated from model pseudopotential fits to spectroscopic data are potential candidates to satisfy these conditions. Although these radii (and their linear combinations) have been utilized in the past, their functional forms are largely justified with heuristic arguments. Here we show that machine learning methods naturally uncover the functional forms that mimic most frequently used features in the literature, thereby providing a mathematical basis for feature set construction without a priori assumptions. We apply these principles to study two broad materials classes: (i) wide band gap AB compounds and (ii) rare earth-main group RM intermetallics. The AB compounds serve as a prototypical example to demonstrate our approach, whereas the RM intermetallics show how these concepts can be used to rapidly design new ductile materials. Our predictive models indicate that ScCo, ScIr, and YCd should be ductile, whereas each was previously proposed to be brittle. PMID:26304800

QSPR prediction of the hydroxyl radical rate constant of water contaminants.

PubMed

Borhani, Tohid Nejad Ghaffar; Saniedanesh, Mohammadhossein; Bagheri, Mehdi; Lim, Jeng Shiun

2016-07-01

In advanced oxidation processes (AOPs), the aqueous hydroxyl radical (HO) acts as a strong oxidant to react with organic contaminants. The hydroxyl radical rate constant (kHO) is important for evaluating and modelling of the AOPs. In this study, quantitative structure-property relationship (QSPR) method is applied to model the hydroxyl radical rate constant for a diverse dataset of 457 water contaminants from 27 various chemical classes. The constricted binary particle swarm optimization and multiple-linear regression (BPSO-MLR) are used to obtain the best model with eight theoretical descriptors. An optimized feed forward neural network (FFNN) is developed to investigate the complex performance of the selected molecular parameters with kHO. Although the FFNN prediction results are more accurate than those obtained using BPSO-MLR, the application of the latter is much more convenient. Various internal and external validation techniques indicate that the obtained models could predict the logarithmic hydroxyl radical rate constants of a large number of water contaminants with less than 4% absolute relative error. Finally, the above-mentioned proposed models are compared to those reported earlier and the structural factors contributing to the AOP degradation efficiency are discussed. Copyright © 2016 Elsevier Ltd. All rights reserved.

In Silico Identification of Epitopes in Mycobacterium avium subsp. paratuberculosis Proteins That Were Upregulated under Stress Conditions

PubMed Central

Gurung, Ratna B.; Purdie, Auriol C.; Begg, Douglas J.

2012-01-01

Johne's disease in ruminants is caused by Mycobacterium avium subsp. paratuberculosis. Diagnosis of M. avium subsp. paratuberculosis infection is difficult, especially in the early stages. To date, ideal antigen candidates are not available for efficient immunization or immunodiagnosis. This study reports the in silico selection and subsequent analysis of epitopes of M. avium subsp. paratuberculosis proteins that were found to be upregulated under stress conditions as a means to identify immunogenic candidate proteins. Previous studies have reported differential regulation of proteins when M. avium subsp. paratuberculosis is exposed to stressors which induce a response similar to dormancy. Dormancy may be involved in evading host defense mechanisms, and the host may also mount an immune response against these proteins. Twenty-five M. avium subsp. paratuberculosis proteins that were previously identified as being upregulated under in vitro stress conditions were analyzed for B and T cell epitopes by use of the prediction tools at the Immune Epitope Database and Analysis Resource. Major histocompatibility complex class I T cell epitopes were predicted using an artificial neural network method, and class II T cell epitopes were predicted using the consensus method. Conformational B cell epitopes were predicted from the relevant three-dimensional structure template for each protein. Based on the greatest number of predicted epitopes, eight proteins (MAP2698c [encoded by desA2], MAP2312c [encoded by fadE19], MAP3651c [encoded by fadE3_2], MAP2872c [encoded by fabG5_2], MAP3523c [encoded by oxcA], MAP0187c [encoded by sodA], and the hypothetical proteins MAP3567 and MAP1168c) were identified as potential candidates for study of antibody- and cell-mediated immune responses within infected hosts. PMID:22496492

Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach.

PubMed

Nielsen, Morten; Lundegaard, Claus; Worning, Peder; Hvid, Christina Sylvester; Lamberth, Kasper; Buus, Søren; Brunak, Søren; Lund, Ole

2004-06-12

Prediction of which peptides will bind a specific major histocompatibility complex (MHC) constitutes an important step in identifying potential T-cell epitopes suitable as vaccine candidates. MHC class II binding peptides have a broad length distribution complicating such predictions. Thus, identifying the correct alignment is a crucial part of identifying the core of an MHC class II binding motif. In this context, we wish to describe a novel Gibbs motif sampler method ideally suited for recognizing such weak sequence motifs. The method is based on the Gibbs sampling method, and it incorporates novel features optimized for the task of recognizing the binding motif of MHC classes I and II. The method locates the binding motif in a set of sequences and characterizes the motif in terms of a weight-matrix. Subsequently, the weight-matrix can be applied to identifying effectively potential MHC binding peptides and to guiding the process of rational vaccine design. We apply the motif sampler method to the complex problem of MHC class II binding. The input to the method is amino acid peptide sequences extracted from the public databases of SYFPEITHI and MHCPEP and known to bind to the MHC class II complex HLA-DR4(B1*0401). Prior identification of information-rich (anchor) positions in the binding motif is shown to improve the predictive performance of the Gibbs sampler. Similarly, a consensus solution obtained from an ensemble average over suboptimal solutions is shown to outperform the use of a single optimal solution. In a large-scale benchmark calculation, the performance is quantified using relative operating characteristics curve (ROC) plots and we make a detailed comparison of the performance with that of both the TEPITOPE method and a weight-matrix derived using the conventional alignment algorithm of ClustalW. The calculation demonstrates that the predictive performance of the Gibbs sampler is higher than that of ClustalW and in most cases also higher than that of the TEPITOPE method.

Patterns of Adolescent Sexual Behavior Predicting Young Adult Sexually Transmitted Infections: A Latent Class Analysis Approach

PubMed Central

Vasilenko, Sara A.; Kugler, Kari C.; Butera, Nicole M.; Lanza, Stephanie T.

2014-01-01

Adolescent sexual behavior is multidimensional, yet most studies of the topic use variable-oriented methods that reduce behaviors to a single dimension. In this study, we used a person-oriented approach to model adolescent sexual behavior comprehensively, using data from the National Longitudinal Study of Adolescent Health. We identified five latent classes of adolescent sexual behavior: Abstinent (39%), Oral Sex (10%), Low-Risk (25%), Multi-Partner Normative (12%), and Multi-Partner Early (13%). Membership in riskier classes of sexual behavior was predicted by substance use and depressive symptoms. Class membership was also associated with young adult STI outcomes although these associations differed by gender. Male adolescents' STI rates increased with membership in classes with more risky behaviors whereas females' rates were consistent among all sexually active classes. These findings demonstrate the advantages of examining adolescent sexuality in a way that emphasizes its complexity. PMID:24449152

Patterns of adolescent sexual behavior predicting young adult sexually transmitted infections: a latent class analysis approach.

PubMed

Vasilenko, Sara A; Kugler, Kari C; Butera, Nicole M; Lanza, Stephanie T

2015-04-01

Adolescent sexual behavior is multidimensional, yet most studies of the topic use variable-oriented methods that reduce behaviors to a single dimension. In this study, we used a person-oriented approach to model adolescent sexual behavior comprehensively, using data from the National Longitudinal Study of Adolescent Health. We identified five latent classes of adolescent sexual behavior: Abstinent (39%), Oral Sex (10%), Low-Risk (25%), Multi-Partner Normative (12%), and Multi-Partner Early (13%). Membership in riskier classes of sexual behavior was predicted by substance use and depressive symptoms. Class membership was also associated with young adult STI outcomes although these associations differed by gender. Male adolescents' STI rates increased with membership in classes with more risky behaviors whereas females' rates were consistent among all sexually active classes. These findings demonstrate the advantages of examining adolescent sexuality in a way that emphasizes its complexity.

The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models

ERIC Educational Resources Information Center

Park, Jungkyu; Yu, Hsiu-Ting

2016-01-01

The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…

Structural and Functional Analysis of the Human HDAC4 Catalytic Domain Reveals a Regulatory Structural Zinc-binding Domain*S⃞

PubMed Central

Bottomley, Matthew J.; Lo Surdo, Paola; Di Giovine, Paolo; Cirillo, Agostino; Scarpelli, Rita; Ferrigno, Federica; Jones, Philip; Neddermann, Petra; De Francesco, Raffaele; Steinkühler, Christian; Gallinari, Paola; Carfí, Andrea

2008-01-01

Histone deacetylases (HDACs) regulate chromatin status and gene expression, and their inhibition is of significant therapeutic interest. To date, no biological substrate for class IIa HDACs has been identified, and only low activity on acetylated lysines has been demonstrated. Here, we describe inhibitor-bound and inhibitor-free structures of the histone deacetylase-4 catalytic domain (HDAC4cd) and of an HDAC4cd active site mutant with enhanced enzymatic activity toward acetylated lysines. The structures presented, coupled with activity data, provide the molecular basis for the intrinsically low enzymatic activity of class IIa HDACs toward acetylated lysines and reveal active site features that may guide the design of class-specific inhibitors. In addition, these structures reveal a conformationally flexible structural zinc-binding domain conserved in all class IIa enzymes. Importantly, either the mutation of residues coordinating the structural zinc ion or the binding of a class IIa selective inhibitor prevented the association of HDAC4 with the N-CoR·HDAC3 repressor complex. Together, these data suggest a key role of the structural zinc-binding domain in the regulation of class IIa HDAC functions. PMID:18614528

Structural and functional analysis of the human HDAC4 catalytic domain reveals a regulatory structural zinc-binding domain.

PubMed

Bottomley, Matthew J; Lo Surdo, Paola; Di Giovine, Paolo; Cirillo, Agostino; Scarpelli, Rita; Ferrigno, Federica; Jones, Philip; Neddermann, Petra; De Francesco, Raffaele; Steinkühler, Christian; Gallinari, Paola; Carfí, Andrea

2008-09-26

Histone deacetylases (HDACs) regulate chromatin status and gene expression, and their inhibition is of significant therapeutic interest. To date, no biological substrate for class IIa HDACs has been identified, and only low activity on acetylated lysines has been demonstrated. Here, we describe inhibitor-bound and inhibitor-free structures of the histone deacetylase-4 catalytic domain (HDAC4cd) and of an HDAC4cd active site mutant with enhanced enzymatic activity toward acetylated lysines. The structures presented, coupled with activity data, provide the molecular basis for the intrinsically low enzymatic activity of class IIa HDACs toward acetylated lysines and reveal active site features that may guide the design of class-specific inhibitors. In addition, these structures reveal a conformationally flexible structural zinc-binding domain conserved in all class IIa enzymes. Importantly, either the mutation of residues coordinating the structural zinc ion or the binding of a class IIa selective inhibitor prevented the association of HDAC4 with the N-CoR.HDAC3 repressor complex. Together, these data suggest a key role of the structural zinc-binding domain in the regulation of class IIa HDAC functions.

The clustering-based case-based reasoning for imbalanced business failure prediction: a hybrid approach through integrating unsupervised process with supervised process

NASA Astrophysics Data System (ADS)

Li, Hui; Yu, Jun-Ling; Yu, Le-An; Sun, Jie

2014-05-01

Case-based reasoning (CBR) is one of the main forecasting methods in business forecasting, which performs well in prediction and holds the ability of giving explanations for the results. In business failure prediction (BFP), the number of failed enterprises is relatively small, compared with the number of non-failed ones. However, the loss is huge when an enterprise fails. Therefore, it is necessary to develop methods (trained on imbalanced samples) which forecast well for this small proportion of failed enterprises and performs accurately on total accuracy meanwhile. Commonly used methods constructed on the assumption of balanced samples do not perform well in predicting minority samples on imbalanced samples consisting of the minority/failed enterprises and the majority/non-failed ones. This article develops a new method called clustering-based CBR (CBCBR), which integrates clustering analysis, an unsupervised process, with CBR, a supervised process, to enhance the efficiency of retrieving information from both minority and majority in CBR. In CBCBR, various case classes are firstly generated through hierarchical clustering inside stored experienced cases, and class centres are calculated out by integrating cases information in the same clustered class. When predicting the label of a target case, its nearest clustered case class is firstly retrieved by ranking similarities between the target case and each clustered case class centre. Then, nearest neighbours of the target case in the determined clustered case class are retrieved. Finally, labels of the nearest experienced cases are used in prediction. In the empirical experiment with two imbalanced samples from China, the performance of CBCBR was compared with the classical CBR, a support vector machine, a logistic regression and a multi-variant discriminate analysis. The results show that compared with the other four methods, CBCBR performed significantly better in terms of sensitivity for identifying the minority samples and generated high total accuracy meanwhile. The proposed approach makes CBR useful in imbalanced forecasting.

Mixed time integration methods for transient thermal analysis of structures

NASA Technical Reports Server (NTRS)

Liu, W. K.

1982-01-01

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

Mixed time integration methods for transient thermal analysis of structures

NASA Technical Reports Server (NTRS)

Liu, W. K.

1983-01-01

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

EPIPOX: Immunoinformatic Characterization of the Shared T-Cell Epitome between Variola Virus and Related Pathogenic Orthopoxviruses.

PubMed

Molero-Abraham, Magdalena; Glutting, John-Paul; Flower, Darren R; Lafuente, Esther M; Reche, Pedro A

2015-01-01

Concerns that variola viruses might be used as bioweapons have renewed the interest in developing new and safer smallpox vaccines. Variola virus genomes are now widely available, allowing computational characterization of the entire T-cell epitome and the use of such information to develop safe and yet effective vaccines. To this end, we identified 124 proteins shared between various species of pathogenic orthopoxviruses including variola minor and major, monkeypox, cowpox, and vaccinia viruses, and we targeted them for T-cell epitope prediction. We recognized 8,106, and 8,483 unique class I and class II MHC-restricted T-cell epitopes that are shared by all mentioned orthopoxviruses. Subsequently, we developed an immunological resource, EPIPOX, upon the predicted T-cell epitome. EPIPOX is freely available online and it has been designed to facilitate reverse vaccinology. Thus, EPIPOX includes key epitope-focused protein annotations: time point expression, presence of leader and transmembrane signals, and known location on outer membrane structures of the infective viruses. These features can be used to select specific T-cell epitopes suitable for experimental validation restricted by single MHC alleles, as combinations thereof, or by MHC supertypes.

EPIPOX: Immunoinformatic Characterization of the Shared T-Cell Epitome between Variola Virus and Related Pathogenic Orthopoxviruses

PubMed Central

Molero-Abraham, Magdalena; Glutting, John-Paul; Flower, Darren R.; Lafuente, Esther M.; Reche, Pedro A.

2015-01-01

Concerns that variola viruses might be used as bioweapons have renewed the interest in developing new and safer smallpox vaccines. Variola virus genomes are now widely available, allowing computational characterization of the entire T-cell epitome and the use of such information to develop safe and yet effective vaccines. To this end, we identified 124 proteins shared between various species of pathogenic orthopoxviruses including variola minor and major, monkeypox, cowpox, and vaccinia viruses, and we targeted them for T-cell epitope prediction. We recognized 8,106, and 8,483 unique class I and class II MHC-restricted T-cell epitopes that are shared by all mentioned orthopoxviruses. Subsequently, we developed an immunological resource, EPIPOX, upon the predicted T-cell epitome. EPIPOX is freely available online and it has been designed to facilitate reverse vaccinology. Thus, EPIPOX includes key epitope-focused protein annotations: time point expression, presence of leader and transmembrane signals, and known location on outer membrane structures of the infective viruses. These features can be used to select specific T-cell epitopes suitable for experimental validation restricted by single MHC alleles, as combinations thereof, or by MHC supertypes. PMID:26605344

Automated compound classification using a chemical ontology.

PubMed

Bobach, Claudia; Böhme, Timo; Laube, Ulf; Püschel, Anett; Weber, Lutz

2012-12-29

Classification of chemical compounds into compound classes by using structure derived descriptors is a well-established method to aid the evaluation and abstraction of compound properties in chemical compound databases. MeSH and recently ChEBI are examples of chemical ontologies that provide a hierarchical classification of compounds into general compound classes of biological interest based on their structural as well as property or use features. In these ontologies, compounds have been assigned manually to their respective classes. However, with the ever increasing possibilities to extract new compounds from text documents using name-to-structure tools and considering the large number of compounds deposited in databases, automated and comprehensive chemical classification methods are needed to avoid the error prone and time consuming manual classification of compounds. In the present work we implement principles and methods to construct a chemical ontology of classes that shall support the automated, high-quality compound classification in chemical databases or text documents. While SMARTS expressions have already been used to define chemical structure class concepts, in the present work we have extended the expressive power of such class definitions by expanding their structure-based reasoning logic. Thus, to achieve the required precision and granularity of chemical class definitions, sets of SMARTS class definitions are connected by OR and NOT logical operators. In addition, AND logic has been implemented to allow the concomitant use of flexible atom lists and stereochemistry definitions. The resulting chemical ontology is a multi-hierarchical taxonomy of concept nodes connected by directed, transitive relationships. A proposal for a rule based definition of chemical classes has been made that allows to define chemical compound classes more precisely than before. The proposed structure-based reasoning logic allows to translate chemistry expert knowledge into a computer interpretable form, preventing erroneous compound assignments and allowing automatic compound classification. The automated assignment of compounds in databases, compound structure files or text documents to their related ontology classes is possible through the integration with a chemical structure search engine. As an application example, the annotation of chemical structure files with a prototypic ontology is demonstrated.

Automated compound classification using a chemical ontology

PubMed Central

2012-01-01

Background Classification of chemical compounds into compound classes by using structure derived descriptors is a well-established method to aid the evaluation and abstraction of compound properties in chemical compound databases. MeSH and recently ChEBI are examples of chemical ontologies that provide a hierarchical classification of compounds into general compound classes of biological interest based on their structural as well as property or use features. In these ontologies, compounds have been assigned manually to their respective classes. However, with the ever increasing possibilities to extract new compounds from text documents using name-to-structure tools and considering the large number of compounds deposited in databases, automated and comprehensive chemical classification methods are needed to avoid the error prone and time consuming manual classification of compounds. Results In the present work we implement principles and methods to construct a chemical ontology of classes that shall support the automated, high-quality compound classification in chemical databases or text documents. While SMARTS expressions have already been used to define chemical structure class concepts, in the present work we have extended the expressive power of such class definitions by expanding their structure-based reasoning logic. Thus, to achieve the required precision and granularity of chemical class definitions, sets of SMARTS class definitions are connected by OR and NOT logical operators. In addition, AND logic has been implemented to allow the concomitant use of flexible atom lists and stereochemistry definitions. The resulting chemical ontology is a multi-hierarchical taxonomy of concept nodes connected by directed, transitive relationships. Conclusions A proposal for a rule based definition of chemical classes has been made that allows to define chemical compound classes more precisely than before. The proposed structure-based reasoning logic allows to translate chemistry expert knowledge into a computer interpretable form, preventing erroneous compound assignments and allowing automatic compound classification. The automated assignment of compounds in databases, compound structure files or text documents to their related ontology classes is possible through the integration with a chemical structure search engine. As an application example, the annotation of chemical structure files with a prototypic ontology is demonstrated. PMID:23273256

Class Inclusion and Role-Taking: Structural Mediation.

ERIC Educational Resources Information Center

Gash, Hugh

The mediational linkage between class inclusion and role-taking skills was investigated by studying the effects of a successive perspective-taking training technique on the consolidation of class inclusion structures. Sixty preoperational Irish boys were given two pretest measures of class inclusion and two of role-taking. They were then grouped…

Evaluation of objective structured clinical examination for advanced orthodontic education 12 years after introduction.

PubMed

Fields, Henry W; Kim, Do-Gyoon; Jeon, Minjeong; Firestone, Allen R; Sun, Zongyang; Shanker, Shiva; Mercado, Ana M; Deguchi, Toru; Vig, Katherine W L

2017-05-01

Advanced education programs in orthodontics must ensure student competency in clinical skills. An objective structure clinical examination has been used in 1 program for over a decade. The results were analyzed cross-sectionally and longitudinally to provide insights regarding the achievement of competency, student growth, question difficulty, question discrimination, and question predictive ability. In this study, we analyzed 218 (82 first-year, 68 second-year, and 68 third-year classes) scores of each station from 85 orthodontic students. The grades originated from 13 stations and were collected anonymously for 12 consecutive years during the first 2 decades of the 2000s. The stations tested knowledge and skills regarding dental relationships, analyzing a cephalometric tracing, performing a diagnostic skill, identifying cephalometric points, bracket placement, placing first-order and second-order bends, forming a loop, placing accentuated third-order bends, identifying problems and planning mixed dentition treatment, identifying problems and planning adolescent dentition treatment, identifying problems and planning nongrowing skeletal treatment, superimposing cephalometric tracings, and interpreting cephalometric superimpositions. Results were evaluated using multivariate analysis of variance, chi-square tests, and latent growth analysis. The multivariate analysis of variance showed that all stations except 3 (analyzing a cephalometric tracing, forming a loop, and identifying cephalometric points) had significantly lower mean scores for the first-year student class than the second- and third-year classes (P <0.028); scores between the second- and third-year student classes were not significantly different (P >0.108). The chi-square analysis of the distribution of the number of noncompetent item responses decreased from the first to the second years (P <0.0003), from the second to the third years (P <0.0042), and from the first to the third years (P <0.00003). The latent growth analysis showed a wide range of difficulty and discrimination between questions. It also showed continuous growth for some areas and the ability of 6 questions to predict competency at greater than the 80% level. Objective structure clinical examinations can provide a method of evaluating student performance and curriculum impact over time, but cross-sectional and longitudinal analyses of the results may not be complementary. Significant learning appears to occur during all years of a 3-year program. Valuable questions were both easy and difficult, discriminating and not discriminating, and came from all domains: diagnostic, technical, and evaluation/synthesis. Copyright © 2017 American Association of Orthodontists. Published by Elsevier Inc. All rights reserved.

Medical X-ray Image Hierarchical Classification Using a Merging and Splitting Scheme in Feature Space.

PubMed

Fesharaki, Nooshin Jafari; Pourghassem, Hossein

2013-07-01

Due to the daily mass production and the widespread variation of medical X-ray images, it is necessary to classify these for searching and retrieving proposes, especially for content-based medical image retrieval systems. In this paper, a medical X-ray image hierarchical classification structure based on a novel merging and splitting scheme and using shape and texture features is proposed. In the first level of the proposed structure, to improve the classification performance, similar classes with regard to shape contents are grouped based on merging measures and shape features into the general overlapped classes. In the next levels of this structure, the overlapped classes split in smaller classes based on the classification performance of combination of shape and texture features or texture features only. Ultimately, in the last levels, this procedure is also continued forming all the classes, separately. Moreover, to optimize the feature vector in the proposed structure, we use orthogonal forward selection algorithm according to Mahalanobis class separability measure as a feature selection and reduction algorithm. In other words, according to the complexity and inter-class distance of each class, a sub-space of the feature space is selected in each level and then a supervised merging and splitting scheme is applied to form the hierarchical classification. The proposed structure is evaluated on a database consisting of 2158 medical X-ray images of 18 classes (IMAGECLEF 2005 database) and accuracy rate of 93.6% in the last level of the hierarchical structure for an 18-class classification problem is obtained.

Proposed actions are no actions: re-modeling an ontology design pattern with a realist top-level ontology.

PubMed

Seddig-Raufie, Djamila; Jansen, Ludger; Schober, Daniel; Boeker, Martin; Grewe, Niels; Schulz, Stefan

2012-09-21

Ontology Design Patterns (ODPs) are representational artifacts devised to offer solutions for recurring ontology design problems. They promise to enhance the ontology building process in terms of flexibility, re-usability and expansion, and to make the result of ontology engineering more predictable. In this paper, we analyze ODP repositories and investigate their relation with upper-level ontologies. In particular, we compare the BioTop upper ontology to the Action ODP from the NeOn an ODP repository. In view of the differences in the respective approaches, we investigate whether the Action ODP can be embedded into BioTop. We demonstrate that this requires re-interpreting the meaning of classes of the NeOn Action ODP in the light of the precepts of realist ontologies. As a result, the re-design required clarifying the ontological commitment of the ODP classes by assigning them to top-level categories. Thus, ambiguous definitions are avoided. Classes of real entities are clearly distinguished from classes of information artifacts. The proposed approach avoids the commitment to the existence of unclear future entities which underlies the NeOn Action ODP. Our re-design is parsimonious in the sense that existing BioTop content proved to be largely sufficient to define the different types of actions and plans. The proposed model demonstrates that an expressive upper-level ontology provides enough resources and expressivity to represent even complex ODPs, here shown with the different flavors of Action as proposed in the NeOn ODP. The advantage of ODP inclusion into a top-level ontology is the given predetermined dependency of each class, an existing backbone structure and well-defined relations. Our comparison shows that the use of some ODPs is more likely to cause problems for ontology developers, rather than to guide them. Besides the structural properties, the explanation of classification results were particularly hard to grasp for 'self-sufficient' ODPs as compared with implemented and 'embedded' upper-level structures which, for example in the case of BioTop, offer a detailed description of classes and relations in an axiomatic network. This ensures unambiguous interpretation and provides more concise constraints to leverage on in the ontology engineering process.

Longitudinal relationships between individual and class norms supporting dating violence and perpetration of dating violence.

PubMed

Taylor, Katherine A; Sullivan, Terri N; Farrell, Albert D

2015-03-01

Dating violence is commonly perpetrated in adolescence, making it imperative to understand risk factors in order to inform prevention efforts. Although individual norms supporting dating violence are strongly related to its perpetration, few studies have examined their longitudinal impact. Moreover, the influence of class norms (i.e., norms for students in the same grade, cohort, and school) supporting dating violence on perpetration has rarely been studied. The current study examined longitudinal relationships between individual and class norms supporting dating violence and perpetration of physical and psychological dating violence. Participants were two cohorts of sixth graders from 37 schools who were in dating relationships at Wave 1 and 6 months later at Wave 2 (N = 2,022; 43% female; 52% African American, 21% Latino/a, 20% White, and 7% other). The analyses used a multilevel approach, with students represented at Level 1 and classes (n = 74) at Level 2. The models tested direct effects of Wave 1 individual and class norms supporting dating violence on subsequent changes in perpetration of dating violence at Wave 2 and the extent to which gender moderated these relationships. The findings indicated that greater individual norms supporting male dating violence predicted greater change in perpetration of physical and psychological dating violence and greater individual norms supporting female dating violence predicted greater change in perpetration of psychological dating violence. Greater class norms supporting male dating violence predicted greater change in perpetration of physical dating violence; whereas greater class norms supporting female dating violence predicted less change in perpetration of physical dating violence. These findings highlight the need to address norms in early adolescence.

The UCL low-density lipoprotein receptor gene variant database: pathogenicity update

PubMed Central

Futema, Marta; Whittall, Ros; Taylor-Beadling, Alison; Williams, Maggie; den Dunnen, Johan T; Humphries, Steve E

2017-01-01

Background Familial hypercholesterolaemia (OMIM 143890) is most frequently caused by variations in the low-density lipoprotein receptor (LDLR) gene. Predicting whether novel variants are pathogenic may not be straightforward, especially for missense and synonymous variants. In 2013, the Association of Clinical Genetic Scientists published guidelines for the classification of variants, with categories 1 and 2 representing clearly not or unlikely pathogenic, respectively, 3 representing variants of unknown significance (VUS), and 4 and 5 representing likely to be or clearly pathogenic, respectively. Here, we update the University College London (UCL) LDLR variant database according to these guidelines. Methods PubMed searches and alerts were used to identify novel LDLR variants for inclusion in the database. Standard in silico tools were used to predict potential pathogenicity. Variants were designated as class 4/5 only when the predictions from the different programs were concordant and as class 3 when predictions were discordant. Results The updated database (http://www.lovd.nl/LDLR) now includes 2925 curated variants, representing 1707 independent events. All 129 nonsense variants, 337 small frame-shifting and 117/118 large rearrangements were classified as 4 or 5. Of the 795 missense variants, 115 were in classes 1 and 2, 605 in class 4 and 75 in class 3. 111/181 intronic variants, 4/34 synonymous variants and 14/37 promoter variants were assigned to classes 4 or 5. Overall, 112 (7%) of reported variants were class 3. Conclusions This study updates the LDLR variant database and identifies a number of reported VUS where additional family and in vitro studies will be required to confirm or refute their pathogenicity. PMID:27821657

The Experiences of Working-Class College Students Who Became University Presidents

ERIC Educational Resources Information Center

Springer, Mary E.

2012-01-01

Working-class students enter college lacking necessary capital to predict their academic and personal success making college success less likely than for middle class students (Bufton, 2003; Mack, 2006; Paulsen & St. John, 2002; Rose, 1997; Wegner, 1973). This same social class origin helps to define experiences, provides context for…