Motion prediction of a non-cooperative space target

NASA Astrophysics Data System (ADS)

Zhou, Bang-Zhao; Cai, Guo-Ping; Liu, Yun-Meng; Liu, Pan

2018-01-01

Capturing a non-cooperative space target is a tremendously challenging research topic. Effective acquisition of motion information of the space target is the premise to realize target capture. In this paper, motion prediction of a free-floating non-cooperative target in space is studied and a motion prediction algorithm is proposed. In order to predict the motion of the free-floating non-cooperative target, dynamic parameters of the target must be firstly identified (estimated), such as inertia, angular momentum and kinetic energy and so on; then the predicted motion of the target can be acquired by substituting these identified parameters into the Euler's equations of the target. Accurate prediction needs precise identification. This paper presents an effective method to identify these dynamic parameters of a free-floating non-cooperative target. This method is based on two steps, (1) the rough estimation of the parameters is computed using the motion observation data to the target, and (2) the best estimation of the parameters is found by an optimization method. In the optimization problem, the objective function is based on the difference between the observed and the predicted motion, and the interior-point method (IPM) is chosen as the optimization algorithm, which starts at the rough estimate obtained in the first step and finds a global minimum to the objective function with the guidance of objective function's gradient. So the speed of IPM searching for the global minimum is fast, and an accurate identification can be obtained in time. The numerical results show that the proposed motion prediction algorithm is able to predict the motion of the target.

Common features of microRNA target prediction tools

PubMed Central

Peterson, Sarah M.; Thompson, Jeffrey A.; Ufkin, Melanie L.; Sathyanarayana, Pradeep; Liaw, Lucy; Congdon, Clare Bates

2014-01-01

The human genome encodes for over 1800 microRNAs (miRNAs), which are short non-coding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one miRNA to target multiple gene transcripts, miRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of miRNA targets is a critical initial step in identifying miRNA:mRNA target interactions for experimental validation. The available tools for miRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to miRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all miRNA target prediction tools, four main aspects of the miRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MiRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output. PMID:24600468

Common features of microRNA target prediction tools.

PubMed

Peterson, Sarah M; Thompson, Jeffrey A; Ufkin, Melanie L; Sathyanarayana, Pradeep; Liaw, Lucy; Congdon, Clare Bates

2014-01-01

The human genome encodes for over 1800 microRNAs (miRNAs), which are short non-coding RNA molecules that function to regulate gene expression post-transcriptionally. Due to the potential for one miRNA to target multiple gene transcripts, miRNAs are recognized as a major mechanism to regulate gene expression and mRNA translation. Computational prediction of miRNA targets is a critical initial step in identifying miRNA:mRNA target interactions for experimental validation. The available tools for miRNA target prediction encompass a range of different computational approaches, from the modeling of physical interactions to the incorporation of machine learning. This review provides an overview of the major computational approaches to miRNA target prediction. Our discussion highlights three tools for their ease of use, reliance on relatively updated versions of miRBase, and range of capabilities, and these are DIANA-microT-CDS, miRanda-mirSVR, and TargetScan. In comparison across all miRNA target prediction tools, four main aspects of the miRNA:mRNA target interaction emerge as common features on which most target prediction is based: seed match, conservation, free energy, and site accessibility. This review explains these features and identifies how they are incorporated into currently available target prediction tools. MiRNA target prediction is a dynamic field with increasing attention on development of new analysis tools. This review attempts to provide a comprehensive assessment of these tools in a manner that is accessible across disciplines. Understanding the basis of these prediction methodologies will aid in user selection of the appropriate tools and interpretation of the tool output.

Predicting oligonucleotide affinity to nucleic acid targets.

PubMed Central

Mathews, D H; Burkard, M E; Freier, S M; Wyatt, J R; Turner, D H

1999-01-01

A computer program, OligoWalk, is reported that predicts the equilibrium affinity of complementary DNA or RNA oligonucleotides to an RNA target. This program considers the predicted stability of the oligonucleotide-target helix and the competition with predicted secondary structure of both the target and the oligonucleotide. Both unimolecular and bimolecular oligonucleotide self structure are considered with a user-defined concentration. The application of OligoWalk is illustrated with three comparisons to experimental results drawn from the literature. PMID:10580474

Predictive acute toxicity tests with pesticides.

PubMed

Brown, V K

1983-01-01

By definition pesticides are biocidal products and this implies a probability that pesticides may be acutely toxic to species other than the designated target species. The ways in which pesticides are manufactured, formulated, packaged, distributed and used necessitates a potential for the exposure of non-target species although the technology exists to minimize adventitious exposure. The occurrence of deliberate exposure of non-target species due to the misuse of pesticides is known to happen. The array of predictive acute toxicity tests carried out on pesticides and involving the use of laboratory animals can be justified as providing data on which hazard assessment can be based. This paper addresses the justification and rationale of this statement.

22 Years of predictive testing for Huntington's disease: the experience of the UK Huntington's Prediction Consortium.

PubMed

Baig, Sheharyar S; Strong, Mark; Rosser, Elisabeth; Taverner, Nicola V; Glew, Ruth; Miedzybrodzka, Zosia; Clarke, Angus; Craufurd, David; Quarrell, Oliver W

2016-10-01

Huntington's disease (HD) is a progressive neurodegenerative condition. At-risk individuals have accessed predictive testing via direct mutation testing since 1993. The UK Huntington's Prediction Consortium has collected anonymised data on UK predictive tests, annually, from 1993 to 2014: 9407 predictive tests were performed across 23 UK centres. Where gender was recorded, 4077 participants were male (44.3%) and 5122 were female (55.7%). The median age of participants was 37 years. The most common reason for predictive testing was to reduce uncertainty (70.5%). Of the 8441 predictive tests on individuals at 50% prior risk, 4629 (54.8%) were reported as mutation negative and 3790 (44.9%) were mutation positive, with 22 (0.3%) in the database being uninterpretable. Using a prevalence figure of 12.3 × 10(-5), the cumulative uptake of predictive testing in the 50% at-risk UK population from 1994 to 2014 was estimated at 17.4% (95% CI: 16.9-18.0%). We present the largest study conducted on predictive testing in HD. Our findings indicate that the vast majority of individuals at risk of HD (>80%) have not undergone predictive testing. Future therapies in HD will likely target presymptomatic individuals; therefore, identifying the at-risk population whose gene status is unknown is of significant public health value.

Drug-Target Interactions: Prediction Methods and Applications.

PubMed

Anusuya, Shanmugam; Kesherwani, Manish; Priya, K Vishnu; Vimala, Antonydhason; Shanmugam, Gnanendra; Velmurugan, Devadasan; Gromiha, M Michael

2018-01-01

Identifying the interactions between drugs and target proteins is a key step in drug discovery. This not only aids to understand the disease mechanism, but also helps to identify unexpected therapeutic activity or adverse side effects of drugs. Hence, drug-target interaction prediction becomes an essential tool in the field of drug repurposing. The availability of heterogeneous biological data on known drug-target interactions enabled many researchers to develop various computational methods to decipher unknown drug-target interactions. This review provides an overview on these computational methods for predicting drug-target interactions along with available webservers and databases for drug-target interactions. Further, the applicability of drug-target interactions in various diseases for identifying lead compounds has been outlined. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

22 Years of predictive testing for Huntington's disease: the experience of the UK Huntington's Prediction Consortium

PubMed Central

Baig, Sheharyar S; Strong, Mark; Rosser, Elisabeth; Taverner, Nicola V; Glew, Ruth; Miedzybrodzka, Zosia; Clarke, Angus; Craufurd, David; Quarrell, Oliver W

2016-01-01

Huntington's disease (HD) is a progressive neurodegenerative condition. At-risk individuals have accessed predictive testing via direct mutation testing since 1993. The UK Huntington's Prediction Consortium has collected anonymised data on UK predictive tests, annually, from 1993 to 2014: 9407 predictive tests were performed across 23 UK centres. Where gender was recorded, 4077 participants were male (44.3%) and 5122 were female (55.7%). The median age of participants was 37 years. The most common reason for predictive testing was to reduce uncertainty (70.5%). Of the 8441 predictive tests on individuals at 50% prior risk, 4629 (54.8%) were reported as mutation negative and 3790 (44.9%) were mutation positive, with 22 (0.3%) in the database being uninterpretable. Using a prevalence figure of 12.3 × 10−5, the cumulative uptake of predictive testing in the 50% at-risk UK population from 1994 to 2014 was estimated at 17.4% (95% CI: 16.9–18.0%). We present the largest study conducted on predictive testing in HD. Our findings indicate that the vast majority of individuals at risk of HD (>80%) have not undergone predictive testing. Future therapies in HD will likely target presymptomatic individuals; therefore, identifying the at-risk population whose gene status is unknown is of significant public health value. PMID:27165004

MOST: most-similar ligand based approach to target prediction.

PubMed

Huang, Tao; Mi, Hong; Lin, Cheng-Yuan; Zhao, Ling; Zhong, Linda L D; Liu, Feng-Bin; Zhang, Ge; Lu, Ai-Ping; Bian, Zhao-Xiang

2017-03-11

Many computational approaches have been used for target prediction, including machine learning, reverse docking, bioactivity spectra analysis, and chemical similarity searching. Recent studies have suggested that chemical similarity searching may be driven by the most-similar ligand. However, the extent of bioactivity of most-similar ligands has been oversimplified or even neglected in these studies, and this has impaired the prediction power. Here we propose the MOst-Similar ligand-based Target inference approach, namely MOST, which uses fingerprint similarity and explicit bioactivity of the most-similar ligands to predict targets of the query compound. Performance of MOST was evaluated by using combinations of different fingerprint schemes, machine learning methods, and bioactivity representations. In sevenfold cross-validation with a benchmark Ki dataset from CHEMBL release 19 containing 61,937 bioactivity data of 173 human targets, MOST achieved high average prediction accuracy (0.95 for pKi ≥ 5, and 0.87 for pKi ≥ 6). Morgan fingerprint was shown to be slightly better than FP2. Logistic Regression and Random Forest methods performed better than Naïve Bayes. In a temporal validation, the Ki dataset from CHEMBL19 were used to train models and predict the bioactivity of newly deposited ligands in CHEMBL20. MOST also performed well with high accuracy (0.90 for pKi ≥ 5, and 0.76 for pKi ≥ 6), when Logistic Regression and Morgan fingerprint were employed. Furthermore, the p values associated with explicit bioactivity were found be a robust index for removing false positive predictions. Implicit bioactivity did not offer this capability. Finally, p values generated with Logistic Regression, Morgan fingerprint and explicit activity were integrated with a false discovery rate (FDR) control procedure to reduce false positives in multiple-target prediction scenario, and the success of this strategy it was demonstrated with a case of fluanisone

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

PubMed

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

2016-05-01

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

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

NASA Astrophysics Data System (ADS)

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

2016-05-01

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

Quantitative self-assembly prediction yields targeted nanomedicines

NASA Astrophysics Data System (ADS)

Shamay, Yosi; Shah, Janki; Işık, Mehtap; Mizrachi, Aviram; Leibold, Josef; Tschaharganeh, Darjus F.; Roxbury, Daniel; Budhathoki-Uprety, Januka; Nawaly, Karla; Sugarman, James L.; Baut, Emily; Neiman, Michelle R.; Dacek, Megan; Ganesh, Kripa S.; Johnson, Darren C.; Sridharan, Ramya; Chu, Karen L.; Rajasekhar, Vinagolu K.; Lowe, Scott W.; Chodera, John D.; Heller, Daniel A.

2018-02-01

Development of targeted nanoparticle drug carriers often requires complex synthetic schemes involving both supramolecular self-assembly and chemical modification. These processes are generally difficult to predict, execute, and control. We describe herein a targeted drug delivery system that is accurately and quantitatively predicted to self-assemble into nanoparticles based on the molecular structures of precursor molecules, which are the drugs themselves. The drugs assemble with the aid of sulfated indocyanines into particles with ultrahigh drug loadings of up to 90%. We devised quantitative structure-nanoparticle assembly prediction (QSNAP) models to identify and validate electrotopological molecular descriptors as highly predictive indicators of nano-assembly and nanoparticle size. The resulting nanoparticles selectively targeted kinase inhibitors to caveolin-1-expressing human colon cancer and autochthonous liver cancer models to yield striking therapeutic effects while avoiding pERK inhibition in healthy skin. This finding enables the computational design of nanomedicines based on quantitative models for drug payload selection.

Hydrocode predictions of collisional outcomes: Effects of target size

NASA Technical Reports Server (NTRS)

Ryan, Eileen V.; Asphaug, Erik; Melosh, H. J.

1991-01-01

Traditionally, laboratory impact experiments, designed to simulate asteroid collisions, attempted to establish a predictive capability for collisional outcomes given a particular set of initial conditions. Unfortunately, laboratory experiments are restricted to using targets considerably smaller than the modelled objects. It is therefore necessary to develop some methodology for extrapolating the extensive experimental results to the size regime of interest. Results are reported obtained through the use of two dimensional hydrocode based on 2-D SALE and modified to include strength effects and the fragmentation equations. The hydrocode was tested by comparing its predictions for post-impact fragment size distributions to those observed in laboratory impact experiments.

Brainstorming: weighted voting prediction of inhibitors for protein targets.

PubMed

Plewczynski, Dariusz

2011-09-01

The "Brainstorming" approach presented in this paper is a weighted voting method that can improve the quality of predictions generated by several machine learning (ML) methods. First, an ensemble of heterogeneous ML algorithms is trained on available experimental data, then all solutions are gathered and a consensus is built between them. The final prediction is performed using a voting procedure, whereby the vote of each method is weighted according to a quality coefficient calculated using multivariable linear regression (MLR). The MLR optimization procedure is very fast, therefore no additional computational cost is introduced by using this jury approach. Here, brainstorming is applied to selecting actives from large collections of compounds relating to five diverse biological targets of medicinal interest, namely HIV-reverse transcriptase, cyclooxygenase-2, dihydrofolate reductase, estrogen receptor, and thrombin. The MDL Drug Data Report (MDDR) database was used for selecting known inhibitors for these protein targets, and experimental data was then used to train a set of machine learning methods. The benchmark dataset (available at http://bio.icm.edu.pl/∼darman/chemoinfo/benchmark.tar.gz ) can be used for further testing of various clustering and machine learning methods when predicting the biological activity of compounds. Depending on the protein target, the overall recall value is raised by at least 20% in comparison to any single machine learning method (including ensemble methods like random forest) and unweighted simple majority voting procedures.

Macromolecular target prediction by self-organizing feature maps.

PubMed

Schneider, Gisbert; Schneider, Petra

2017-03-01

Rational drug discovery would greatly benefit from a more nuanced appreciation of the activity of pharmacologically active compounds against a diverse panel of macromolecular targets. Already, computational target-prediction models assist medicinal chemists in library screening, de novo molecular design, optimization of active chemical agents, drug re-purposing, in the spotting of potential undesired off-target activities, and in the 'de-orphaning' of phenotypic screening hits. The self-organizing map (SOM) algorithm has been employed successfully for these and other purposes. Areas covered: The authors recapitulate contemporary artificial neural network methods for macromolecular target prediction, and present the basic SOM algorithm at a conceptual level. Specifically, they highlight consensus target-scoring by the employment of multiple SOMs, and discuss the opportunities and limitations of this technique. Expert opinion: Self-organizing feature maps represent a straightforward approach to ligand clustering and classification. Some of the appeal lies in their conceptual simplicity and broad applicability domain. Despite known algorithmic shortcomings, this computational target prediction concept has been proven to work in prospective settings with high success rates. It represents a prototypic technique for future advances in the in silico identification of the modes of action and macromolecular targets of bioactive molecules.

HomoTarget: a new algorithm for prediction of microRNA targets in Homo sapiens.

PubMed

Ahmadi, Hamed; Ahmadi, Ali; Azimzadeh-Jamalkandi, Sadegh; Shoorehdeli, Mahdi Aliyari; Salehzadeh-Yazdi, Ali; Bidkhori, Gholamreza; Masoudi-Nejad, Ali

2013-02-01

MiRNAs play an essential role in the networks of gene regulation by inhibiting the translation of target mRNAs. Several computational approaches have been proposed for the prediction of miRNA target-genes. Reports reveal a large fraction of under-predicted or falsely predicted target genes. Thus, there is an imperative need to develop a computational method by which the target mRNAs of existing miRNAs can be correctly identified. In this study, combined pattern recognition neural network (PRNN) and principle component analysis (PCA) architecture has been proposed in order to model the complicated relationship between miRNAs and their target mRNAs in humans. The results of several types of intelligent classifiers and our proposed model were compared, showing that our algorithm outperformed them with higher sensitivity and specificity. Using the recent release of the mirBase database to find potential targets of miRNAs, this model incorporated twelve structural, thermodynamic and positional features of miRNA:mRNA binding sites to select target candidates. Copyright © 2012 Elsevier Inc. All rights reserved.

Predicting Drug-Target Interactions With Multi-Information Fusion.

PubMed

Peng, Lihong; Liao, Bo; Zhu, Wen; Li, Zejun; Li, Keqin

2017-03-01

Identifying potential associations between drugs and targets is a critical prerequisite for modern drug discovery and repurposing. However, predicting these associations is difficult because of the limitations of existing computational methods. Most models only consider chemical structures and protein sequences, and other models are oversimplified. Moreover, datasets used for analysis contain only true-positive interactions, and experimentally validated negative samples are unavailable. To overcome these limitations, we developed a semi-supervised based learning framework called NormMulInf through collaborative filtering theory by using labeled and unlabeled interaction information. The proposed method initially determines similarity measures, such as similarities among samples and local correlations among the labels of the samples, by integrating biological information. The similarity information is then integrated into a robust principal component analysis model, which is solved using augmented Lagrange multipliers. Experimental results on four classes of drug-target interaction networks suggest that the proposed approach can accurately classify and predict drug-target interactions. Part of the predicted interactions are reported in public databases. The proposed method can also predict possible targets for new drugs and can be used to determine whether atropine may interact with alpha1B- and beta1- adrenergic receptors. Furthermore, the developed technique identifies potential drugs for new targets and can be used to assess whether olanzapine and propiomazine may target 5HT2B. Finally, the proposed method can potentially address limitations on studies of multitarget drugs and multidrug targets.

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

Drug-target interaction prediction: A Bayesian ranking approach.

PubMed

Peska, Ladislav; Buza, Krisztian; Koller, Júlia

2017-12-01

In silico prediction of drug-target interactions (DTI) could provide valuable information and speed-up the process of drug repositioning - finding novel usage for existing drugs. In our work, we focus on machine learning algorithms supporting drug-centric repositioning approach, which aims to find novel usage for existing or abandoned drugs. We aim at proposing a per-drug ranking-based method, which reflects the needs of drug-centric repositioning research better than conventional drug-target prediction approaches. We propose Bayesian Ranking Prediction of Drug-Target Interactions (BRDTI). The method is based on Bayesian Personalized Ranking matrix factorization (BPR) which has been shown to be an excellent approach for various preference learning tasks, however, it has not been used for DTI prediction previously. In order to successfully deal with DTI challenges, we extended BPR by proposing: (i) the incorporation of target bias, (ii) a technique to handle new drugs and (iii) content alignment to take structural similarities of drugs and targets into account. Evaluation on five benchmark datasets shows that BRDTI outperforms several state-of-the-art approaches in terms of per-drug nDCG and AUC. BRDTI results w.r.t. nDCG are 0.929, 0.953, 0.948, 0.897 and 0.690 for G-Protein Coupled Receptors (GPCR), Ion Channels (IC), Nuclear Receptors (NR), Enzymes (E) and Kinase (K) datasets respectively. Additionally, BRDTI significantly outperformed other methods (BLM-NII, WNN-GIP, NetLapRLS and CMF) w.r.t. nDCG in 17 out of 20 cases. Furthermore, BRDTI was also shown to be able to predict novel drug-target interactions not contained in the original datasets. The average recall at top-10 predicted targets for each drug was 0.762, 0.560, 1.000 and 0.404 for GPCR, IC, NR, and E datasets respectively. Based on the evaluation, we can conclude that BRDTI is an appropriate choice for researchers looking for an in silico DTI prediction technique to be used in drug

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

PubMed

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

2017-12-04

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

DrugE-Rank: improving drug–target interaction prediction of new candidate drugs or targets by ensemble learning to rank

PubMed Central

Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-01-01

Motivation: Identifying drug–target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug–target interactions of new candidate drugs or targets. Methods: Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. Results: The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. Availability: http://datamining-iip.fudan.edu.cn/service/DrugE-Rank Contact: zhusf@fudan.edu.cn Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307615

DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank.

PubMed

Yuan, Qingjun; Gao, Junning; Wu, Dongliang; Zhang, Shihua; Mamitsuka, Hiroshi; Zhu, Shanfeng

2016-06-15

Identifying drug-target interactions is an important task in drug discovery. To reduce heavy time and financial cost in experimental way, many computational approaches have been proposed. Although these approaches have used many different principles, their performance is far from satisfactory, especially in predicting drug-target interactions of new candidate drugs or targets. Approaches based on machine learning for this problem can be divided into two types: feature-based and similarity-based methods. Learning to rank is the most powerful technique in the feature-based methods. Similarity-based methods are well accepted, due to their idea of connecting the chemical and genomic spaces, represented by drug and target similarities, respectively. We propose a new method, DrugE-Rank, to improve the prediction performance by nicely combining the advantages of the two different types of methods. That is, DrugE-Rank uses LTR, for which multiple well-known similarity-based methods can be used as components of ensemble learning. The performance of DrugE-Rank is thoroughly examined by three main experiments using data from DrugBank: (i) cross-validation on FDA (US Food and Drug Administration) approved drugs before March 2014; (ii) independent test on FDA approved drugs after March 2014; and (iii) independent test on FDA experimental drugs. Experimental results show that DrugE-Rank outperforms competing methods significantly, especially achieving more than 30% improvement in Area under Prediction Recall curve for FDA approved new drugs and FDA experimental drugs. http://datamining-iip.fudan.edu.cn/service/DrugE-Rank zhusf@fudan.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Drug-target interaction prediction from PSSM based evolutionary information.

PubMed

Mousavian, Zaynab; Khakabimamaghani, Sahand; Kavousi, Kaveh; Masoudi-Nejad, Ali

2016-01-01

The labor-intensive and expensive experimental process of drug-target interaction prediction has motivated many researchers to focus on in silico prediction, which leads to the helpful information in supporting the experimental interaction data. Therefore, they have proposed several computational approaches for discovering new drug-target interactions. Several learning-based methods have been increasingly developed which can be categorized into two main groups: similarity-based and feature-based. In this paper, we firstly use the bi-gram features extracted from the Position Specific Scoring Matrix (PSSM) of proteins in predicting drug-target interactions. Our results demonstrate the high-confidence prediction ability of the Bigram-PSSM model in terms of several performance indicators specifically for enzymes and ion channels. Moreover, we investigate the impact of negative selection strategy on the performance of the prediction, which is not widely taken into account in the other relevant studies. This is important, as the number of non-interacting drug-target pairs are usually extremely large in comparison with the number of interacting ones in existing drug-target interaction data. An interesting observation is that different levels of performance reduction have been attained for four datasets when we change the sampling method from the random sampling to the balanced sampling. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering.

PubMed

Shi, Jian-Yu; Yiu, Siu-Ming; Li, Yiming; Leung, Henry C M; Chin, Francis Y L

2015-07-15

Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting Drug-Target Interaction Networks Based on Functional Groups and Biological Features

PubMed Central

Shi, Xiao-He; Hu, Le-Le; Kong, Xiangyin; Cai, Yu-Dong; Chou, Kuo-Chen

2010-01-01

Background Study of drug-target interaction networks is an important topic for drug development. It is both time-consuming and costly to determine compound-protein interactions or potential drug-target interactions by experiments alone. As a complement, the in silico prediction methods can provide us with very useful information in a timely manner. Methods/Principal Findings To realize this, drug compounds are encoded with functional groups and proteins encoded by biological features including biochemical and physicochemical properties. The optimal feature selection procedures are adopted by means of the mRMR (Maximum Relevance Minimum Redundancy) method. Instead of classifying the proteins as a whole family, target proteins are divided into four groups: enzymes, ion channels, G-protein- coupled receptors and nuclear receptors. Thus, four independent predictors are established using the Nearest Neighbor algorithm as their operation engine, with each to predict the interactions between drugs and one of the four protein groups. As a result, the overall success rates by the jackknife cross-validation tests achieved with the four predictors are 85.48%, 80.78%, 78.49%, and 85.66%, respectively. Conclusion/Significance Our results indicate that the network prediction system thus established is quite promising and encouraging. PMID:20300175

Predictive model of outcome of targeted nodal assessment in colorectal cancer.

PubMed

Nissan, Aviram; Protic, Mladjan; Bilchik, Anton; Eberhardt, John; Peoples, George E; Stojadinovic, Alexander

2010-02-01

Improvement in staging accuracy is the principal aim of targeted nodal assessment in colorectal carcinoma. Technical factors independently predictive of false negative (FN) sentinel lymph node (SLN) mapping should be identified to facilitate operative decision making. To define independent predictors of FN SLN mapping and to develop a predictive model that could support surgical decisions. Data was analyzed from 2 completed prospective clinical trials involving 278 patients with colorectal carcinoma undergoing SLN mapping. Clinical outcome of interest was FN SLN(s), defined as one(s) with no apparent tumor cells in the presence of non-SLN metastases. To assess the independent predictive effect of a covariate for a nominal response (FN SLN), a logistic regression model was constructed and parameters estimated using maximum likelihood. A probabilistic Bayesian model was also trained and cross validated using 10-fold train-and-test sets to predict FN SLN mapping. Area under the curve (AUC) from receiver operating characteristics curves of these predictions was calculated to determine the predictive value of the model. Number of SLNs (<3; P = 0.03) and tumor-replaced nodes (P < 0.01) independently predicted FN SLN. Cross validation of the model created with Bayesian Network Analysis effectively predicted FN SLN (area under the curve = 0.84-0.86). The positive and negative predictive values of the model are 83% and 97%, respectively. This study supports a minimum threshold of 3 nodes for targeted nodal assessment in colorectal cancer, and establishes sufficient basis to conclude that SLN mapping and biopsy cannot be justified in the presence of clinically apparent tumor-replaced nodes.

TAPIR, a web server for the prediction of plant microRNA targets, including target mimics.

PubMed

Bonnet, Eric; He, Ying; Billiau, Kenny; Van de Peer, Yves

2010-06-15

We present a new web server called TAPIR, designed for the prediction of plant microRNA targets. The server offers the possibility to search for plant miRNA targets using a fast and a precise algorithm. The precise option is much slower but guarantees to find less perfectly paired miRNA-target duplexes. Furthermore, the precise option allows the prediction of target mimics, which are characterized by a miRNA-target duplex having a large loop, making them undetectable by traditional tools. The TAPIR web server can be accessed at: http://bioinformatics.psb.ugent.be/webtools/tapir. Supplementary data are available at Bioinformatics online.

A novel multi-target regression framework for time-series prediction of drug efficacy.

PubMed

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-18

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task.

DIANA-microT web server: elucidating microRNA functions through target prediction.

PubMed

Maragkakis, M; Reczko, M; Simossis, V A; Alexiou, P; Papadopoulos, G L; Dalamagas, T; Giannopoulos, G; Goumas, G; Koukis, E; Kourtis, K; Vergoulis, T; Koziris, N; Sellis, T; Tsanakas, P; Hatzigeorgiou, A G

2009-07-01

Computational microRNA (miRNA) target prediction is one of the key means for deciphering the role of miRNAs in development and disease. Here, we present the DIANA-microT web server as the user interface to the DIANA-microT 3.0 miRNA target prediction algorithm. The web server provides extensive information for predicted miRNA:target gene interactions with a user-friendly interface, providing extensive connectivity to online biological resources. Target gene and miRNA functions may be elucidated through automated bibliographic searches and functional information is accessible through Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. The web server offers links to nomenclature, sequence and protein databases, and users are facilitated by being able to search for targeted genes using different nomenclatures or functional features, such as the genes possible involvement in biological pathways. The target prediction algorithm supports parameters calculated individually for each miRNA:target gene interaction and provides a signal-to-noise ratio and a precision score that helps in the evaluation of the significance of the predicted results. Using a set of miRNA targets recently identified through the pSILAC method, the performance of several computational target prediction programs was assessed. DIANA-microT 3.0 achieved there with 66% the highest ratio of correctly predicted targets over all predicted targets. The DIANA-microT web server is freely available at www.microrna.gr/microT.

A study of the 200-metre fast walk test as a possible new assessment tool to predict maximal heart rate and define target heart rate for exercise training of coronary heart disease patients.

PubMed

Casillas, Jean-Marie; Joussain, Charles; Gremeaux, Vincent; Hannequin, Armelle; Rapin, Amandine; Laurent, Yves; Benaïm, Charles

2015-02-01

To develop a new predictive model of maximal heart rate based on two walking tests at different speeds (comfortable and brisk walking) as an alternative to a cardiopulmonary exercise test during cardiac rehabilitation. Evaluation of a clinical assessment tool. A Cardiac Rehabilitation Department in France. A total of 148 patients (133 men), mean age of 59 ±9 years, at the end of an outpatient cardiac rehabilitation programme. Patients successively performed a 6-minute walk test, a 200 m fast-walk test (200mFWT), and a cardiopulmonary exercise test, with measure of heart rate at the end of each test. An all-possible regression procedure was used to determine the best predictive regression models of maximal heart rate. The best model was compared with the Fox equation in term of predictive error of maximal heart rate using the paired t-test. Results of the two walking tests correlated significantly with maximal heart rate determined during the cardiopulmonary exercise test, whereas anthropometric parameters and resting heart rate did not. The simplified predictive model with the most acceptable mean error was: maximal heart rate = 130 - 0.6 × age + 0.3 × HR200mFWT (R(2) = 0.24). This model was superior to the Fox formula (R(2) = 0.138). The relationship between training target heart rate calculated from measured reserve heart rate and that established using this predictive model was statistically significant (r = 0.528, p < 10(-6)). A formula combining heart rate measured during a safe simple fast walk test and age is more efficient than an equation only including age to predict maximal heart rate and training target heart rate. © The Author(s) 2014.

Prediction methodologies for target scene generation in the aerothermal targets analysis program (ATAP)

NASA Astrophysics Data System (ADS)

Hudson, Douglas J.; Torres, Manuel; Dougherty, Catherine; Rajendran, Natesan; Thompson, Rhoe A.

2003-09-01

The Air Force Research Laboratory (AFRL) Aerothermal Targets Analysis Program (ATAP) is a user-friendly, engineering-level computational tool that features integrated aerodynamics, six-degree-of-freedom (6-DoF) trajectory/motion, convective and radiative heat transfer, and thermal/material response to provide an optimal blend of accuracy and speed for design and analysis applications. ATAP is sponsored by the Kinetic Kill Vehicle Hardware-in-the-Loop Simulator (KHILS) facility at Eglin AFB, where it is used with the CHAMP (Composite Hardbody and Missile Plume) technique for rapid infrared (IR) signature and imagery predictions. ATAP capabilities include an integrated 1-D conduction model for up to 5 in-depth material layers (with options for gaps/voids with radiative heat transfer), fin modeling, several surface ablation modeling options, a materials library with over 250 materials, options for user-defined materials, selectable/definable atmosphere and earth models, multiple trajectory options, and an array of aerodynamic prediction methods. All major code modeling features have been validated with ground-test data from wind tunnels, shock tubes, and ballistics ranges, and flight-test data for both U.S. and foreign strategic and theater systems. Numerous applications include the design and analysis of interceptors, booster and shroud configurations, window environments, tactical missiles, and reentry vehicles.

Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.

PubMed

Zhang, Wen; Chen, Yanlin; Li, Dingfang

2017-11-25

Interactions between drugs and target proteins provide important information for the drug discovery. Currently, experiments identified only a small number of drug-target interactions. Therefore, the development of computational methods for drug-target interaction prediction is an urgent task of theoretical interest and practical significance. In this paper, we propose a label propagation method with linear neighborhood information (LPLNI) for predicting unobserved drug-target interactions. Firstly, we calculate drug-drug linear neighborhood similarity in the feature spaces, by considering how to reconstruct data points from neighbors. Then, we take similarities as the manifold of drugs, and assume the manifold unchanged in the interaction space. At last, we predict unobserved interactions between known drugs and targets by using drug-drug linear neighborhood similarity and known drug-target interactions. The experiments show that LPLNI can utilize only known drug-target interactions to make high-accuracy predictions on four benchmark datasets. Furthermore, we consider incorporating chemical structures into LPLNI models. Experimental results demonstrate that the model with integrated information (LPLNI-II) can produce improved performances, better than other state-of-the-art methods. The known drug-target interactions are an important information source for computational predictions. The usefulness of the proposed method is demonstrated by cross validation and the case study.

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

PubMed Central

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

2011-01-01

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

Drug-target interaction prediction using ensemble learning and dimensionality reduction.

PubMed

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

2017-10-01

Experimental prediction of drug-target interactions is expensive, time-consuming and tedious. Fortunately, computational methods help narrow down the search space for interaction candidates to be further examined via wet-lab techniques. Nowadays, the number of attributes/features for drugs and targets, as well as the amount of their interactions, are increasing, making these computational methods inefficient or occasionally prohibitive. This motivates us to derive a reduced feature set for prediction. In addition, since ensemble learning techniques are widely used to improve the classification performance, it is also worthwhile to design an ensemble learning framework to enhance the performance for drug-target interaction prediction. In this paper, we propose a framework for drug-target interaction prediction leveraging both feature dimensionality reduction and ensemble learning. First, we conducted feature subspacing to inject diversity into the classifier ensemble. Second, we applied three different dimensionality reduction methods to the subspaced features. Third, we trained homogeneous base learners with the reduced features and then aggregated their scores to derive the final predictions. For base learners, we selected two classifiers, namely Decision Tree and Kernel Ridge Regression, resulting in two variants of ensemble models, EnsemDT and EnsemKRR, respectively. In our experiments, we utilized AUC (Area under ROC Curve) as an evaluation metric. We compared our proposed methods with various state-of-the-art methods under 5-fold cross validation. Experimental results showed EnsemKRR achieving the highest AUC (94.3%) for predicting drug-target interactions. In addition, dimensionality reduction helped improve the performance of EnsemDT. In conclusion, our proposed methods produced significant improvements for drug-target interaction prediction. Copyright © 2017 Elsevier Inc. All rights reserved.

Towards crystal structure prediction of complex organic compounds – a report on the fifth blind test

PubMed Central

Bardwell, David A.; Adjiman, Claire S.; Arnautova, Yelena A.; Bartashevich, Ekaterina; Boerrigter, Stephan X. M.; Braun, Doris E.; Cruz-Cabeza, Aurora J.; Day, Graeme M.; Della Valle, Raffaele G.; Desiraju, Gautam R.; van Eijck, Bouke P.; Facelli, Julio C.; Ferraro, Marta B.; Grillo, Damian; Habgood, Matthew; Hofmann, Detlef W. M.; Hofmann, Fridolin; Jose, K. V. Jovan; Karamertzanis, Panagiotis G.; Kazantsev, Andrei V.; Kendrick, John; Kuleshova, Liudmila N.; Leusen, Frank J. J.; Maleev, Andrey V.; Misquitta, Alston J.; Mohamed, Sharmarke; Needs, Richard J.; Neumann, Marcus A.; Nikylov, Denis; Orendt, Anita M.; Pal, Rumpa; Pantelides, Constantinos C.; Pickard, Chris J.; Price, Louise S.; Price, Sarah L.; Scheraga, Harold A.; van de Streek, Jacco; Thakur, Tejender S.; Tiwari, Siddharth; Venuti, Elisabetta; Zhitkov, Ilia K.

2011-01-01

Following on from the success of the previous crystal structure prediction blind tests (CSP1999, CSP2001, CSP2004 and CSP2007), a fifth such collaborative project (CSP2010) was organized at the Cambridge Crystallographic Data Centre. A range of methodologies was used by the participating groups in order to evaluate the ability of the current computational methods to predict the crystal structures of the six organic molecules chosen as targets for this blind test. The first four targets, two rigid molecules, one semi-flexible molecule and a 1:1 salt, matched the criteria for the targets from CSP2007, while the last two targets belonged to two new challenging categories – a larger, much more flexible molecule and a hydrate with more than one polymorph. Each group submitted three predictions for each target it attempted. There was at least one successful prediction for each target, and two groups were able to successfully predict the structure of the large flexible molecule as their first place submission. The results show that while not as many groups successfully predicted the structures of the three smallest molecules as in CSP2007, there is now evidence that methodologies such as dispersion-corrected density functional theory (DFT-D) are able to reliably do so. The results also highlight the many challenges posed by more complex systems and show that there are still issues to be overcome. PMID:22101543

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 novel multi-target regression framework for time-series prediction of drug efficacy

PubMed Central

Li, Haiqing; Zhang, Wei; Chen, Ying; Guo, Yumeng; Li, Guo-Zheng; Zhu, Xiaoxin

2017-01-01

Excavating from small samples is a challenging pharmacokinetic problem, where statistical methods can be applied. Pharmacokinetic data is special due to the small samples of high dimensionality, which makes it difficult to adopt conventional methods to predict the efficacy of traditional Chinese medicine (TCM) prescription. The main purpose of our study is to obtain some knowledge of the correlation in TCM prescription. Here, a novel method named Multi-target Regression Framework to deal with the problem of efficacy prediction is proposed. We employ the correlation between the values of different time sequences and add predictive targets of previous time as features to predict the value of current time. Several experiments are conducted to test the validity of our method and the results of leave-one-out cross-validation clearly manifest the competitiveness of our framework. Compared with linear regression, artificial neural networks, and partial least squares, support vector regression combined with our framework demonstrates the best performance, and appears to be more suitable for this task. PMID:28098186

Testing an earthquake prediction algorithm

USGS Publications Warehouse

Kossobokov, V.G.; Healy, J.H.; Dewey, J.W.

1997-01-01

A test to evaluate earthquake prediction algorithms is being applied to a Russian algorithm known as M8. The M8 algorithm makes intermediate term predictions for earthquakes to occur in a large circle, based on integral counts of transient seismicity in the circle. In a retroactive prediction for the period January 1, 1985 to July 1, 1991 the algorithm as configured for the forward test would have predicted eight of ten strong earthquakes in the test area. A null hypothesis, based on random assignment of predictions, predicts eight earthquakes in 2.87% of the trials. The forward test began July 1, 1991 and will run through December 31, 1997. As of July 1, 1995, the algorithm had forward predicted five out of nine earthquakes in the test area, which success ratio would have been achieved in 53% of random trials with the null hypothesis.

29 mm Diameter Target Test Report

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

Woloshun, Keith Albert; Olivas, Eric Richard; Dale, Gregory E.

After numerous delays, the test of the 29 mm diameter target was conducted on 8/18/2017. The complete target design report, dated 8/15/2016, is reproduced below for completeness. This describes in detail the 10 disk target with varying thickness disks. The report presents and discusses the test results. In brief summary, there appears to have been multiple instrumentation errors. Measured temperatures, pressures and IR camera window temperature measurement are all suspect. All tests were done at 35 MeV, with 171 μA current, or 6 kW of beam power.

Computational Predictions Provide Insights into the Biology of TAL Effector Target Sites

PubMed Central

Grau, Jan; Wolf, Annett; Reschke, Maik; Bonas, Ulla; Posch, Stefan; Boch, Jens

2013-01-01

Transcription activator-like (TAL) effectors are injected into host plant cells by Xanthomonas bacteria to function as transcriptional activators for the benefit of the pathogen. The DNA binding domain of TAL effectors is composed of conserved amino acid repeat structures containing repeat-variable diresidues (RVDs) that determine DNA binding specificity. In this paper, we present TALgetter, a new approach for predicting TAL effector target sites based on a statistical model. In contrast to previous approaches, the parameters of TALgetter are estimated from training data computationally. We demonstrate that TALgetter successfully predicts known TAL effector target sites and often yields a greater number of predictions that are consistent with up-regulation in gene expression microarrays than an existing approach, Target Finder of the TALE-NT suite. We study the binding specificities estimated by TALgetter and approve that different RVDs are differently important for transcriptional activation. In subsequent studies, the predictions of TALgetter indicate a previously unreported positional preference of TAL effector target sites relative to the transcription start site. In addition, several TAL effectors are predicted to bind to the TATA-box, which might constitute one general mode of transcriptional activation by TAL effectors. Scrutinizing the predicted target sites of TALgetter, we propose several novel TAL effector virulence targets in rice and sweet orange. TAL-mediated induction of the candidates is supported by gene expression microarrays. Validity of these targets is also supported by functional analogy to known TAL effector targets, by an over-representation of TAL effector targets with similar function, or by a biological function related to pathogen infection. Hence, these predicted TAL effector virulence targets are promising candidates for studying the virulence function of TAL effectors. TALgetter is implemented as part of the open-source Java library

In vitro perturbations of targets in cancer hallmark processes predict rodent chemical carcinogenesis.

PubMed

Kleinstreuer, Nicole C; Dix, David J; Houck, Keith A; Kavlock, Robert J; Knudsen, Thomas B; Martin, Matthew T; Paul, Katie B; Reif, David M; Crofton, Kevin M; Hamilton, Kerry; Hunter, Ronald; Shah, Imran; Judson, Richard S

2013-01-01

Thousands of untested chemicals in the environment require efficient characterization of carcinogenic potential in humans. A proposed solution is rapid testing of chemicals using in vitro high-throughput screening (HTS) assays for targets in pathways linked to disease processes to build models for priority setting and further testing. We describe a model for predicting rodent carcinogenicity based on HTS data from 292 chemicals tested in 672 assays mapping to 455 genes. All data come from the EPA ToxCast project. The model was trained on a subset of 232 chemicals with in vivo rodent carcinogenicity data in the Toxicity Reference Database (ToxRefDB). Individual HTS assays strongly associated with rodent cancers in ToxRefDB were linked to genes, pathways, and hallmark processes documented to be involved in tumor biology and cancer progression. Rodent liver cancer endpoints were linked to well-documented pathways such as peroxisome proliferator-activated receptor signaling and TP53 and novel targets such as PDE5A and PLAUR. Cancer hallmark genes associated with rodent thyroid tumors were found to be linked to human thyroid tumors and autoimmune thyroid disease. A model was developed in which these genes/pathways function as hypothetical enhancers or promoters of rat thyroid tumors, acting secondary to the key initiating event of thyroid hormone disruption. A simple scoring function was generated to identify chemicals with significant in vitro evidence that was predictive of in vivo carcinogenicity in different rat tissues and organs. This scoring function was applied to an external test set of 33 compounds with carcinogenicity classifications from the EPA's Office of Pesticide Programs and successfully (p = 0.024) differentiated between chemicals classified as "possible"/"probable"/"likely" carcinogens and those designated as "not likely" or with "evidence of noncarcinogenicity." This model represents a chemical carcinogenicity prioritization tool supporting targeted

Predicting selective drug targets in cancer through metabolic networks

PubMed Central

Folger, Ori; Jerby, Livnat; Frezza, Christian; Gottlieb, Eyal; Ruppin, Eytan; Shlomi, Tomer

2011-01-01

The interest in studying metabolic alterations in cancer and their potential role as novel targets for therapy has been rejuvenated in recent years. Here, we report the development of the first genome-scale network model of cancer metabolism, validated by correctly identifying genes essential for cellular proliferation in cancer cell lines. The model predicts 52 cytostatic drug targets, of which 40% are targeted by known, approved or experimental anticancer drugs, and the rest are new. It further predicts combinations of synthetic lethal drug targets, whose synergy is validated using available drug efficacy and gene expression measurements across the NCI-60 cancer cell line collection. Finally, potential selective treatments for specific cancers that depend on cancer type-specific downregulation of gene expression and somatic mutations are compiled. PMID:21694718

Predictive distractor context facilitates attentional selection of high, but not intermediate and low, salience targets.

PubMed

Töllner, Thomas; Conci, Markus; Müller, Hermann J

2015-03-01

It is well established that we can focally attend to a specific region in visual space without shifting our eyes, so as to extract action-relevant sensory information from covertly attended locations. The underlying mechanisms that determine how fast we engage our attentional spotlight in visual-search scenarios, however, remain controversial. One dominant view advocated by perceptual decision-making models holds that the times taken for focal-attentional selection are mediated by an internal template that biases perceptual coding and selection decisions exclusively through target-defining feature coding. This notion directly predicts that search times remain unaffected whether or not participants can anticipate the upcoming distractor context. Here we tested this hypothesis by employing an illusory-figure localization task that required participants to search for an invariant target amongst a variable distractor context, which gradually changed--either randomly or predictably--as a function of distractor-target similarity. We observed a graded decrease in internal focal-attentional selection times--correlated with external behavioral latencies--for distractor contexts of higher relative to lower similarity to the target. Critically, for low but not intermediate and high distractor-target similarity, these context-driven effects were cortically and behaviorally amplified when participants could reliably predict the type of distractors. This interactive pattern demonstrates that search guidance signals can integrate information about distractor, in addition to target, identities to optimize distractor-target competition for focal-attentional selection. © 2014 Wiley Periodicals, Inc.

Report on the sixth blind test of organic crystal structure prediction methods

PubMed Central

Reilly, Anthony M.; Cooper, Richard I.; Adjiman, Claire S.; Bhattacharya, Saswata; Boese, A. Daniel; Brandenburg, Jan Gerit; Bygrave, Peter J.; Bylsma, Rita; Campbell, Josh E.; Car, Roberto; Case, David H.; Chadha, Renu; Cole, Jason C.; Cosburn, Katherine; Cuppen, Herma M.; Curtis, Farren; Day, Graeme M.; DiStasio Jr, Robert A.; Dzyabchenko, Alexander; van Eijck, Bouke P.; Elking, Dennis M.; van den Ende, Joost A.; Facelli, Julio C.; Ferraro, Marta B.; Fusti-Molnar, Laszlo; Gatsiou, Christina-Anna; Gee, Thomas S.; de Gelder, René; Ghiringhelli, Luca M.; Goto, Hitoshi; Grimme, Stefan; Guo, Rui; Hofmann, Detlef W. M.; Hoja, Johannes; Hylton, Rebecca K.; Iuzzolino, Luca; Jankiewicz, Wojciech; de Jong, Daniël T.; Kendrick, John; de Klerk, Niek J. J.; Ko, Hsin-Yu; Kuleshova, Liudmila N.; Li, Xiayue; Lohani, Sanjaya; Leusen, Frank J. J.; Lund, Albert M.; Lv, Jian; Ma, Yanming; Marom, Noa; Masunov, Artëm E.; McCabe, Patrick; McMahon, David P.; Meekes, Hugo; Metz, Michael P.; Misquitta, Alston J.; Mohamed, Sharmarke; Monserrat, Bartomeu; Needs, Richard J.; Neumann, Marcus A.; Nyman, Jonas; Obata, Shigeaki; Oberhofer, Harald; Oganov, Artem R.; Orendt, Anita M.; Pagola, Gabriel I.; Pantelides, Constantinos C.; Pickard, Chris J.; Podeszwa, Rafal; Price, Louise S.; Price, Sarah L.; Pulido, Angeles; Read, Murray G.; Reuter, Karsten; Schneider, Elia; Schober, Christoph; Shields, Gregory P.; Singh, Pawanpreet; Sugden, Isaac J.; Szalewicz, Krzysztof; Taylor, Christopher R.; Tkatchenko, Alexandre; Tuckerman, Mark E.; Vacarro, Francesca; Vasileiadis, Manolis; Vazquez-Mayagoitia, Alvaro; Vogt, Leslie; Wang, Yanchao; Watson, Rona E.; de Wijs, Gilles A.; Yang, Jack; Zhu, Qiang; Groom, Colin R.

2016-01-01

The sixth blind test of organic crystal structure prediction (CSP) methods has been held, with five target systems: a small nearly rigid molecule, a polymorphic former drug candidate, a chloride salt hydrate, a co-crystal and a bulky flexible molecule. This blind test has seen substantial growth in the number of participants, with the broad range of prediction methods giving a unique insight into the state of the art in the field. Significant progress has been seen in treating flexible molecules, usage of hierarchical approaches to ranking structures, the application of density-functional approximations, and the establishment of new workflows and ‘best practices’ for performing CSP calculations. All of the targets, apart from a single potentially disordered Z′ = 2 polymorph of the drug candidate, were predicted by at least one submission. Despite many remaining challenges, it is clear that CSP methods are becoming more applicable to a wider range of real systems, including salts, hydrates and larger flexible molecules. The results also highlight the potential for CSP calculations to complement and augment experimental studies of organic solid forms. PMID:27484368

Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy.

PubMed

Fang, Jiansong; Wu, Zengrui; Cai, Chuipu; Wang, Qi; Tang, Yun; Cheng, Feixiong

2017-11-27

Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.

Texture metric that predicts target detection performance

NASA Astrophysics Data System (ADS)

Culpepper, Joanne B.

2015-12-01

Two texture metrics based on gray level co-occurrence error (GLCE) are used to predict probability of detection and mean search time. The two texture metrics are local clutter metrics and are based on the statistics of GLCE probability distributions. The degree of correlation between various clutter metrics and the target detection performance of the nine military vehicles in complex natural scenes found in the Search_2 dataset are presented. Comparison is also made between four other common clutter metrics found in the literature: root sum of squares, Doyle, statistical variance, and target structure similarity. The experimental results show that the GLCE energy metric is a better predictor of target detection performance when searching for targets in natural scenes than the other clutter metrics studied.

Integrating Transcriptomics with Metabolic Modeling Predicts Biomarkers and Drug Targets for Alzheimer's Disease

PubMed Central

Stempler, Shiri; Yizhak, Keren; Ruppin, Eytan

2014-01-01

Accumulating evidence links numerous abnormalities in cerebral metabolism with the progression of Alzheimer's disease (AD), beginning in its early stages. Here, we integrate transcriptomic data from AD patients with a genome-scale computational human metabolic model to characterize the altered metabolism in AD, and employ state-of-the-art metabolic modelling methods to predict metabolic biomarkers and drug targets in AD. The metabolic descriptions derived are first tested and validated on a large scale versus existing AD proteomics and metabolomics data. Our analysis shows a significant decrease in the activity of several key metabolic pathways, including the carnitine shuttle, folate metabolism and mitochondrial transport. We predict several metabolic biomarkers of AD progression in the blood and the CSF, including succinate and prostaglandin D2. Vitamin D and steroid metabolism pathways are enriched with predicted drug targets that could mitigate the metabolic alterations observed. Taken together, this study provides the first network wide view of the metabolic alterations associated with AD progression. Most importantly, it offers a cohort of new metabolic leads for the diagnosis of AD and its treatment. PMID:25127241

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

NASA Astrophysics Data System (ADS)

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

2017-01-01

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

Hypothesis testing and earthquake prediction.

PubMed

Jackson, D D

1996-04-30

Requirements for testing include advance specification of the conditional rate density (probability per unit time, area, and magnitude) or, alternatively, probabilities for specified intervals of time, space, and magnitude. Here I consider testing fully specified hypotheses, with no parameter adjustments or arbitrary decisions allowed during the test period. Because it may take decades to validate prediction methods, it is worthwhile to formulate testable hypotheses carefully in advance. Earthquake prediction generally implies that the probability will be temporarily higher than normal. Such a statement requires knowledge of "normal behavior"--that is, it requires a null hypothesis. Hypotheses can be tested in three ways: (i) by comparing the number of actual earth-quakes to the number predicted, (ii) by comparing the likelihood score of actual earthquakes to the predicted distribution, and (iii) by comparing the likelihood ratio to that of a null hypothesis. The first two tests are purely self-consistency tests, while the third is a direct comparison of two hypotheses. Predictions made without a statement of probability are very difficult to test, and any test must be based on the ratio of earthquakes in and out of the forecast regions.

Hypothesis testing and earthquake prediction.

PubMed Central

Jackson, D D

1996-01-01

Requirements for testing include advance specification of the conditional rate density (probability per unit time, area, and magnitude) or, alternatively, probabilities for specified intervals of time, space, and magnitude. Here I consider testing fully specified hypotheses, with no parameter adjustments or arbitrary decisions allowed during the test period. Because it may take decades to validate prediction methods, it is worthwhile to formulate testable hypotheses carefully in advance. Earthquake prediction generally implies that the probability will be temporarily higher than normal. Such a statement requires knowledge of "normal behavior"--that is, it requires a null hypothesis. Hypotheses can be tested in three ways: (i) by comparing the number of actual earth-quakes to the number predicted, (ii) by comparing the likelihood score of actual earthquakes to the predicted distribution, and (iii) by comparing the likelihood ratio to that of a null hypothesis. The first two tests are purely self-consistency tests, while the third is a direct comparison of two hypotheses. Predictions made without a statement of probability are very difficult to test, and any test must be based on the ratio of earthquakes in and out of the forecast regions. PMID:11607663

Multi-target QSPR modeling for simultaneous prediction of multiple gas-phase kinetic rate constants of diverse chemicals

NASA Astrophysics Data System (ADS)

Basant, Nikita; Gupta, Shikha

2018-03-01

The reactions of molecular ozone (O3), hydroxyl (•OH) and nitrate (NO3) radicals are among the major pathways of removal of volatile organic compounds (VOCs) in the atmospheric environment. The gas-phase kinetic rate constants (kO3, kOH, kNO3) are thus, important in assessing the ultimate fate and exposure risk of atmospheric VOCs. Experimental data for rate constants are not available for many emerging VOCs and the computational methods reported so far address a single target modeling only. In this study, we have developed a multi-target (mt) QSPR model for simultaneous prediction of multiple kinetic rate constants (kO3, kOH, kNO3) of diverse organic chemicals considering an experimental data set of VOCs for which values of all the three rate constants are available. The mt-QSPR model identified and used five descriptors related to the molecular size, degree of saturation and electron density in a molecule, which were mechanistically interpretable. These descriptors successfully predicted three rate constants simultaneously. The model yielded high correlations (R2 = 0.874-0.924) between the experimental and simultaneously predicted endpoint rate constant (kO3, kOH, kNO3) values in test arrays for all the three systems. The model also passed all the stringent statistical validation tests for external predictivity. The proposed multi-target QSPR model can be successfully used for predicting reactivity of new VOCs simultaneously for their exposure risk assessment.

MultiMiTar: a novel multi objective optimization based miRNA-target prediction method.

PubMed

Mitra, Ramkrishna; Bandyopadhyay, Sanghamitra

2011-01-01

Machine learning based miRNA-target prediction algorithms often fail to obtain a balanced prediction accuracy in terms of both sensitivity and specificity due to lack of the gold standard of negative examples, miRNA-targeting site context specific relevant features and efficient feature selection process. Moreover, all the sequence, structure and machine learning based algorithms are unable to distribute the true positive predictions preferentially at the top of the ranked list; hence the algorithms become unreliable to the biologists. In addition, these algorithms fail to obtain considerable combination of precision and recall for the target transcripts that are translationally repressed at protein level. In the proposed article, we introduce an efficient miRNA-target prediction system MultiMiTar, a Support Vector Machine (SVM) based classifier integrated with a multiobjective metaheuristic based feature selection technique. The robust performance of the proposed method is mainly the result of using high quality negative examples and selection of biologically relevant miRNA-targeting site context specific features. The features are selected by using a novel feature selection technique AMOSA-SVM, that integrates the multi objective optimization technique Archived Multi-Objective Simulated Annealing (AMOSA) and SVM. MultiMiTar is found to achieve much higher Matthew's correlation coefficient (MCC) of 0.583 and average class-wise accuracy (ACA) of 0.8 compared to the others target prediction methods for a completely independent test data set. The obtained MCC and ACA values of these algorithms range from -0.269 to 0.155 and 0.321 to 0.582, respectively. Moreover, it shows a more balanced result in terms of precision and sensitivity (recall) for the translationally repressed data set as compared to all the other existing methods. An important aspect is that the true positive predictions are distributed preferentially at the top of the ranked list that makes Multi

On Why Targets Evoke P3 Components in Prediction Tasks: Drawing an Analogy between Prediction and Matching Tasks

PubMed Central

Verleger, Rolf; Cäsar, Stephanie; Siller, Bastian; Śmigasiewicz, Kamila

2017-01-01

P3 is the most conspicuous component in recordings of stimulus-evoked EEG potentials from the human scalp, occurring whenever some task has to be performed with the stimuli. The process underlying P3 has been assumed to be the updating of expectancies. More recently, P3 has been related to decision processing and to activation of established stimulus-response associations (S/R-link hypothesis). However, so far this latter approach has not provided a conception about how to explain the occurrence of P3 with predicted stimuli, although P3 was originally discovered in a prediction task. The present article proposes such a conception. We assume that the internal responses right or wrong both become associatively linked to each predicted target and that one of these two response alternatives gets activated as a function of match or mismatch of the target to the preceding prediction. This seems similar to comparison tasks where responses depend on the matching of the target stimulus with a preceding first stimulus (S1). Based on this idea, this study compared the effects of frequencies of first events (predictions or S1) on target-evoked P3s in prediction and comparison tasks. Indeed, frequencies not only of targets but also of first events had similar effects across tasks on target-evoked P3s. These results support the notion that P3 evoked by predicted stimuli reflects activation of appropriate internal “match” or “mismatch” responses, which is compatible with S/R-link hypothesis. PMID:29066965

Allegany Ballistics Lab: sensor test target system

NASA Astrophysics Data System (ADS)

Eaton, Deran S.

2011-06-01

Leveraging the Naval Surface Warfare Center, Indian Head Division's historical experience in weapon simulation, Naval Sea Systems Command commissioned development of a remote-controlled, digitally programmable Sensor Test Target as part of a modern, outdoor hardware-in-the-loop test system for ordnance-related guidance, navigation and control systems. The overall Target system design invokes a sciences-based, "design of automated experiments" approach meant to close the logistical distance between sensor engineering and developmental T&E in outdoor conditions over useful real world distances. This enables operating modes that employ broad spectrum electromagnetic energy in many a desired combination, variably generated using a Jet Engine Simulator, a multispectral infrared emitter array, optically enhanced incandescent Flare Simulators, Emitter/Detector mounts, and an RF corner reflector kit. As assembled, the recently tested Sensor Test Target prototype being presented can capably provide a full array of useful RF and infrared target source simulations for RDT&E use with developmental and existing sensors. Certain Target technologies are patent pending, with potential spinoffs in aviation, metallurgy and biofuels processing, while others are variations on well-established technology. The Sensor Test Target System is planned for extended installation at Allegany Ballistics Laboratory (Rocket Center, WV).

Drug Target Prediction and Repositioning Using an Integrated Network-Based Approach

PubMed Central

Emig, Dorothea; Ivliev, Alexander; Pustovalova, Olga; Lancashire, Lee; Bureeva, Svetlana; Nikolsky, Yuri; Bessarabova, Marina

2013-01-01

The discovery of novel drug targets is a significant challenge in drug development. Although the human genome comprises approximately 30,000 genes, proteins encoded by fewer than 400 are used as drug targets in the treatment of diseases. Therefore, novel drug targets are extremely valuable as the source for first in class drugs. On the other hand, many of the currently known drug targets are functionally pleiotropic and involved in multiple pathologies. Several of them are exploited for treating multiple diseases, which highlights the need for methods to reliably reposition drug targets to new indications. Network-based methods have been successfully applied to prioritize novel disease-associated genes. In recent years, several such algorithms have been developed, some focusing on local network properties only, and others taking the complete network topology into account. Common to all approaches is the understanding that novel disease-associated candidates are in close overall proximity to known disease genes. However, the relevance of these methods to the prediction of novel drug targets has not yet been assessed. Here, we present a network-based approach for the prediction of drug targets for a given disease. The method allows both repositioning drug targets known for other diseases to the given disease and the prediction of unexploited drug targets which are not used for treatment of any disease. Our approach takes as input a disease gene expression signature and a high-quality interaction network and outputs a prioritized list of drug targets. We demonstrate the high performance of our method and highlight the usefulness of the predictions in three case studies. We present novel drug targets for scleroderma and different types of cancer with their underlying biological processes. Furthermore, we demonstrate the ability of our method to identify non-suspected repositioning candidates using diabetes type 1 as an example. PMID:23593264

In silico target prediction for elucidating the mode of action of herbicides including prospective validation.

PubMed

Chiddarwar, Rucha K; Rohrer, Sebastian G; Wolf, Antje; Tresch, Stefan; Wollenhaupt, Sabrina; Bender, Andreas

2017-01-01

The rapid emergence of pesticide resistance has given rise to a demand for herbicides with new mode of action (MoA). In the agrochemical sector, with the availability of experimental high throughput screening (HTS) data, it is now possible to utilize in silico target prediction methods in the early discovery phase to suggest the MoA of a compound via data mining of bioactivity data. While having been established in the pharmaceutical context, in the agrochemical area this approach poses rather different challenges, as we have found in this work, partially due to different chemistry, but even more so due to different (usually smaller) amounts of data, and different ways of conducting HTS. With the aim to apply computational methods for facilitating herbicide target identification, 48,000 bioactivity data against 16 herbicide targets were processed to train Laplacian modified Naïve Bayesian (NB) classification models. The herbicide target prediction model ("HerbiMod") is an ensemble of 16 binary classification models which are evaluated by internal, external and prospective validation sets. In addition to the experimental inactives, 10,000 random agrochemical inactives were included in the training process, which showed to improve the overall balanced accuracy of our models up to 40%. For all the models, performance in terms of balanced accuracy of≥80% was achieved in five-fold cross validation. Ranking target predictions was addressed by means of z-scores which improved predictivity over using raw scores alone. An external testset of 247 compounds from ChEMBL and a prospective testset of 394 compounds from BASF SE tested against five well studied herbicide targets (ACC, ALS, HPPD, PDS and PROTOX) were used for further validation. Only 4% of the compounds in the external testset lied in the applicability domain and extrapolation (and correct prediction) was hence impossible, which on one hand was surprising, and on the other hand illustrated the utilization of

Predicting essential genes for identifying potential drug targets in Aspergillus fumigatus.

PubMed

Lu, Yao; Deng, Jingyuan; Rhodes, Judith C; Lu, Hui; Lu, Long Jason

2014-06-01

Aspergillus fumigatus (Af) is a ubiquitous and opportunistic pathogen capable of causing acute, invasive pulmonary disease in susceptible hosts. Despite current therapeutic options, mortality associated with invasive Af infections remains unacceptably high, increasing 357% since 1980. Therefore, there is an urgent need for the development of novel therapeutic strategies, including more efficacious drugs acting on new targets. Thus, as noted in a recent review, "the identification of essential genes in fungi represents a crucial step in the development of new antifungal drugs". Expanding the target space by rapidly identifying new essential genes has thus been described as "the most important task of genomics-based target validation". In previous research, we were the first to show that essential gene annotation can be reliably transferred between distantly related four Prokaryotic species. In this study, we extend our machine learning approach to the much more complex Eukaryotic fungal species. A compendium of essential genes is predicted in Af by transferring known essential gene annotations from another filamentous fungus Neurospora crassa. This approach predicts essential genes by integrating diverse types of intrinsic and context-dependent genomic features encoded in microbial genomes. The predicted essential datasets contained 1674 genes. We validated our results by comparing our predictions with known essential genes in Af, comparing our predictions with those predicted by homology mapping, and conducting conditional expressed alleles. We applied several layers of filters and selected a set of potential drug targets from the predicted essential genes. Finally, we have conducted wet lab knockout experiments to verify our predictions, which further validates the accuracy and wide applicability of the machine learning approach. The approach presented here significantly extended our ability to predict essential genes beyond orthologs and made it possible to

Turing Trade: A Hybrid of a Turing Test and a Prediction Market

NASA Astrophysics Data System (ADS)

Farfel, Joseph; Conitzer, Vincent

We present Turing Trade, a web-based game that is a hybrid of a Turing test and a prediction market. In this game, there is a mystery conversation partner, the “target,” who is trying to appear human, but may in reality be either a human or a bot. There are multiple judges (or “bettors”), who interrogate the target in order to assess whether it is a human or a bot. Throughout the interrogation, each bettor bets on the nature of the target by buying or selling human (or bot) securities, which pay out if the target is a human (bot). The resulting market price represents the bettors’ aggregate belief that the target is a human. This game offers multiple advantages over standard variants of the Turing test. Most significantly, our game gathers much more fine-grained data, since we obtain not only the judges’ final assessment of the target’s humanity, but rather the entire progression of their aggregate belief over time. This gives us the precise moments in conversations where the target’s response caused a significant shift in the aggregate belief, indicating that the response was decidedly human or unhuman. An additional benefit is that (we believe) the game is more enjoyable to participants than a standard Turing test. This is important because otherwise, we will fail to collect significant amounts of data. In this paper, we describe in detail how Turing Trade works, exhibit some example logs, and analyze how well Turing Trade functions as a prediction market by studying the calibration and sharpness of its forecasts (from real user data).

DeepMirTar: a deep-learning approach for predicting human miRNA targets.

PubMed

Wen, Ming; Cong, Peisheng; Zhang, Zhimin; Lu, Hongmei; Li, Tonghua

2018-06-01

MicroRNAs (miRNAs) are small noncoding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates. In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed, and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance. DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA. lith@tongji.edu.cn, hongmeilu@csu.edu.cn. Supplementary data are available at Bioinformatics online.

New support vector machine-based method for microRNA target prediction.

PubMed

Li, L; Gao, Q; Mao, X; Cao, Y

2014-06-09

MicroRNA (miRNA) plays important roles in cell differentiation, proliferation, growth, mobility, and apoptosis. An accurate list of precise target genes is necessary in order to fully understand the importance of miRNAs in animal development and disease. Several computational methods have been proposed for miRNA target-gene identification. However, these methods still have limitations with respect to their sensitivity and accuracy. Thus, we developed a new miRNA target-prediction method based on the support vector machine (SVM) model. The model supplies information of two binding sites (primary and secondary) for a radial basis function kernel as a similarity measure for SVM features. The information is categorized based on structural, thermodynamic, and sequence conservation. Using high-confidence datasets selected from public miRNA target databases, we obtained a human miRNA target SVM classifier model with high performance and provided an efficient tool for human miRNA target gene identification. Experiments have shown that our method is a reliable tool for miRNA target-gene prediction, and a successful application of an SVM classifier. Compared with other methods, the method proposed here improves the sensitivity and accuracy of miRNA prediction. Its performance can be further improved by providing more training examples.

Target Soil Impact Verification: Experimental Testing and Kayenta Constitutive Modeling.

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

Broome, Scott Thomas; Flint, Gregory Mark; Dewers, Thomas

2015-11-01

This report details experimental testing and constitutive modeling of sandy soil deformation under quasi - static conditions. This is driven by the need to understand constitutive response of soil to target/component behavior upon impact . An experimental and constitutive modeling program was followed to determine elastic - plastic properties and a compressional failure envelope of dry soil . One hydrostatic, one unconfined compressive stress (UCS), nine axisymmetric compression (ACS) , and one uniaxial strain (US) test were conducted at room temperature . Elastic moduli, assuming isotropy, are determined from unload/reload loops and final unloading for all tests pre - failuremore » and increase monotonically with mean stress. Very little modulus degradation was discernable from elastic results even when exposed to mean stresses above 200 MPa . The failure envelope and initial yield surface were determined from peak stresses and observed onset of plastic yielding from all test results. Soil elasto - plastic behavior is described using the Brannon et al. (2009) Kayenta constitutive model. As a validation exercise, the ACS - parameterized Kayenta model is used to predict response of the soil material under uniaxial strain loading. The resulting parameterized and validated Kayenta model is of high quality and suitable for modeling sandy soil deformation under a range of conditions, including that for impact prediction.« less

Informatics Approaches for Predicting, Understanding, and Testing Cancer Drug Combinations.

PubMed

Tang, Jing

2017-01-01

Making cancer treatment more effective is one of the grand challenges in our health care system. However, many drugs have entered clinical trials but so far showed limited efficacy or induced rapid development of resistance. We urgently need multi-targeted drug combinations, which shall selectively inhibit the cancer cells and block the emergence of drug resistance. The book chapter focuses on mathematical and computational tools to facilitate the discovery of the most promising drug combinations to improve efficacy and prevent resistance. Data integration approaches that leverage drug-target interactions, cancer molecular features, and signaling pathways for predicting, understanding, and testing drug combinations are critically reviewed.

Visuo-vestibular interaction: predicting the position of a visual target during passive body rotation.

PubMed

Mackrous, I; Simoneau, M

2011-11-10

Following body rotation, optimal updating of the position of a memorized target is attained when retinal error is perceived and corrective saccade is performed. Thus, it appears that these processes may enable the calibration of the vestibular system by facilitating the sharing of information between both reference frames. Here, it is assessed whether having sensory information regarding body rotation in the target reference frame could enhance an individual's learning rate to predict the position of an earth-fixed target. During rotation, participants had to respond when they felt their body midline had crossed the position of the target and received knowledge of result. During practice blocks, for two groups, visual cues were displayed in the same reference frame of the target, whereas a third group relied on vestibular information (vestibular-only group) to predict the location of the target. Participants, unaware of the role of the visual cues (visual cues group), learned to predict the location of the target and spatial error decreased from 16.2 to 2.0°, reflecting a learning rate of 34.08 trials (determined from fitting a falling exponential model). In contrast, the group aware of the role of the visual cues (explicit visual cues group) showed a faster learning rate (i.e. 2.66 trials) but similar final spatial error 2.9°. For the vestibular-only group, similar accuracy was achieved (final spatial error of 2.3°), but their learning rate was much slower (i.e. 43.29 trials). Transferring to the Post-test (no visual cues and no knowledge of result) increased the spatial error of the explicit visual cues group (9.5°), but it did not change the performance of the vestibular group (1.2°). Overall, these results imply that cognition assists the brain in processing the sensory information within the target reference frame. Copyright © 2011 IBRO. Published by Elsevier Ltd. All rights reserved.

DDR: efficient computational method to predict drug-target interactions using graph mining and machine learning approaches.

PubMed

Olayan, Rawan S; Ashoor, Haitham; Bajic, Vladimir B

2018-04-01

Finding computationally drug-target interactions (DTIs) is a convenient strategy to identify new DTIs at low cost with reasonable accuracy. However, the current DTI prediction methods suffer the high false positive prediction rate. We developed DDR, a novel method that improves the DTI prediction accuracy. DDR is based on the use of a heterogeneous graph that contains known DTIs with multiple similarities between drugs and multiple similarities between target proteins. DDR applies non-linear similarity fusion method to combine different similarities. Before fusion, DDR performs a pre-processing step where a subset of similarities is selected in a heuristic process to obtain an optimized combination of similarities. Then, DDR applies a random forest model using different graph-based features extracted from the DTI heterogeneous graph. Using 5-repeats of 10-fold cross-validation, three testing setups, and the weighted average of area under the precision-recall curve (AUPR) scores, we show that DDR significantly reduces the AUPR score error relative to the next best start-of-the-art method for predicting DTIs by 34% when the drugs are new, by 23% when targets are new and by 34% when the drugs and the targets are known but not all DTIs between them are not known. Using independent sources of evidence, we verify as correct 22 out of the top 25 DDR novel predictions. This suggests that DDR can be used as an efficient method to identify correct DTIs. The data and code are provided at https://bitbucket.org/RSO24/ddr/. vladimir.bajic@kaust.edu.sa. Supplementary data are available at Bioinformatics online.

Projection of controlled repeatable real-time moving targets to test and evaluate motion imagery quality

NASA Astrophysics Data System (ADS)

Scopatz, Stephen D.; Mendez, Michael; Trent, Randall

2015-05-01

The projection of controlled moving targets is key to the quantitative testing of video capture and post processing for Motion Imagery. This presentation will discuss several implementations of target projectors with moving targets or apparent moving targets creating motion to be captured by the camera under test. The targets presented are broadband (UV-VIS-IR) and move in a predictable, repeatable and programmable way; several short videos will be included in the presentation. Among the technical approaches will be targets that move independently in the camera's field of view, as well targets that change size and shape. The development of a rotating IR and VIS 4 bar target projector with programmable rotational velocity and acceleration control for testing hyperspectral cameras is discussed. A related issue for motion imagery is evaluated by simulating a blinding flash which is an impulse of broadband photons in fewer than 2 milliseconds to assess the camera's reaction to a large, fast change in signal. A traditional approach of gimbal mounting the camera in combination with the moving target projector is discussed as an alternative to high priced flight simulators. Based on the use of the moving target projector several standard tests are proposed to provide a corresponding test to MTF (resolution), SNR and minimum detectable signal at velocity. Several unique metrics are suggested for Motion Imagery including Maximum Velocity Resolved (the measure of the greatest velocity that is accurately tracked by the camera system) and Missing Object Tolerance (measurement of tracking ability when target is obscured in the images). These metrics are applicable to UV-VIS-IR wavelengths and can be used to assist in camera and algorithm development as well as comparing various systems by presenting the exact scenes to the cameras in a repeatable way.

Thermal Test on Target with Pressed Disks

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

Woloshun, Keith Albert; Dale, Gregory E.; Olivas, Eric Richard

A thorough test of the thermal performance of a target for Mo 99 production using solid Mo 100 target to produce the Mo 99 via a gamma-n reaction has previously been conducted at Argonne National Laboratory (ANL). The results are reported in “Zero Degree Line Mo Target Thermal Test Results and Analysis,” LANL report Number LA-UR-15-23134 dated 3/27/15. This target was comprised of 25 disks 1 mm thick and 12 mm in diameter, separated by helium coolant gaps 0.5 mm wide. The test reported in the above referenced report was conducted with natural Mo disks all cut from commercial rod.more » The production plant will have Mo 100 disks pressed and sintered using a process being developed at Oak Ridge National Laboratory (ORNL). The structural integrity of press-and-sinter disks is of some concern. The test reported herein included 4 disks made by the ORNL process and placed in the high heat, and therefore high thermal stress, region of the target. The electron beam energy was 23 MeV for these tests. Beam spot size was 3.5 mm horizontal and 3 mm vertical, FWHM. The thermal stress test of pressed-and-sintered disks resulted in no mechanical failures. The induced thermal stresses were below yield stress for natural Mo, indicating that up to that stress state no inherent deficiencies in the mechanical properties of the fabricated disks were evident.« less

Global analysis of bacterial transcription factors to predict cellular target processes.

PubMed

Doerks, Tobias; Andrade, Miguel A; Lathe, Warren; von Mering, Christian; Bork, Peer

2004-03-01

Whole-genome sequences are now available for >100 bacterial species, giving unprecedented power to comparative genomics approaches. We have applied genome-context methods to predict target processes that are regulated by transcription factors (TFs). Of 128 orthologous groups of proteins annotated as TFs, to date, 36 are functionally uncharacterized; in our analysis we predict a probable cellular target process or biochemical pathway for half of these functionally uncharacterized TFs.

Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites.

PubMed

Betel, Doron; Koppal, Anjali; Agius, Phaedra; Sander, Chris; Leslie, Christina

2010-01-01

mirSVR is a new machine learning method for ranking microRNA target sites by a down-regulation score. The algorithm trains a regression model on sequence and contextual features extracted from miRanda-predicted target sites. In a large-scale evaluation, miRanda-mirSVR is competitive with other target prediction methods in identifying target genes and predicting the extent of their downregulation at the mRNA or protein levels. Importantly, the method identifies a significant number of experimentally determined non-canonical and non-conserved sites.

Predicting spillover risk to non-target plants pre-release: Bikasha collaris a potential biological control agent of Chinese tallowtree (Triadica sebifera)

USDA-ARS?s Scientific Manuscript database

Quarantine host range tests accurately predict direct risk of biological control agents to non-target species. However, a well-known indirect effect of biological control of weeds releases is spillover damage to non-target species. Spillover damage may occur when the population of agents achieves ou...

Modified linear predictive coding approach for moving target tracking by Doppler radar

NASA Astrophysics Data System (ADS)

Ding, Yipeng; Lin, Xiaoyi; Sun, Ke-Hui; Xu, Xue-Mei; Liu, Xi-Yao

2016-07-01

Doppler radar is a cost-effective tool for moving target tracking, which can support a large range of civilian and military applications. A modified linear predictive coding (LPC) approach is proposed to increase the target localization accuracy of the Doppler radar. Based on the time-frequency analysis of the received echo, the proposed approach first real-time estimates the noise statistical parameters and constructs an adaptive filter to intelligently suppress the noise interference. Then, a linear predictive model is applied to extend the available data, which can help improve the resolution of the target localization result. Compared with the traditional LPC method, which empirically decides the extension data length, the proposed approach develops an error array to evaluate the prediction accuracy and thus, adjust the optimum extension data length intelligently. Finally, the prediction error array is superimposed with the predictor output to correct the prediction error. A series of experiments are conducted to illustrate the validity and performance of the proposed techniques.

Research of maneuvering target prediction and tracking technology based on IMM algorithm

NASA Astrophysics Data System (ADS)

Cao, Zheng; Mao, Yao; Deng, Chao; Liu, Qiong; Chen, Jing

2016-09-01

Maneuvering target prediction and tracking technology is widely used in both military and civilian applications, the study of those technologies is all along the hotspot and difficulty. In the Electro-Optical acquisition-tracking-pointing system (ATP), the primary traditional maneuvering targets are ballistic target, large aircraft and other big targets. Those targets have the features of fast velocity and a strong regular trajectory and Kalman Filtering and polynomial fitting have good effects when they are used to track those targets. In recent years, the small unmanned aerial vehicles developed rapidly for they are small, nimble and simple operation. The small unmanned aerial vehicles have strong maneuverability in the observation system of ATP although they are close-in, slow and small targets. Moreover, those vehicles are under the manual operation, therefore, the acceleration of them changes greatly and they move erratically. So the prediction and tracking precision is low when traditional algorithms are used to track the maneuvering fly of those targets, such as speeding up, turning, climbing and so on. The interacting multiple model algorithm (IMM) use multiple models to match target real movement trajectory, there are interactions between each model. The IMM algorithm can switch model based on a Markov chain to adapt to the change of target movement trajectory, so it is suitable to solve the prediction and tracking problems of the small unmanned aerial vehicles because of the better adaptability of irregular movement. This paper has set up model set of constant velocity model (CV), constant acceleration model (CA), constant turning model (CT) and current statistical model. And the results of simulating and analyzing the real movement trajectory data of the small unmanned aerial vehicles show that the prediction and tracking technology based on the interacting multiple model algorithm can get relatively lower tracking error and improve tracking precision

Predictive Control of Speededness in Adaptive Testing

ERIC Educational Resources Information Center

van der Linden, Wim J.

2009-01-01

An adaptive testing method is presented that controls the speededness of a test using predictions of the test takers' response times on the candidate items in the pool. Two different types of predictions are investigated: posterior predictions given the actual response times on the items already administered and posterior predictions that use the…

Risk of co-occuring psychopathology: testing a prediction of expectancy theory.

PubMed

Capron, Daniel W; Norr, Aaron M; Schmidt, Norman B

2013-01-01

Despite the high impact of anxiety sensitivity (AS; a fear of anxiety related sensations) research, almost no research attention has been paid to its parent theory, Reiss' expectancy theory (ET). ET has gone largely unexamined to this point, including the prediction that AS is a better predictor of number of fears than current anxiety. To test Reiss' prediction, we used a large (N = 317) clinical sample of anxiety outpatients. Specifically, we examined whether elevated AS predicted number of comorbid anxiety and non-anxiety disorder diagnoses in this sample. Consistent with ET, findings indicated that AS predicted number of comorbid anxiety disorder diagnoses above and beyond current anxiety symptoms. Also, AS did not predict the number of comorbid non-anxiety diagnoses when current anxiety symptoms were accounted for. These findings represent an important examination of a prediction of Reiss' ET and are consistent with the idea that AS may be a useful transdiagnostic treatment target. Copyright © 2012 Elsevier Ltd. All rights reserved.

Using the Integrative Model of Behavioral Prediction to Understand College Students' STI Testing Beliefs, Intentions, and Behaviors.

PubMed

Wombacher, Kevin; Dai, Minhao; Matig, Jacob J; Harrington, Nancy Grant

2018-03-22

To identify salient behavioral determinants related to STI testing among college students by testing a model based on the integrative model of behavioral (IMBP) prediction. 265 undergraduate students from a large university in the Southeastern US. Formative and survey research to test an IMBP-based model that explores the relationships between determinants and STI testing intention and behavior. Results of path analyses supported a model in which attitudinal beliefs predicted intention and intention predicted behavior. Normative beliefs and behavioral control beliefs were not significant in the model; however, select individual normative and control beliefs were significantly correlated with intention and behavior. Attitudinal beliefs are the strongest predictor of STI testing intention and behavior. Future efforts to increase STI testing rates should identify and target salient attitudinal beliefs.

ACTP: A webserver for predicting potential targets and relevant pathways of autophagy-modulating compounds

PubMed Central

Ouyang, Liang; Cai, Haoyang; Liu, Bo

2016-01-01

Autophagy (macroautophagy) is well known as an evolutionarily conserved lysosomal degradation process for long-lived proteins and damaged organelles. Recently, accumulating evidence has revealed a series of small-molecule compounds that may activate or inhibit autophagy for therapeutic potential on human diseases. However, targeting autophagy for drug discovery still remains in its infancy. In this study, we developed a webserver called Autophagic Compound-Target Prediction (ACTP) (http://actp.liu-lab.com/) that could predict autophagic targets and relevant pathways for a given compound. The flexible docking of submitted small-molecule compound (s) to potential autophagic targets could be performed by backend reverse docking. The webpage would return structure-based scores and relevant pathways for each predicted target. Thus, these results provide a basis for the rapid prediction of potential targets/pathways of possible autophagy-activating or autophagy-inhibiting compounds without labor-intensive experiments. Moreover, ACTP will be helpful to shed light on identifying more novel autophagy-activating or autophagy-inhibiting compounds for future therapeutic implications. PMID:26824420

Target-motion prediction for robotic search and rescue in wilderness environments.

PubMed

Macwan, Ashish; Nejat, Goldie; Benhabib, Beno

2011-10-01

This paper presents a novel modular methodology for predicting a lost person's (motion) behavior for autonomous coordinated multirobot wilderness search and rescue. The new concept of isoprobability curves is introduced and developed, which represents a unique mechanism for identifying the target's probable location at any given time within the search area while accounting for influences such as terrain topology, target physiology and psychology, clues found, etc. The isoprobability curves are propagated over time and space. The significant tangible benefit of the proposed target-motion prediction methodology is demonstrated through a comparison to a nonprobabilistic approach, as well as through a simulated realistic wilderness search scenario.

Andean microrefugia: testing the Holocene to predict the Anthropocene.

PubMed

Valencia, Bryan G; Matthews-Bird, Frazer; Urrego, Dunia H; Williams, Joseph J; Gosling, William D; Bush, Mark

2016-10-01

Microrefugia are important for supporting populations during periods of unfavourable climate change and in facilitating rapid migration as conditions ameliorate. With ongoing anthropogenic climate change, microrefugia could have an important conservation value; however, a simple tool has not been developed and tested to predict which settings are microrefugial. We provide a tool based on terrain ruggedness modelling of individual catchments to predict Andean microrefugia. We tested the predictions using nine Holocene Polylepis pollen records. We used the mid-Holocene dry event, a period of peak aridity for the last 100 000 yr, as an analogue climate scenario for the near future. The results suggest that sites with high terrain rugosity have the greatest chance of sustaining mesic conditions under drier-than-modern climates. Fire is a feature of all catchments; however, an increase in fire is only recorded in settings with low rugosity. Owing to rising temperatures and greater precipitation variability, Andean ecosystems are threatened by increasing moisture stress. Our results suggest that high terrain rugosity helps to create more resilient catchments by trapping moisture through orographic rainfall and providing firebreaks that shelter forest from fire. On this basis, conservation policy should target protection and management of catchments with high terrain rugosity. © 2016 The Authors. New Phytologist © 2016 New Phytologist Trust.

Predicting Drug-Target Interactions Based on Small Positive Samples.

PubMed

Hu, Pengwei; Chan, Keith C C; Hu, Yanxing

2018-01-01

A basic task in drug discovery is to find new medication in the form of candidate compounds that act on a target protein. In other words, a drug has to interact with a target and such drug-target interaction (DTI) is not expected to be random. Significant and interesting patterns are expected to be hidden in them. If these patterns can be discovered, new drugs are expected to be more easily discoverable. Currently, a number of computational methods have been proposed to predict DTIs based on their similarity. However, such as approach does not allow biochemical features to be directly considered. As a result, some methods have been proposed to try to discover patterns in physicochemical interactions. Since the number of potential negative DTIs are very high both in absolute terms and in comparison to that of the known ones, these methods are rather computationally expensive and they can only rely on subsets, rather than the full set, of negative DTIs for training and validation. As there is always a relatively high chance for negative DTIs to be falsely identified and as only partial subset of such DTIs is considered, existing approaches can be further improved to better predict DTIs. In this paper, we present a novel approach, called ODT (one class drug target interaction prediction), for such purpose. One main task of ODT is to discover association patterns between interacting drugs and proteins from the chemical structure of the former and the protein sequence network of the latter. ODT does so in two phases. First, the DTI-network is transformed to a representation by structural properties. Second, it applies a oneclass classification algorithm to build a prediction model based only on known positive interactions. We compared the best AUROC scores of the ODT with several state-of-art approaches on Gold standard data. The prediction accuracy of the ODT is superior in comparison with all the other methods at GPCRs dataset and Ion channels dataset. Performance

Test Information Targeting Strategies for Adaptive Multistage Testing Designs.

ERIC Educational Resources Information Center

Luecht, Richard M.; Burgin, William

Adaptive multistage testlet (MST) designs appear to be gaining popularity for many large-scale computer-based testing programs. These adaptive MST designs use a modularized configuration of preconstructed testlets and embedded score-routing schemes to prepackage different forms of an adaptive test. The conditional information targeting (CIT)…

Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference

PubMed Central

Jiang, Jing; Lu, Weiqiang; Li, Weihua; Liu, Guixia; Zhou, Weixing; Huang, Jin; Tang, Yun

2012-01-01

Drug-target interaction (DTI) is the basis of drug discovery and design. It is time consuming and costly to determine DTI experimentally. Hence, it is necessary to develop computational methods for the prediction of potential DTI. Based on complex network theory, three supervised inference methods were developed here to predict DTI and used for drug repositioning, namely drug-based similarity inference (DBSI), target-based similarity inference (TBSI) and network-based inference (NBI). Among them, NBI performed best on four benchmark data sets. Then a drug-target network was created with NBI based on 12,483 FDA-approved and experimental drug-target binary links, and some new DTIs were further predicted. In vitro assays confirmed that five old drugs, namely montelukast, diclofenac, simvastatin, ketoconazole, and itraconazole, showed polypharmacological features on estrogen receptors or dipeptidyl peptidase-IV with half maximal inhibitory or effective concentration ranged from 0.2 to 10 µM. Moreover, simvastatin and ketoconazole showed potent antiproliferative activities on human MDA-MB-231 breast cancer cell line in MTT assays. The results indicated that these methods could be powerful tools in prediction of DTIs and drug repositioning. PMID:22589709

Patient-Customized Drug Combination Prediction and Testing for T-cell Prolymphocytic Leukemia Patients.

PubMed

He, Liye; Tang, Jing; Andersson, Emma I; Timonen, Sanna; Koschmieder, Steffen; Wennerberg, Krister; Mustjoki, Satu; Aittokallio, Tero

2018-05-01

The molecular pathways that drive cancer progression and treatment resistance are highly redundant and variable between individual patients with the same cancer type. To tackle this complex rewiring of pathway cross-talk, personalized combination treatments targeting multiple cancer growth and survival pathways are required. Here we implemented a computational-experimental drug combination prediction and testing (DCPT) platform for efficient in silico prioritization and ex vivo testing in patient-derived samples to identify customized synergistic combinations for individual cancer patients. DCPT used drug-target interaction networks to traverse the massive combinatorial search spaces among 218 compounds (a total of 23,653 pairwise combinations) and identified cancer-selective synergies by using differential single-compound sensitivity profiles between patient cells and healthy controls, hence reducing the likelihood of toxic combination effects. A polypharmacology-based machine learning modeling and network visualization made use of baseline genomic and molecular profiles to guide patient-specific combination testing and clinical translation phases. Using T-cell prolymphocytic leukemia (T-PLL) as a first case study, we show how the DCPT platform successfully predicted distinct synergistic combinations for each of the three T-PLL patients, each presenting with different resistance patterns and synergy mechanisms. In total, 10 of 24 (42%) of selective combination predictions were experimentally confirmed to show synergy in patient-derived samples ex vivo The identified selective synergies among approved drugs, including tacrolimus and temsirolimus combined with BCL-2 inhibitor venetoclax, may offer novel drug repurposing opportunities for treating T-PLL. Significance: An integrated use of functional drug screening combined with genomic and molecular profiling enables patient-customized prediction and testing of drug combination synergies for T-PLL patients. Cancer

Progress in sensor performance testing, modeling and range prediction using the TOD method: an overview

NASA Astrophysics Data System (ADS)

Bijl, Piet; Hogervorst, Maarten A.; Toet, Alexander

2017-05-01

The Triangle Orientation Discrimination (TOD) methodology includes i) a widely applicable, accurate end-to-end EO/IR sensor test, ii) an image-based sensor system model and iii) a Target Acquisition (TA) range model. The method has been extensively validated against TA field performance for a wide variety of well- and under-sampled imagers, systems with advanced image processing techniques such as dynamic super resolution and local adaptive contrast enhancement, and sensors showing smear or noise drift, for both static and dynamic test stimuli and as a function of target contrast. Recently, significant progress has been made in various directions. Dedicated visual and NIR test charts for lab and field testing are available and thermal test benches are on the market. Automated sensor testing using an objective synthetic human observer is within reach. Both an analytical and an image-based TOD model have recently been developed and are being implemented in the European Target Acquisition model ECOMOS and in the EOSTAR TDA. Further, the methodology is being applied for design optimization of high-end security camera systems. Finally, results from a recent perception study suggest that DRI ranges for real targets can be predicted by replacing the relevant distinctive target features by TOD test patterns of the same characteristic size and contrast, enabling a new TA modeling approach. This paper provides an overview.

Prediction of Drug-Target Interaction Networks from the Integration of Protein Sequences and Drug Chemical Structures.

PubMed

Meng, Fan-Rong; You, Zhu-Hong; Chen, Xing; Zhou, Yong; An, Ji-Yong

2017-07-05

Knowledge of drug-target interaction (DTI) plays an important role in discovering new drug candidates. Unfortunately, there are unavoidable shortcomings; including the time-consuming and expensive nature of the experimental method to predict DTI. Therefore, it motivates us to develop an effective computational method to predict DTI based on protein sequence. In the paper, we proposed a novel computational approach based on protein sequence, namely PDTPS (Predicting Drug Targets with Protein Sequence) to predict DTI. The PDTPS method combines Bi-gram probabilities (BIGP), Position Specific Scoring Matrix (PSSM), and Principal Component Analysis (PCA) with Relevance Vector Machine (RVM). In order to evaluate the prediction capacity of the PDTPS, the experiment was carried out on enzyme, ion channel, GPCR, and nuclear receptor datasets by using five-fold cross-validation tests. The proposed PDTPS method achieved average accuracy of 97.73%, 93.12%, 86.78%, and 87.78% on enzyme, ion channel, GPCR and nuclear receptor datasets, respectively. The experimental results showed that our method has good prediction performance. Furthermore, in order to further evaluate the prediction performance of the proposed PDTPS method, we compared it with the state-of-the-art support vector machine (SVM) classifier on enzyme and ion channel datasets, and other exiting methods on four datasets. The promising comparison results further demonstrate that the efficiency and robust of the proposed PDTPS method. This makes it a useful tool and suitable for predicting DTI, as well as other bioinformatics tasks.

Predicting the Noise of High Power Fluid Targets Using Computational Fluid Dynamics

NASA Astrophysics Data System (ADS)

Moore, Michael; Covrig Dusa, Silviu

The 2.5 kW liquid hydrogen (LH2) target used in the Qweak parity violation experiment is the highest power LH2 target in the world and the first to be designed with Computational Fluid Dynamics (CFD) at Jefferson Lab. The Qweak experiment determined the weak charge of the proton by measuring the parity-violating elastic scattering asymmetry of longitudinally polarized electrons from unpolarized liquid hydrogen at small momentum transfer (Q2 = 0 . 025 GeV2). This target satisfied the design goals of < 1 % luminosity reduction and < 5 % contribution to the total asymmetry width (the Qweak target achieved 2 % or 55ppm). State of the art time dependent CFD simulations are being developed to improve the predictions of target noise on the time scale of the electron beam helicity period. These predictions will be bench-marked with the Qweak target data. This work is an essential component in future designs of very high power low noise targets like MOLLER (5 kW, target noise asymmetry contribution < 25 ppm) and MESA (4.5 kW).

A Waveform Detector that Targets Template-Decorrelated Signals and Achieves its Predicted Performance: Demonstration with IMS Data

NASA Astrophysics Data System (ADS)

Carmichael, J.

2016-12-01

Waveform correlation detectors used in seismic monitoring scan multichannel data to test two competing hypotheses: that data contain (1) a noisy, amplitude-scaled version of a template waveform, or, (2) only noise. In reality, seismic wavefields include signals triggered by non-target sources (background seismicity) and target signals that are only partially correlated with the waveform template. We reform the waveform correlation detector hypothesis test to accommodate deterministic uncertainty in template/target waveform similarity and thereby derive a new detector from convex set projections (the "cone detector") for use in explosion monitoring. Our analyses give probability density functions that quantify the detectors' degraded performance with decreasing waveform similarity. We then apply our results to three announced North Korean nuclear tests and use International Monitoring System (IMS) arrays to determine the probability that low magnitude, off-site explosions can be reliably detected with a given waveform template. We demonstrate that cone detectors provide (1) an improved predictive capability over correlation detectors to identify such spatially separated explosive sources, (2) competitive detection rates, and (3) reduced false alarms on background seismicity. Figure Caption: Observed and predicted receiver operating characteristic curves for correlation statistic r(x) (left) and cone statistic s(x) (right) versus semi-empirical explosion magnitude. a: Shaded region shows range of ROC curves for r(x) that give the predicted detection performance in noise conditions recorded over 24 hrs on 8 October 2006. Superimposed stair plot shows the empirical detection performance (recorded detections/total events) averaged over 24 hr of data. Error bars indicate the demeaned range in observed detection probability over the day; means are removed to avoid risk of misinterpreting range to indicate probabilities can exceed one. b: Shaded region shows range of ROC

Comprehensive predictions of target proteins based on protein-chemical interaction using virtual screening and experimental verifications.

PubMed

Kobayashi, Hiroki; Harada, Hiroko; Nakamura, Masaomi; Futamura, Yushi; Ito, Akihiro; Yoshida, Minoru; Iemura, Shun-Ichiro; Shin-Ya, Kazuo; Doi, Takayuki; Takahashi, Takashi; Natsume, Tohru; Imoto, Masaya; Sakakibara, Yasubumi

2012-04-05

Identification of the target proteins of bioactive compounds is critical for elucidating the mode of action; however, target identification has been difficult in general, mostly due to the low sensitivity of detection using affinity chromatography followed by CBB staining and MS/MS analysis. We applied our protocol of predicting target proteins combining in silico screening and experimental verification for incednine, which inhibits the anti-apoptotic function of Bcl-xL by an unknown mechanism. One hundred eighty-two target protein candidates were computationally predicted to bind to incednine by the statistical prediction method, and the predictions were verified by in vitro binding of incednine to seven proteins, whose expression can be confirmed in our cell system.As a result, 40% accuracy of the computational predictions was achieved successfully, and we newly found 3 incednine-binding proteins. This study revealed that our proposed protocol of predicting target protein combining in silico screening and experimental verification is useful, and provides new insight into a strategy for identifying target proteins of small molecules.

A Report on Molecular Diagnostic Testing for Inherited Retinal Dystrophies by Targeted Genetic Analyses.

PubMed

Ramkumar, Hema L; Gudiseva, Harini V; Kishaba, Kameron T; Suk, John J; Verma, Rohan; Tadimeti, Keerti; Thorson, John A; Ayyagari, Radha

2017-02-01

To test the utility of targeted sequencing as a method of clinical molecular testing in patients diagnosed with inherited retinal degeneration (IRD). After genetic counseling, peripheral blood was drawn from 188 probands and 36 carriers of IRD. Single gene testing was performed on each patient in a Clinical Laboratory Improvement Amendment (CLIA) certified laboratory. DNA was isolated, and all exons in the gene of interest were analyzed along with 20 base pairs of flanking intronic sequence. Genetic testing was most often performed on ABCA4, CTRP5, ELOV4, BEST1, CRB1, and PRPH2. Pathogenicity of novel sequence changes was predicted by PolyPhen2 and sorting intolerant from tolerant (SIFT). Of the 225 genetic tests performed, 150 were for recessive IRD, and 75 were for dominant IRD. A positive molecular diagnosis was made in 70 (59%) of probands with recessive IRD and 19 (26%) probands with dominant IRD. Analysis confirmed 12 (34%) of individuals as carriers of familial mutations associated with IRD. Thirty-two novel variants were identified; among these, 17 sequence changes in four genes were predicted to be possibly or probably damaging including: ABCA4 (14), BEST1 (2), PRPH2 (1), and TIMP3 (1). Targeted analysis of clinically suspected genes in 225 subjects resulted in a positive molecular diagnosis in 26% of patients with dominant IRD and 59% of patients with recessive IRD. Novel damaging mutations were identified in four genes. Single gene screening is not an ideal method for diagnostic testing given the phenotypic and genetic heterogeneity among IRD cases. High-throughput sequencing of all genes associated with retinal degeneration may be more efficient for molecular diagnosis.

Ensemble Methods for MiRNA Target Prediction from Expression Data.

PubMed

Le, Thuc Duy; Zhang, Junpeng; Liu, Lin; Li, Jiuyong

2015-01-01

microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each of the methods is developed based on some assumptions and constraints, for instance, assuming linear relationships between variables. For such reasons, computational methods are often subject to the problem of inconsistent performance across different datasets. On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory. In this paper, we investigate the performance of some ensemble methods over the commonly used miRNA target prediction methods. We apply eight different popular miRNA target prediction methods to three cancer datasets, and compare their performance with the ensemble methods which integrate the results from each combination of the individual methods. The validation results using experimentally confirmed databases show that the results of the ensemble methods complement those obtained by the individual methods and the ensemble methods perform better than the individual methods across different datasets. The ensemble method, Pearson+IDA+Lasso, which combines methods in different approaches, including a correlation method, a causal inference method, and a regression method, is the best performed ensemble method in this study. Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched. The source codes, datasets, miRNA target predictions by all methods, and the ground truth

Ensemble Methods for MiRNA Target Prediction from Expression Data

PubMed Central

Le, Thuc Duy; Zhang, Junpeng; Liu, Lin; Li, Jiuyong

2015-01-01

Background microRNAs (miRNAs) are short regulatory RNAs that are involved in several diseases, including cancers. Identifying miRNA functions is very important in understanding disease mechanisms and determining the efficacy of drugs. An increasing number of computational methods have been developed to explore miRNA functions by inferring the miRNA-mRNA regulatory relationships from data. Each of the methods is developed based on some assumptions and constraints, for instance, assuming linear relationships between variables. For such reasons, computational methods are often subject to the problem of inconsistent performance across different datasets. On the other hand, ensemble methods integrate the results from individual methods and have been proved to outperform each of their individual component methods in theory. Results In this paper, we investigate the performance of some ensemble methods over the commonly used miRNA target prediction methods. We apply eight different popular miRNA target prediction methods to three cancer datasets, and compare their performance with the ensemble methods which integrate the results from each combination of the individual methods. The validation results using experimentally confirmed databases show that the results of the ensemble methods complement those obtained by the individual methods and the ensemble methods perform better than the individual methods across different datasets. The ensemble method, Pearson+IDA+Lasso, which combines methods in different approaches, including a correlation method, a causal inference method, and a regression method, is the best performed ensemble method in this study. Further analysis of the results of this ensemble method shows that the ensemble method can obtain more targets which could not be found by any of the single methods, and the discovered targets are more statistically significant and functionally enriched. The source codes, datasets, miRNA target predictions by all methods, and

Identification of HMX1 target genes: A predictive promoter model approach

PubMed Central

Boulling, Arnaud; Wicht, Linda

2013-01-01

Purpose A homozygous mutation in the H6 family homeobox 1 (HMX1) gene is responsible for a new oculoauricular defect leading to eye and auricular developmental abnormalities as well as early retinal degeneration (MIM 612109). However, the HMX1 pathway remains poorly understood, and in the first approach to better understand the pathway’s function, we sought to identify the target genes. Methods We developed a predictive promoter model (PPM) approach using a comparative transcriptomic analysis in the retina at P15 of a mouse model lacking functional Hmx1 (dmbo mouse) and its respective wild-type. This PPM was based on the hypothesis that HMX1 binding site (HMX1-BS) clusters should be more represented in promoters of HMX1 target genes. The most differentially expressed genes in the microarray experiment that contained HMX1-BS clusters were used to generate the PPM, which was then statistically validated. Finally, we developed two genome-wide target prediction methods: one that focused on conserving PPM features in human and mouse and one that was based on the co-occurrence of HMX1-BS pairs fitting the PPM, in human or in mouse, independently. Results The PPM construction revealed that sarcoglycan, gamma (35kDa dystrophin-associated glycoprotein) (Sgcg), teashirt zinc finger homeobox 2 (Tshz2), and solute carrier family 6 (neurotransmitter transporter, glycine) (Slc6a9) genes represented Hmx1 targets in the mouse retina at P15. Moreover, the genome-wide target prediction revealed that mouse genes belonging to the retinal axon guidance pathway were targeted by Hmx1. Expression of these three genes was experimentally validated using a quantitative reverse transcription PCR approach. The inhibitory activity of Hmx1 on Sgcg, as well as protein tyrosine phosphatase, receptor type, O (Ptpro) and Sema3f, two targets identified by the PPM, were validated with luciferase assay. Conclusions Gene expression analysis between wild-type and dmbo mice allowed us to develop a PPM

Test Operations Procedure (TOP) 03-2-827 Test Procedures for Video Target Scoring Using Calibration Lights

DTIC Science & Technology

2016-04-04

Final 3. DATES COVERED (From - To) 4. TITLE AND SUBTITLE Test Operations Procedure (TOP) 03-2-827 Test Procedures for Video Target Scoring Using...ABSTRACT This Test Operations Procedure (TOP) describes typical equipment and procedures to setup and operate a Video Target Scoring System (VTSS) to...lights. 15. SUBJECT TERMS Video Target Scoring System, VTSS, witness screens, camera, target screen, light pole 16. SECURITY

Changes in predictive cuing modulate the hemispheric distribution of the P1 inhibitory response to attentional targets.

PubMed

Lasaponara, Stefano; D' Onofrio, Marianna; Dragone, Alessio; Pinto, Mario; Caratelli, Ludovica; Doricchi, Fabrizio

2017-05-01

Brain activity related to orienting of attention with spatial cues and brain responses to attentional targets are influenced the probabilistic contingency between cues and targets. Compared to predictive cues, cues predicting at chance the location of targets reduce the filtering out of uncued locations and the costs in reorienting attention to targets presented at these locations. Slagter et al. (2016) have recently suggested that the larger target related P1 component that is found in the hemisphere ipsilateral to validly cued targets reflects stimulus-driven inhibition in the processing of the unstimulated side of space contralateral to the same hemisphere. Here we verified whether the strength of this inhibition and the amplitude of the corresponding P1 wave are modulated by the probabilistic link between cues and targets. Healthy participants performed a task of endogenous orienting once with predictive and once with non-predictive directional cues. In the non-predictive condition we observed a drop in the amplitude of the P1 ipsilateral to the target and in the costs of reorienting. No change in the inter-hemispheric latencies of the P1 was found between the two predictive conditions. The N1 facilitatory component was unaffected by predictive cuing. These results show that the predictive context modulates the strength of the inhibitory P1 response and that this modulation is not matched with changes in the inter-hemispheric interaction between the P1 generators of the two hemispheres. Copyright © 2017. Published by Elsevier Ltd.

Prediction and Testing of Biological Networks Underlying Intestinal Cancer

PubMed Central

Mariadason, John M.; Wang, Donghai; Augenlicht, Leonard H.; Chance, Mark R.

2010-01-01

Colorectal cancer progresses through an accumulation of somatic mutations, some of which reside in so-called “driver” genes that provide a growth advantage to the tumor. To identify points of intersection between driver gene pathways, we implemented a network analysis framework using protein interactions to predict likely connections – both precedented and novel – between key driver genes in cancer. We applied the framework to find significant connections between two genes, Apc and Cdkn1a (p21), known to be synergistic in tumorigenesis in mouse models. We then assessed the functional coherence of the resulting Apc-Cdkn1a network by engineering in vivo single node perturbations of the network: mouse models mutated individually at Apc (Apc1638N+/−) or Cdkn1a (Cdkn1a−/−), followed by measurements of protein and gene expression changes in intestinal epithelial tissue. We hypothesized that if the predicted network is biologically coherent (functional), then the predicted nodes should associate more specifically with dysregulated genes and proteins than stochastically selected genes and proteins. The predicted Apc-Cdkn1a network was significantly perturbed at the mRNA-level by both single gene knockouts, and the predictions were also strongly supported based on physical proximity and mRNA coexpression of proteomic targets. These results support the functional coherence of the proposed Apc-Cdkn1a network and also demonstrate how network-based predictions can be statistically tested using high-throughput biological data. PMID:20824133

Electrical test prediction using hybrid metrology and machine learning

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Enhanced clinical pharmacy service targeting tools: risk-predictive algorithms.

PubMed

El Hajji, Feras W D; Scullin, Claire; Scott, Michael G; McElnay, James C

2015-04-01

This study aimed to determine the value of using a mix of clinical pharmacy data and routine hospital admission spell data in the development of predictive algorithms. Exploration of risk factors in hospitalized patients, together with the targeting strategies devised, will enable the prioritization of clinical pharmacy services to optimize patient outcomes. Predictive algorithms were developed using a number of detailed steps using a 75% sample of integrated medicines management (IMM) patients, and validated using the remaining 25%. IMM patients receive targeted clinical pharmacy input throughout their hospital stay. The algorithms were applied to the validation sample, and predicted risk probability was generated for each patient from the coefficients. Risk threshold for the algorithms were determined by identifying the cut-off points of risk scores at which the algorithm would have the highest discriminative performance. Clinical pharmacy staffing levels were obtained from the pharmacy department staffing database. Numbers of previous emergency admissions and admission medicines together with age-adjusted co-morbidity and diuretic receipt formed a 12-month post-discharge and/or readmission risk algorithm. Age-adjusted co-morbidity proved to be the best index to predict mortality. Increased numbers of clinical pharmacy staff at ward level was correlated with a reduction in risk-adjusted mortality index (RAMI). Algorithms created were valid in predicting risk of in-hospital and post-discharge mortality and risk of hospital readmission 3, 6 and 12 months post-discharge. The provision of ward-based clinical pharmacy services is a key component to reducing RAMI and enabling the full benefits of pharmacy input to patient care to be realized. © 2014 John Wiley & Sons, Ltd.

Prediction of Bispectral Index during Target-controlled Infusion of Propofol and Remifentanil: A Deep Learning Approach.

PubMed

Lee, Hyung-Chul; Ryu, Ho-Geol; Chung, Eun-Jin; Jung, Chul-Woo

2018-03-01

The discrepancy between predicted effect-site concentration and measured bispectral index is problematic during intravenous anesthesia with target-controlled infusion of propofol and remifentanil. We hypothesized that bispectral index during total intravenous anesthesia would be more accurately predicted by a deep learning approach. Long short-term memory and the feed-forward neural network were sequenced to simulate the pharmacokinetic and pharmacodynamic parts of an empirical model, respectively, to predict intraoperative bispectral index during combined use of propofol and remifentanil. Inputs of long short-term memory were infusion histories of propofol and remifentanil, which were retrieved from target-controlled infusion pumps for 1,800 s at 10-s intervals. Inputs of the feed-forward network were the outputs of long short-term memory and demographic data such as age, sex, weight, and height. The final output of the feed-forward network was the bispectral index. The performance of bispectral index prediction was compared between the deep learning model and previously reported response surface model. The model hyperparameters comprised 8 memory cells in the long short-term memory layer and 16 nodes in the hidden layer of the feed-forward network. The model training and testing were performed with separate data sets of 131 and 100 cases. The concordance correlation coefficient (95% CI) were 0.561 (0.560 to 0.562) in the deep learning model, which was significantly larger than that in the response surface model (0.265 [0.263 to 0.266], P < 0.001). The deep learning model-predicted bispectral index during target-controlled infusion of propofol and remifentanil more accurately compared to the traditional model. The deep learning approach in anesthetic pharmacology seems promising because of its excellent performance and extensibility.

Target-Independent Prediction of Drug Synergies Using Only Drug Lipophilicity

PubMed Central

2015-01-01

Physicochemical properties of compounds have been instrumental in selecting lead compounds with increased drug-likeness. However, the relationship between physicochemical properties of constituent drugs and the tendency to exhibit drug interaction has not been systematically studied. We assembled physicochemical descriptors for a set of antifungal compounds (“drugs”) previously examined for interaction. Analyzing the relationship between molecular weight, lipophilicity, H-bond donor, and H-bond acceptor values for drugs and their propensity to show pairwise antifungal drug synergy, we found that combinations of two lipophilic drugs had a greater tendency to show drug synergy. We developed a more refined decision tree model that successfully predicted drug synergy in stringent cross-validation tests based on only lipophilicity of drugs. Our predictions achieved a precision of 63% and allowed successful prediction for 58% of synergistic drug pairs, suggesting that this phenomenon can extend our understanding for a substantial fraction of synergistic drug interactions. We also generated and analyzed a large-scale synergistic human toxicity network, in which we observed that combinations of lipophilic compounds show a tendency for increased toxicity. Thus, lipophilicity, a simple and easily determined molecular descriptor, is a powerful predictor of drug synergy. It is well established that lipophilic compounds (i) are promiscuous, having many targets in the cell, and (ii) often penetrate into the cell via the cellular membrane by passive diffusion. We discuss the positive relationship between drug lipophilicity and drug synergy in the context of potential drug synergy mechanisms. PMID:25026390

Early Adoption of a Multi-target Stool DNA Test for Colorectal Cancer Screening

PubMed Central

Finney Rutten, Lila J.; Jacobson, Robert M.; Wilson, Patrick M.; Jacobson, Debra J.; Fan, Chun; Kisiel, John B.; Sweetser, Seth R.; Tulledge-Scheitel, Sidna M.; St. Sauver, Jennifer L.

2017-01-01

Objective To characterize early adoption of a novelmulti-target stool deoxyribonucleic acid (MTsDNA) screening test for colorectal cancer (CRC) and test the hypothesis that adoption differs by demographic characteristics, prior CRC screening behavior, and proceeds predictably over time. Patients and Methods We used the Rochester Epidemiology Project infrastructure to assess MTsDNA screening test use among adults aged 50–75 years, and identified 27,147 individuals eligible/due for screening colonoscopy from November 1, 2014 through November 30, 2015, and living in Olmsted County, Minnesota in2014. We used electronic Current Procedure Terminology and Health Care Common Procedure codes to evaluate early adoption of MTsDNA screening test in this population and to test whether early adoption varies by age, sex, race, and prior screening behavior. Results Overall, 2,193 (8.1%) and 974 (3.6%) of individuals were screened by colonoscopy and MT-sDNA, respectively. Age, sex, race, and prior screening were significantly and independently associated with MT-sDNA screening use compared to colonoscopy use after adjustment for all other variables. Rates of adoption of MTsDNA screening increased over time and were highest among those aged 50–54 years, females, whites, and had a prior history of screening. MT-sDNA screening use varied predictably by insurance coverage. Rates of colonoscopy decreased over time, while overall CRC screening rates remained steady. Conclusion Our results are generally consistent with predictions derived from prior research and Diffusion of Innovation framework, pointing to increasing use of the new screening test over time, and early adoption by younger patients, females, whites and those with prior CRC screening. PMID:28473037

Recent advances in the development and use of molecular tests to predict antimicrobial resistance in Neisseria gonorrhoeae.

PubMed

Donà, Valentina; Low, Nicola; Golparian, Daniel; Unemo, Magnus

2017-09-01

The number of genetic tests, mostly real-time PCRs, to detect antimicrobial resistance (AMR) determinants and predict AMR in Neisseria gonorrhoeae is increasing. Several of these assays are promising, but there are important shortcomings and few assays have been adequately validated and quality assured. Areas covered: Recent advances, focusing on publications since 2012, in the development and use of molecular tests to predict gonococcal AMR for surveillance and for clinical use, advantages and disadvantages of these tests and of molecular AMR prediction compared with phenotypic AMR testing, and future perspectives for effective use of molecular AMR tests for different purposes. Expert commentary: Several challenges for direct testing of clinical, especially extra-genital, specimens remain. The choice of molecular assay needs to consider the assay target, quality controls, sample types, limitations intrinsic to molecular technologies, and specific to the chosen methodology, and the intended use of the test. Improved molecular- and particularly genome-sequencing-based methods will supplement AMR testing for surveillance purposes, and translate into point-of-care tests that will lead to personalized treatments, while sparing the last available empiric treatment option (ceftriaxone). However, genetic AMR prediction will never completely replace phenotypic AMR testing, which detects also AMR due to unknown AMR determinants.

Preparatory Drilling Test on Martian Target Windjana

NASA Image and Video Library

2014-04-30

NASA Curiosity Mars rover completed a shallow mini drill test April 29, 2014, in preparation for full-depth drilling at a rock target called Windjana. The hole results from the test is 0.63 inch across and about 0.8 inch deep.

SELF-BLM: Prediction of drug-target interactions via self-training SVM.

PubMed

Keum, Jongsoo; Nam, Hojung

2017-01-01

Predicting drug-target interactions is important for the development of novel drugs and the repositioning of drugs. To predict such interactions, there are a number of methods based on drug and target protein similarity. Although these methods, such as the bipartite local model (BLM), show promise, they often categorize unknown interactions as negative interaction. Therefore, these methods are not ideal for finding potential drug-target interactions that have not yet been validated as positive interactions. Thus, here we propose a method that integrates machine learning techniques, such as self-training support vector machine (SVM) and BLM, to develop a self-training bipartite local model (SELF-BLM) that facilitates the identification of potential interactions. The method first categorizes unlabeled interactions and negative interactions among unknown interactions using a clustering method. Then, using the BLM method and self-training SVM, the unlabeled interactions are self-trained and final local classification models are constructed. When applied to four classes of proteins that include enzymes, G-protein coupled receptors (GPCRs), ion channels, and nuclear receptors, SELF-BLM showed the best performance for predicting not only known interactions but also potential interactions in three protein classes compare to other related studies. The implemented software and supporting data are available at https://github.com/GIST-CSBL/SELF-BLM.

Identifying On-Orbit Test Targets for Space Fence Operational Testing

NASA Astrophysics Data System (ADS)

Pechkis, D.; Pacheco, N.; Botting, T.

2014-09-01

Space Fence will be an integrated system of two ground-based, S-band (2 to 4 GHz) phased-array radars located in Kwajalein and perhaps Western Australia [1]. Space Fence will cooperate with other Space Surveillance Network sensors to provide space object tracking and radar characterization data to support U.S. Strategic Command space object catalog maintenance and other space situational awareness needs. We present a rigorous statistical test design intended to test Space Fence to the letter of the program requirements as well as to characterize the system performance across the entire operational envelope. The design uses altitude, size, and inclination as independent factors in statistical tests of dependent variables (e.g., observation accuracy) linked to requirements. The analysis derives the type and number of necessary test targets. Comparing the resulting sample sizes with the number of currently known targets, we identify those areas where modelling and simulation methods are needed. Assuming hypothetical Kwajalein radar coverage and a conservative number of radar passes per object per day, we conclude that tests involving real-world space objects should take no more than 25 days to evaluate all operational requirements; almost 60 percent of the requirements can be tested in a single day and nearly 90 percent can be tested in one week or less. Reference: [1] L. Haines and P. Phu, Space Fence PDR Concept Development Phase, 2011 AMOS Conference Technical Papers.

Test target for characterizing 3D resolution of optical coherence tomography

NASA Astrophysics Data System (ADS)

Hu, Zhixiong; Hao, Bingtao; Liu, Wenli; Hong, Baoyu; Li, Jiao

2014-12-01

Optical coherence tomography (OCT) is a non-invasive 3D imaging technology which has been applied or investigated in many diagnostic fields including ophthalmology, dermatology, dentistry, cardiovasology, endoscopy, brain imaging and so on. Optical resolution is an important characteristic that can describe the quality and utility of an image acquiring system. We employ 3D printing technology to design and fabricate a test target for characterizing 3D resolution of optical coherence tomography. The test target which mimics USAF 1951 test chart was produced with photopolymer. By measuring the 3D test target, axial resolution as well as lateral resolution of a spectral domain OCT system was evaluated. For comparison, conventional microscope and surface profiler were employed to characterize the 3D test targets. The results demonstrate that the 3D resolution test targets have the potential of qualitatively and quantitatively validating the performance of OCT systems.

International variation in the prevalence of preclinical colorectal cancer: Implications for predictive values of noninvasive screening tests and potential target populations for screening

PubMed Central

Stock, Christian; Brenner, Hermann

2017-01-01

Screening for colorectal cancer (CRC) is implemented in an increasing number of countries. We aimed to assess international variation in the prevalence of preclinical CRC and the resulting variation in positive and negative predictive values (PPVs, NPVs) of existing and potential CRC screening tests in various countries. Using age‐ and sex‐specific CRC incidence data and transition rates from preclinical to clinical CRC we estimated overall and age‐ and sex‐specific prevalence of preclinical CRC in the target population aged 50–74 years in different parts of the world. These prevalence estimates were used to derive PPVs and NPVs for existing and potential noninvasive screening tests with varying levels of sensitivity and specificity. Within all regions and countries, prevalence strongly increases with age and is higher in men than in women. In addition, major variation was seen between regions and countries, with overall prevalence varying between 1 and 0.1%. As a result, PPVs are expected to strongly vary between ∼10% for men in high incidence countries, such as Australia and Germany, and 1% for women in low incidence countries, whereas NPVs are expected to be consistently well above 99%. Variation in CRC prevalence profoundly affects expected PPVs of screening tests, and PPVs should be carefully considered when decisions on screening tests and strategies are made for specific populations and health care systems. Here, we provide estimates of preclinical CRC and expected PPVs and NPVs of noninvasive screening tests, which may enhance the empirical basis for planning of population‐based CRC screening strategies. PMID:28670788

Predicting miRNA targets for head and neck squamous cell carcinoma using an ensemble method.

PubMed

Gao, Hong; Jin, Hui; Li, Guijun

2018-01-01

This study aimed to uncover potential microRNA (miRNA) targets in head and neck squamous cell carcinoma (HNSCC) using an ensemble method which combined 3 different methods: Pearson's correlation coefficient (PCC), Lasso and a causal inference method (i.e., intervention calculus when the directed acyclic graph (DAG) is absent [IDA]), based on Borda count election. The Borda count election method was used to integrate the top 100 predicted targets of each miRNA generated by individual methods. Afterwards, to validate the performance ability of our method, we checked the TarBase v6.0, miRecords v2013, miRWalk v2.0 and miRTarBase v4.5 databases to validate predictions for miRNAs. Pathway enrichment analysis of target genes in the top 1,000 miRNA-messenger RNA (mRNA) interactions was conducted to focus on significant KEGG pathways. Finally, we extracted target genes based on occurrence frequency ≥3. Based on an absolute value of PCC >0.7, we found 33 miRNAs and 288 mRNAs for further analysis. We extracted 10 target genes with predicted frequencies not less than 3. The target gene MYO5C possessed the highest frequency, which was predicted by 7 different miRNAs. Significantly, a total of 8 pathways were identified; the pathways of cytokine-cytokine receptor interaction and chemokine signaling pathway were the most significant. We successfully predicted target genes and pathways for HNSCC relying on miRNA expression data, mRNA expression profile, an ensemble method and pathway information. Our results may offer new information for the diagnosis and estimation of the prognosis of HNSCC.

Drug-therapy networks and the prediction of novel drug targets

PubMed Central

Spiro, Zoltan; Kovacs, Istvan A; Csermely, Peter

2008-01-01

A recent study in BMC Pharmacology presents a network of drugs and the therapies in which they are used. Network approaches open new ways of predicting novel drug targets and overcoming the cellular robustness that can prevent drugs from working. PMID:18710588

Predicting Drug Combination Index and Simulating the Network-Regulation Dynamics by Mathematical Modeling of Drug-Targeted EGFR-ERK Signaling Pathway

NASA Astrophysics Data System (ADS)

Huang, Lu; Jiang, Yuyang; Chen, Yuzong

2017-01-01

Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems.

Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model.

PubMed

Xianfang, Wang; Junmei, Wang; Xiaolei, Wang; Yue, Zhang

2017-01-01

The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the F value is used to measure the significance level of the feature for the result, and the attribute with smaller F value is filtered by rough selection. Secondly, redundancy degree is calculated by Pearson Correlation Coefficient. And the threshold is set to filter attributes with weak independence to get the result of the refinement. Finally, SVM is used to predict the types of ion channel-targeted conotoxins. The experimental results show the proposed AVC-SVM model reaches an overall accuracy of 91.98%, an average accuracy of 92.17%, and the total number of parameters of 68. The proposed model provides highly useful information for further experimental research. The prediction model will be accessed free of charge at our web server.

Predicting the Types of Ion Channel-Targeted Conotoxins Based on AVC-SVM Model

PubMed Central

Xiaolei, Wang

2017-01-01

The conotoxin proteins are disulfide-rich small peptides. Predicting the types of ion channel-targeted conotoxins has great value in the treatment of chronic diseases, epilepsy, and cardiovascular diseases. To solve the problem of information redundancy existing when using current methods, a new model is presented to predict the types of ion channel-targeted conotoxins based on AVC (Analysis of Variance and Correlation) and SVM (Support Vector Machine). First, the F value is used to measure the significance level of the feature for the result, and the attribute with smaller F value is filtered by rough selection. Secondly, redundancy degree is calculated by Pearson Correlation Coefficient. And the threshold is set to filter attributes with weak independence to get the result of the refinement. Finally, SVM is used to predict the types of ion channel-targeted conotoxins. The experimental results show the proposed AVC-SVM model reaches an overall accuracy of 91.98%, an average accuracy of 92.17%, and the total number of parameters of 68. The proposed model provides highly useful information for further experimental research. The prediction model will be accessed free of charge at our web server. PMID:28497044

When the Cop Is the Victim: A Test of Target Congruence Theory on Intimate Partner Violence Victimization Experienced by Police Officers.

PubMed

Zavala, Egbert

2017-05-01

This study analyzed data from the Police Stress and Domestic Violence in Police Families in Baltimore, Maryland, 1997-1999 ( N = 753) to examine propositions derived from target congruence theory in the context of intimate partner violence (IPV) victimization experienced by police officers. Specifically, this study tested the influence of target vulnerability, target gratifiability, and target antagonism on IPV victimization. Results from logistic regression models showed that all three theoretical constructs positively and significantly predicted IPV victimization. Results, as well as the study's limitations and directions for future research, are discussed.

Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space

PubMed Central

Bustos-Korts, Daniela; Malosetti, Marcos; Chapman, Scott; Biddulph, Ben; van Eeuwijk, Fred

2016-01-01

Genome-enabled prediction provides breeders with the means to increase the number of genotypes that can be evaluated for selection. One of the major challenges in genome-enabled prediction is how to construct a training set of genotypes from a calibration set that represents the target population of genotypes, where the calibration set is composed of a training and validation set. A random sampling protocol of genotypes from the calibration set will lead to low quality coverage of the total genetic space by the training set when the calibration set contains population structure. As a consequence, predictive ability will be affected negatively, because some parts of the genotypic diversity in the target population will be under-represented in the training set, whereas other parts will be over-represented. Therefore, we propose a training set construction method that uniformly samples the genetic space spanned by the target population of genotypes, thereby increasing predictive ability. To evaluate our method, we constructed training sets alongside with the identification of corresponding genomic prediction models for four genotype panels that differed in the amount of population structure they contained (maize Flint, maize Dent, wheat, and rice). Training sets were constructed using uniform sampling, stratified-uniform sampling, stratified sampling and random sampling. We compared these methods with a method that maximizes the generalized coefficient of determination (CD). Several training set sizes were considered. We investigated four genomic prediction models: multi-locus QTL models, GBLUP models, combinations of QTL and GBLUPs, and Reproducing Kernel Hilbert Space (RKHS) models. For the maize and wheat panels, construction of the training set under uniform sampling led to a larger predictive ability than under stratified and random sampling. The results of our methods were similar to those of the CD method. For the rice panel, all training set construction

Comparing sixteen scoring functions for predicting biological activities of ligands for protein targets.

PubMed

Xu, Weijun; Lucke, Andrew J; Fairlie, David P

2015-04-01

Accurately predicting relative binding affinities and biological potencies for ligands that interact with proteins remains a significant challenge for computational chemists. Most evaluations of docking and scoring algorithms have focused on enhancing ligand affinity for a protein by optimizing docking poses and enrichment factors during virtual screening. However, there is still relatively limited information on the accuracy of commercially available docking and scoring software programs for correctly predicting binding affinities and biological activities of structurally related inhibitors of different enzyme classes. Presented here is a comparative evaluation of eight molecular docking programs (Autodock Vina, Fitted, FlexX, Fred, Glide, GOLD, LibDock, MolDock) using sixteen docking and scoring functions to predict the rank-order activity of different ligand series for six pharmacologically important protein and enzyme targets (Factor Xa, Cdk2 kinase, Aurora A kinase, COX-2, pla2g2a, β Estrogen receptor). Use of Fitted gave an excellent correlation (Pearson 0.86, Spearman 0.91) between predicted and experimental binding only for Cdk2 kinase inhibitors. FlexX and GOLDScore produced good correlations (Pearson>0.6) for hydrophilic targets such as Factor Xa, Cdk2 kinase and Aurora A kinase. By contrast, pla2g2a and COX-2 emerged as difficult targets for scoring functions to predict ligand activities. Although possessing a high hydrophobicity in its binding site, β Estrogen receptor produced reasonable correlations using LibDock (Pearson 0.75, Spearman 0.68). These findings can assist medicinal chemists to better match scoring functions with ligand-target systems for hit-to-lead optimization using computer-aided drug design approaches. Copyright © 2015 Elsevier Inc. All rights reserved.

Predicting Drug-Target Interactions for New Drug Compounds Using a Weighted Nearest Neighbor Profile.

PubMed

van Laarhoven, Twan; Marchiori, Elena

2013-01-01

In silico discovery of interactions between drug compounds and target proteins is of core importance for improving the efficiency of the laborious and costly experimental determination of drug-target interaction. Drug-target interaction data are available for many classes of pharmaceutically useful target proteins including enzymes, ion channels, GPCRs and nuclear receptors. However, current drug-target interaction databases contain a small number of drug-target pairs which are experimentally validated interactions. In particular, for some drug compounds (or targets) there is no available interaction. This motivates the need for developing methods that predict interacting pairs with high accuracy also for these 'new' drug compounds (or targets). We show that a simple weighted nearest neighbor procedure is highly effective for this task. We integrate this procedure into a recent machine learning method for drug-target interaction we developed in previous work. Results of experiments indicate that the resulting method predicts true interactions with high accuracy also for new drug compounds and achieves results comparable or better than those of recent state-of-the-art algorithms. Software is publicly available at http://cs.ru.nl/~tvanlaarhoven/drugtarget2013/.

In Silico Prediction and Validation of Gfap as an miR-3099 Target in Mouse Brain.

PubMed

Abidin, Shahidee Zainal; Leong, Jia-Wen; Mahmoudi, Marzieh; Nordin, Norshariza; Abdullah, Syahril; Cheah, Pike-See; Ling, King-Hwa

2017-08-01

MicroRNAs are small non-coding RNAs that play crucial roles in the regulation of gene expression and protein synthesis during brain development. MiR-3099 is highly expressed throughout embryogenesis, especially in the developing central nervous system. Moreover, miR-3099 is also expressed at a higher level in differentiating neurons in vitro, suggesting that it is a potential regulator during neuronal cell development. This study aimed to predict the target genes of miR-3099 via in-silico analysis using four independent prediction algorithms (miRDB, miRanda, TargetScan, and DIANA-micro-T-CDS) with emphasis on target genes related to brain development and function. Based on the analysis, a total of 3,174 miR-3099 target genes were predicted. Those predicted by at least three algorithms (324 genes) were subjected to DAVID bioinformatics analysis to understand their overall functional themes and representation. The analysis revealed that nearly 70% of the target genes were expressed in the nervous system and a significant proportion were associated with transcriptional regulation and protein ubiquitination mechanisms. Comparison of in situ hybridization (ISH) expression patterns of miR-3099 in both published and in-house-generated ISH sections with the ISH sections of target genes from the Allen Brain Atlas identified 7 target genes (Dnmt3a, Gabpa, Gfap, Itga4, Lxn, Smad7, and Tbx18) having expression patterns complementary to miR-3099 in the developing and adult mouse brain samples. Of these, we validated Gfap as a direct downstream target of miR-3099 using the luciferase reporter gene system. In conclusion, we report the successful prediction and validation of Gfap as an miR-3099 target gene using a combination of bioinformatics resources with enrichment of annotations based on functional ontologies and a spatio-temporal expression dataset.

Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor

DOE PAGES

Faulon, Jean-Loup; Misra, Milind; Martin, Shawn; ...

2007-11-23

Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures. Additionally, there is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein–chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. Results: Our method relies on expressing proteins and chemicals with a common cheminformaticsmore » representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Lastly, such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets.« less

Predictive Models of target organ and Systemic toxicities (BOSC)

EPA Science Inventory

The objective of this work is to predict the hazard classification and point of departure (PoD) of untested chemicals in repeat-dose animal testing studies. We used supervised machine learning to objectively evaluate the predictive accuracy of different classification and regress...

Predicting Ideological Prejudice

PubMed Central

Brandt, Mark J.

2017-01-01

A major shortcoming of current models of ideological prejudice is that although they can anticipate the direction of the association between participants’ ideology and their prejudice against a range of target groups, they cannot predict the size of this association. I developed and tested models that can make specific size predictions for this association. A quantitative model that used the perceived ideology of the target group as the primary predictor of the ideology-prejudice relationship was developed with a representative sample of Americans (N = 4,940) and tested against models using the perceived status of and choice to belong to the target group as predictors. In four studies (total N = 2,093), ideology-prejudice associations were estimated, and these observed estimates were compared with the models’ predictions. The model that was based only on perceived ideology was the most parsimonious with the smallest errors. PMID:28394693

The search for drug-targetable diagnostic, prognostic and predictive biomarkers in chronic graft-versus-host disease.

PubMed

Ren, Hong-Gang; Adom, Djamilatou; Paczesny, Sophie

2018-05-01

Chronic graft-versus-host disease (cGVHD) continues to be the leading cause of late morbidity and mortality after allogeneic hematopoietic stem cell transplantation (allo-HSCT), which is an increasingly applied curative method for both benign and malignant hematologic disorders. Biomarker identification is crucial for the development of noninvasive and cost-effective cGVHD diagnostic, prognostic, and predictive test for use in clinic. Furthermore, biomarkers may help to gain a better insight on ongoing pathophysiological processes. The recent widespread application of omics technologies including genomics, transcriptomics, proteomics and cytomics provided opportunities to discover novel biomarkers. Areas covered: This review focuses on biomarkers identified through omics that play a critical role in target identification for drug development, and that were verified in at least two independent cohorts. It also summarizes the current status on omics tools used to identify these useful cGVHD targets. We briefly list the biomarkers identified and verified so far. We further address challenges associated to their exploitation and application in the management of cGVHD patients. Finally, insights on biomarkers that are drug targetable and represent potential therapeutic targets are discussed. Expert commentary: We focus on biomarkers that play an essential role in target identification.

Smooth Pursuit Eye Movement Deficits in Patients With Whiplash and Neck Pain are Modulated by Target Predictability.

PubMed

Janssen, Malou; Ischebeck, Britta K; de Vries, Jurryt; Kleinrensink, Gert-Jan; Frens, Maarten A; van der Geest, Jos N

2015-10-01

This is a cross-sectional study. The purpose of this study is to support and extend previous observations on oculomotor disturbances in patients with neck pain and whiplash-associated disorders (WADs) by systematically investigating the effect of static neck torsion on smooth pursuit in response to both predictably and unpredictably moving targets using video-oculography. Previous studies showed that in patients with neck complaints, for instance due to WAD, extreme static neck torsion deteriorates smooth pursuit eye movements in response to predictably moving targets compared with healthy controls. Eye movements in response to a smoothly moving target were recorded with video-oculography in a heterogeneous group of 55 patients with neck pain (including 11 patients with WAD) and 20 healthy controls. Smooth pursuit performance was determined while the trunk was fixed in 7 static rotations relative to the head (from 45° to the left to 45° to right), using both predictably and unpredictably moving stimuli. Patients had reduced smooth pursuit gains and smooth pursuit gain decreased due to neck torsion. Healthy controls showed higher gains for predictably moving targets compared with unpredictably moving targets, whereas patients with neck pain had similar gains in response to both types of target movements. In 11 patients with WAD, increased neck torsion decreased smooth pursuit performance, but only for predictably moving targets. Smooth pursuit of patients with neck pain is affected. The previously reported WAD-specific decline in smooth pursuit due to increased neck torsion seems to be modulated by the predictability of the movement of the target. The observed oculomotor disturbances in patients with WAD are therefore unlikely to be induced by impaired neck proprioception alone. 3.

Testing Saliency Parameters for Automatic Target Recognition

NASA Technical Reports Server (NTRS)

Pandya, Sagar

2012-01-01

A bottom-up visual attention model (the saliency model) is tested to enhance the performance of Automated Target Recognition (ATR). JPL has developed an ATR system that identifies regions of interest (ROI) using a trained OT-MACH filter, and then classifies potential targets as true- or false-positives using machine-learning techniques. In this project, saliency is used as a pre-processing step to reduce the space for performing OT-MACH filtering. Saliency parameters, such as output level and orientation weight, are tuned to detect known target features. Preliminary results are promising and future work entails a rigrous and parameter-based search to gain maximum insight about this method.

Multiplex primer prediction software for divergent targets

PubMed Central

Gardner, Shea N.; Hiddessen, Amy L.; Williams, Peter L.; Hara, Christine; Wagner, Mark C.; Colston, Bill W.

2009-01-01

We describe a Multiplex Primer Prediction (MPP) algorithm to build multiplex compatible primer sets to amplify all members of large, diverse and unalignable sets of target sequences. The MPP algorithm is scalable to larger target sets than other available software, and it does not require a multiple sequence alignment. We applied it to questions in viral detection, and demonstrated that there are no universally conserved priming sequences among viruses and that it could require an unfeasibly large number of primers (∼3700 18-mers or ∼2000 10-mers) to generate amplicons from all sequenced viruses. We then designed primer sets separately for each viral family, and for several diverse species such as foot-and-mouth disease virus (FMDV), hemagglutinin (HA) and neuraminidase (NA) segments of influenza A virus, Norwalk virus, and HIV-1. We empirically demonstrated the application of the software with a multiplex set of 16 short (10 nt) primers designed to amplify the Poxviridae family to produce a specific amplicon from vaccinia virus. PMID:19759213

Predicting targets of compounds against neurological diseases using cheminformatic methodology

NASA Astrophysics Data System (ADS)

Nikolic, Katarina; Mavridis, Lazaros; Bautista-Aguilera, Oscar M.; Marco-Contelles, José; Stark, Holger; do Carmo Carreiras, Maria; Rossi, Ilaria; Massarelli, Paola; Agbaba, Danica; Ramsay, Rona R.; Mitchell, John B. O.

2015-02-01

Recently developed multi-targeted ligands are novel drug candidates able to interact with monoamine oxidase A and B; acetylcholinesterase and butyrylcholinesterase; or with histamine N-methyltransferase and histamine H3-receptor (H3R). These proteins are drug targets in the treatment of depression, Alzheimer's disease, obsessive disorders, and Parkinson's disease. A probabilistic method, the Parzen-Rosenblatt window approach, was used to build a "predictor" model using data collected from the ChEMBL database. The model can be used to predict both the primary pharmaceutical target and off-targets of a compound based on its structure. Molecular structures were represented based on the circular fingerprint methodology. The same approach was used to build a "predictor" model from the DrugBank dataset to determine the main pharmacological groups of the compound. The study of off-target interactions is now recognised as crucial to the understanding of both drug action and toxicology. Primary pharmaceutical targets and off-targets for the novel multi-target ligands were examined by use of the developed cheminformatic method. Several multi-target ligands were selected for further study, as compounds with possible additional beneficial pharmacological activities. The cheminformatic targets identifications were in agreement with four 3D-QSAR (H3R/D1R/D2R/5-HT2aR) models and by in vitro assays for serotonin 5-HT1a and 5-HT2a receptor binding of the most promising ligand ( 71/MBA-VEG8).

Neuropsychological tests for predicting cognitive decline in older adults

PubMed Central

Baerresen, Kimberly M; Miller, Karen J; Hanson, Eric R; Miller, Justin S; Dye, Richelin V; Hartman, Richard E; Vermeersch, David; Small, Gary W

2015-01-01

Summary Aim To determine neuropsychological tests likely to predict cognitive decline. Methods A sample of nonconverters (n = 106) was compared with those who declined in cognitive status (n = 24). Significant univariate logistic regression prediction models were used to create multivariate logistic regression models to predict decline based on initial neuropsychological testing. Results Rey–Osterrieth Complex Figure Test (RCFT) Retention predicted conversion to mild cognitive impairment (MCI) while baseline Buschke Delay predicted conversion to Alzheimer’s disease (AD). Due to group sample size differences, additional analyses were conducted using a subsample of demographically matched nonconverters. Analyses indicated RCFT Retention predicted conversion to MCI and AD, and Buschke Delay predicted conversion to AD. Conclusion Results suggest RCFT Retention and Buschke Delay may be useful in predicting cognitive decline. PMID:26107318

A Systematic Prediction of Drug-Target Interactions Using Molecular Fingerprints and Protein Sequences.

PubMed

Huang, Yu-An; You, Zhu-Hong; Chen, Xing

2018-01-01

Drug-Target Interactions (DTI) play a crucial role in discovering new drug candidates and finding new proteins to target for drug development. Although the number of detected DTI obtained by high-throughput techniques has been increasing, the number of known DTI is still limited. On the other hand, the experimental methods for detecting the interactions among drugs and proteins are costly and inefficient. Therefore, computational approaches for predicting DTI are drawing increasing attention in recent years. In this paper, we report a novel computational model for predicting the DTI using extremely randomized trees model and protein amino acids information. More specifically, the protein sequence is represented as a Pseudo Substitution Matrix Representation (Pseudo-SMR) descriptor in which the influence of biological evolutionary information is retained. For the representation of drug molecules, a novel fingerprint feature vector is utilized to describe its substructure information. Then the DTI pair is characterized by concatenating the two vector spaces of protein sequence and drug substructure. Finally, the proposed method is explored for predicting the DTI on four benchmark datasets: Enzyme, Ion Channel, GPCRs and Nuclear Receptor. The experimental results demonstrate that this method achieves promising prediction accuracies of 89.85%, 87.87%, 82.99% and 81.67%, respectively. For further evaluation, we compared the performance of Extremely Randomized Trees model with that of the state-of-the-art Support Vector Machine classifier. And we also compared the proposed model with existing computational models, and confirmed 15 potential drug-target interactions by looking for existing databases. The experiment results show that the proposed method is feasible and promising for predicting drug-target interactions for new drug candidate screening based on sizeable features. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Real-Time Target Motion Animation for Missile Warning System Testing

DTIC Science & Technology

2006-04-01

T. Perkins, R. Sundberg, J. Cordell, Z. Tun , and M. Owen, Real-time Target Motion Animation for Missile Warning System Testing, Proc. SPIE Vol 6208...Z39-18 Real-time target motion animation for missile warning system testing Timothy Perkins*a, Robert Sundberga, John Cordellb, Zaw Tunb, Mark

Testing 40 Predictions from the Transtheoretical Model Again, with Confidence

ERIC Educational Resources Information Center

Velicer, Wayne F.; Brick, Leslie Ann D.; Fava, Joseph L.; Prochaska, James O.

2013-01-01

Testing Theory-based Quantitative Predictions (TTQP) represents an alternative to traditional Null Hypothesis Significance Testing (NHST) procedures and is more appropriate for theory testing. The theory generates explicit effect size predictions and these effect size estimates, with related confidence intervals, are used to test the predictions.…

Can working memory predict target-to-target interval effects in the P300?

PubMed

Steiner, Genevieve Z; Barry, Robert J; Gonsalvez, Craig J

2013-09-01

It has been suggested that the P300 component of the ERP is an electrophysiological index of memory-updating processes associated with task-relevant stimuli. Component magnitude varies with the time separating target stimuli (target-to-target interval: TTI), with longer TTIs eliciting larger P300 amplitudes. According to the template-update perspective, TTI effects observable in the P300 reflect the updating of stimulus-templates in working memory (WM). The current study explored whether young adults' memory-task ability could predict TTI effects in P300. EEG activity was recorded from 50 university students (aged 18-25 years) while they completed an auditory equiprobable Go/NoGo task with manipulations of TTIs. Participants also completed a CogState® battery and were sorted according to their WM score. ERPs were analysed using a temporal PCA. Two P300 components, P3b and the Slow Wave, were found to linearly increase in amplitude to longer TTIs. This TTI effect differed between groups only for the P3b component: The high WM group showed a steeper increase in P3b amplitude with TTI than the low WM group. These results suggest that TTI effects in P300 are directly related to WM processes. © 2013.

A new fetal RHD genotyping test: costs and benefits of mass testing to target antenatal anti-D prophylaxis in England and Wales.

PubMed

Szczepura, Ala; Osipenko, Leeza; Freeman, Karoline

2011-01-18

Postnatal and antenatal anti-D prophylaxis have dramatically reduced maternal sensitisations and cases of rhesus disease in babies born to women with RhD negative blood group. Recent scientific advances mean that non-invasive prenatal diagnosis (NIPD), based on the presence of cell-free fetal DNA in maternal plasma, could be used to target prophylaxis on "at risk" pregnancies where the fetus is RhD positive. This paper provides the first assessment of cost-effectiveness of NIPD-targeted prophylaxis compared to current policies. We conducted an economic analysis of NIPD implementation in England and Wales. Two scenarios were considered. Scenario 1 assumed that NIPD will be only used to target antenatal prophylaxis with serology tests continuing to direct post-delivery prophylaxis. In Scenario 2, NIPD would also displace postnatal serology testing if an RhD negative fetus was identified. Costs were estimated from the provider's perspective for both scenarios together with a threshold royalty fee per test. Incremental costs were compared with clinical implications. The basic cost of an NIPD in-house test is £16.25 per sample (excluding royalty fee). The two-dose antenatal prophylaxis policy recommended by NICE is estimated to cost the NHS £3.37 million each year. The estimated threshold royalty fee is £2.18 and £8.83 for Scenarios 1 and 2 respectively. At a £2.00 royalty fee, mass NIPD testing would produce no saving for Scenario 1 and £507,154 per annum for Scenario 2. Incremental cost-effectiveness analysis indicates that, at a test sensitivity of 99.7% and this royalty fee, NIPD testing in Scenario 2 will generate one additional sensitisation for every £9,190 saved. If a single-dose prophylaxis policy were implemented nationally, as recently recommended by NICE, Scenario 2 savings would fall. Currently, NIPD testing to target anti-D prophylaxis is unlikely to be sufficiently cost-effective to warrant its large scale introduction in England and Wales. Only

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

PubMed

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

2012-05-01

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

Binding Site and Affinity Prediction of General Anesthetics to Protein Targets Using Docking

PubMed Central

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

2012-01-01

Background The protein targets for general anesthetics remain unclear. A tool to predict anesthetic binding for potential binding targets is needed. In this study, we explore whether a computational method, AutoDock, could serve as such a tool. Methods High-resolution crystal data of water soluble proteins (cytochrome C, apoferritin and human serum albumin), and a membrane protein (a pentameric ligand-gated ion channel from Gloeobacter violaceus, GLIC) were used. Isothermal titration calorimetry (ITC) experiments were performed to determine anesthetic affinity in solution conditions for apoferritin. Docking calculations were performed using DockingServer with the Lamarckian genetic algorithm and the Solis and Wets local search method (https://www.dockingserver.com/web). Twenty general anesthetics were docked into apoferritin. The predicted binding constants are compared with those obtained from ITC experiments for potential correlations. In the case of apoferritin, details of the binding site and their interactions were compared with recent co-crystallization data. Docking calculations for six general anesthetics currently used in clinical settings (isoflurane, sevoflurane, desflurane, halothane, propofol, and etomidate) with known EC50 were also performed in all tested proteins. The binding constants derived from docking experiments were compared with known EC50s and octanol/water partition coefficients for the six general anesthetics. Results All 20 general anesthetics docked unambiguously into the anesthetic binding site identified in the crystal structure of apoferritin. The binding constants for 20 anesthetics obtained from the docking calculations correlate significantly with those obtained from ITC experiments (p=0.04). In the case of GLIC, the identified anesthetic binding sites in the crystal structure are among the docking predicted binding sites, but not the top ranked site. Docking calculations suggest a most probable binding site located in the

Differential Expression of MicroRNA and Predicted Targets in Pulmonary Sarcoidosis

PubMed Central

Crouser, Elliott D.; Julian, Mark W.; Crawford, Melissa; Shao, Guohong; Yu, Lianbo; Planck, Stephen R.; Rosenbaum, James T.; Nana-Sinkam, S. Patrick

2014-01-01

Background Recent studies show that various inflammatory diseases are regulated at the level of RNA translation by small non-coding RNAs, termed microRNAs (miRNAs). We sought to determine whether sarcoidosis tissues harbor a distinct pattern of miRNA expression and then considered their potential molecular targets. Methods and Results Genome-wide microarray analysis of miRNA expression in lung tissue and peripheral blood mononuclear cells (PBMCs) was performed and differentially expressed (DE)-miRNAs were then validated by real-time PCR. A distinct pattern of DE-miRNA expression was identified in both lung tissue and PBMCs of sarcoidosis patients. A subgroup of DE-miRNAs common to lung and lymph node tissues were predicted to target transforming growth factor (TGFβ)-regulated pathways. Likewise, the DE-miRNAs identified in PBMCs of sarcoidosis patients were predicted to target the TGFβ-regulated “wingless and integrase-1” (WNT) pathway. Conclusions This study is the first to profile miRNAs in sarcoidosis tissues and to consider their possible roles in disease pathogenesis. Our results suggest that miRNA regulate TGFβ and related WNT pathways in sarcoidosis tissues, pathways previously incriminated in the pathogenesis of sarcoidosis. PMID:22209793

Predictive saccade in the absence of smooth pursuit: interception of moving targets in the archer fish.

PubMed

Ben-Simon, Avi; Ben-Shahar, Ohad; Vasserman, Genadiy; Segev, Ronen

2012-12-15

Interception of fast-moving targets is a demanding task many animals solve. To handle it successfully, mammals employ both saccadic and smooth pursuit eye movements in order to confine the target to their area centralis. But how can non-mammalian vertebrates, which lack smooth pursuit, intercept moving targets? We studied this question by exploring eye movement strategies employed by archer fish, an animal that possesses an area centralis, lacks smooth pursuit eye movements, but can intercept moving targets by shooting jets of water at them. We tracked the gaze direction of fish during interception of moving targets and found that they employ saccadic eye movements based on prediction of target position when it is hit. The fish fixates on the target's initial position for ∼0.2 s from the onset of its motion, a time period used to predict whether a shot can be made before the projection of the target exits the area centralis. If the prediction indicates otherwise, the fish performs a saccade that overshoots the center of gaze beyond the present target projection on the retina, such that after the saccade the moving target remains inside the area centralis long enough to prepare and perform a shot. These results add to the growing body of knowledge on biological target tracking and may shed light on the mechanism underlying this behavior in other animals with no neural system for the generation of smooth pursuit eye movements.

Flight-Test Evaluation of Flutter-Prediction Methods

NASA Technical Reports Server (NTRS)

Lind, RIck; Brenner, Marty

2003-01-01

The flight-test community routinely spends considerable time and money to determine a range of flight conditions, called a flight envelope, within which an aircraft is safe to fly. The cost of determining a flight envelope could be greatly reduced if there were a method of safely and accurately predicting the speed associated with the onset of an instability called flutter. Several methods have been developed with the goal of predicting flutter speeds to improve the efficiency of flight testing. These methods include (1) data-based methods, in which one relies entirely on information obtained from the flight tests and (2) model-based approaches, in which one relies on a combination of flight data and theoretical models. The data-driven methods include one based on extrapolation of damping trends, one that involves an envelope function, one that involves the Zimmerman-Weissenburger flutter margin, and one that involves a discrete-time auto-regressive model. An example of a model-based approach is that of the flutterometer. These methods have all been shown to be theoretically valid and have been demonstrated on simple test cases; however, until now, they have not been thoroughly evaluated in flight tests. An experimental apparatus called the Aerostructures Test Wing (ATW) was developed to test these prediction methods.

Targeted Nanoparticle Tested in Patients with Cancer

Cancer.gov

By packaging molecules of the chemotherapy drug docetaxel in nanoparticles, researchers aim to deliver a high dose directly to tumors and reduce the drug's toxicity. A trial to test the targeted nanoparticle is underway in humans.

Computational Prediction of Neutralization Epitopes Targeted by Human Anti-V3 HIV Monoclonal Antibodies

PubMed Central

Shmelkov, Evgeny; Krachmarov, Chavdar; Grigoryan, Arsen V.; Pinter, Abraham; Statnikov, Alexander; Cardozo, Timothy

2014-01-01

The extreme diversity of HIV-1 strains presents a formidable challenge for HIV-1 vaccine design. Although antibodies (Abs) can neutralize HIV-1 and potentially protect against infection, antibodies that target the immunogenic viral surface protein gp120 have widely variable and poorly predictable cross-strain reactivity. Here, we developed a novel computational approach, the Method of Dynamic Epitopes, for identification of neutralization epitopes targeted by anti-HIV-1 monoclonal antibodies (mAbs). Our data demonstrate that this approach, based purely on calculated energetics and 3D structural information, accurately predicts the presence of neutralization epitopes targeted by V3-specific mAbs 2219 and 447-52D in any HIV-1 strain. The method was used to calculate the range of conservation of these specific epitopes across all circulating HIV-1 viruses. Accurately identifying an Ab-targeted neutralization epitope in a virus by computational means enables easy prediction of the breadth of reactivity of specific mAbs across the diversity of thousands of different circulating HIV-1 variants and facilitates rational design and selection of immunogens mimicking specific mAb-targeted epitopes in a multivalent HIV-1 vaccine. The defined epitopes can also be used for the purpose of epitope-specific analyses of breakthrough sequences recorded in vaccine clinical trials. Thus, our study is a prototype for a valuable tool for rational HIV-1 vaccine design. PMID:24587168

Non-Targeted Effects Models Predict Significantly Higher Mars Mission Cancer Risk than Targeted Effects Models

DOE PAGES

Cucinotta, Francis A.; Cacao, Eliedonna

2017-05-12

Cancer risk is an important concern for galactic cosmic ray (GCR) exposures, which consist of a wide-energy range of protons, heavy ions and secondary radiation produced in shielding and tissues. Relative biological effectiveness (RBE) factors for surrogate cancer endpoints in cell culture models and tumor induction in mice vary considerable, including significant variations for different tissues and mouse strains. Many studies suggest non-targeted effects (NTE) occur for low doses of high linear energy transfer (LET) radiation, leading to deviation from the linear dose response model used in radiation protection. Using the mouse Harderian gland tumor experiment, the only extensive data-setmore » for dose response modelling with a variety of particle types (>4), for the first-time a particle track structure model of tumor prevalence is used to investigate the effects of NTEs in predictions of chronic GCR exposure risk. The NTE model led to a predicted risk 2-fold higher compared to a targeted effects model. The scarcity of data with animal models for tissues that dominate human radiation cancer risk, including lung, colon, breast, liver, and stomach, suggest that studies of NTEs in other tissues are urgently needed prior to long-term space missions outside the protection of the Earth’s geomagnetic sphere.« less

Non-Targeted Effects Models Predict Significantly Higher Mars Mission Cancer Risk than Targeted Effects Models

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

Cucinotta, Francis A.; Cacao, Eliedonna

Cancer risk is an important concern for galactic cosmic ray (GCR) exposures, which consist of a wide-energy range of protons, heavy ions and secondary radiation produced in shielding and tissues. Relative biological effectiveness (RBE) factors for surrogate cancer endpoints in cell culture models and tumor induction in mice vary considerable, including significant variations for different tissues and mouse strains. Many studies suggest non-targeted effects (NTE) occur for low doses of high linear energy transfer (LET) radiation, leading to deviation from the linear dose response model used in radiation protection. Using the mouse Harderian gland tumor experiment, the only extensive data-setmore » for dose response modelling with a variety of particle types (>4), for the first-time a particle track structure model of tumor prevalence is used to investigate the effects of NTEs in predictions of chronic GCR exposure risk. The NTE model led to a predicted risk 2-fold higher compared to a targeted effects model. The scarcity of data with animal models for tissues that dominate human radiation cancer risk, including lung, colon, breast, liver, and stomach, suggest that studies of NTEs in other tissues are urgently needed prior to long-term space missions outside the protection of the Earth’s geomagnetic sphere.« less

Prediction of psychological functioning one year after the predictive test for Huntington's disease and impact of the test result on reproductive decision making.

PubMed Central

Decruyenaere, M; Evers-Kiebooms, G; Boogaerts, A; Cassiman, J J; Cloostermans, T; Demyttenaere, K; Dom, R; Fryns, J P; Van den Berghe, H

1996-01-01

For people at risk for Huntington's disease, the anxiety and uncertainty about the future may be very burdensome and may be an obstacle to personal decision making about important life issues, for example, procreation. For some at risk persons, this situation is the reason for requesting predictive DNA testing. The aim of this paper is two-fold. First, we want to evaluate whether knowing one's carrier status reduces anxiety and uncertainty and whether it facilitates decision making about procreation. Second, we endeavour to identify pretest predictors of psychological adaptation one year after the predictive test (psychometric evaluation of general anxiety, depression level, and ego strength). The impact of the predictive test result was assessed in 53 subjects tested, using pre- and post-test psychometric measurement and self-report data of follow up interviews. Mean anxiety and depression levels were significantly decreased one year after a good test result; there was no significant change in the case of a bad test result. The mean personality profile, including ego strength, remained unchanged one year after the test. The study further shows that the test result had a definite impact on reproductive decision making. Stepwise multiple regression analyses were used to select the best predictors of the subject's post-test reactions. The results indicate that a careful evaluation of pretest ego strength, depression level, and coping strategies may be helpful in predicting post-test reactions, independently of the carrier status. Test result (carrier/ non-carrier), gender, and age did not significantly contribute to the prediction. About one third of the variance of post-test anxiety and depression level and more than half of the variance of ego strength was explained, implying that other psychological or social aspects should also be taken into account when predicting individual post-test reactions. PMID:8880572

How to test for partially predictable chaos.

PubMed

Wernecke, Hendrik; Sándor, Bulcsú; Gros, Claudius

2017-04-24

For a chaotic system pairs of initially close-by trajectories become eventually fully uncorrelated on the attracting set. This process of decorrelation can split into an initial exponential decrease and a subsequent diffusive process on the chaotic attractor causing the final loss of predictability. Both processes can be either of the same or of very different time scales. In the latter case the two trajectories linger within a finite but small distance (with respect to the overall extent of the attractor) for exceedingly long times and remain partially predictable. Standard tests for chaos widely use inter-orbital correlations as an indicator. However, testing partially predictable chaos yields mostly ambiguous results, as this type of chaos is characterized by attractors of fractally broadened braids. For a resolution we introduce a novel 0-1 indicator for chaos based on the cross-distance scaling of pairs of initially close trajectories. This test robustly discriminates chaos, including partially predictable chaos, from laminar flow. Additionally using the finite time cross-correlation of pairs of initially close trajectories, we are able to identify laminar flow as well as strong and partially predictable chaos in a 0-1 manner solely from the properties of pairs of trajectories.

Results of thermal test of metallic molybdenum disk target and fast-acting valve testing

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

Virgo, M.; Chemerisov, S.; Gromov, R.

2016-12-01

This report describes the irradiation conditions for thermal testing of helium-cooled metallic disk targets that was conducted on March 9, 2016, at the Argonne National Laboratory electron linac. The four disks in this irradiation were pressed and sintered by Oak Ridge National Laboratory from molybdenum metal powder. Two of those disks were instrumented with thermocouples. Also reported are results of testing a fast-acting-valve system, which was designed to protect the accelerator in case of a target-window failure.

Role of retinal slip in the prediction of target motion during smooth and saccadic pursuit.

PubMed

de Brouwer, S; Missal, M; Lefèvre, P

2001-08-01

Visual tracking of moving targets requires the combination of smooth pursuit eye movements with catch-up saccades. In primates, catch-up saccades usually take place only during pursuit initiation because pursuit gain is close to unity. This contrasts with the lower and more variable gain of smooth pursuit in cats, where smooth eye movements are intermingled with catch-up saccades during steady-state pursuit. In this paper, we studied in detail the role of retinal slip in the prediction of target motion during smooth and saccadic pursuit in the cat. We found that the typical pattern of pursuit in the cat was a combination of smooth eye movements with saccades. During smooth pursuit initiation, there was a correlation between peak eye acceleration and target velocity. During pursuit maintenance, eye velocity oscillated at approximately 3 Hz around a steady-state value. The average gain of smooth pursuit was approximately 0.5. Trained cats were able to continue pursuing in the absence of a visible target, suggesting a role of the prediction of future target motion in this species. The analysis of catch-up saccades showed that the smooth-pursuit motor command is added to the saccadic command during catch-up saccades and that both position error and retinal slip are taken into account in their programming. The influence of retinal slip on catch-up saccades showed that prediction about future target motion is used in the programming of catch-up saccades. Altogether, these results suggest that pursuit systems in primates and cats are qualitatively similar, with a lower average gain in the cat and that prediction affects both saccades and smooth eye movements during pursuit.

Six-minute walking test predicts maximal fat oxidation in obese children.

PubMed

Makni, E; Moalla, W; Trabelsi, Y; Lac, G; Brun, J F; Tabka, Z; Elloumi, M

2012-07-01

Obesity is associated with reduced exercise maximal fat oxidation rate (FATmax), which is generally assessed by cardiopulmonary cycling test. The six-minute walking test (6MWT) presents an alternative method in patients. The aim of this study was to establish a practical reference equation facilitating the prediction of FATmax from the 6 MWT in obese children of both genders. This study is a cross-sectional study using mixed linear and multiple regression models. Anthropometric measurements were recorded and submaximal cycling test and 6 MWT conducted for 131 school-aged obese children, 68 boys and 63 girls. A multiple regression analysis for FATmax, including six-minute walking distance (6 MWD), anthropometric and cardiac parameters as the dependent variables, was performed for the two genders separately. Mean 6 MWD and FATmax were 564.9 ± 53.7 m and 126.5 ± 12.1 mg min(-1) for boys and 506.7 ± 55.0 m and 120.7 ± 10.0 mg min(-1) for girls, respectively. The 6MWD, body mass index, Z-score, fat-free mass, waist and hip circumferences (WC and HC), rest heart rate, and systolic and diastolic blood pressures were highly correlated with FATmax for both genders. There was a significant correlation between 6 MWD and FATmax in both boys and girls (r = 0.88 and r = 0.81, P<0.001, respectively). Stepwise regression analyses revealed that the combinations of 6 MWD with HC for boys and 6MWD with WC for girls improved the predictability of the model (R(2) = 0.81 for boys and R(2) = 0.72 for girls; P<0.001). In obese children, the 6MWT can be used to predict FATmax when formal test of exercise capacity and gas exchange analysis are unavailable or impractical. It is therefore possible to prescript targeted exercises at FATmax, without performing indirect calorimetry, just from a field test.

miRTar2GO: a novel rule-based model learning method for cell line specific microRNA target prediction that integrates Ago2 CLIP-Seq and validated microRNA-target interaction data.

PubMed

Ahadi, Alireza; Sablok, Gaurav; Hutvagner, Gyorgy

2017-04-07

MicroRNAs (miRNAs) are ∼19-22 nucleotides (nt) long regulatory RNAs that regulate gene expression by recognizing and binding to complementary sequences on mRNAs. The key step in revealing the function of a miRNA, is the identification of miRNA target genes. Recent biochemical advances including PAR-CLIP and HITS-CLIP allow for improved miRNA target predictions and are widely used to validate miRNA targets. Here, we present miRTar2GO, which is a model, trained on the common rules of miRNA-target interactions, Argonaute (Ago) CLIP-Seq data and experimentally validated miRNA target interactions. miRTar2GO is designed to predict miRNA target sites using more relaxed miRNA-target binding characteristics. More importantly, miRTar2GO allows for the prediction of cell-type specific miRNA targets. We have evaluated miRTar2GO against other widely used miRNA target prediction algorithms and demonstrated that miRTar2GO produced significantly higher F1 and G scores. Target predictions, binding specifications, results of the pathway analysis and gene ontology enrichment of miRNA targets are freely available at http://www.mirtar2go.org. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Test-Retest Reliability and Predictive Validity of the Implicit Association Test in Children

ERIC Educational Resources Information Center

Rae, James R.; Olson, Kristina R.

2018-01-01

The Implicit Association Test (IAT) is increasingly used in developmental research despite minimal evidence of whether children's IAT scores are reliable across time or predictive of behavior. When test-retest reliability and predictive validity have been assessed, the results have been mixed, and because these studies have differed on many…

Predicting the size-dependent tissue accumulation of agents released from vascular targeted nanoconstructs

NASA Astrophysics Data System (ADS)

de Tullio, Marco D.; Singh, Jaykrishna; Pascazio, Giuseppe; Decuzzi, Paolo

2014-03-01

Vascular targeted nanoparticles have been developed for the delivery of therapeutic and imaging agents in cancer and cardiovascular diseases. However, at authors' knowledge, a comprehensive systematic analysis on their delivery efficiency is still missing. Here, a computational model is developed to predict the vessel wall accumulation of agents released from vascular targeted nanoconstructs. The transport problem for the released agent is solved using a finite volume scheme in terms of three governing parameters: the local wall shear rate , ranging from to ; the wall filtration velocity , varying from to ; and the agent diffusion coefficient , ranging from to . It is shown that the percentage of released agent adsorbing on the vessel walls in the vicinity of the vascular targeted nanoconstructs reduces with an increase in shear rate , and with a decrease in filtration velocity and agent diffusivity . In particular, in tumor microvessels, characterized by lower shear rates () and higher filtration velocities (), an agent with a diffusivity (i.e. a 50 nm particle) is predicted to deposit on the vessel wall up to of the total released dose. Differently, drug molecules, exhibiting a smaller size and much higher diffusion coefficient (), are predicted to accumulate up to . In healthy vessels, characterized by higher and lower , the largest majority of the released agent is redistributed directly in the circulation. These data suggest that drug molecules and small nanoparticles only can be efficiently released from vascular targeted nanoconstructs towards the diseased vessel walls and tissue.

A new approach to human microRNA target prediction using ensemble pruning and rotation forest.

PubMed

Mousavi, Reza; Eftekhari, Mahdi; Haghighi, Mehdi Ghezelbash

2015-12-01

MicroRNAs (miRNAs) are small non-coding RNAs that have important functions in gene regulation. Since finding miRNA target experimentally is costly and needs spending much time, the use of machine learning methods is a growing research area for miRNA target prediction. In this paper, a new approach is proposed by using two popular ensemble strategies, i.e. Ensemble Pruning and Rotation Forest (EP-RTF), to predict human miRNA target. For EP, the approach utilizes Genetic Algorithm (GA). In other words, a subset of classifiers from the heterogeneous ensemble is first selected by GA. Next, the selected classifiers are trained based on the RTF method and then are combined using weighted majority voting. In addition to seeking a better subset of classifiers, the parameter of RTF is also optimized by GA. Findings of the present study confirm that the newly developed EP-RTF outperforms (in terms of classification accuracy, sensitivity, and specificity) the previously applied methods over four datasets in the field of human miRNA target. Diversity-error diagrams reveal that the proposed ensemble approach constructs individual classifiers which are more accurate and usually diverse than the other ensemble approaches. Given these experimental results, we highly recommend EP-RTF for improving the performance of miRNA target prediction.

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

PubMed

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

2007-06-01

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

Prediction of Multi-Target Networks of Neuroprotective Compounds with Entropy Indices and Synthesis, Assay, and Theoretical Study of New Asymmetric 1,2-Rasagiline Carbamates

PubMed Central

Romero Durán, Francisco J.; Alonso, Nerea; Caamaño, Olga; García-Mera, Xerardo; Yañez, Matilde; Prado-Prado, Francisco J.; González-Díaz, Humberto

2014-01-01

In a multi-target complex network, the links (Lij) represent the interactions between the drug (di) and the target (tj), characterized by different experimental measures (Ki, Km, IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (cj). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%–90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally. PMID:25255029

Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates.

PubMed

Romero Durán, Francisco J; Alonso, Nerea; Caamaño, Olga; García-Mera, Xerardo; Yañez, Matilde; Prado-Prado, Francisco J; González-Díaz, Humberto

2014-09-24

In a multi-target complex network, the links (L(ij)) represent the interactions between the drug (d(i)) and the target (t(j)), characterized by different experimental measures (K(i), K(m), IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (c(j)). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%-90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.

Use of thermodynamic coupling between antibody-antigen binding and phospholipid acyl chain phase transition energetics to predict immunoliposome targeting affinity.

PubMed

Klegerman, Melvin E; Zou, Yuejiao; Golunski, Eva; Peng, Tao; Huang, Shao-Ling; McPherson, David D

2014-09-01

Thermodynamic analysis of ligand-target binding has been a useful tool for dissecting the nature of the binding mechanism and, therefore, potentially can provide valuable information regarding the utility of targeted formulations. Based on a consistent coupling of antibody-antigen binding and gel-liquid crystal transition energetics observed for antibody-phosphatidylethanolamine (Ab-PE) conjugates, we hypothesized that the thermodynamic parameters and the affinity for antigen of the Ab-PE conjugates could be effectively predicted once the corresponding information for the unconjugated antibody is determined. This hypothesis has now been tested in nine different antibody-targeted echogenic liposome (ELIP) preparations, where antibody is conjugated to dipalmitoylphosphatidylethanolamine (DPPE) head groups through a thioether linkage. Predictions were satisfactory (affinity not significantly different from the population of values found) in five cases (55.6%), but the affinity of the unconjugated antibody was not significantly different from the population of values found in six cases (66.7%), indicating that the affinities of the conjugated antibody tended not to deviate appreciably from those of the free antibody. While knowledge of the affinities of free antibodies may be sufficient to judge their suitability as targeting agents, thermodynamic analysis may still provide valuable information regarding their usefulness for specific applications.

Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

PubMed

Chua, Huey Eng; Bhowmick, Sourav S; Tucker-Kellogg, Lisa

2017-10-01

Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

Getting NuSTAR on target: predicting mast motion

NASA Astrophysics Data System (ADS)

Forster, Karl; Madsen, Kristin K.; Miyasaka, Hiromasa; Craig, William W.; Harrison, Fiona A.; Rana, Vikram R.; Markwardt, Craig B.; Grefenstette, Brian W.

2016-07-01

The Nuclear Spectroscopic Telescope Array (NuSTAR) is the first focusing high energy (3-79 keV) X-ray observatory operating for four years from low Earth orbit. The X-ray detector arrays are located on the spacecraft bus with the optics modules mounted on a flexible mast of 10.14m length. The motion of the telescope optical axis on the detectors during each observation is measured by a laser metrology system and matches the pre-launch predictions of the thermal flexing of the mast as the spacecraft enters and exits the Earths shadow each orbit. However, an additional motion of the telescope field of view was discovered during observatory commissioning that is associated with the spacecraft attitude control system and an additional flexing of the mast correlated with the Solar aspect angle for the observation. We present the methodology developed to predict where any particular target coordinate will fall on the NuSTAR detectors based on the Solar aspect angle at the scheduled time of an observation. This may be applicable to future observatories that employ optics deployed on extendable masts. The automation of the prediction system has greatly improved observatory operations efficiency and the reliability of observation planning.

Getting NuSTAR on Target: Predicting Mast Motion

NASA Technical Reports Server (NTRS)

Forster, Karl; Madsen, Kristin K.; Miyasaka, Hiroshima; Craig, William W.; Harrison, Fiona A.; Rana, Vikram R.; Markwardt, Craig B.; Grenfenstette, Brian W.

2017-01-01

The Nuclear Spectroscopic Telescope Array (NuSTAR) is the first focusing high energy (3-79 keV) X-ray observatory operating for four years from low Earth orbit. The X-ray detector arrays are located on the spacecraft bus with the optics modules mounted on a flexible mast of 10.14m length. The motion of the telescope optical axis on the detectors during each observation is measured by a laser metrology system and matches the pre-launch predictions of the thermal flexing of the mast as the spacecraft enters and exits the Earths shadow each orbit. However, an additional motion of the telescope field of view was discovered during observatory commissioning that is associated with the spacecraft attitude control system and an additional flexing of the mast correlated with the Solar aspect angle for the observation. We present the methodology developed to predict where any particular target coordinate will fall on the NuSTAR detectors based on the Solar aspect angle at the scheduled time of an observation. This may be applicable to future observatories that employ optics deployed on extendable masts. The automation of the prediction system has greatly improved observatory operations efficiency and the reliability of observation planning.

Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.

PubMed

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

2018-02-06

While novel technologies such as high-throughput screening have advanced together with significant investment by pharmaceutical companies during the past decades, the success rate for drug development has not yet been improved prompting researchers looking for new strategies of drug discovery. Drug repositioning is a potential approach to solve this dilemma. However, experimental identification and validation of potential drug targets encoded by the human genome is both costly and time-consuming. Therefore, effective computational approaches have been proposed to facilitate drug repositioning, which have proved to be successful in drug discovery. Doubtlessly, the availability of open-accessible data from basic chemical biology research and the success of human genome sequencing are crucial to develop effective in silico drug repositioning methods allowing the identification of potential targets for existing drugs. In this work, we review several chemogenomic data-driven computational algorithms with source codes publicly accessible for predicting drug-target interactions (DTIs). We organize these algorithms by model properties and model evolutionary relationships. We re-implemented five representative algorithms in R programming language, and compared these algorithms by means of mean percentile ranking, a new recall-based evaluation metric in the DTI prediction research field. We anticipate that this review will be objective and helpful to researchers who would like to further improve existing algorithms or need to choose appropriate algorithms to infer potential DTIs in the projects. The source codes for DTI predictions are available at: https://github.com/minghao2016/chemogenomicAlg4DTIpred. Published by Oxford University Press 2018. This work is written by US Government employees and is in the public domain in the US.

HPV-testing versus HPV-cytology co-testing to predict the outcome after conization.

PubMed

Bruhn, Laerke Valsøe; Andersen, Sisse Josephine; Hariri, Jalil

2018-06-01

The purpose of this study was to determine the feasibility of human Papillomavirus (HPV) testing alone as a prognostic tool to predict recurrent disease within a three-year follow-up period after treatment for cervical intraepithelial neoplasia (CIN)2 + . Retrospectively, 128 women with histologically verified CIN2 + who had a conization performed at Southern Jutland Hospital in Denmark between 1 January 2013 and 31 December 2013 were included. Histology, cytology and HPV test results were obtained for a three-year follow-up period. 4.7% (6/128) of the cases developed recurrent disease during follow-up. Of the cases without free margins, recurrent dysplasia was detected normal in 10.4% (5/48), whereas in the group with free margins it was 1.3% (1/80). The post-conization HPV test was negative in 67.2% (86/128) and Pap smear normal in 93.7% (120/128). Combining resection margins, cytology and HPV had sensitivity for prediction of recurrent dysplasia of 100%. Specificity was 45.8%, positive predictive value (PPV) 8.5% and negative predictive value (NPV) 100%. Using HPV test alone as a predictor of recurrent dysplasia gave a sensitivity of 83.3%, specificity 69.7%, PPV 11.9% and NPV 98.8%. Combining resection margin and HPV test had a sensitivity of 100%, specificity 45.9%, PPV 8.3% and NPV 100%. HPV test at six months control post-conization gave an NPV of 98.8% and can be used as a solitary test to identify women at risk for recurrent disease three years after treatment for precursor lesions. Using both resection margin and HPV test had a sensitivity of 100% and NPV 100%. Adding cytology did not increase the predictive value. © 2018 Nordic Federation of Societies of Obstetrics and Gynecology.

Targeting as the basis for pre-test market of lithium-ion battery

NASA Astrophysics Data System (ADS)

Yuniaristanto, Zakaria, R.; Saputri, V. H. L.; Sutopo, W.; Kadir, E. A.

2017-11-01

This article discusses about market segmentation and targeting as a first step in pre-test market of a new technology. The benefits of targeting towards pre-test market are pre-test market can be conducted to focus on selected target markets so there is no bias during the pre-test market. In determining the target market then do some surveys to identify the state of market in the future, so that the marketing process is not misplaced. Lithium ion battery which is commercialized through start-up companies is the case study. This start-up companies must be able to respond the changes and bring in customers as well as maintain them so that companies can survive and evolve to achieve its objectives. The research aims to determine market segments and target market effectively. Marketing strategy (segmentation and targeting) is used to make questionnaire and cluster analysis in data processing. Respondents were selected by purposive sampling and have obtained data as many as 80 samples. As the results study, there are three segments for lithium ion battery with their own distinguished characteristics and there are two segments that can be used as the target market for the company.

Tongue Blade Bite Test Predicts Mandible Fractures

PubMed Central

Neiner, John; Free, Rachael; Caldito, Gloria; Moore-Medlin, Tara; Nathan, Cherie-Ann

2015-01-01

The aim of the study is to evaluate the utility of a simple tongue blade bite test in predicting mandible fractures and use this test as an alternative screening tool for further workup. This is a retrospective chart review. An institutional review board approved the retrospective review of patients evaluated by the Department of Otolaryngology at a single institution for facial trauma performed from November 1, 2011, to February 27, 2014. Patients who had a bite test documented were included in the study. CT was performed in all cases and was used as the gold standard to diagnose mandible fractures. Variables analyzed included age, sex, fracture type/location on CT, bite test positivity, and operative intervention. A total of 86 patients met the inclusion criteria and of those 12 were pediatric patients. Majority of the patients were male (80.2%) and adult (86.0%; average age: 34.3 years). Fifty-seven patients had a negative bite test and on CT scans had no mandible fracture. Twenty-three patients had a positive bite test and a CT scan confirmed fracture. The bite test revealed a sensitivity of 88.5% (95% CI: 69.8–97.6%), specificity of 95.0% (95% CI:86.1–99%), positive predictive value [PPV] of 88.5% (95% CI: 69.8–97.6%), and negative predictive value [NPV] of 95.0% (95% CI: 86.1–99.0%). Among pediatric patients, the sensitivity was 100% (95% CI: 29.9–100%), specificity was 88.9% (95% CI: 68.4–100%), PPV was 75.0% (95% CI: 19.4–99.4%), and NPV was 100% (95% CI: 63.1–100%). The tongue blade bite test is a quick inexpensive diagnostic tool for the otolaryngologist with high sensitivity and specificity for predicting mandible fractures. In the pediatric population, where avoidance of unnecessary CT scans is of highest priority, a wider range of data collection should be undertaken to better assess its utility. PMID:27162567

A new fetal RHD genotyping test: Costs and benefits of mass testing to target antenatal anti-D prophylaxis in England and Wales

PubMed Central

2011-01-01

Background Postnatal and antenatal anti-D prophylaxis have dramatically reduced maternal sensitisations and cases of rhesus disease in babies born to women with RhD negative blood group. Recent scientific advances mean that non-invasive prenatal diagnosis (NIPD), based on the presence of cell-free fetal DNA in maternal plasma, could be used to target prophylaxis on "at risk" pregnancies where the fetus is RhD positive. This paper provides the first assessment of cost-effectiveness of NIPD-targeted prophylaxis compared to current policies. Methods We conducted an economic analysis of NIPD implementation in England and Wales. Two scenarios were considered. Scenario 1 assumed that NIPD will be only used to target antenatal prophylaxis with serology tests continuing to direct post-delivery prophylaxis. In Scenario 2, NIPD would also displace postnatal serology testing if an RhD negative fetus was identified. Costs were estimated from the provider's perspective for both scenarios together with a threshold royalty fee per test. Incremental costs were compared with clinical implications. Results The basic cost of an NIPD in-house test is £16.25 per sample (excluding royalty fee). The two-dose antenatal prophylaxis policy recommended by NICE is estimated to cost the NHS £3.37 million each year. The estimated threshold royalty fee is £2.18 and £8.83 for Scenarios 1 and 2 respectively. At a £2.00 royalty fee, mass NIPD testing would produce no saving for Scenario 1 and £507,154 per annum for Scenario 2. Incremental cost-effectiveness analysis indicates that, at a test sensitivity of 99.7% and this royalty fee, NIPD testing in Scenario 2 will generate one additional sensitisation for every £9,190 saved. If a single-dose prophylaxis policy were implemented nationally, as recently recommended by NICE, Scenario 2 savings would fall. Conclusions Currently, NIPD testing to target anti-D prophylaxis is unlikely to be sufficiently cost-effective to warrant its large scale

Cultural group selection is plausible, but the predictions of its hypotheses should be tested with real-world data.

PubMed

Turchin, Peter; Currie, Thomas E

2016-01-01

The evidence compiled in the target article demonstrates that the assumptions of cultural group selection (CGS) theory are often met, and it is therefore a useful framework for generating plausible hypotheses. However, more can be said about how we can test the predictions of CGS hypotheses against competing explanations using historical, archaeological, and anthropological data.

Fine motor skills predict performance in the Jebsen Taylor Hand Function Test after stroke.

PubMed

Allgöwer, Kathrin; Hermsdörfer, Joachim

2017-10-01

To determine factors characterizing the differences in fine motor performance between stroke patients and controls. To confirm the relevance of the factors by analyzing their predictive power with regard to the Jebsen Taylor Hand Function Test (JTHFT), a common clinical test of fine motor control. Twenty-two people with slight paresis in an early chronic phase following stroke and twenty-two healthy controls were examined. Performance on the JTHFT, Nine-Hole Peg Test and 2-point discrimination was evaluated. To analyze object manipulation skills, grip forces and temporal measures were examined during (1) lifting actions with variations of weight and surface (2) cyclic movements (3) predictive/reactive catching tasks. Three other aspects of force control included (4) visuomotor tracking (5) fast force changes and (6) grip strength. Based on 9 parameters which significantly distinguished fine motor performance in the two groups, we identified three principal components (factors): grip force scaling, motor coordination and speed of movement. The three factors are shown to predict JTHFT scores via linear regression (R 2 =0.687, p<0.001). We revealed a factor structure behind fine motor impairments following stroke and showed that it explains JTHFT results to a large extend. This result can serve as a basis for improving diagnostics and enabling more targeted therapy. Copyright © 2017 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

Prediction of microRNA target genes using an efficient genetic algorithm-based decision tree.

PubMed

Rabiee-Ghahfarrokhi, Behzad; Rafiei, Fariba; Niknafs, Ali Akbar; Zamani, Behzad

2015-01-01

MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression in almost all plants and animals. They play an important role in key processes, such as proliferation, apoptosis, and pathogen-host interactions. Nevertheless, the mechanisms by which miRNAs act are not fully understood. The first step toward unraveling the function of a particular miRNA is the identification of its direct targets. This step has shown to be quite challenging in animals primarily because of incomplete complementarities between miRNA and target mRNAs. In recent years, the use of machine-learning techniques has greatly increased the prediction of miRNA targets, avoiding the need for costly and time-consuming experiments to achieve miRNA targets experimentally. Among the most important machine-learning algorithms are decision trees, which classify data based on extracted rules. In the present work, we used a genetic algorithm in combination with C4.5 decision tree for prediction of miRNA targets. We applied our proposed method to a validated human datasets. We nearly achieved 93.9% accuracy of classification, which could be related to the selection of best rules.

Prediction of microRNA target genes using an efficient genetic algorithm-based decision tree

PubMed Central

Rabiee-Ghahfarrokhi, Behzad; Rafiei, Fariba; Niknafs, Ali Akbar; Zamani, Behzad

2015-01-01

MicroRNAs (miRNAs) are small, non-coding RNA molecules that regulate gene expression in almost all plants and animals. They play an important role in key processes, such as proliferation, apoptosis, and pathogen–host interactions. Nevertheless, the mechanisms by which miRNAs act are not fully understood. The first step toward unraveling the function of a particular miRNA is the identification of its direct targets. This step has shown to be quite challenging in animals primarily because of incomplete complementarities between miRNA and target mRNAs. In recent years, the use of machine-learning techniques has greatly increased the prediction of miRNA targets, avoiding the need for costly and time-consuming experiments to achieve miRNA targets experimentally. Among the most important machine-learning algorithms are decision trees, which classify data based on extracted rules. In the present work, we used a genetic algorithm in combination with C4.5 decision tree for prediction of miRNA targets. We applied our proposed method to a validated human datasets. We nearly achieved 93.9% accuracy of classification, which could be related to the selection of best rules. PMID:26649272

3D flexible alignment using 2D maximum common substructure: dependence of prediction accuracy on target-reference chemical similarity.

PubMed

Kawabata, Takeshi; Nakamura, Haruki

2014-07-28

A protein-bound conformation of a target molecule can be predicted by aligning the target molecule on the reference molecule obtained from the 3D structure of the compound-protein complex. This strategy is called "similarity-based docking". For this purpose, we develop the flexible alignment program fkcombu, which aligns the target molecule based on atomic correspondences with the reference molecule. The correspondences are obtained by the maximum common substructure (MCS) of 2D chemical structures, using our program kcombu. The prediction performance was evaluated using many target-reference pairs of superimposed ligand 3D structures on the same protein in the PDB, with different ranges of chemical similarity. The details of atomic correspondence largely affected the prediction success. We found that topologically constrained disconnected MCS (TD-MCS) with the simple element-based atomic classification provides the best prediction. The crashing potential energy with the receptor protein improved the performance. We also found that the RMSD between the predicted and correct target conformations significantly correlates with the chemical similarities between target-reference molecules. Generally speaking, if the reference and target compounds have more than 70% chemical similarity, then the average RMSD of 3D conformations is <2.0 Å. We compared the performance with a rigid-body molecular alignment program based on volume-overlap scores (ShaEP). Our MCS-based flexible alignment program performed better than the rigid-body alignment program, especially when the target and reference molecules were sufficiently similar.

EOS/AMSU: A Blackbody Spacecraft Test Targets Operation and Maintenance Manual

NASA Technical Reports Server (NTRS)

1998-01-01

This report describes the spacecraft test targets and readout console as described in section 5.3.3 of the performance specification S-480-80. The spacecraft targets are to be used to provide a well-known radiometric reference for testing the functionality of the AMSU-A instruments at the spacecraft contractor's facility.

Quantitative PET Imaging with Novel HER3 Targeted Peptides Selected by Phage Display to Predict Androgen Independent Prostate Cancer Progression

DTIC Science & Technology

2017-08-01

9 4 1. Introduction The subject of this research is the design and testing of a PET imaging agent for the detection and...AWARD NUMBER: W81XWH-16-1-0447 TITLE: Quantitative PET Imaging with Novel HER3-Targeted Peptides Selected by Phage Display to Predict Androgen...MA 02114 REPORT DATE: August 2017 TYPE OF REPORT: Annual PREPARED FOR: U.S. Army Medical Research and Materiel Command Fort Detrick, Maryland

Prediction of intracellular exposure bridges the gap between target- and cell-based drug discovery

PubMed Central

Gordon, Laurie J.; Wayne, Gareth J.; Almqvist, Helena; Axelsson, Hanna; Seashore-Ludlow, Brinton; Treyer, Andrea; Lundbäck, Thomas; West, Andy; Hann, Michael M.; Artursson, Per

2017-01-01

Inadequate target exposure is a major cause of high attrition in drug discovery. Here, we show that a label-free method for quantifying the intracellular bioavailability (Fic) of drug molecules predicts drug access to intracellular targets and hence, pharmacological effect. We determined Fic in multiple cellular assays and cell types representing different targets from a number of therapeutic areas, including cancer, inflammation, and dementia. Both cytosolic targets and targets localized in subcellular compartments were investigated. Fic gives insights on membrane-permeable compounds in terms of cellular potency and intracellular target engagement, compared with biochemical potency measurements alone. Knowledge of the amount of drug that is locally available to bind intracellular targets provides a powerful tool for compound selection in early drug discovery. PMID:28701380

Happy eating: the single target implicit association test predicts overeating after positive emotions.

PubMed

Bongers, Peggy; Jansen, Anita; Houben, Katrijn; Roefs, Anne

2013-08-01

For many years, questionnaires have been considered the standard when examining emotional eating behavior. However, recently, some controversy has arisen about these questionnaires, and their usefulness in identifying emotional eaters has been questioned. The current study aimed to investigate the Single Target Implicit Association Test (ST-IAT) as a measure of emotional eating. Two ST-IATs (assessing food-positive and food-negative associations respectively) and the Dutch Eating Behaviour Questionnaire (DEBQ) were compared in undergraduate students. A positive, negative or neutral mood was induced by means of a film clip, and milkshake consumption was measured during and after the mood induction. It was hypothesized that participants with strong emotion-food associations on the ST-IATs (i.e., IAT-emotional eaters) would consume more food in the emotion induction condition corresponding to that emotion, as compared to those with weak emotion-food associations as well as to those in the neutral condition. Participants who scored high on both the positive and negative ST-IATs ate more during a positive mood induction than during a negative mood induction. This effect did not extend to milkshake consumption after the mood induction procedure. In addition, IAT-positive emotional eaters consumed more food than IAT-non-emotional eaters. No effects of the DEBQ on milkshake consumption were found. It is concluded that the ST-IAT has potential as a measure of emotional eating. © 2013 Elsevier Ltd. All rights reserved.

Differential Prediction Generalization in College Admissions Testing

ERIC Educational Resources Information Center

Aguinis, Herman; Culpepper, Steven A.; Pierce, Charles A.

2016-01-01

We introduce the concept of "differential prediction generalization" in the context of college admissions testing. Specifically, we assess the extent to which predicted first-year college grade point average (GPA) based on high-school grade point average (HSGPA) and SAT scores depends on a student's ethnicity and gender and whether this…

Predictive encoding of moving target trajectory by neurons in the parabigeminal nucleus

PubMed Central

Ma, Rui; Cui, He; Lee, Sang-Hun; Anastasio, Thomas J.

2013-01-01

Intercepting momentarily invisible moving objects requires internally generated estimations of target trajectory. We demonstrate here that the parabigeminal nucleus (PBN) encodes such estimations, combining sensory representations of target location, extrapolated positions of briefly obscured targets, and eye position information. Cui and Malpeli (Cui H, Malpeli JG. J Neurophysiol 89: 3128–3142, 2003) reported that PBN activity for continuously visible tracked targets is determined by retinotopic target position. Here we show that when cats tracked moving, blinking targets the relationship between activity and target position was similar for ON and OFF phases (400 ms for each phase). The dynamic range of activity evoked by virtual targets was 94% of that of real targets for the first 200 ms after target offset and 64% for the next 200 ms. Activity peaked at about the same best target position for both real and virtual targets. PBN encoding of target position takes into account changes in eye position resulting from saccades, even without visual feedback. Since PBN response fields are retinotopically organized, our results suggest that activity foci associated with real and virtual targets at a given target position lie in the same physical location in the PBN, i.e., a retinotopic as well as a rate encoding of virtual-target position. We also confirm that PBN activity is specific to the intended target of a saccade and is predictive of which target will be chosen if two are offered. A Bayesian predictor-corrector model is presented that conceptually explains the differences in the dynamic ranges of PBN neuronal activity evoked during tracking of real and virtual targets. PMID:23365185

Ethical Issues of Predictive Genetic Testing for Diabetes

PubMed Central

Haga, Susanne B.

2009-01-01

With the rising number of individuals affected with diabetes and the significant health care costs of treatment, the emphasis on prevention is key to controlling the health burden of this disease. Several genetic and genomic studies have identified genetic variants associated with increased risk to diabetes. As a result, commercial testing is available to predict an individual's genetic risk. Although the clinical benefits of testing have not yet been demonstrated, it is worth considering some of the ethical implications of testing for this common chronic disease. In this article, I discuss several issues that should be considered during the translation of predictive testing for diabetes, including familial implications, improvement of risk communication, implications for behavioral change and health outcomes, the Genetic Information Nondiscrimination Act, direct-to-consumer testing, and appropriate age of testing. PMID:20144329

Genome-wide prediction of vaccine targets for human herpes simplex viruses using Vaxign reverse vaccinology

PubMed Central

2013-01-01

Herpes simplex virus (HSV) types 1 and 2 (HSV-1 and HSV-2) are the most common infectious agents of humans. No safe and effective HSV vaccines have been licensed. Reverse vaccinology is an emerging and revolutionary vaccine development strategy that starts with the prediction of vaccine targets by informatics analysis of genome sequences. Vaxign (http://www.violinet.org/vaxign) is the first web-based vaccine design program based on reverse vaccinology. In this study, we used Vaxign to analyze 52 herpesvirus genomes, including 3 HSV-1 genomes, one HSV-2 genome, 8 other human herpesvirus genomes, and 40 non-human herpesvirus genomes. The HSV-1 strain 17 genome that contains 77 proteins was used as the seed genome. These 77 proteins are conserved in two other HSV-1 strains (strain F and strain H129). Two envelope glycoproteins gJ and gG do not have orthologs in HSV-2 or 8 other human herpesviruses. Seven HSV-1 proteins (including gJ and gG) do not have orthologs in all 40 non-human herpesviruses. Nineteen proteins are conserved in all human herpesviruses, including capsid scaffold protein UL26.5 (NP_044628.1). As the only HSV-1 protein predicted to be an adhesin, UL26.5 is a promising vaccine target. The MHC Class I and II epitopes were predicted by the Vaxign Vaxitop prediction program and IEDB prediction programs recently installed and incorporated in Vaxign. Our comparative analysis found that the two programs identified largely the same top epitopes but also some positive results predicted from one program might not be positive from another program. Overall, our Vaxign computational prediction provides many promising candidates for rational HSV vaccine development. The method is generic and can also be used to predict other viral vaccine targets. PMID:23514126

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

PubMed

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

2005-05-24

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

A test to evaluate the earthquake prediction algorithm, M8

USGS Publications Warehouse

Healy, John H.; Kossobokov, Vladimir G.; Dewey, James W.

1992-01-01

A test of the algorithm M8 is described. The test is constructed to meet four rules, which we propose to be applicable to the test of any method for earthquake prediction:  1. An earthquake prediction technique should be presented as a well documented, logical algorithm that can be used by  investigators without restrictions. 2. The algorithm should be coded in a common programming language and implementable on widely available computer systems. 3. A test of the earthquake prediction technique should involve future predictions with a black box version of the algorithm in which potentially adjustable parameters are fixed in advance. The source of the input data must be defined and ambiguities in these data must be resolved automatically by the algorithm. 4. At least one reasonable null hypothesis should be stated in advance of testing the earthquake prediction method, and it should be stated how this null hypothesis will be used to estimate the statistical significance of the earthquake predictions. The M8 algorithm has successfully predicted several destructive earthquakes, in the sense that the earthquakes occurred inside regions with linear dimensions from 384 to 854 km that the algorithm had identified as being in times of increased probability for strong earthquakes. In addition, M8 has successfully "post predicted" high percentages of strong earthquakes in regions to which it has been applied in retroactive studies. The statistical significance of previous predictions has not been established, however, and post-prediction studies in general are notoriously subject to success-enhancement through hindsight. Nor has it been determined how much more precise an M8 prediction might be than forecasts and probability-of-occurrence estimates made by other techniques. We view our test of M8 both as a means to better determine the effectiveness of M8 and as an experimental structure within which to make observations that might lead to improvements in the algorithm

Prediction of Academic Achievement with the McCarthy Screening Test and Metropolitan Readiness Test.

ERIC Educational Resources Information Center

Gullo, Dominic F.; And Others

1984-01-01

Examined the efficacy of the McCarthy Screening Test (MST) and Metropolitan Readiness Test (MRT) to predict academic readiness after kindergarten and achievement at the end of first grade. The MST significantly predicted children's scores of the MRT and SFAT. Additionally, the MRT was a significant predictor of the SFAT. (JAC)

Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory.

PubMed

Durán, Claudio; Daminelli, Simone; Thomas, Josephine M; Haupt, V Joachim; Schroeder, Michael; Cannistraci, Carlo Vittorio

2017-04-26

The bipartite network representation of the drug-target interactions (DTIs) in a biosystem enhances understanding of the drugs' multifaceted action modes, suggests therapeutic switching for approved drugs and unveils possible side effects. As experimental testing of DTIs is costly and time-consuming, computational predictors are of great aid. Here, for the first time, state-of-the-art DTI supervised predictors custom-made in network biology were compared-using standard and innovative validation frameworks-with unsupervised pure topological-based models designed for general-purpose link prediction in bipartite networks. Surprisingly, our results show that the bipartite topology alone, if adequately exploited by means of the recently proposed local-community-paradigm (LCP) theory-initially detected in brain-network topological self-organization and afterwards generalized to any complex network-is able to suggest highly reliable predictions, with comparable performance with the state-of-the-art-supervised methods that exploit additional (non-topological, for instance biochemical) DTI knowledge. Furthermore, a detailed analysis of the novel predictions revealed that each class of methods prioritizes distinct true interactions; hence, combining methodologies based on diverse principles represents a promising strategy to improve drug-target discovery. To conclude, this study promotes the power of bio-inspired computing, demonstrating that simple unsupervised rules inspired by principles of topological self-organization and adaptiveness arising during learning in living intelligent systems (like the brain) can efficiently equal perform complicated algorithms based on advanced, supervised and knowledge-based engineering. © The Author 2017. Published by Oxford University Press.

Impact of Target Distance, Target Size, and Visual Acuity on the Video Head Impulse Test.

PubMed

Judge, Paul D; Rodriguez, Amanda I; Barin, Kamran; Janky, Kristen L

2018-05-01

The video head impulse test (vHIT) assesses the vestibulo-ocular reflex. Few have evaluated whether environmental factors or visual acuity influence the vHIT. The purpose of this study was to evaluate the influence of target distance, target size, and visual acuity on vHIT outcomes. Thirty-eight normal controls and 8 subjects with vestibular loss (VL) participated. vHIT was completed at 3 distances and with 3 target sizes. Normal controls were subdivided on the basis of visual acuity. Corrective saccade frequency, corrective saccade amplitude, and gain were tabulated. In the normal control group, there were no significant effects of target size or visual acuity for any vHIT outcome parameters; however, gain increased as target distance decreased. The VL group demonstrated higher corrective saccade frequency and amplitude and lower gain as compared with controls. In conclusion, decreasing target distance increases gain for normal controls but not subjects with VL. Preliminarily, visual acuity does not affect vHIT outcomes.

Transcriptome-wide identification of Rauvolfia serpentina microRNAs and prediction of their potential targets.

PubMed

Prakash, Pravin; Rajakani, Raja; Gupta, Vikrant

2016-04-01

MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 19-24 nucleotides (nt) in length and considered as potent regulators of gene expression at transcriptional and post-transcriptional levels. Here we report the identification and characterization of 15 conserved miRNAs belonging to 13 families from Rauvolfia serpentina through in silico analysis of available nucleotide dataset. The identified mature R. serpentina miRNAs (rse-miRNAs) ranged between 20 and 22nt in length, and the average minimal folding free energy index (MFEI) value of rse-miRNA precursor sequences was found to be -0.815 kcal/mol. Using the identified rse-miRNAs as query, their potential targets were predicted in R. serpentina and other plant species. Gene Ontology (GO) annotation showed that predicted targets of rse-miRNAs include transcription factors as well as genes involved in diverse biological processes such as primary and secondary metabolism, stress response, disease resistance, growth, and development. Few rse-miRNAs were predicted to target genes of pharmaceutically important secondary metabolic pathways such as alkaloids and anthocyanin biosynthesis. Phylogenetic analysis showed the evolutionary relationship of rse-miRNAs and their precursor sequences to homologous pre-miRNA sequences from other plant species. The findings under present study besides giving first hand information about R. serpentina miRNAs and their targets, also contributes towards the better understanding of miRNA-mediated gene regulatory processes in plants. Copyright © 2015 Elsevier Ltd. All rights reserved.

Parametric bicubic spline and CAD tools for complex targets shape modelling in physical optics radar cross section prediction

NASA Astrophysics Data System (ADS)

Delogu, A.; Furini, F.

1991-09-01

Increasing interest in radar cross section (RCS) reduction is placing new demands on theoretical, computation, and graphic techniques for calculating scattering properties of complex targets. In particular, computer codes capable of predicting the RCS of an entire aircraft at high frequency and of achieving RCS control with modest structural changes, are becoming of paramount importance in stealth design. A computer code, evaluating the RCS of arbitrary shaped metallic objects that are computer aided design (CAD) generated, and its validation with measurements carried out using ALENIA RCS test facilities are presented. The code, based on the physical optics method, is characterized by an efficient integration algorithm with error control, in order to contain the computer time within acceptable limits, and by an accurate parametric representation of the target surface in terms of bicubic splines.

Pretest Predictions for Ventilation Tests

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

Y. Sun; H. Yang; H.N. Kalia

The objective of this calculation is to predict the temperatures of the ventilating air, waste package surface, concrete pipe walls, and insulation that will be developed during the ventilation tests involving various test conditions. The results will be used as input to the following three areas: (1) Decisions regarding testing set-up and performance. (2) Assessing how best to scale the test phenomena measured. (3) Validating numerical approach for modeling continuous ventilation. The scope of the calculation is to identify the physical mechanisms and parameters related to thermal response in the ventilation tests, and develop and describe numerical methods that canmore » be used to calculate the effects of continuous ventilation. Sensitivity studies to assess the impact of variation of linear power densities (linear heat loads) and ventilation air flow rates are included. The calculation is limited to thermal effect only.« less

How reliable are ligand-centric methods for Target Fishing?

NASA Astrophysics Data System (ADS)

Peon, Antonio; Dang, Cuong; Ballester, Pedro

2016-04-01

Computational methods for Target Fishing (TF), also known as Target Prediction or Polypharmacology Prediction, can be used to discover new targets for small-molecule drugs. This may result in repositioning the drug in a new indication or improving our current understanding of its efficacy and side effects. While there is a substantial body of research on TF methods, there is still a need to improve their validation, which is often limited to a small part of the available targets and not easily interpretable by the user. Here we discuss how target-centric TF methods are inherently limited by the number of targets that can possibly predict (this number is by construction much larger in ligand-centric techniques). We also propose a new benchmark to validate TF methods, which is particularly suited to analyse how predictive performance varies with the query molecule. On average over approved drugs, we estimate that only five predicted targets will have to be tested to find two true targets with submicromolar potency (a strong variability in performance is however observed). In addition, we find that an approved drug has currently an average of eight known targets, which reinforces the notion that polypharmacology is a common and strong event. Furthermore, with the assistance of a control group of randomly-selected molecules, we show that the targets of approved drugs are generally harder to predict.

Identification of the feedforward component in manual control with predictable target signals.

PubMed

Drop, Frank M; Pool, Daan M; Damveld, Herman J; van Paassen, Marinus M; Mulder, Max

2013-12-01

In the manual control of a dynamic system, the human controller (HC) often follows a visible and predictable reference path. Compared with a purely feedback control strategy, performance can be improved by making use of this knowledge of the reference. The operator could effectively introduce feedforward control in conjunction with a feedback path to compensate for errors, as hypothesized in literature. However, feedforward behavior has never been identified from experimental data, nor have the hypothesized models been validated. This paper investigates human control behavior in pursuit tracking of a predictable reference signal while being perturbed by a quasi-random multisine disturbance signal. An experiment was done in which the relative strength of the target and disturbance signals were systematically varied. The anticipated changes in control behavior were studied by means of an ARX model analysis and by fitting three parametric HC models: two different feedback models and a combined feedforward and feedback model. The ARX analysis shows that the experiment participants employed control action on both the error and the target signal. The control action on the target was similar to the inverse of the system dynamics. Model fits show that this behavior can be modeled best by the combined feedforward and feedback model.

[Anti-tumor target prediction and activity verification of Ganoderma lucidum triterpenoids].

PubMed

Du, Guo-Hua; Wang, Hong-Xu; Yan, Zheng; Liu, Li-Ying; Chen, Ruo-Yun

2017-02-01

It has reported that Ganoderma lucidum triterpenoids had anti-tumor activity. However, the anti-tumor target is still unclear. The present study was designed to investigate the anti-tumor activity of G. lucidum triterpenoids on different tumor cells, and predict their potential targets by virtual screening. In this experiment, molecular docking was used to simulate the interactions of 26 triterpenoids isolated from G. lucidum and 11 target proteins by LibDock module of Discovery Studio2016 software, then the anti-tumor targets of triterpenoids were predicted. In addition, the in vitro anti-tumor effects of triterpenoids were evaluated by MTT assay by determining the inhibition of proliferation in 5 tumor cell lines. The docking results showed that the poses were greater than five, and Libdock Scores higher than 100, which can be used to determine whether compounds were activity. Eight triterpenoids might have anti-tumor activity as a result of good docking, five of which had multiple targets. MTT experiments demonstrated that the ganoderic acid Y had a certain inhibitory activity on lung cancer cell H460, with IC₅₀ of 22.4 μmol•L ⁻¹, followed by 7-oxo-ganoderic acid Z2, with IC₅₀ of 43.1 μmol•L ⁻¹. However, the other triterpenoids had no anti-tumor activity in the detected tumor cell lines. Taking together, molecular docking approach established here can be used for preliminary screening of anti-tumor activity of G.lucidum ingredients. Through this screening method, combined with the MTT assay, we can conclude that ganoderic acid Y had antitumor activity, especially anti-lung cancer, and 7-oxo-ganoderic acid Z2 as well as ganoderon B, to a certain extent, had anti-tumor activity. These findings can provide basis for the development of anti-tumor drugs. However, the anti-tumor mechanisms need to be further studied. Copyright© by the Chinese Pharmaceutical Association.

[Study of the predictive value of detection tests for silent aspirations].

PubMed

Woisard, V; Réhault, E; Brouard, C; Fichaux-Bourin, P; Puech, M; Grand, S

2009-01-01

Screening for aspiration in patients with swallowing disorders is important in preventing complications. The tests used in this regard are insufficient due to silent aspiration relating to abnormal protective reflexes in many patients with swallowing problems. The aim of this study is to determine the predictive values of simple tests in screening for silent aspiration. The reference test used was videofluoroscopic examination on swallowing. In the presence of aspiration (FR+) the presence (ME+) or not (ME-) of a cough of throat clearing was noted. The tests being studied were a nasal test with isotonic saline and swallowing according to a set time. For screening for aspiration the presence of a "wet voice" was considered to be a sign of reduced protective reflexes. 1) During the nasal test, the results are 100% for the positive predictive value (VPp) and 83.3% for the negative predictive value (VPn); 2) These results are respectively 84.6% and 35.9% during the swallowing test. Regarding screening for silent aspiration, 1) during the nasal test, the results are 62.5% for the positive predictive value (VPp) and 36.3% for the negative predictive value (VPn); 2) These results are respectively 54.5% and 26.6% during the swallowing test. This preliminary study points out the lack of predictive value of the nasal test and the swallow test for the silent aspirations. However the results could be useful for other researchers developing other tests in this area.

Expansion tube test time predictions

NASA Technical Reports Server (NTRS)

Gourlay, Christopher M.

1988-01-01

The interaction of an interface between two gases and strong expansion is investigated and the effect on flow in an expansion tube is examined. Two mechanisms for the unsteady Pitot-pressure fluctuations found in the test section of an expansion tube are proposed. The first mechanism depends on the Rayleigh-Taylor instability of the driver-test gas interface in the presence of a strong expansion. The second mechanism depends on the reflection of the strong expansion from the interface. Predictions compare favorably with experimental results. The theory is expected to be independent of the absolute values of the initial expansion tube filling pressures.

Identification of novel plant peroxisomal targeting signals by a combination of machine learning methods and in vivo subcellular targeting analyses.

PubMed

Lingner, Thomas; Kataya, Amr R; Antonicelli, Gerardo E; Benichou, Aline; Nilssen, Kjersti; Chen, Xiong-Yan; Siemsen, Tanja; Morgenstern, Burkhard; Meinicke, Peter; Reumann, Sigrun

2011-04-01

In the postgenomic era, accurate prediction tools are essential for identification of the proteomes of cell organelles. Prediction methods have been developed for peroxisome-targeted proteins in animals and fungi but are missing specifically for plants. For development of a predictor for plant proteins carrying peroxisome targeting signals type 1 (PTS1), we assembled more than 2500 homologous plant sequences, mainly from EST databases. We applied a discriminative machine learning approach to derive two different prediction methods, both of which showed high prediction accuracy and recognized specific targeting-enhancing patterns in the regions upstream of the PTS1 tripeptides. Upon application of these methods to the Arabidopsis thaliana genome, 392 gene models were predicted to be peroxisome targeted. These predictions were extensively tested in vivo, resulting in a high experimental verification rate of Arabidopsis proteins previously not known to be peroxisomal. The prediction methods were able to correctly infer novel PTS1 tripeptides, which even included novel residues. Twenty-three newly predicted PTS1 tripeptides were experimentally confirmed, and a high variability of the plant PTS1 motif was discovered. These prediction methods will be instrumental in identifying low-abundance and stress-inducible peroxisomal proteins and defining the entire peroxisomal proteome of Arabidopsis and agronomically important crop plants.

Testing prediction methods: Earthquake clustering versus the Poisson model

USGS Publications Warehouse

Michael, A.J.

1997-01-01

Testing earthquake prediction methods requires statistical techniques that compare observed success to random chance. One technique is to produce simulated earthquake catalogs and measure the relative success of predicting real and simulated earthquakes. The accuracy of these tests depends on the validity of the statistical model used to simulate the earthquakes. This study tests the effect of clustering in the statistical earthquake model on the results. Three simulation models were used to produce significance levels for a VLF earthquake prediction method. As the degree of simulated clustering increases, the statistical significance drops. Hence, the use of a seismicity model with insufficient clustering can lead to overly optimistic results. A successful method must pass the statistical tests with a model that fully replicates the observed clustering. However, a method can be rejected based on tests with a model that contains insufficient clustering. U.S. copyright. Published in 1997 by the American Geophysical Union.

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.

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

Orion Pad Abort 1 Flight Test: Simulation Predictions Versus Flight Data

NASA Technical Reports Server (NTRS)

Stillwater, Ryan Allanque; Merritt, Deborah S.

2011-01-01

The presentation covers the pre-flight simulation predictions of the Orion Pad Abort 1. The pre-flight simulation predictions are compared to the Orion Pad Abort 1 flight test data. Finally the flight test data is compared to the updated simulation predictions, which show a ove rall improvement in the accuracy of the simulation predictions.

Prediction of target genes for miR-140-5p in pulmonary arterial hypertension using bioinformatics methods.

PubMed

Li, Fangwei; Shi, Wenhua; Wan, Yixin; Wang, Qingting; Feng, Wei; Yan, Xin; Wang, Jian; Chai, Limin; Zhang, Qianqian; Li, Manxiang

2017-12-01

The expression of microRNA (miR)-140-5p is known to be reduced in both pulmonary arterial hypertension (PAH) patients and monocrotaline-induced PAH models in rat. Identification of target genes for miR-140-5p with bioinformatics analysis may reveal new pathways and connections in PAH. This study aimed to explore downstream target genes and relevant signaling pathways regulated by miR-140-5p to provide theoretical evidences for further researches on role of miR-140-5p in PAH. Multiple downstream target genes and upstream transcription factors (TFs) of miR-140-5p were predicted in the analysis. Gene ontology (GO) enrichment analysis indicated that downstream target genes of miR-140-5p were enriched in many biological processes, such as biological regulation, signal transduction, response to chemical stimulus, stem cell proliferation, cell surface receptor signaling pathways. Kyoto Encyclopedia of Genes and Genome (KEGG) pathway analysis found that downstream target genes were mainly located in Notch, TGF-beta, PI3K/Akt, and Hippo signaling pathway. According to TF-miRNA-mRNA network, the important downstream target genes of miR-140-5p were PPI, TGF-betaR1, smad4, JAG1, ADAM10, FGF9, PDGFRA, VEGFA, LAMC1, TLR4, and CREB. After thoroughly reviewing published literature, we found that 23 target genes and seven signaling pathways were truly inhibited by miR-140-5p in various tissues or cells; most of these verified targets were in accordance with our present prediction. Other predicted targets still need further verification in vivo and in vitro .

Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.

PubMed

Alaimo, Salvatore; Giugno, Rosalba; Pulvirenti, Alfredo

2016-01-01

The usage of computational methods in drug discovery is a common practice. More recently, by exploiting the wealth of biological knowledge bases, a novel approach called drug repositioning has raised. Several computational methods are available, and these try to make a high-level integration of all the knowledge in order to discover unknown mechanisms. In this chapter, we review drug-target interaction prediction methods based on a recommendation system. We also give some extensions which go beyond the bipartite network case.

Development, Testing, and Validation of a Model-Based Tool to Predict Operator Responses in Unexpected Workload Transitions

NASA Technical Reports Server (NTRS)

Sebok, Angelia; Wickens, Christopher; Sargent, Robert

2015-01-01

One human factors challenge is predicting operator performance in novel situations. Approaches such as drawing on relevant previous experience, and developing computational models to predict operator performance in complex situations, offer potential methods to address this challenge. A few concerns with modeling operator performance are that models need to realistic, and they need to be tested empirically and validated. In addition, many existing human performance modeling tools are complex and require that an analyst gain significant experience to be able to develop models for meaningful data collection. This paper describes an effort to address these challenges by developing an easy to use model-based tool, using models that were developed from a review of existing human performance literature and targeted experimental studies, and performing an empirical validation of key model predictions.

Predictive Service Life Tests for Roofing Membranes

NASA Astrophysics Data System (ADS)

Bailey, David M.; Cash, Carl G.; Davies, Arthur G.

2002-09-01

The average service life of roofing membranes used in low-slope applications on U.S. Army buildings is estimated to be considerably shorter than the industry-presumed 20-year design life, even when installers carefully adhere to the latest guide specifications. This problem is due in large part to market-driven product development cycles, which do not include time for long-term field testing. To reduce delivery costs, contractors may provide untested, interior membranes in place of ones proven satisfactory in long-term service. Federal procurement regulations require that roofing systems and components be selected according to desired properties and generic type, not brand name. The problem is that a material certified to have satisfactory properties at installation time will not necessarily retain those properties in service. The overall objective of this research is to develop a testing program that can be executed in a matter of weeks to adequately predict a membrane's long-term performance in service. This report details accelerated aging tests of 12 popular membrane materials in the laboratory, and describes outdoor experiment stations set up for long-term exposure tests of those same membranes. The laboratory results will later be correlated with the outdoor test results to develop performance models and predictive service life tests.

Mock Target Window OTR and IR Design and Testing

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

Wass, Alexander Joseph

In order to fully verify temperature measurements made on the target window using infrared (IR) optical non-contact methods, actual comparative measurements are made with a real beam distribution as the heat source using Argonne National Laboratory’s (ANL) 35 MeV electron accelerator. Using Monte Carlo N-Particle (MCNP) simulations and thermal Finite Element Analysis (FEA), a cooled mock target window with thermocouple implants is designed to be used in such a test to achieve window temperatures up to 700°C. An uncoated and blackcoated mock window is designed to enhance the IR temperature measurements and verify optical transmitted radiation (OTR) imagery. This allowsmore » us to fully verify and characterize our temperature accuracy with our current IR camera method and any future method we may wish to explore using actual production conditions. This test also provides us with valuable conclusions/concerns regarding the calibration method we developed using our IR test stand at TA-53 in MPF-14.« less

The Development of MST Test Information for the Prediction of Test Performances

ERIC Educational Resources Information Center

Park, Ryoungsun; Kim, Jiseon; Chung, Hyewon; Dodd, Barbara G.

2017-01-01

The current study proposes novel methods to predict multistage testing (MST) performance without conducting simulations. This method, called MST test information, is based on analytic derivation of standard errors of ability estimates across theta levels. We compared standard errors derived analytically to the simulation results to demonstrate the…

Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs.

PubMed

Le, Duc-Hau; Verbeke, Lieven; Son, Le Hoang; Chu, Dinh-Toi; Pham, Van-Huy

2017-11-14

MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable

Prediction of R-curves from small coupon tests

NASA Technical Reports Server (NTRS)

Yeh, J. R.; Bray, G. H.; Bucci, R. J.; Macheret, Y.

1994-01-01

R-curves were predicted for Alclad 2024-T3 and C188-T3 sheet using the results of small-coupon Kahn tear tests in combination with two-dimensional elastic-plastic finite element stress analyses. The predictions were compared to experimental R-curves from 6.3, 16 and 60-inch wide M(T) specimens and good agreement was obtained. The method is an inexpensive alternative to wide panel testing for characterizing the fracture toughness of damage-tolerant sheet alloys. The usefulness of this approach was demonstrated by performing residual strength calculations for a two-bay crack in a representative fuselage structure. C188-T3 was predicted to have a 24 percent higher load carrying capability than 2024-T3 in this application as a result of its superior fracture toughness.

A method for predicting target drug efficiency in cancer based on the analysis of signaling pathway activation.

PubMed

Artemov, Artem; Aliper, Alexander; Korzinkin, Michael; Lezhnina, Ksenia; Jellen, Leslie; Zhukov, Nikolay; Roumiantsev, Sergey; Gaifullin, Nurshat; Zhavoronkov, Alex; Borisov, Nicolas; Buzdin, Anton

2015-10-06

A new generation of anticancer therapeutics called target drugs has quickly developed in the 21st century. These drugs are tailored to inhibit cancer cell growth, proliferation, and viability by specific interactions with one or a few target proteins. However, despite formally known molecular targets for every "target" drug, patient response to treatment remains largely individual and unpredictable. Choosing the most effective personalized treatment remains a major challenge in oncology and is still largely trial and error. Here we present a novel approach for predicting target drug efficacy based on the gene expression signature of the individual tumor sample(s). The enclosed bioinformatic algorithm detects activation of intracellular regulatory pathways in the tumor in comparison to the corresponding normal tissues. According to the nature of the molecular targets of a drug, it predicts whether the drug can prevent cancer growth and survival in each individual case by blocking the abnormally activated tumor-promoting pathways or by reinforcing internal tumor suppressor cascades. To validate the method, we compared the distribution of predicted drug efficacy scores for five drugs (Sorafenib, Bevacizumab, Cetuximab, Sorafenib, Imatinib, Sunitinib) and seven cancer types (Clear Cell Renal Cell Carcinoma, Colon cancer, Lung adenocarcinoma, non-Hodgkin Lymphoma, Thyroid cancer and Sarcoma) with the available clinical trials data for the respective cancer types and drugs. The percent of responders to a drug treatment correlated significantly (Pearson's correlation 0.77 p = 0.023) with the percent of tumors showing high drug scores calculated with the current algorithm.

School Tax Elections: Testing Messages and Targeting Voters

ERIC Educational Resources Information Center

Senden, J. Bradford; Lifto, Don E.

2010-01-01

Anticipating a substantially larger voter turnout in the upcoming election, district officials needed to probe--more precisely than in past tax elections--exactly what demographic groups would most likely go to the polls and support the tax proposal. Message testing and voter targeting became critical components in building a foundation for…

Predictive testing for Huntington's disease: relationship with partners after testing.

PubMed

Decruyenaere, M; Evers-Kiebooms, G; Cloostermans, T; Boogaerts, A; Demyttenaere, K; Dom, R; Fryns, J P

2004-01-01

This study focuses on the partner relationship of tested persons, 5 years after their predictive test result for Huntington's disease (HD). We describe changes in marital status, quality of the relationship, and perceived changes in the relationship. Twenty-six carriers, 14 of their partners, 33 non-carriers, and 17 of their partners participated in the study. Qualitative and quantitative methods were used. For the majority of tested persons (about 70%), the marital status was unchanged 5 years post test. Overall, carriers rated the quality of the relationship higher than their partners did and they perceived more positive changes. Qualitative data show that a test result leading to changed roles may induce significant marital distress. Another consequence of the test may be the changes in dynamics in asymptomatic carrier couples. A pre-test discussion of the possible impact of the test result on the relationship should result in a better preparation for and more understanding of the reactions after testing. Counselling after testing should stimulate an open communication between partners with consideration of needs and anxieties of both partners.

Effects of Targeted Test Preparation on Scores of Two Tests of Oral English as a Second Language

ERIC Educational Resources Information Center

Farnsworth, Tim

2013-01-01

This study investigated the effect of targeted test preparation, or coaching, on oral English as a second language test scores. The tests in question were the Basic English Skills Test Plus (BEST Plus), a scripted oral interview published by the Center for Applied Linguistics, and the Versant English Test (VET), a computer-administered and…

Predicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics

NASA Astrophysics Data System (ADS)

Greulich, Philip; Doležal, Jakub; Scott, Matthew; Evans, Martin R.; Allen, Rosalind J.

2017-12-01

Understanding how antibiotics inhibit bacteria can help to reduce antibiotic use and hence avoid antimicrobial resistance—yet few theoretical models exist for bacterial growth inhibition by a clinically relevant antibiotic treatment regimen. In particular, in the clinic, antibiotic treatment is time-dependent. Here, we use a theoretical model, previously applied to steady-state bacterial growth, to predict the dynamical response of a bacterial cell to a time-dependent dose of ribosome-targeting antibiotic. Our results depend strongly on whether the antibiotic shows reversible transport and/or low-affinity ribosome binding (‘low-affinity antibiotic’) or, in contrast, irreversible transport and/or high affinity ribosome binding (‘high-affinity antibiotic’). For low-affinity antibiotics, our model predicts that growth inhibition depends on the duration of the antibiotic pulse, and can show a transient period of very fast growth following removal of the antibiotic. For high-affinity antibiotics, growth inhibition depends on peak dosage rather than dose duration, and the model predicts a pronounced post-antibiotic effect, due to hysteresis, in which growth can be suppressed for long times after the antibiotic dose has ended. These predictions are experimentally testable and may be of clinical significance.

Predicting the dynamics of bacterial growth inhibition by ribosome-targeting antibiotics

PubMed Central

Greulich, Philip; Doležal, Jakub; Scott, Matthew; Evans, Martin R; Allen, Rosalind J

2017-01-01

Understanding how antibiotics inhibit bacteria can help to reduce antibiotic use and hence avoid antimicrobial resistance—yet few theoretical models exist for bacterial growth inhibition by a clinically relevant antibiotic treatment regimen. In particular, in the clinic, antibiotic treatment is time-dependent. Here, we use a theoretical model, previously applied to steady-state bacterial growth, to predict the dynamical response of a bacterial cell to a time-dependent dose of ribosome-targeting antibiotic. Our results depend strongly on whether the antibiotic shows reversible transport and/or low-affinity ribosome binding (‘low-affinity antibiotic’) or, in contrast, irreversible transport and/or high affinity ribosome binding (‘high-affinity antibiotic’). For low-affinity antibiotics, our model predicts that growth inhibition depends on the duration of the antibiotic pulse, and can show a transient period of very fast growth following removal of the antibiotic. For high-affinity antibiotics, growth inhibition depends on peak dosage rather than dose duration, and the model predicts a pronounced post-antibiotic effect, due to hysteresis, in which growth can be suppressed for long times after the antibiotic dose has ended. These predictions are experimentally testable and may be of clinical significance. PMID:28714461

Optimal test selection for prediction uncertainty reduction

DOE PAGES

Mullins, Joshua; Mahadevan, Sankaran; Urbina, Angel

2016-12-02

Economic factors and experimental limitations often lead to sparse and/or imprecise data used for the calibration and validation of computational models. This paper addresses resource allocation for calibration and validation experiments, in order to maximize their effectiveness within given resource constraints. When observation data are used for model calibration, the quality of the inferred parameter descriptions is directly affected by the quality and quantity of the data. This paper characterizes parameter uncertainty within a probabilistic framework, which enables the uncertainty to be systematically reduced with additional data. The validation assessment is also uncertain in the presence of sparse and imprecisemore » data; therefore, this paper proposes an approach for quantifying the resulting validation uncertainty. Since calibration and validation uncertainty affect the prediction of interest, the proposed framework explores the decision of cost versus importance of data in terms of the impact on the prediction uncertainty. Often, calibration and validation tests may be performed for different input scenarios, and this paper shows how the calibration and validation results from different conditions may be integrated into the prediction. Then, a constrained discrete optimization formulation that selects the number of tests of each type (calibration or validation at given input conditions) is proposed. Furthermore, the proposed test selection methodology is demonstrated on a microelectromechanical system (MEMS) example.« less

Cavitation damage prediction for spallation target vessels by assessment of acoustic vibration

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

Futakawa, Masatoshi; Kogawa, Hiroyuki; Hasegawa, Shoichi

2008-01-01

Liquid-mercury target systems for MW-class spallation neutron sources are being developed around the world. Proton beams are used to induce the spallation reaction. At the moment the proton beam hits the target, pressure waves are generated in the mercury because of the abrupt heat deposition. The pressure waves interact with the target vessel leading to negative pressure that may cause cavitation along the vessel wall. In order to estimate the cavitation erosion, i.e. the pitting damage formed by the collapse of cavitation bubbles, off-beam tests were performed by using an electric magnetic impact testing machine (MIMTM), which can impose equivalentmore » pressure pulses in mercury. The damage potential was defined based on the relationship between the pitting damage and the time-integrated acoustic vibration induced by impact due to the bubble collapses. Additionally, the damage potential was measured in on-beam tests carried out by using the proton beam at WNR (Weapons Neutron Research) facility in Los Alamos Neutron Science Center (LANSCE). In this paper, the concept of the damage potential, the relationship between the pitting damage formation and the damage potential both in off-beam and on-beam tests is shown.« less

Using predictive uncertainty analysis to optimise tracer test design and data acquisition

NASA Astrophysics Data System (ADS)

Wallis, Ilka; Moore, Catherine; Post, Vincent; Wolf, Leif; Martens, Evelien; Prommer, Henning

2014-07-01

Tracer injection tests are regularly-used tools to identify and characterise flow and transport mechanisms in aquifers. Examples of practical applications are manifold and include, among others, managed aquifer recharge schemes, aquifer thermal energy storage systems and, increasingly important, the disposal of produced water from oil and shale gas wells. The hydrogeological and geochemical data collected during the injection tests are often employed to assess the potential impacts of injection on receptors such as drinking water wells and regularly serve as a basis for the development of conceptual and numerical models that underpin the prediction of potential impacts. As all field tracer injection tests impose substantial logistical and financial efforts, it is crucial to develop a solid a-priori understanding of the value of the various monitoring data to select monitoring strategies which provide the greatest return on investment. In this study, we demonstrate the ability of linear predictive uncertainty analysis (i.e. “data worth analysis”) to quantify the usefulness of different tracer types (bromide, temperature, methane and chloride as examples) and head measurements in the context of a field-scale aquifer injection trial of coal seam gas (CSG) co-produced water. Data worth was evaluated in terms of tracer type, in terms of tracer test design (e.g., injection rate, duration of test and the applied measurement frequency) and monitoring disposition to increase the reliability of injection impact assessments. This was followed by an uncertainty targeted Pareto analysis, which allowed the interdependencies of cost and predictive reliability for alternative monitoring campaigns to be compared directly. For the evaluated injection test, the data worth analysis assessed bromide as superior to head data and all other tracers during early sampling times. However, with time, chloride became a more suitable tracer to constrain simulations of physical transport

RFDT: A Rotation Forest-based Predictor for Predicting Drug-Target Interactions Using Drug Structure and Protein Sequence Information.

PubMed

Wang, Lei; You, Zhu-Hong; Chen, Xing; Yan, Xin; Liu, Gang; Zhang, Wei

2018-01-01

Identification of interaction between drugs and target proteins plays an important role in discovering new drug candidates. However, through the experimental method to identify the drug-target interactions remain to be extremely time-consuming, expensive and challenging even nowadays. Therefore, it is urgent to develop new computational methods to predict potential drugtarget interactions (DTI). In this article, a novel computational model is developed for predicting potential drug-target interactions under the theory that each drug-target interaction pair can be represented by the structural properties from drugs and evolutionary information derived from proteins. Specifically, the protein sequences are encoded as Position-Specific Scoring Matrix (PSSM) descriptor which contains information of biological evolutionary and the drug molecules are encoded as fingerprint feature vector which represents the existence of certain functional groups or fragments. Four benchmark datasets involving enzymes, ion channels, GPCRs and nuclear receptors, are independently used for establishing predictive models with Rotation Forest (RF) model. The proposed method achieved the prediction accuracy of 91.3%, 89.1%, 84.1% and 71.1% for four datasets respectively. In order to make our method more persuasive, we compared our classifier with the state-of-theart Support Vector Machine (SVM) classifier. We also compared the proposed method with other excellent methods. Experimental results demonstrate that the proposed method is effective in the prediction of DTI, and can provide assistance for new drug research and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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

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.

Ensemble-sensitivity Analysis Based Observation Targeting for Mesoscale Convection Forecasts and Factors Influencing Observation-Impact Prediction

NASA Astrophysics Data System (ADS)

Hill, A.; Weiss, C.; Ancell, B. C.

2017-12-01

The basic premise of observation targeting is that additional observations, when gathered and assimilated with a numerical weather prediction (NWP) model, will produce a more accurate forecast related to a specific phenomenon. Ensemble-sensitivity analysis (ESA; Ancell and Hakim 2007; Torn and Hakim 2008) is a tool capable of accurately estimating the proper location of targeted observations in areas that have initial model uncertainty and large error growth, as well as predicting the reduction of forecast variance due to the assimilated observation. ESA relates an ensemble of NWP model forecasts, specifically an ensemble of scalar forecast metrics, linearly to earlier model states. A thorough investigation is presented to determine how different factors of the forecast process are impacting our ability to successfully target new observations for mesoscale convection forecasts. Our primary goals for this work are to determine: (1) If targeted observations hold more positive impact over non-targeted (i.e. randomly chosen) observations; (2) If there are lead-time constraints to targeting for convection; (3) How inflation, localization, and the assimilation filter influence impact prediction and realized results; (4) If there exist differences between targeted observations at the surface versus aloft; and (5) how physics errors and nonlinearity may augment observation impacts.Ten cases of dryline-initiated convection between 2011 to 2013 are simulated within a simplified OSSE framework and presented here. Ensemble simulations are produced from a cycling system that utilizes the Weather Research and Forecasting (WRF) model v3.8.1 within the Data Assimilation Research Testbed (DART). A "truth" (nature) simulation is produced by supplying a 3-km WRF run with GFS analyses and integrating the model forward 90 hours, from the beginning of ensemble initialization through the end of the forecast. Target locations for surface and radiosonde observations are computed 6, 12, and

Metacognition of the testing effect: Guiding learners to predict the benefits of retrieval

PubMed Central

Tullis, Jonathan G.; Finley, Jason R.; Benjamin, Aaron S.

2012-01-01

If the mnemonic benefits of testing are to be widely realized in real-world learning circumstances, people must appreciate the value of testing and choose to utilize testing during self-guided learning. Yet metacognitive judgments do not appear to reflect the enhancement provided by testing (Karpicke & Roediger, 2008). In this paper, we show that under judicious conditions learners can indeed reveal an understanding of the beneficial effects of testing as well as the interaction of that effect with delay (Experiment 1). In that experiment, subjects made judgments of learning (JOLs) for previously studied or previously tested items in either a cue-only or cue-target context, and either immediately or after a one-day delay. When subjects made judgments in a cue-only context, their JOLs accurately reflected the effects of testing, both immediately and at a delay. To evaluate the potential of exposure to such conditions for promoting generalized appreciation of testing effects, three further experiments elicited global predictions about re-studied and tested items across two study/test cycles (Experiments 2, 3, and 4). The results indicated that learners’ global naïve metacognitive beliefs increasingly reflect the beneficial effects of testing when learners experience these benefits with increasing external support. If queried under facilitative circumstances, learners appreciate the mnemonic enhancement that testing provides on both an item-by-item and global basis, but generalize that knowledge to future learning only with considerable guidance. PMID:23242770

Petri net-based prediction of therapeutic targets that recover abnormally phosphorylated proteins in muscle atrophy.

PubMed

Jung, Jinmyung; Kwon, Mijin; Bae, Sunghwa; Yim, Soorin; Lee, Doheon

2018-03-05

Muscle atrophy, an involuntary loss of muscle mass, is involved in various diseases and sometimes leads to mortality. However, therapeutics for muscle atrophy thus far have had limited effects. Here, we present a new approach for therapeutic target prediction using Petri net simulation of the status of phosphorylation, with a reasonable assumption that the recovery of abnormally phosphorylated proteins can be a treatment for muscle atrophy. The Petri net model was employed to simulate phosphorylation status in three states, i.e. reference, atrophic and each gene-inhibited state based on the myocyte-specific phosphorylation network. Here, we newly devised a phosphorylation specific Petri net that involves two types of transitions (phosphorylation or de-phosphorylation) and two types of places (activation with or without phosphorylation). Before predicting therapeutic targets, the simulation results in reference and atrophic states were validated by Western blotting experiments detecting five marker proteins, i.e. RELA, SMAD2, SMAD3, FOXO1 and FOXO3. Finally, we determined 37 potential therapeutic targets whose inhibition recovers the phosphorylation status from an atrophic state as indicated by the five validated marker proteins. In the evaluation, we confirmed that the 37 potential targets were enriched for muscle atrophy-related terms such as actin and muscle contraction processes, and they were also significantly overlapping with the genes associated with muscle atrophy reported in the Comparative Toxicogenomics Database (p-value < 0.05). Furthermore, we noticed that they included several proteins that could not be characterized by the shortest path analysis. The three potential targets, i.e. BMPR1B, ROCK, and LEPR, were manually validated with the literature. In this study, we suggest a new approach to predict potential therapeutic targets of muscle atrophy with an analysis of phosphorylation status simulated by Petri net. We generated a list of the potential

Which Neuropsychological Tests Predict Progression to Alzheimer’s Disease in Hispanics?

PubMed Central

Weissberger, Gali H.; Salmon, David P.; Bondi, Mark W.; Gollan, Tamar H.

2013-01-01

Objective To investigate which neuropsychological tests predict eventual progression to Alzheimer’s disease (AD) in both Hispanic and non-Hispanic individuals. Although our approach was exploratory, we predicted that tests that underestimate cognitive ability in healthy aging Hispanics might not be sensitive to future cognitive decline in this cultural group. Method We compared first-year data of 22 older adults (11 Hispanic) who were diagnosed as cognitively normal but eventually developed AD (decliners), to 60 age- and education-matched controls (27 Hispanic) who remained cognitively normal. To identify tests that may be culturally biased in our sample, we compared Hispanic with non-Hispanic controls on all tests and asked which tests were sensitive to future decline in each cultural group. Results Compared to age-, education-, and gender-matched non-Hispanic controls, Hispanic controls obtained lower scores on tests of language, executive function, and some measures of global cognition. Consistent with our predictions, some tests identified non-Hispanic, but not Hispanic, decliners (vocabulary, semantic fluency). Contrary to our predictions, a number of tests on which Hispanics obtained lower scores than non-Hispanics nevertheless predicted eventual progression to AD in both cultural groups (e.g., Boston Naming Test [BNT], Trails A and B). Conclusions Cross-cultural variation in test sensitivity to decline may reflect greater resistance of medium difficulty items to decline and bilingual advantages that initially protect Hispanics against some aspects of cognitive decline commonly observed in non-Hispanics with preclinical AD. These findings highlight a need for further consideration of cross-cultural differences in neuropsychological test performance and development of culturally unbiased measures. PMID:23688216

Videogrammetry Using Projected Circular Targets: Proof-of-Concept Test

NASA Technical Reports Server (NTRS)

Pappa, Richard S.; Black, Jonathan T.

2003-01-01

Videogrammetry is the science of calculating 3D object coordinates as a function of time from image sequences. It expands the method of photogrammetry to multiple time steps enabling the object to be characterized dynamically. Photogrammetry achieves the greatest accuracy with high contrast, solid-colored, circular targets. The high contrast is most often effected using retro-reflective targets attached to the measurement article. Knowledge of the location of each target allows those points to be tracked in a sequence of images, thus yielding dynamic characterization of the overall object. For ultra-lightweight and inflatable gossamer structures (e.g. solar sails, inflatable antennae, sun shields, etc.) where it may be desirable to avoid physically attaching retro-targets, a high-density grid of projected circular targets - called dot projection - is a viable alternative. Over time the object changes shape or position independently of the dots. Dynamic behavior, such as deployment or vibration, can be characterized by tracking the overall 3D shape of the object instead of tracking specific object points. To develop this method, an oscillating rigid object was measured using both retroreflective targets and dot projection. This paper details these tests, compares the results, and discusses the overall accuracy of dot projection videogrammetry.

Videogrammetry Using Projected Circular Targets: Proof-of-Concept Test

NASA Technical Reports Server (NTRS)

Black, Jonathan T.; Pappa, Richard S.

2003-01-01

Videogrammetry is the science of calculating 3D object coordinates as a function of time from image sequences. It expands the method of photogrammetry to multiple time steps enabling the object to be characterized dynamically. Photogrammetry achieves the greatest accuracy with high contrast, solid-colored circular targets. The high contrast is most often effected using retro-reflective targets attached to the measurement article. Knowledge of the location of each target allows those points to be tracked in a sequence of images, thus yielding dynamic characterization of the overall object. For ultra-lightweight and inflatable gossamer structures (e.g. solar sails, inflatable antennae, sun shields, etc.) where it may be desirable to avoid physically attaching retro-targets, a high-density grid of projected circular targets - called dot projection - is a viable alternative. Over time the object changes shape or position independently of the dots. Dynamic behavior, such as deployment or vibration, can be characterized by tracking the overall 3D shape of the object instead of tracking specific object points. To develop this method, an oscillating rigid object was measured using both retro- reflective targets and dot projection. This paper details these tests, compares the results, and discusses the overall accuracy of dot projection videogrammetry.

Prediction of preterm birth in twin gestations using biophysical and biochemical tests

PubMed Central

Conde-Agudelo, Agustin; Romero, Roberto

2018-01-01

The objective of this study was to determine the performance of biophysical and biochemical tests for the prediction of preterm birth in both asymptomatic and symptomatic women with twin gestations. We identified a total of 19 tests proposed to predict preterm birth, mainly in asymptomatic women. In these women, a single measurement of cervical length with transvaginal ultrasound before 25 weeks of gestation appears to be a good test to predict preterm birth. Its clinical potential is enhanced by the evidence that vaginal progesterone administration in asymptomatic women with twin gestations and a short cervix reduces neonatal morbidity and mortality associated with spontaneous preterm delivery. Other tests proposed for the early identification of asymptomatic women at increased risk of preterm birth showed minimal to moderate predictive accuracy. None of the tests evaluated in this review meet the criteria to be considered clinically useful to predict preterm birth among patients with an episode of preterm labor. However, a negative cervicovaginal fetal fibronectin test could be useful in identifying women who are not at risk for delivering within the next week, which could avoid unnecessary hospitalization and treatment. This review underscores the need to develop accurate tests for predicting preterm birth in twin gestations. Moreover, the use of interventions in these patients based on test results should be associated with the improvement of perinatal outcomes. PMID:25072736

Prediction of preterm birth in twin gestations using biophysical and biochemical tests.

PubMed

Conde-Agudelo, Agustin; Romero, Roberto

2014-12-01

The objective of this study was to determine the performance of biophysical and biochemical tests for the prediction of preterm birth in both asymptomatic and symptomatic women with twin gestations. We identified a total of 19 tests proposed to predict preterm birth, mainly in asymptomatic women. In these women, a single measurement of cervical length with transvaginal ultrasound before 25 weeks of gestation appears to be a good test to predict preterm birth. Its clinical potential is enhanced by the evidence that vaginal progesterone administration in asymptomatic women with twin gestations and a short cervix reduces neonatal morbidity and mortality associated with spontaneous preterm delivery. Other tests proposed for the early identification of asymptomatic women at increased risk of preterm birth showed minimal to moderate predictive accuracy. None of the tests evaluated in this review meet the criteria to be considered clinically useful to predict preterm birth among patients with an episode of preterm labor. However, a negative cervicovaginal fetal fibronectin test could be useful in identifying women who are not at risk for delivering within the next week, which could avoid unnecessary hospitalization and treatment. This review underscores the need to develop accurate tests for predicting preterm birth in twin gestations. Moreover, the use of interventions in these patients based on test results should be associated with the improvement of perinatal outcomes. Copyright © 2014. Published by Elsevier Inc.

Prediction of Host-Derived miRNAs with the Potential to Target PVY in Potato Plants

PubMed Central

Iqbal, Muhammad S.; Hafeez, Muhammad N.; Wattoo, Javed I.; Ali, Arfan; Sharif, Muhammad N.; Rashid, Bushra; Tabassum, Bushra; Nasir, Idrees A.

2016-01-01

Potato virus Y has emerged as a threatening problem in all potato growing areas around the globe. PVY reduces the yield and quality of potato cultivars. During the last 30 years, significant genetic changes in PVY strains have been observed with an increased incidence associated with crop damage. In the current study, computational approaches were applied to predict Potato derived miRNA targets in the PVY genome. The PVY genome is approximately 9 thousand nucleotides, which transcribes the following 6 genes:CI, NIa, NIb-Pro, HC-Pro, CP, and VPg. A total of 343 mature miRNAs were retrieved from the miRBase database and were examined for their target sequences in PVY genes using the minimum free energy (mfe), minimum folding energy, sequence complementarity and mRNA-miRNA hybridization approaches. The identified potato miRNAs against viral mRNA targets have antiviral activities, leading to translational inhibition by mRNA cleavage and/or mRNA blockage. We found 86 miRNAs targeting the PVY genome at 151 different sites. Moreover, only 36 miRNAs potentially targeted the PVY genome at 101 loci. The CI gene of the PVY genome was targeted by 32 miRNAs followed by the complementarity of 26, 19, 18, 16, and 13 miRNAs. Most importantly, we found 5 miRNAs (miR160a-5p, miR7997b, miR166c-3p, miR399h, and miR5303d) that could target the CI, NIa, NIb-Pro, HC-Pro, CP, and VPg genes of PVY. The predicted miRNAs can be used for the development of PVY-resistant potato crops in the future. PMID:27683585

Using existing data to predict and quantify the risks of GM forage to a population of a non-target invertebrate species: a New Zealand case study.

PubMed

O'Callaghan, Maureen; Soboleva, Tanya K; Barratt, Barbara I P

2010-01-01

Determining the effects of genetically modified (GM) crops on non-target organisms is essential as many non-target species provide important ecological functions. However, it is simply not possible to collect field data on more than a few potential non-target species present in the receiving environment of a GM crop. While risk assessment must be rigorous, new approaches are necessary to improve the efficiency of the process. Utilisation of published information and existing data on the phenology and population dynamics of test species in the field can be combined with limited amounts of experimental biosafety data to predict possible outcomes on species persistence. This paper presents an example of an approach where data from laboratory experiments and field studies on phenology are combined using predictive modelling. Using the New Zealand native weevil species Nicaeana cervina as a case study, we could predict that oviposition rates of the weevil feeding on a GM ryegrass could be reduced by up to 30% without threat to populations of the weevil in pastoral ecosystems. In addition, an experimentally established correlation between feeding level and oviposition led to the prediction that a consistent reduction in feeding of 50% or higher indicated a significant risk to the species and could potentially lead to local extinctions. This approach to biosafety risk assessment, maximising the use of pre-existing field and laboratory data on non-target species, can make an important contribution to informed decision-making by regulatory authorities and developers of new technologies. © ISBR, EDP Sciences, 2011.

Fatigue life prediction of liquid rocket engine combustor with subscale test verification

NASA Astrophysics Data System (ADS)

Sung, In-Kyung

Reusable rocket systems such as the Space Shuttle introduced a new era in propulsion system design for economic feasibility. Practical reusable systems require an order of magnitude increase in life. To achieve this improved methods are needed to assess failure mechanisms and to predict life cycles of rocket combustor. A general goal of the research was to demonstrate the use of subscale rocket combustor prototype in a cost-effective test program. Life limiting factors and metal behaviors under repeated loads were surveyed and reviewed. The life prediction theories are presented, with an emphasis on studies that used subscale test hardware for model validation. From this review, low cycle fatigue (LCF) and creep-fatigue interaction (ratcheting) were identified as the main life limiting factors of the combustor. Several life prediction methods such as conventional and advanced viscoplastic models were used to predict life cycle due to low cycle thermal stress, transient effects, and creep rupture damage. Creep-fatigue interaction and cyclic hardening were also investigated. A prediction method based on 2D beam theory was modified using 3D plate deformation theory to provide an extended prediction method. For experimental validation two small scale annular plug nozzle thrusters were designed, built and tested. The test article was composed of a water-cooled liner, plug annular nozzle and 200 psia precombustor that used decomposed hydrogen peroxide as the oxidizer and JP-8 as the fuel. The first combustor was tested cyclically at the Advanced Propellants and Combustion Laboratory at Purdue University. Testing was stopped after 140 cycles due to an unpredicted failure mechanism due to an increasing hot spot in the location where failure was predicted. A second combustor was designed to avoid the previous failure, however, it was over pressurized and deformed beyond repair during cold-flow test. The test results are discussed and compared to the analytical and numerical

Predicting ground level impacts of solid rocket motor testing

NASA Technical Reports Server (NTRS)

Douglas, Willard L.; Eagan, Ellen E.; Kennedy, Carolyn D.; Mccaleb, Rebecca C.

1993-01-01

Beginning in August of 1988 and continuing until the present, NASA at Stennis Space Center, Mississippi has conducted environmental monitoring of selected static test firings of the solid rocket motor used on the Space Shuttle. The purpose of the study was to assess the modeling protocol adapted for use in predicting plume behavior for the Advanced Solid Rocket Motor that is to be tested in Mississippi beginning in the mid-1990's. Both motors use an aluminum/ammonium perchlorate fuel that produces HCl and Al2O3 particulates as the major combustion products of concern. A combination of COMBUS.sr and PRISE.sr subroutines and the INPUFF model are used to predict the centerline stabilization height, the maximum concentration of HCl and Al2O3 at ground level, and distance to maximum concentration. Ground studies were conducted to evaluate the ability of the model to make these predictions. The modeling protocol was found to be conservative in the prediction of plume stabilization height and in the concentrations of the two emission products predicted.

Blind test of physics-based prediction of protein structures.

PubMed

Shell, M Scott; Ozkan, S Banu; Voelz, Vincent; Wu, Guohong Albert; Dill, Ken A

2009-02-01

We report here a multiprotein blind test of a computer method to predict native protein structures based solely on an all-atom physics-based force field. We use the AMBER 96 potential function with an implicit (GB/SA) model of solvation, combined with replica-exchange molecular-dynamics simulations. Coarse conformational sampling is performed using the zipping and assembly method (ZAM), an approach that is designed to mimic the putative physical routes of protein folding. ZAM was applied to the folding of six proteins, from 76 to 112 monomers in length, in CASP7, a community-wide blind test of protein structure prediction. Because these predictions have about the same level of accuracy as typical bioinformatics methods, and do not utilize information from databases of known native structures, this work opens up the possibility of predicting the structures of membrane proteins, synthetic peptides, or other foldable polymers, for which there is little prior knowledge of native structures. This approach may also be useful for predicting physical protein folding routes, non-native conformations, and other physical properties from amino acid sequences.

Blind Test of Physics-Based Prediction of Protein Structures

PubMed Central

Shell, M. Scott; Ozkan, S. Banu; Voelz, Vincent; Wu, Guohong Albert; Dill, Ken A.

2009-01-01

We report here a multiprotein blind test of a computer method to predict native protein structures based solely on an all-atom physics-based force field. We use the AMBER 96 potential function with an implicit (GB/SA) model of solvation, combined with replica-exchange molecular-dynamics simulations. Coarse conformational sampling is performed using the zipping and assembly method (ZAM), an approach that is designed to mimic the putative physical routes of protein folding. ZAM was applied to the folding of six proteins, from 76 to 112 monomers in length, in CASP7, a community-wide blind test of protein structure prediction. Because these predictions have about the same level of accuracy as typical bioinformatics methods, and do not utilize information from databases of known native structures, this work opens up the possibility of predicting the structures of membrane proteins, synthetic peptides, or other foldable polymers, for which there is little prior knowledge of native structures. This approach may also be useful for predicting physical protein folding routes, non-native conformations, and other physical properties from amino acid sequences. PMID:19186130

Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data.

PubMed

Qeli, Ermir; Omasits, Ulrich; Goetze, Sandra; Stekhoven, Daniel J; Frey, Juerg E; Basler, Konrad; Wollscheid, Bernd; Brunner, Erich; Ahrens, Christian H

2014-08-28

The in silico prediction of the best-observable "proteotypic" peptides in mass spectrometry-based workflows is a challenging problem. Being able to accurately predict such peptides would enable the informed selection of proteotypic peptides for targeted quantification of previously observed and non-observed proteins for any organism, with a significant impact for clinical proteomics and systems biology studies. Current prediction algorithms rely on physicochemical parameters in combination with positive and negative training sets to identify those peptide properties that most profoundly affect their general detectability. Here we present PeptideRank, an approach that uses learning to rank algorithm for peptide detectability prediction from shotgun proteomics data, and that eliminates the need to select a negative dataset for the training step. A large number of different peptide properties are used to train ranking models in order to predict a ranking of the best-observable peptides within a protein. Empirical evaluation with rank accuracy metrics showed that PeptideRank complements existing prediction algorithms. Our results indicate that the best performance is achieved when it is trained on organism-specific shotgun proteomics data, and that PeptideRank is most accurate for short to medium-sized and abundant proteins, without any loss in prediction accuracy for the important class of membrane proteins. Targeted proteomics approaches have been gaining a lot of momentum and hold immense potential for systems biology studies and clinical proteomics. However, since only very few complete proteomes have been reported to date, for a considerable fraction of a proteome there is no experimental proteomics evidence that would allow to guide the selection of the best-suited proteotypic peptides (PTPs), i.e. peptides that are specific to a given proteoform and that are repeatedly observed in a mass spectrometer. We describe a novel, rank-based approach for the prediction

Attitudes to predictive DNA testing in familial adenomatous polyposis.

PubMed Central

Whitelaw, S; Northover, J M; Hodgson, S V

1996-01-01

Attitudes to predictive DNA testing for familial adenomatous polyposis were documented in 62 affected adults. Patient views on prenatal testing and termination of pregnancy for this disorder were sought, as were opinions on the most suitable age to offer predictive testing for at risk children and the most appropriate age to begin screening. While 15 (24%) of those questioned stated that they would proceed to termination of pregnancy if a prenatal test indicated that the unborn baby was affected, in clinical practice no one has yet requested this option. Six (10%) people who had refrained from having children for fear of passing on the polyposis gene felt that the arrival of prenatal testing would enable them to consider planning a family. The majority of patients (93%) said they would like their children tested by DNA analysis at birth or in infancy, but felt that 10 to 12 years was the most appropriate time to discuss the diagnosis with the child. PMID:8818937

Mathematical modeling of antibody drug conjugates with the target and tubulin dynamics to predict AUC.

PubMed

Byun, Jong Hyuk; Jung, Il Hyo

2018-04-14

Antibody drug conjugates (ADCs)are one of the most recently developed chemotherapeutics to treat some types of tumor cells. They consist of monoclonal antibodies (mAbs), linkers, and potent cytotoxic drugs. Unlike common chemotherapies, ADCs combine selectively with a target at the surface of the tumor cell, and a potent cytotoxic drug (payload) effectively prevents microtubule polymerization. In this work, we construct an ADC model that considers both the target of antibodies and the receptor (tubulin) of the cytotoxic payloads. The model is simulated with brentuximab vedotin, one of ADCs, and used to investigate the pharmacokinetic (PK) characteristics of ADCs in vivo. It also predicts area under the curve (AUC) of ADCs and the payloads by identifying the half-life. The results show that dynamical behaviors fairly coincide with the observed data and half-life and capture AUC. Thus, the model can be used for estimating some parameters, fitting experimental observations, predicting AUC, and exploring various dynamical behaviors of the target and the receptor. Copyright © 2018 Elsevier Ltd. All rights reserved.

Revolutionizing Toxicity Testing For Predicting Developmental Outcomes (DNT4)

EPA Science Inventory

Characterizing risk from environmental chemical exposure currently requires extensive animal testing; however, alternative approaches are being researched to increase throughput of chemicals screened, decrease reliance on animal testing, and improve accuracy in predicting adverse...

Prediction of biodegradability from chemical structure: Modeling or ready biodegradation test data

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

Loonen, H.; Lindgren, F.; Hansen, B.

1999-08-01

Biodegradation data were collected and evaluated for 894 substances with widely varying chemical structures. All data were determined according to the Japanese Ministry of International Trade and Industry (MITI) I test protocol. The MITI I test is a screening test for ready biodegradability and has been described by Organization for Economic Cooperation and Development (OECD) test guideline 301 C and European Union (EU) test guideline C4F. The chemicals were characterized by a set of 127 predefined structural fragments. This data set was used to develop a model for the prediction of the biodegradability of chemicals under standardized OECD and EUmore » ready biodegradation test conditions. Partial least squares (PLS) discriminant analysis was used for the model development. The model was evaluated by means of internal cross-validation and repeated external validation. The importance of various structural fragments and fragment interactions was investigated. The most important fragments include the presence of a long alkyl chain; hydroxy, ester, and acid groups (enhancing biodegradation); and the presence of one or more aromatic rings and halogen substituents (regarding biodegradation). More than 85% of the model predictions were correct for using the complete data set. The not readily biodegradable predictions were slightly better than the readily biodegradable predictions (86 vs 84%). The average percentage of correct predictions from four external validation studies was 83%. Model optimization by including fragment interactions improve the model predicting capabilities to 89%. It can be concluded that the PLS model provides predictions of high reliability for a diverse range of chemical structures. The predictions conform to the concept of readily biodegradable (or not readily biodegradable) as defined by OECD and EU test guidelines.« less

The prospect of predictive testing for personal risk: attitudes and decision making.

PubMed

Wroe, A L; Salkovskis, P M; Rimes, K A

1998-06-01

As predictive tests for medical problems such as genetic disorders become more widely available, it becomes increasingly important to understand the processes involved in the decision whether or not to seek testing. This study investigates the decision to pursue the possibility of testing. Individuals (one group who had already contemplated the possibility of predictive testing and one group who had not) were asked to consider predictive testing for several diseases. They rated the likelihood of opting for testing and specified the reasons which they believed had affected their decision. The ratio of the numbers of reasons stated for testing and the numbers of reasons stated against testing was a good predictor of the stated likelihood of testing, particularly when the reasons were weighted by utility (importance). Those who had previously contemplated testing specified more emotional reasons. It is proposed that the decision process is internally logical although it may seem illogical to others due to there being idiosyncratic premises (or reasons) upon which the decision is based. It is concluded that the Utility Theory is a useful basis for describing how people make decisions related to predictive testing; modifications of the theory are proposed.

Plant microRNA-Target Interaction Identification Model Based on the Integration of Prediction Tools and Support Vector Machine

PubMed Central

Meng, Jun; Shi, Lin; Luan, Yushi

2014-01-01

Background Confident identification of microRNA-target interactions is significant for studying the function of microRNA (miRNA). Although some computational miRNA target prediction methods have been proposed for plants, results of various methods tend to be inconsistent and usually lead to more false positive. To address these issues, we developed an integrated model for identifying plant miRNA–target interactions. Results Three online miRNA target prediction toolkits and machine learning algorithms were integrated to identify and analyze Arabidopsis thaliana miRNA-target interactions. Principle component analysis (PCA) feature extraction and self-training technology were introduced to improve the performance. Results showed that the proposed model outperformed the previously existing methods. The results were validated by using degradome sequencing supported Arabidopsis thaliana miRNA-target interactions. The proposed model constructed on Arabidopsis thaliana was run over Oryza sativa and Vitis vinifera to demonstrate that our model is effective for other plant species. Conclusions The integrated model of online predictors and local PCA-SVM classifier gained credible and high quality miRNA-target interactions. The supervised learning algorithm of PCA-SVM classifier was employed in plant miRNA target identification for the first time. Its performance can be substantially improved if more experimentally proved training samples are provided. PMID:25051153

DrugECs: An Ensemble System with Feature Subspaces for Accurate Drug-Target Interaction Prediction

PubMed Central

Jiang, Jinjian; Wang, Nian; Zhang, Jun

2017-01-01

Background Drug-target interaction is key in drug discovery, especially in the design of new lead compound. However, the work to find a new lead compound for a specific target is complicated and hard, and it always leads to many mistakes. Therefore computational techniques are commonly adopted in drug design, which can save time and costs to a significant extent. Results To address the issue, a new prediction system is proposed in this work to identify drug-target interaction. First, drug-target pairs are encoded with a fragment technique and the software “PaDEL-Descriptor.” The fragment technique is for encoding target proteins, which divides each protein sequence into several fragments in order and encodes each fragment with several physiochemical properties of amino acids. The software “PaDEL-Descriptor” creates encoding vectors for drug molecules. Second, the dataset of drug-target pairs is resampled and several overlapped subsets are obtained, which are then input into kNN (k-Nearest Neighbor) classifier to build an ensemble system. Conclusion Experimental results on the drug-target dataset showed that our method performs better and runs faster than the state-of-the-art predictors. PMID:28744468

Comparative Analysis of Predicted Plastid-Targeted Proteomes of Sequenced Higher Plant Genomes

PubMed Central

Schaeffer, Scott; Harper, Artemus; Raja, Rajani; Jaiswal, Pankaj; Dhingra, Amit

2014-01-01

Plastids are actively involved in numerous plant processes critical to growth, development and adaptation. They play a primary role in photosynthesis, pigment and monoterpene synthesis, gravity sensing, starch and fatty acid synthesis, as well as oil, and protein storage. We applied two complementary methods to analyze the recently published apple genome (Malus × domestica) to identify putative plastid-targeted proteins, the first using TargetP and the second using a custom workflow utilizing a set of predictive programs. Apple shares roughly 40% of its 10,492 putative plastid-targeted proteins with that of the Arabidopsis (Arabidopsis thaliana) plastid-targeted proteome as identified by the Chloroplast 2010 project and ∼57% of its entire proteome with Arabidopsis. This suggests that the plastid-targeted proteomes between apple and Arabidopsis are different, and interestingly alludes to the presence of differential targeting of homologs between the two species. Co-expression analysis of 2,224 genes encoding putative plastid-targeted apple proteins suggests that they play a role in plant developmental and intermediary metabolism. Further, an inter-specific comparison of Arabidopsis, Prunus persica (Peach), Malus × domestica (Apple), Populus trichocarpa (Black cottonwood), Fragaria vesca (Woodland Strawberry), Solanum lycopersicum (Tomato) and Vitis vinifera (Grapevine) also identified a large number of novel species-specific plastid-targeted proteins. This analysis also revealed the presence of alternatively targeted homologs across species. Two separate analyses revealed that a small subset of proteins, one representing 289 protein clusters and the other 737 unique protein sequences, are conserved between seven plastid-targeted angiosperm proteomes. Majority of the novel proteins were annotated to play roles in stress response, transport, catabolic processes, and cellular component organization. Our results suggest that the current state of knowledge regarding

Cognitive versus Software-Assisted Registration: Development of a New Nomogram Predicting Prostate Cancer at MRI-Targeted Biopsies.

PubMed

Kaufmann, Sascha; Russo, Giorgio I; Thaiss, Wolfgang; Notohamiprodjo, Mike; Bamberg, Fabian; Bedke, Jens; Morgia, Giuseppe; Nikolaou, Konstantin; Stenzl, Arnulf; Kruck, Stephan

2018-04-03

Multiparametric magnetic resonance imaging (mpMRI) is gaining acceptance to guide targeted biopsy (TB) in prostate cancer (PC) diagnosis. We aimed to compare the detection rate of software-assisted fusion TB (SA-TB) versus cognitive fusion TB (COG-TB) for PC and to evaluate potential clinical features in detecting PC and clinically significant PC (csPC) at TB. This was a retrospective cohort study of patients with rising and/or persistently elevated prostate-specific antigen (PSA) undergoing mpMRI followed by either transperineal SA-TB or transrectal COG-TB. The analysis showed a matched-paired analysis between SA-TB versus COG-TB without differences in clinical or radiological characteristics. Differences among detection of PC/csPC among groups were analyzed. A multivariable logistic regression model predicting PC at TB was fitted. The model was evaluated using the receiver operating characteristic-derived area under the curve, goodness of fit test, and decision-curve analyses. One hundred ninety-one and 87 patients underwent SA-TB or COG-TB, respectively. The multivariate logistic analysis showed that SA-TB was associated with overall PC (odds ratio [OR], 5.70; P < .01) and PC at TB (OR, 3.00; P < .01) but not with overall csPC (P = .40) and csPC at TB (P = .40). A nomogram predicting PC at TB was constructed using the Prostate Imaging Reporting and Data System version 2.0, age, PSA density and biopsy technique, showing improved clinical risk prediction against a threshold probability of 10% with a c-index of 0.83. In patients with suspected PC, software-assisted biopsy detects most cancers and outperforms the cognitive approach in targeting magnetic resonance imaging-visible lesions. Furthermore, we introduced a prebiopsy nomogram for the probability of PC in TB. Copyright © 2018 Elsevier Inc. All rights reserved.

Slash prediction: a test in commercial thinnings in northeasrern California

Treesearch

C. Phillip Weatherspoon; Gary O. Fiddler

1984-01-01

Two slash prediction handbooks commonly used in California do not use data from California. To test predictions of the handbooks in northeastern California, logging residues from commercially thinned young-growth stands were surveyed. Measured residues were compared to handbook predictions. Species represented were ponderosa pine, California white fir, California red...

The predictive value of multi-targeted fluorescent in-situ hybridization in patients with history of bladder cancer.

PubMed

Gofrit, Ofer N; Zorn, Kevin C; Silvestre, Josephine; Shalhav, Arieh L; Zagaja, Gregory P; Msezane, Lambda P; Steinberg, Gary D

2008-01-01

UroVysion (Abbott Molecular Inc., Des Plaines, IL) is a multi-target fluorescent in-situ hybridization (FISH) assay that detects aneuploidy of chromosomes 3, 7, and 17, and loss of the 9p21 locus in exfoliated cells in urine. In this study, we evaluated if UroVysion can predict tumor recurrence in patients with negative cystoscopy and urinary cytology at the time of (FISH) assay. The study population included patients with history of non-muscle invasive bladder cancer treated by transurethral resection. Follow-up included cystoscopy, barbotage, urinary cytology, and UroVysion testing. Patients were followed for at least 6 months after their initial UroVysion testing. A total of 64 patients (37 males) were enrolled into the study. Mean patient age was 62 years (S.D. 13.2 years). Initial highest tumor stage was Ta in 42 patients (65.6%), T1 in 21 patients (33%), and isolated Tis in a single patient. Abnormal UroVysion results were observed in 40 patients (62.5%). After a median follow-up of 13.5 months, 21 patients (33%) developed tumor recurrence (Ta in 13 patients, T1 in 5, and Tis in 3). Recurrent tumors developed in 45% of the patients with abnormal UroVysion test compared with 12.5% of the patients with normal assay (P = 0.01). An abnormal UroVysion result preceded the diagnosis of tumor recurrence in 18/21 cases (86%), including all high-grade recurrences. This data suggest that UroVysion may be a useful tool for predicting tumor recurrence. Cystoscopy may be spared and surveillance intervals widened in patients with history of low grade tumors and a normal UroVysion test.

[MicroRNA Target Prediction Based on Support Vector Machine Ensemble Classification Algorithm of Under-sampling Technique].

PubMed

Chen, Zhiru; Hong, Wenxue

2016-02-01

Considering the low accuracy of prediction in the positive samples and poor overall classification effects caused by unbalanced sample data of MicroRNA (miRNA) target, we proposes a support vector machine (SVM)-integration of under-sampling and weight (IUSM) algorithm in this paper, an under-sampling based on the ensemble learning algorithm. The algorithm adopts SVM as learning algorithm and AdaBoost as integration framework, and embeds clustering-based under-sampling into the iterative process, aiming at reducing the degree of unbalanced distribution of positive and negative samples. Meanwhile, in the process of adaptive weight adjustment of the samples, the SVM-IUSM algorithm eliminates the abnormal ones in negative samples with robust sample weights smoothing mechanism so as to avoid over-learning. Finally, the prediction of miRNA target integrated classifier is achieved with the combination of multiple weak classifiers through the voting mechanism. The experiment revealed that the SVM-IUSW, compared with other algorithms on unbalanced dataset collection, could not only improve the accuracy of positive targets and the overall effect of classification, but also enhance the generalization ability of miRNA target classifier.

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

PubMed

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

2009-04-01

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

The accomplishments of lithium target and test facility validation activities in the IFMIF/EVEDA phase

NASA Astrophysics Data System (ADS)

Arbeiter, Frederik; Baluc, Nadine; Favuzza, Paolo; Gröschel, Friedrich; Heidinger, Roland; Ibarra, Angel; Knaster, Juan; Kanemura, Takuji; Kondo, Hiroo; Massaut, Vincent; Saverio Nitti, Francesco; Miccichè, Gioacchino; O'hira, Shigeru; Rapisarda, David; Sugimoto, Masayoshi; Wakai, Eiichi; Yokomine, Takehiko

2018-01-01

As part of the engineering validation and engineering design activities (EVEDA) phase for the international fusion materials irradiation facility IFMIF, major elements of a lithium target facility and the test facility were designed, prototyped and validated. For the lithium target facility, the EVEDA lithium test loop was built at JAEA and used to test the stability (waves and long term) of the lithium flow in the target, work out the startup procedures, and test lithium purification and analysis. It was confirmed by experiments in the Lifus 6 plant at ENEA that lithium corrosion on ferritic martensitic steels is acceptably low. Furthermore, complex remote handling procedures for the remote maintenance of the target in the test cell environment were successfully practiced. For the test facility, two variants of a high flux test module were prototyped and tested in helium loops, demonstrating their good capabilities of maintaining the material specimens at the desired temperature with a low temperature spread. Irradiation tests were performed for heated specimen capsules and irradiation instrumentation in the BR2 reactor at SCK-CEN. The small specimen test technique, essential for obtaining material test results with limited irradiation volume, was advanced by evaluating specimen shape and test technique influences.

Predictive values of thermal and electrical dental pulp tests: a clinical study.

PubMed

Villa-Chávez, Carlos E; Patiño-Marín, Nuria; Loyola-Rodríguez, Juan P; Zavala-Alonso, Norma V; Martínez-Castañón, Gabriel A; Medina-Solís, Carlo E

2013-08-01

For a diagnostic test to be useful, it is necessary to determine the probability that the test will provide the correct diagnosis. Therefore, it is necessary to calculate the predictive value of diagnostics. The aim of the present study was to identify the sensitivity, specificity, positive and negative predictive values, accuracy, and reproducibility of thermal and electrical tests of pulp sensitivity. The thermal tests studied were the 1, 1, 1, 2-tetrafluoroethane (cold) and hot gutta-percha (hot) tests. For the electrical test, the Analytic Technology Pulp Tester (Analytic Technology, Redmond, WA) was used. A total of 110 teeth were tested: 60 teeth with vital pulp and 50 teeth with necrotic pulps (disease prevalence of 45%). The ideal standard was established by direct pulp inspection. The sensitivities of the diagnostic tests were 0.88 for the cold test, 0.86 for the heat test, and 0.76 for the electrical test, and the specificity was 1.0 for all 3 tests. The negative predictive value was 0.90 for the cold test, 0.89 for the heat test, and 0.83 for the electrical test, and the positive predictive value was 1.0 for all 3 tests. The highest accuracy (0.94) and reproducibility (0.88) were observed for the cold test. The cold test was the most accurate method for diagnostic testing. Copyright © 2013 American Association of Endodontists. Published by Elsevier Inc. All rights reserved.

Validity of Integrity Tests for Predicting Drug and Alcohol Abuse

DTIC Science & Technology

1993-08-31

Wiinkler and Sheridan (1989) found that employees who entered employee assistance programs for treating drug addiction were more likely be absent...August 31, 1993 Final 4. TITLE AND SUBTITLE S. FUNDING NUMBERS Validity of Integrity Tests for Predicting Drug and Alcohol Abuse C No. N00014-92-J...words) This research used psychometric meta-analysis (Hunter & Schmidt, 1990b) to examine the validity of integrity tests for predicting drug and

Pretest Predictions for Phase II Ventilation Tests

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

Yiming Sun

The objective of this calculation is to predict the temperatures of the ventilating air, waste package surface, and concrete pipe walls that will be developed during the Phase II ventilation tests involving various test conditions. The results will be used as inputs to validating numerical approach for modeling continuous ventilation, and be used to support the repository subsurface design. The scope of the calculation is to identify the physical mechanisms and parameters related to thermal response in the Phase II ventilation tests, and describe numerical methods that are used to calculate the effects of continuous ventilation. The calculation is limitedmore » to thermal effect only. This engineering work activity is conducted in accordance with the ''Technical Work Plan for: Subsurface Performance Testing for License Application (LA) for Fiscal Year 2001'' (CRWMS M&O 2000d). This technical work plan (TWP) includes an AP-2.21Q, ''Quality Determinations and Planning for Scientific, Engineering, and Regulatory Compliance Activities'', activity evaluation (CRWMS M&O 2000d, Addendum A) that has determined this activity is subject to the YMP quality assurance (QA) program. The calculation is developed in accordance with the AP-3.12Q procedure, ''Calculations''. Additional background information regarding this activity is contained in the ''Development Plan for Ventilation Pretest Predictive Calculation'' (DP) (CRWMS M&O 2000a).« less

Predicting bending strength of fire-retardant-treated plywood from screw-withdrawal tests

Treesearch

J. E. Winandy; P. K. Lebow; W. Nelson

This report describes the development of a test method and predictive model to estimate the residual bending strength of fire-retardant-treated plywood roof sheathing from measurement of screw-withdrawal force. The preferred test methodology is described in detail. Models were developed to predict loss in mean and lower prediction bounds for plywood bending strength as...

Test prediction for the German PKL Test K5A using RELAP4/MOD6

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

Chen, Y.S.; Haigh, W.S.; Sullivan, L.H.

RELAP4/MOD6 is the most recent modification in the series of RELAP4 computer programs developed to describe the thermal-hydraulic conditions attendant to postulated transients in light water reactor systems. The major new features in RELAP4/MOD6 include best-estimate pressurized water reactor (PWR) reflood transient analytical models for core heat transfer, local entrainment, and core vapor superheat, and a new set of heat transfer correlations for PWR blowdown and reflood. These new features were used for a test prediction of the Kraftwerk Union three-loop PRIMAR KREISLAUF (PKL) Reflood Test K5A. The results of the prediction were in good agreement with the experimental thermalmore » and hydraulic system data. Comparisons include heater rod surface temperature, system pressure, mass flow rates, and core mixture level. It is concluded that RELAP4/MOD6 is capable of accurately predicting transient reflood phenomena in the 200% cold-leg break test configuration of the PKL reflood facility.« less

Combining on-chip synthesis of a focused combinatorial library with computational target prediction reveals imidazopyridine GPCR ligands.

PubMed

Reutlinger, Michael; Rodrigues, Tiago; Schneider, Petra; Schneider, Gisbert

2014-01-07

Using the example of the Ugi three-component reaction we report a fast and efficient microfluidic-assisted entry into the imidazopyridine scaffold, where building block prioritization was coupled to a new computational method for predicting ligand-target associations. We identified an innovative GPCR-modulating combinatorial chemotype featuring ligand-efficient adenosine A1/2B and adrenergic α1A/B receptor antagonists. Our results suggest the tight integration of microfluidics-assisted synthesis with computer-based target prediction as a viable approach to rapidly generate bioactivity-focused combinatorial compound libraries with high success rates. Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Routine blood tests to predict liver fibrosis in chronic hepatitis C.

PubMed

Hsieh, Yung-Yu; Tung, Shui-Yi; Lee, Kamfai; Wu, Cheng-Shyong; Wei, Kuo-Liang; Shen, Chien-Heng; Chang, Te-Sheng; Lin, Yi-Hsiung

2012-02-28

To verify the usefulness of FibroQ for predicting fibrosis in patients with chronic hepatitis C, compared with other noninvasive tests. This retrospective cohort study included 237 consecutive patients with chronic hepatitis C who had undergone percutaneous liver biopsy before treatment. FibroQ, aspartate aminotransferase (AST)/alanine aminotransferase ratio (AAR), AST to platelet ratio index, cirrhosis discriminant score, age-platelet index (API), Pohl score, FIB-4 index, and Lok's model were calculated and compared. FibroQ, FIB-4, AAR, API and Lok's model results increased significantly as fibrosis advanced (analysis of variance test: P < 0.001). FibroQ trended to be superior in predicting significant fibrosis score in chronic hepatitis C compared with other noninvasive tests. FibroQ is a simple and useful test for predicting significant fibrosis in patients with chronic hepatitis C.

Helicopter external noise prediction and correlation with flight test

NASA Technical Reports Server (NTRS)

Gupta, B. P.

1978-01-01

Mathematical analysis procedures for predicting the main and tail rotor rotational and broadband noise are presented. The aerodynamic and acoustical data from Operational Loads Survey (OLS) flight program are used for validating the analysis and noise prediction methodology. For the long method of rotational noise prediction, the spanwise, chordwise, and azimuthwise airloading is used. In the short method, the airloads are assumed to be concentrated at a single spanwise station and for higher harmonics an airloading harmonic exponent of 2.0 is assumed. For the same flight condition, the predictions from long and short methods of rotational noise prediction are compared with the flight test results. The short method correlates as well or better than the long method.

Predictive Accuracy of Exercise Stress Testing the Healthy Adult.

ERIC Educational Resources Information Center

Lamont, Linda S.

1981-01-01

Exercise stress testing provides information on the aerobic capacity, heart rate, and blood pressure responses to graded exercises of a healthy adult. The reliability of exercise tests as a diagnostic procedure is discussed in relation to sensitivity and specificity and predictive accuracy. (JN)

Do functional tests predict low back pain?

PubMed

Takala, E P; Viikari-Juntura, E

2000-08-15

A cohort of 307 nonsymptomatic workers and another cohort of 123 workers with previous episodes of low back pain were followed up for 2 years. The outcomes were measured by symptoms, medical consultations, and sick leaves due to low back disorders. To study the predictive value of a set of tests measuring the physical performance of the back in a working population. The hypothesis was that subjects with poor functional capacity are liable to back disorders. Reduced functional performance has been associated with back pain. There are few data to show whether reduced functional capacity is a cause or a consequence of pain. Mobility of the trunk in forward and side bending, maximal isokinetic trunk extension, flexion and lifting strength, and static endurance of back extension were measured. Standing balance and foot reaction time were recorded with a force plate. Clinical tests for the provocation of back or leg pain were performed. Gender, workload, age, and anthropometrics were managed as potential confounders in the analysis. Marked overlapping was seen in the measures of the subjects with different outcomes. Among the nonsymptomatic subjects, low performance in tests of mobility and standing balance was associated with future back disorders. Among workers with previous episodes of back pain, low isokinetic extension strength, poor standing balance, and positive clinical signs predicted future pain. Some associations were found between the functional tests and future low back pain. The wide variation in the results questions the value of the tests in health examinations (e.g., in screening or surveillance of low back disorders).

Development of HWIL Testing Capabilities for Satellite Target Emulation at AEDC

NASA Astrophysics Data System (ADS)

Lowry, H.; Crider, D.; Burns, J.; Thompson, R.; Goldsmith, G., II; Sholes, W.

Programs involved in Space Situational Awareness (SSA) need the capability to test satellite sensors in a Hardware-in-the-Loop (HWIL) environment. Testing in a ground system avoids the significant cost of on-orbit test targets and the resulting issues such as debris mitigation, and in-space testing implications. The space sensor test facilities at AEDC consist of cryo-vacuum chambers that have been developed to project simulated targets to air-borne, space-borne, and ballistic platforms. The 7V chamber performs calibration and characterization of surveillance and seeker systems, as well as some mission simulation. The 10V chamber is being upgraded to provide real-time target simulation during the detection, acquisition, discrimination, and terminal phases of a seeker mission. The objective of the Satellite Emulation project is to upgrade this existing capability to support the ability to discern and track other satellites and orbital debris in a HWIL capability. It would provide a baseline for realistic testing of satellite surveillance sensors, which would be operated in a controlled environment. Many sensor functions could be tested, including scene recognition and maneuvering control software, using real interceptor hardware and software. Statistically significant and repeatable datasets produced by the satellite emulation system can be acquired during such test and saved for further analysis. In addition, the robustness of the discrimination and tracking algorithms can be investigated by a parametric analysis using slightly different scenarios; this will be used to determine critical points where a sensor system might fail. The radiometric characteristics of satellites are expected to be similar to the targets and decoys that make up a typical interceptor mission scenario, since they are near ambient temperature. Their spectral reflectivity, emissivity, and shape must also be considered, but the projection systems employed in the 7V and 10V chambers should be

Measurements to predict the time of target replacement of a helical tomotherapy.

PubMed

Kampfer, Severin; Schell, Stefan; Duma, Marciana N; Wilkens, Jan J; Kneschaurek, Peter

2011-11-15

Intensity-modulated radiation therapy (IMRT) requires more beam-on time than normal open field treatment. Consequently, the machines wear out and need more spare parts. A helical tomotherapy treatment unit needs a periodical tungsten target replacement, which is a time consuming event. To be able to predict the next replacement would be quite valuable. We observed unexpected variations towards the end of the target lifetime in the performed pretreatment measurements for patient plan verification. Thus, we retrospectively analyze the measurements of our quality assurance program. The time dependence of the quotient of two simultaneous dose measurements at different depths within a phantom for a fixed open field irradiation is evaluated. We also assess the time-dependent changes of an IMRT plan measurement and of a relative depth dose curve measurement. Additionally, we performed a Monte Carlo simulation with Geant4 to understand the physical reasons for the measured values. Our measurements show that the dose at a specified depth compared to the dose in shallower regions of the phantom declines towards the end of the target lifetime. This reproducible effect can be due to the lowering of the mean energy of the X-ray spectrum. These results are supported by the measurements of the IMRT plan, as well as the study of the relative depth dose curve. Furthermore, the simulation is consistent with these findings since it provides a possible explanation for the reduction of the mean energy for thinner targets. It could be due to the lowering of low energy photon self-absorption in a worn out and therefore thinner target. We state a threshold value for our measurement at which a target replacement should be initiated. Measurements to observe a change in the energy are good predictors of the need for a target replacement. However, since all results support the softening of the spectrum hypothesis, all depth-dependent setups are viable for analyzing the deterioration of the

Current target acquisition methodology in force on force simulations

NASA Astrophysics Data System (ADS)

Hixson, Jonathan G.; Miller, Brian; Mazz, John P.

2017-05-01

The U.S. Army RDECOM CERDEC NVESD MSD's target acquisition models have been used for many years by the military community in force on force simulations for training, testing, and analysis. There have been significant improvements to these models over the past few years. The significant improvements are the transition of ACQUIRE TTP-TAS (ACQUIRE Targeting Task Performance Target Angular Size) methodology for all imaging sensors and the development of new discrimination criteria for urban environments and humans. This paper is intended to provide an overview of the current target acquisition modeling approach and provide data for the new discrimination tasks. This paper will discuss advances and changes to the models and methodologies used to: (1) design and compare sensors' performance, (2) predict expected target acquisition performance in the field, (3) predict target acquisition performance for combat simulations, and (4) how to conduct model data validation for combat simulations.

Neuropsychological Testing Predicts Cerebrospinal Fluid Aβ in Mild Cognitive Impairment (MCI)

PubMed Central

Kandel, Benjamin M.; Avants, Brian B.; Gee, James C.; Arnold, Steven E.; Wolk, David A.

2015-01-01

Background Psychometric tests predict conversion of Mild Cognitive Impairment (MCI) to probable Alzheimer's Disease (AD). Because the definition of clinical AD relies on those same psychometric tests, the ability of these tests to identify underlying AD pathology remains unclear. Objective To determine the degree to which psychometric testing predicts molecular evidence of AD amyloid pathology, as indicated by CSF Aβ1–42, in patients with MCI, as compared to neuroimaging biomarkers. Methods We identified 408 MCI subjects with CSF Aβ levels, psychometric test data, FDG-PET scans, and acceptable volumetric MR scans from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). We used psychometric tests and imaging biomarkers in univariate and multivariate models to predict Aβ status. Results The 30-minute delayed recall score of the Rey Auditory Verbal Learning Test (AVLT) was the best predictor of Aβ status among the psychometric tests, achieving an AUC of 0.67±0.02 and odds ratio of 2.5±0.4. FDG-PET was the best imaging-based biomarker (AUC 0.67±0.03, OR 3.2±1.2), followed by hippocampal volume (AUC 0.64±0.02,,OR 2.4±0.3). A multivariate analysis based on the psychometric tests improved on the univariate predictors, achieving an AUC of 0.68±0.03 (OR 3.38±1.2). Adding imaging biomarkers to the multivariate analysis did not improve the AUC. Conclusion Psychometric tests perform as well as imaging biomarkers to predict presence of molecular markers of AD pathology in MCI patients and should be considered in the determination of the likelihood that MCI is due to AD. PMID:25881908

Proteome-wide prediction of targets for aspirin: new insight into the molecular mechanism of aspirin

PubMed Central

Dai, Shao-Xing; Li, Wen-Xing

2016-01-01

Besides its anti-inflammatory, analgesic and anti-pyretic properties, aspirin is used for the prevention of cardiovascular disease and various types of cancer. The multiple activities of aspirin likely involve several molecular targets and pathways rather than a single target. Therefore, systematic identification of these targets of aspirin can help us understand the underlying mechanisms of the activities. In this study, we identified 23 putative targets of aspirin in the human proteome by using binding pocket similarity detecting tool combination with molecular docking, free energy calculation and pathway analysis. These targets have diverse folds and are derived from different protein family. However, they have similar aspirin-binding pockets. The binding free energy with aspirin for newly identified targets is comparable to that for the primary targets. Pathway analysis revealed that the targets were enriched in several pathways such as vascular endothelial growth factor (VEGF) signaling, Fc epsilon RI signaling and arachidonic acid metabolism, which are strongly involved in inflammation, cardiovascular disease and cancer. Therefore, the predicted target profile of aspirin suggests a new explanation for the disease prevention ability of aspirin. Our findings provide a new insight of aspirin and its efficacy of disease prevention in a systematic and global view. PMID:26989626

Proteome-wide prediction of targets for aspirin: new insight into the molecular mechanism of aspirin.

PubMed

Dai, Shao-Xing; Li, Wen-Xing; Li, Gong-Hua; Huang, Jing-Fei

2016-01-01

Besides its anti-inflammatory, analgesic and anti-pyretic properties, aspirin is used for the prevention of cardiovascular disease and various types of cancer. The multiple activities of aspirin likely involve several molecular targets and pathways rather than a single target. Therefore, systematic identification of these targets of aspirin can help us understand the underlying mechanisms of the activities. In this study, we identified 23 putative targets of aspirin in the human proteome by using binding pocket similarity detecting tool combination with molecular docking, free energy calculation and pathway analysis. These targets have diverse folds and are derived from different protein family. However, they have similar aspirin-binding pockets. The binding free energy with aspirin for newly identified targets is comparable to that for the primary targets. Pathway analysis revealed that the targets were enriched in several pathways such as vascular endothelial growth factor (VEGF) signaling, Fc epsilon RI signaling and arachidonic acid metabolism, which are strongly involved in inflammation, cardiovascular disease and cancer. Therefore, the predicted target profile of aspirin suggests a new explanation for the disease prevention ability of aspirin. Our findings provide a new insight of aspirin and its efficacy of disease prevention in a systematic and global view.

Drug Target Protein-Protein Interaction Networks: A Systematic Perspective

PubMed Central

2017-01-01

The identification and validation of drug targets are crucial in biomedical research and many studies have been conducted on analyzing drug target features for getting a better understanding on principles of their mechanisms. But most of them are based on either strong biological hypotheses or the chemical and physical properties of those targets separately. In this paper, we investigated three main ways to understand the functional biomolecules based on the topological features of drug targets. There are no significant differences between targets and common proteins in the protein-protein interactions network, indicating the drug targets are neither hub proteins which are dominant nor the bridge proteins. According to some special topological structures of the drug targets, there are significant differences between known targets and other proteins. Furthermore, the drug targets mainly belong to three typical communities based on their modularity. These topological features are helpful to understand how the drug targets work in the PPI network. Particularly, it is an alternative way to predict potential targets or extract nontargets to test a new drug target efficiently and economically. By this way, a drug target's homologue set containing 102 potential target proteins is predicted in the paper. PMID:28691014

lncRNATargets: A platform for lncRNA target prediction based on nucleic acid thermodynamics.

PubMed

Hu, Ruifeng; Sun, Xiaobo

2016-08-01

Many studies have supported that long noncoding RNAs (lncRNAs) perform various functions in various critical biological processes. Advanced experimental and computational technologies allow access to more information on lncRNAs. Determining the functions and action mechanisms of these RNAs on a large scale is urgently needed. We provided lncRNATargets, which is a web-based platform for lncRNA target prediction based on nucleic acid thermodynamics. The nearest-neighbor (NN) model was used to calculate binging-free energy. The main principle of NN model for nucleic acid assumes that identity and orientation of neighbor base pairs determine stability of a given base pair. lncRNATargets features the following options: setting of a specific temperature that allow use not only for human but also for other animals or plants; processing all lncRNAs in high throughput without RNA size limitation that is superior to any other existing tool; and web-based, user-friendly interface, and colored result displays that allow easy access for nonskilled computer operators and provide better understanding of results. This technique could provide accurate calculation on the binding-free energy of lncRNA-target dimers to predict if these structures are well targeted together. lncRNATargets provides high accuracy calculations, and this user-friendly program is available for free at http://www.herbbol.org:8001/lrt/ .

Tiltrotor Aeroacoustic Code (TRAC) Prediction Assessment and Initial Comparisons with Tram Test Data

NASA Technical Reports Server (NTRS)

Burley, Casey L.; Brooks, Thomas F.; Charles, Bruce D.; McCluer, Megan

1999-01-01

A prediction sensitivity assessment to inputs and blade modeling is presented for the TiltRotor Aeroacoustic Code (TRAC). For this study, the non-CFD prediction system option in TRAC is used. Here, the comprehensive rotorcraft code, CAMRAD.Mod1, coupled with the high-resolution sectional loads code HIRES, predicts unsteady blade loads to be used in the noise prediction code WOPWOP. The sensitivity of the predicted blade motions, blade airloads, wake geometry, and acoustics is examined with respect to rotor rpm, blade twist and chord, and to blade dynamic modeling. To accomplish this assessment, an interim input-deck for the TRAM test model and an input-deck for a reference test model are utilized in both rigid and elastic modes. Both of these test models are regarded as near scale models of the V-22 proprotor (tiltrotor). With basic TRAC sensitivities established, initial TRAC predictions are compared to results of an extensive test of an isolated model proprotor. The test was that of the TiltRotor Aeroacoustic Model (TRAM) conducted in the Duits-Nederlandse Windtunnel (DNW). Predictions are compared to measured noise for the proprotor operating over an extensive range of conditions. The variation of predictions demonstrates the great care that must be taken in defining the blade motion. However, even with this variability, the predictions using the different blade modeling successfully capture (bracket) the levels and trends of the noise for conditions ranging from descent to ascent.

Tiltrotor Aeroacoustic Code (TRAC) Prediction Assessment and Initial Comparisons With TRAM Test Data

NASA Technical Reports Server (NTRS)

Burley, Casey L.; Brooks, Thomas F.; Charles, Bruce D.; McCluer, Megan

1999-01-01

A prediction sensitivity assessment to inputs and blade modeling is presented for the TiltRotor Aeroacoustic Code (TRAC). For this study, the non-CFD prediction system option in TRAC is used. Here, the comprehensive rotorcraft code, CAMRAD.Mod 1, coupled with the high-resolution sectional loads code HIRES, predicts unsteady blade loads to be used in the noise prediction code WOPWOP. The sensitivity of the predicted blade motions, blade airloads, wake geometry, and acoustics is examined with respect to rotor rpm, blade twist and chord, and to blade dynamic modeling. To accomplish this assessment. an interim input-deck for the TRAM test model and an input-deck for a reference test model are utilized in both rigid and elastic modes. Both of these test models are regarded as near scale models of the V-22 proprotor (tiltrotor). With basic TRAC sensitivities established, initial TRAC predictions are compared to results of an extensive test of an isolated model proprotor. The test was that of the TiltRotor Aeroacoustic Model (TRAM) conducted in the Duits-Nederlandse Windtunnel (DNW). Predictions are compared to measured noise for the proprotor operating over an extensive range of conditions. The variation of predictions demonstrates the great care that must be taken in defining the blade motion. However, even with this variability, the predictions using the different blade modeling successfully capture (bracket) the levels and trends of the noise for conditions ranging from descent to ascent.

International Test Comparisons: Reviewing Translation Error in Different Source Language-Target Language Combinations

ERIC Educational Resources Information Center

Zhao, Xueyu; Solano-Flores, Guillermo; Qian, Ming

2018-01-01

This article addresses test translation review in international test comparisons. We investigated the applicability of the theory of test translation error--a theory of the multidimensionality and inevitability of test translation error--across source language-target language combinations in the translation of PISA (Programme of International…

SeedVicious: Analysis of microRNA target and near-target sites.

PubMed

Marco, Antonio

2018-01-01

Here I describe seedVicious, a versatile microRNA target site prediction software that can be easily fitted into annotation pipelines and run over custom datasets. SeedVicious finds microRNA canonical sites plus other, less efficient, target sites. Among other novel features, seedVicious can compute evolutionary gains/losses of target sites using maximum parsimony, and also detect near-target sites, which have one nucleotide different from a canonical site. Near-target sites are important to study population variation in microRNA regulation. Some analyses suggest that near-target sites may also be functional sites, although there is no conclusive evidence for that, and they may actually be target alleles segregating in a population. SeedVicious does not aim to outperform but to complement existing microRNA prediction tools. For instance, the precision of TargetScan is almost doubled (from 11% to ~20%) when we filter predictions by the distance between target sites using this program. Interestingly, two adjacent canonical target sites are more likely to be present in bona fide target transcripts than pairs of target sites at slightly longer distances. The software is written in Perl and runs on 64-bit Unix computers (Linux and MacOS X). Users with no computing experience can also run the program in a dedicated web-server by uploading custom data, or browse pre-computed predictions. SeedVicious and its associated web-server and database (SeedBank) are distributed under the GPL/GNU license.

Testing and Life Prediction for Composite Rotor Hub Flexbeams

NASA Technical Reports Server (NTRS)

Murri, Gretchen B.

2004-01-01

A summary of several studies of delamination in tapered composite laminates with internal ply-drops is presented. Initial studies used 2D FE models to calculate interlaminar stresses at the ply-ending locations in linear tapered laminates under tension loading. Strain energy release rates for delamination in these laminates indicated that delamination would likely start at the juncture of the tapered and thin regions and grow unstably in both directions. Tests of glass/epoxy and graphite/epoxy linear tapered laminates under axial tension delaminated as predicted. Nonlinear tapered specimens were cut from a full-size helicopter rotor hub and were tested under combined constant axial tension and cyclic transverse bending loading to simulate the loading experienced by a rotorhub flexbeam in flight. For all the tested specimens, delamination began at the tip of the outermost dropped ply group and grew first toward the tapered region. A 2D FE model was created that duplicated the test flexbeam layup, geometry, and loading. Surface strains calculated by the model agreed very closely with the measured surface strains in the specimens. The delamination patterns observed in the tests were simulated in the model by releasing pairs of MPCs along those interfaces. Strain energy release rates associated with the delamination growth were calculated for several configurations and using two different FE analysis codes. Calculations from the codes agreed very closely. The strain energy release rate results were used with material characterization data to predict fatigue delamination onset lives for nonlinear tapered flexbeams with two different ply-dropping schemes. The predicted curves agreed well with the test data for each case studied.

Pneumococcal vaccine targeting strategy for older adults: customized risk profiling.

PubMed

Balicer, Ran D; Cohen, Chandra J; Leibowitz, Morton; Feldman, Becca S; Brufman, Ilan; Roberts, Craig; Hoshen, Moshe

2014-02-12

Current pneumococcal vaccine campaigns take a broad, primarily age-based approach to immunization targeting, overlooking many clinical and administrative considerations necessary in disease prevention and resource planning for specific patient populations. We aim to demonstrate the utility of a population-specific predictive model for hospital-treated pneumonia to direct effective vaccine targeting. Data was extracted for 1,053,435 members of an Israeli HMO, age 50 and older, during the study period 2008-2010. We developed and validated a logistic regression model to predict hospital-treated pneumonia using training and test samples, including a set of standard and population-specific risk factors. The model's predictive value was tested for prospectively identifying cases of pneumonia and invasive pneumococcal disease (IPD), and was compared to the existing international paradigm for patient immunization targeting. In a multivariate regression, age, co-morbidity burden and previous pneumonia events were most strongly positively associated with hospital-treated pneumonia. The model predicting hospital-treated pneumonia yielded a c-statistic of 0.80. Utilizing the predictive model, the top 17% highest-risk within the study validation population were targeted to detect 54% of those members who were subsequently treated for hospitalized pneumonia in the follow up period. The high-risk population identified through this model included 46% of the follow-up year's IPD cases, and 27% of community-treated pneumonia cases. These outcomes were compared with international guidelines for risk for pneumococcal diseases that accurately identified only 35% of hospitalized pneumonia, 41% of IPD cases and 21% of community-treated pneumonia. We demonstrate that a customized model for vaccine targeting performs better than international guidelines, and therefore, risk modeling may allow for more precise vaccine targeting and resource allocation than current national and international

Aptitude Tests and Successful College Students: The Predictive Validity of the General Aptitude Test (GAT) in Saudi Arabia

ERIC Educational Resources Information Center

Alnahdi, Ghaleb Hamad

2015-01-01

Aptitude tests should predict student success at the university level. This study examined the predictive validity of the General Aptitude Test (GAT) in Saudi Arabia. Data for 27420 students enrolled at Prince Sattam bin Abdulaziz University were analyzed. Of these students, 17565 were male students, and 9855 were female students. Multiple…

AGR-5/6/7 Irradiation Test Predictions using PARFUME

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

Skerjanc, William F.

PARFUME, (PARticle FUel ModEl) a fuel performance modeling code used for high temperature gas-cooled reactors (HTGRs), was used to model the Advanced Gas Reactor (AGR)-5/6/7 irradiation test using predicted physics and thermal hydraulics data. The AGR-5/6/7 test consists of the combined fifth, sixth, and seventh planned irradiations of the AGR Fuel Development and Qualification Program. The AGR-5/6/7 test train is a multi-capsule, instrumented experiment that is designed for irradiation in the 133.4-mm diameter north east flux trap (NEFT) position of Advanced Test Reactor (ATR). Each capsule contains compacts filled with uranium oxycarbide (UCO) unaltered fuel particles. This report documents themore » calculations performed to predict the failure probability of tristructural isotropic (TRISO)-coated fuel particles during the AGR-5/6/7 experiment. In addition, this report documents the calculated source term from the driver fuel. The calculations include modeling of the AGR-5/6/7 irradiation that is scheduled to occur from October 2017 to April 2021 over a total of 13 ATR cycles, including nine normal cycles and four Power Axial Locator Mechanism (PALM) cycle for a total between 500 – 550 effective full power days (EFPD). The irradiation conditions and material properties of the AGR-5/6/7 test predicted zero fuel particle failures in Capsules 1, 2, and 4. Fuel particle failures were predicted in Capsule 3 due to internal particle pressure. These failures were predicted in the highest temperature compacts. Capsule 5 fuel particle failures were due to inner pyrolytic carbon (IPyC) cracking causing localized stresses concentrations in the SiC layer. This capsule predicted the highest particle failures due to the lower irradiation temperature. In addition, shrinkage of the buffer and IPyC layer during irradiation resulted in formation of a buffer-IPyC gap. The two capsules at the two ends of the test train, Capsules 1 and 5 experienced the smallest buffer-IPyC gap

Identification of novel microRNAs in Hevea brasiliensis and computational prediction of their targets

PubMed Central

2012-01-01

Background Plants respond to external stimuli through fine regulation of gene expression partially ensured by small RNAs. Of these, microRNAs (miRNAs) play a crucial role. They negatively regulate gene expression by targeting the cleavage or translational inhibition of target messenger RNAs (mRNAs). In Hevea brasiliensis, environmental and harvesting stresses are known to affect natural rubber production. This study set out to identify abiotic stress-related miRNAs in Hevea using next-generation sequencing and bioinformatic analysis. Results Deep sequencing of small RNAs was carried out on plantlets subjected to severe abiotic stress using the Solexa technique. By combining the LeARN pipeline, data from the Plant microRNA database (PMRD) and Hevea EST sequences, we identified 48 conserved miRNA families already characterized in other plant species, and 10 putatively novel miRNA families. The results showed the most abundant size for miRNAs to be 24 nucleotides, except for seven families. Several MIR genes produced both 20-22 nucleotides and 23-27 nucleotides. The two miRNA class sizes were detected for both conserved and putative novel miRNA families, suggesting their functional duality. The EST databases were scanned with conserved and novel miRNA sequences. MiRNA targets were computationally predicted and analysed. The predicted targets involved in "responses to stimuli" and to "antioxidant" and "transcription activities" are presented. Conclusions Deep sequencing of small RNAs combined with transcriptomic data is a powerful tool for identifying conserved and novel miRNAs when the complete genome is not yet available. Our study provided additional information for evolutionary studies and revealed potentially specific regulation of the control of redox status in Hevea. PMID:22330773

Prediction of clinical response to drugs in ovarian cancer using the chemotherapy resistance test (CTR-test).

PubMed

Kischkel, Frank Christian; Meyer, Carina; Eich, Julia; Nassir, Mani; Mentze, Monika; Braicu, Ioana; Kopp-Schneider, Annette; Sehouli, Jalid

2017-10-27

In order to validate if the test result of the Chemotherapy Resistance Test (CTR-Test) is able to predict the resistances or sensitivities of tumors in ovarian cancer patients to drugs, the CTR-Test result and the corresponding clinical response of individual patients were correlated retrospectively. Results were compared to previous recorded correlations. The CTR-Test was performed on tumor samples from 52 ovarian cancer patients for specific chemotherapeutic drugs. Patients were treated with monotherapies or drug combinations. Resistances were classified as extreme (ER), medium (MR) or slight (SR) resistance in the CTR-Test. Combination treatment resistances were transformed by a scoring system into these classifications. Accurate sensitivity prediction was accomplished in 79% of the cases and accurate prediction of resistance in 100% of the cases in the total data set. The data set of single agent treatment and drug combination treatment were analyzed individually. Single agent treatment lead to an accurate sensitivity in 44% of the cases and the drug combination to 95% accuracy. The detection of resistances was in both cases to 100% correct. ROC curve analysis indicates that the CTR-Test result correlates with the clinical response, at least for the combination chemotherapy. Those values are similar or better than the values from a publication from 1990. Chemotherapy resistance testing in vitro via the CTR-Test is able to accurately detect resistances in ovarian cancer patients. These numbers confirm and even exceed results published in 1990. Better sensitivity detection might be caused by a higher percentage of drug combinations tested in 2012 compared to 1990. Our study confirms the functionality of the CTR-Test to plan an efficient chemotherapeutic treatment for ovarian cancer patients.

"Eyeball test" of thermographic patterns for predicting a successful lateral infraclavicular block.

PubMed

Andreasen, Asger M; Linnet, Karen E; Asghar, Semera; Rothe, Christian; Rosenstock, Charlotte V; Lange, Kai H W; Lundstrøm, Lars H

2017-11-01

Increased distal skin temperature can be used to predict the success of lateral infraclavicular (LIC) block. We hypothesized that an "eyeball test" of specific infrared thermographic patterns after LIC block could be used to determine block success. In this observational study, five observers trained in four distinct thermographic patterns independently evaluated thermographic images of the hands of 40 patients at baseline and at one-minute intervals for 30 min after a LIC block. Sensitivity, specificity, and predictive values of a positive and a negative test were estimated to evaluate the validity of specific thermographic patterns for predicting a successful block. Sensory and motor block of the musculocutaneous, radial, ulnar, and median nerves defined block success. Fleiss' kappa statistics of multiple interobserver agreements were used to evaluate reliability. As a diagnostic test, the defined specific thermographic patterns of the hand predicted a successful block with increasing accuracy over the 30-min observation period. Block success was predicted with a sensitivity of 92.4% (95% confidence interval [CI], 86.8 to 96.2) and with a specificity of 84.0% (95% CI, 70.3 to 92.4) at min 30. The Fleiss' kappa for the five observers was 0.87 (95% CI, 0.77 to 0.96). We conclude that visual evaluation by an eyeball test of specific thermographic patterns of the blocked hands may be useful as a valid and reliable diagnostic test for predicting a successful LIC block.

Evolutionary Ensemble for In Silico Prediction of Ames Test Mutagenicity

NASA Astrophysics Data System (ADS)

Chen, Huanhuan; Yao, Xin

Driven by new regulations and animal welfare, the need to develop in silico models has increased recently as alternative approaches to safety assessment of chemicals without animal testing. This paper describes a novel machine learning ensemble approach to building an in silico model for the prediction of the Ames test mutagenicity, one of a battery of the most commonly used experimental in vitro and in vivo genotoxicity tests for safety evaluation of chemicals. Evolutionary random neural ensemble with negative correlation learning (ERNE) [1] was developed based on neural networks and evolutionary algorithms. ERNE combines the method of bootstrap sampling on training data with the method of random subspace feature selection to ensure diversity in creating individuals within an initial ensemble. Furthermore, while evolving individuals within the ensemble, it makes use of the negative correlation learning, enabling individual NNs to be trained as accurate as possible while still manage to maintain them as diverse as possible. Therefore, the resulting individuals in the final ensemble are capable of cooperating collectively to achieve better generalization of prediction. The empirical experiment suggest that ERNE is an effective ensemble approach for predicting the Ames test mutagenicity of chemicals.

The Predictive Validity of the Metropolitan Readiness Tests, 1976 Edition.

ERIC Educational Resources Information Center

Nagle, Richard J.

1979-01-01

A sample of 176 first-grade children was tested on the Metropolitan Readiness Tests, 1976 Edition (MRT), during the initial month of school and was retested eight months later on the Stanford Achievement Test. Results demonstrated substantial validity of the MRT for predicting first-grade achievement. (Author/CTM)

Computational-experimental approach to drug-target interaction mapping: A case study on kinase inhibitors

PubMed Central

Ravikumar, Balaguru; Parri, Elina; Timonen, Sanna; Airola, Antti; Wennerberg, Krister

2017-01-01

Due to relatively high costs and labor required for experimental profiling of the full target space of chemical compounds, various machine learning models have been proposed as cost-effective means to advance this process in terms of predicting the most potent compound-target interactions for subsequent verification. However, most of the model predictions lack direct experimental validation in the laboratory, making their practical benefits for drug discovery or repurposing applications largely unknown. Here, we therefore introduce and carefully test a systematic computational-experimental framework for the prediction and pre-clinical verification of drug-target interactions using a well-established kernel-based regression algorithm as the prediction model. To evaluate its performance, we first predicted unmeasured binding affinities in a large-scale kinase inhibitor profiling study, and then experimentally tested 100 compound-kinase pairs. The relatively high correlation of 0.77 (p < 0.0001) between the predicted and measured bioactivities supports the potential of the model for filling the experimental gaps in existing compound-target interaction maps. Further, we subjected the model to a more challenging task of predicting target interactions for such a new candidate drug compound that lacks prior binding profile information. As a specific case study, we used tivozanib, an investigational VEGF receptor inhibitor with currently unknown off-target profile. Among 7 kinases with high predicted affinity, we experimentally validated 4 new off-targets of tivozanib, namely the Src-family kinases FRK and FYN A, the non-receptor tyrosine kinase ABL1, and the serine/threonine kinase SLK. Our sub-sequent experimental validation protocol effectively avoids any possible information leakage between the training and validation data, and therefore enables rigorous model validation for practical applications. These results demonstrate that the kernel-based modeling approach

A Compact Methodology to Understand, Evaluate, and Predict the Performance of Automatic Target Recognition

PubMed Central

Li, Yanpeng; Li, Xiang; Wang, Hongqiang; Chen, Yiping; Zhuang, Zhaowen; Cheng, Yongqiang; Deng, Bin; Wang, Liandong; Zeng, Yonghu; Gao, Lei

2014-01-01

This paper offers a compacted mechanism to carry out the performance evaluation work for an automatic target recognition (ATR) system: (a) a standard description of the ATR system's output is suggested, a quantity to indicate the operating condition is presented based on the principle of feature extraction in pattern recognition, and a series of indexes to assess the output in different aspects are developed with the application of statistics; (b) performance of the ATR system is interpreted by a quality factor based on knowledge of engineering mathematics; (c) through a novel utility called “context-probability” estimation proposed based on probability, performance prediction for an ATR system is realized. The simulation result shows that the performance of an ATR system can be accounted for and forecasted by the above-mentioned measures. Compared to existing technologies, the novel method can offer more objective performance conclusions for an ATR system. These conclusions may be helpful in knowing the practical capability of the tested ATR system. At the same time, the generalization performance of the proposed method is good. PMID:24967605

Personalized Prediction of Glaucoma Progression Under Different Target Intraocular Pressure Levels Using Filtered Forecasting Methods.

PubMed

Kazemian, Pooyan; Lavieri, Mariel S; Van Oyen, Mark P; Andrews, Chris; Stein, Joshua D

2018-04-01

To generate personalized forecasts of how patients with open-angle glaucoma (OAG) experience disease progression at different intraocular pressure (IOP) levels to aid clinicians with setting personalized target IOPs. Secondary analyses using longitudinal data from 2 randomized controlled trials. Participants with moderate or advanced OAG from the Collaborative Initial Glaucoma Treatment Study (CIGTS) or the Advanced Glaucoma Intervention Study (AGIS). By using perimetric and tonometric data from trial participants, we developed and validated Kalman Filter (KF) models for fast-, slow-, and nonprogressing patients with OAG. The KF can generate personalized and dynamically updated forecasts of OAG progression under different target IOP levels. For each participant, we determined how mean deviation (MD) would change if the patient maintains his/her IOP at 1 of 7 levels (6, 9, 12, 15, 18, 21, or 24 mmHg) over the next 5 years. We also model and predict changes to MD over the same time horizon if IOP is increased or decreased by 3, 6, and 9 mmHg from the level attained in the trials. Personalized estimates of the change in MD under different target IOP levels. A total of 571 participants (mean age, 64.2 years; standard deviation, 10.9) were followed for a mean of 6.5 years (standard deviation, 2.8). Our models predicted that, on average, fast progressors would lose 2.1, 6.7, and 11.2 decibels (dB) MD under target IOPs of 6, 15, and 24 mmHg, respectively, over 5 years. In contrast, on average, slow progressors would lose 0.8, 2.1, and 4.1 dB MD under the same target IOPs and time frame. When using our tool to quantify the OAG progression dynamics for all 571 patients, we found no statistically significant differences over 5 years between progression for black versus white, male versus female, and CIGTS versus AGIS participants under different target IOPs (P > 0.05 for all). To our knowledge, this is the first clinical decision-making tool that generates personalized

Predicting First-Quarter Test Scores from the New Medical College Admission Test.

ERIC Educational Resources Information Center

Cullen, Thomas J.; And Others

1980-01-01

The predictive validity of the new Medical College Admission Test as it relates to end-of-quarter examinations in anatomy, histology, physiology, biochemistry, and "ages of man" is presented. Results indicate that the Science Knowledge assessment areas of chemistry and physics and the Science Problems subtest were most useful in…

Steering and positioning targets for HWIL IR testing at cryogenic conditions

NASA Astrophysics Data System (ADS)

Perkes, D. W.; Jensen, G. L.; Higham, D. L.; Lowry, H. S.; Simpson, W. R.

2006-05-01

In order to increase the fidelity of hardware-in-the-loop ground-truth testing, it is desirable to create a dynamic scene of multiple, independently controlled IR point sources. ATK-Mission Research has developed and supplied the steering mirror systems for the 7V and 10V Space Simulation Test Chambers at the Arnold Engineering Development Center (AEDC), Air Force Materiel Command (AFMC). A portion of the 10V system incorporates multiple target sources beam-combined at the focal point of a 20K cryogenic collimator. Each IR source consists of a precision blackbody with cryogenic aperture and filter wheels mounted on a cryogenic two-axis translation stage. This point source target scene is steered by a high-speed steering mirror to produce further complex motion. The scene changes dynamically in order to simulate an actual operational scene as viewed by the System Under Test (SUT) as it executes various dynamic look-direction changes during its flight to a target. Synchronization and real-time hardware-in-the-loop control is accomplished using reflective memory for each subsystem control and feedback loop. This paper focuses on the steering mirror system and the required tradeoffs of optical performance, precision, repeatability and high-speed motion as well as the complications of encoder feedback calibration and operation at 20K.

Computer-based prediction of mitochondria-targeting peptides.

PubMed

Martelli, Pier Luigi; Savojardo, Castrense; Fariselli, Piero; Tasco, Gianluca; Casadio, Rita

2015-01-01

Computational methods are invaluable when protein sequences, directly derived from genomic data, need functional and structural annotation. Subcellular localization is a feature necessary for understanding the protein role and the compartment where the mature protein is active and very difficult to characterize experimentally. Mitochondrial proteins encoded on the cytosolic ribosomes carry specific patterns in the precursor sequence from where it is possible to recognize a peptide targeting the protein to its final destination. Here we discuss to which extent it is feasible to develop computational methods for detecting mitochondrial targeting peptides in the precursor sequences and benchmark our and other methods on the human mitochondrial proteins endowed with experimentally characterized targeting peptides. Furthermore, we illustrate our newly implemented web server and its usage on the whole human proteome in order to infer mitochondrial targeting peptides, their cleavage sites, and whether the targeting peptide regions contain or not arginine-rich recurrent motifs. By this, we add some other 2,800 human proteins to the 124 ones already experimentally annotated with a mitochondrial targeting peptide.

A weighted generalized score statistic for comparison of predictive values of diagnostic tests.

PubMed

Kosinski, Andrzej S

2013-03-15

Positive and negative predictive values are important measures of a medical diagnostic test performance. We consider testing equality of two positive or two negative predictive values within a paired design in which all patients receive two diagnostic tests. The existing statistical tests for testing equality of predictive values are either Wald tests based on the multinomial distribution or the empirical Wald and generalized score tests within the generalized estimating equations (GEE) framework. As presented in the literature, these test statistics have considerably complex formulas without clear intuitive insight. We propose their re-formulations that are mathematically equivalent but algebraically simple and intuitive. As is clearly seen with a new re-formulation we presented, the generalized score statistic does not always reduce to the commonly used score statistic in the independent samples case. To alleviate this, we introduce a weighted generalized score (WGS) test statistic that incorporates empirical covariance matrix with newly proposed weights. This statistic is simple to compute, always reduces to the score statistic in the independent samples situation, and preserves type I error better than the other statistics as demonstrated by simulations. Thus, we believe that the proposed WGS statistic is the preferred statistic for testing equality of two predictive values and for corresponding sample size computations. The new formulas of the Wald statistics may be useful for easy computation of confidence intervals for difference of predictive values. The introduced concepts have potential to lead to development of the WGS test statistic in a general GEE setting. Copyright © 2012 John Wiley & Sons, Ltd.

A weighted generalized score statistic for comparison of predictive values of diagnostic tests

PubMed Central

Kosinski, Andrzej S.

2013-01-01

Positive and negative predictive values are important measures of a medical diagnostic test performance. We consider testing equality of two positive or two negative predictive values within a paired design in which all patients receive two diagnostic tests. The existing statistical tests for testing equality of predictive values are either Wald tests based on the multinomial distribution or the empirical Wald and generalized score tests within the generalized estimating equations (GEE) framework. As presented in the literature, these test statistics have considerably complex formulas without clear intuitive insight. We propose their re-formulations which are mathematically equivalent but algebraically simple and intuitive. As is clearly seen with a new re-formulation we present, the generalized score statistic does not always reduce to the commonly used score statistic in the independent samples case. To alleviate this, we introduce a weighted generalized score (WGS) test statistic which incorporates empirical covariance matrix with newly proposed weights. This statistic is simple to compute, it always reduces to the score statistic in the independent samples situation, and it preserves type I error better than the other statistics as demonstrated by simulations. Thus, we believe the proposed WGS statistic is the preferred statistic for testing equality of two predictive values and for corresponding sample size computations. The new formulas of the Wald statistics may be useful for easy computation of confidence intervals for difference of predictive values. The introduced concepts have potential to lead to development of the weighted generalized score test statistic in a general GEE setting. PMID:22912343

Pre-Test Analysis Predictions for the Shell Buckling Knockdown Factor Checkout Tests - TA01 and TA02

NASA Technical Reports Server (NTRS)

Thornburgh, Robert P.; Hilburger, Mark W.

2011-01-01

This report summarizes the pre-test analysis predictions for the SBKF-P2-CYL-TA01 and SBKF-P2-CYL-TA02 shell buckling tests conducted at the Marshall Space Flight Center (MSFC) in support of the Shell Buckling Knockdown Factor (SBKF) Project, NASA Engineering and Safety Center (NESC) Assessment. The test article (TA) is an 8-foot-diameter aluminum-lithium (Al-Li) orthogrid cylindrical shell with similar design features as that of the proposed Ares-I and Ares-V barrel structures. In support of the testing effort, detailed structural analyses were conducted and the results were used to monitor the behavior of the TA during the testing. A summary of predicted results for each of the five load sequences is presented herein.

Target Fortification of Breast Milk: Predicting the Final Osmolality of the Feeds

PubMed Central

Choi, Arum; Fusch, Gerhard; Rochow, Niels; Fusch, Christoph

2016-01-01

For preterm infants, it is common practice to add human milk fortifiers to native breast milk to enhance protein and calorie supply because the growth rates and nutritional requirements of preterm infants are considerably higher than those of term infants. However, macronutrient intake may still be inadequate because the composition of native breast milk has individual inter- and intra-sample variation. Target fortification (TFO) of breast milk is a new nutritional regime aiming to reduce such variations by individually measuring and adding deficient macronutrients. Added TFO components contribute to the final osmolality of milk feeds. It is important to predict the final osmolality of TFO breast milk to ensure current osmolality recommendations are followed to minimize feeding intolerance and necrotizing enterocolitis. This study aims to develop and validate equations to predict the osmolality of TFO milk batches. To establish prediction models, the osmolalities of either native or supplemented breast milk with known amounts of fat, protein, and carbohydrates were analyzed. To validate prediction models, the osmolalities of each macronutrient and combinations of macronutrients were measured in an independent sample set. Additionally, osmolality was measured in TFO milk samples obtained from a previous clinical study and compared with predicted osmolality using the prediction equations. Following the addition of 1 g of carbohydrates (glucose polymer), 1 g of hydrolyzed protein, or 1 g of whey protein per 100 mL breast milk, the average increase in osmolality was 20, 38, and 4 mOsm/kg respectively. Adding fat decreased osmolality only marginally due to dilution effect. Measured and predicted osmolality of combinations of macronutrients as well as single macronutrient (R2 = 0.93) were highly correlated. Using clinical data (n = 696), the average difference between the measured and predicted osmolality was 3 ± 11 mOsm/kg and was not statistically significant. In

Artificial neural network study on organ-targeting peptides

NASA Astrophysics Data System (ADS)

Jung, Eunkyoung; Kim, Junhyoung; Choi, Seung-Hoon; Kim, Minkyoung; Rhee, Hokyoung; Shin, Jae-Min; Choi, Kihang; Kang, Sang-Kee; Lee, Nam Kyung; Choi, Yun-Jaie; Jung, Dong Hyun

2010-01-01

We report a new approach to studying organ targeting of peptides on the basis of peptide sequence information. The positive control data sets consist of organ-targeting peptide sequences identified by the peroral phage-display technique for four organs, and the negative control data are prepared from random sequences. The capacity of our models to make appropriate predictions is validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). VHSE descriptor produces statistically significant training models and the models with simple neural network architectures show slightly greater predictive power than those with complex ones. The training and test set statistics indicate that our models could discriminate between organ-targeting and random sequences. We anticipate that our models will be applicable to the selection of organ-targeting peptides for generating peptide drugs or peptidomimetics.

Predictive validity of the Biomedical Admissions Test: an evaluation and case study.

PubMed

McManus, I C; Ferguson, Eamonn; Wakeford, Richard; Powis, David; James, David

2011-01-01

There has been an increase in the use of pre-admission selection tests for medicine. Such tests need to show good psychometric properties. Here, we use a paper by Emery and Bell [2009. The predictive validity of the Biomedical Admissions Test for pre-clinical examination performance. Med Educ 43:557-564] as a case study to evaluate and comment on the reporting of psychometric data in the field of medical student selection (and the comments apply to many papers in the field). We highlight pitfalls when reliability data are not presented, how simple zero-order associations can lead to inaccurate conclusions about the predictive validity of a test, and how biases need to be explored and reported. We show with BMAT that it is the knowledge part of the test which does all the predictive work. We show that without evidence of incremental validity it is difficult to assess the value of any selection tests for medicine.

Validating regulatory predictions from diverse bacteria with mutant fitness data

DOE PAGES

Sagawa, Shiori; Price, Morgan N.; Deutschbauer, Adam M.; ...

2017-05-24

Although transcriptional regulation is fundamental to understanding bacterial physiology, the targets of most bacterial transcription factors are not known. Comparative genomics has been used to identify likely targets of some of these transcription factors, but these predictions typically lack experimental support. Here, we used mutant fitness data, which measures the importance of each gene for a bacterium's growth across many conditions, to test regulatory predictions from RegPrecise, a curated collection of comparative genomics predictions. Because characterized transcription factors often have correlated fitness with one of their targets (either positively or negatively), correlated fitness patterns provide support for the comparative genomicsmore » predictions. At a false discovery rate of 3%, we identified significant cofitness for at least one target of 158 TFs in 107 ortholog groups and from 24 bacteria. Thus, high-throughput genetics can be used to identify a high-confidence subset of the sequence-based regulatory predictions.« less

Validating regulatory predictions from diverse bacteria with mutant fitness data

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

Sagawa, Shiori; Price, Morgan N.; Deutschbauer, Adam M.

Although transcriptional regulation is fundamental to understanding bacterial physiology, the targets of most bacterial transcription factors are not known. Comparative genomics has been used to identify likely targets of some of these transcription factors, but these predictions typically lack experimental support. Here, we used mutant fitness data, which measures the importance of each gene for a bacterium's growth across many conditions, to test regulatory predictions from RegPrecise, a curated collection of comparative genomics predictions. Because characterized transcription factors often have correlated fitness with one of their targets (either positively or negatively), correlated fitness patterns provide support for the comparative genomicsmore » predictions. At a false discovery rate of 3%, we identified significant cofitness for at least one target of 158 TFs in 107 ortholog groups and from 24 bacteria. Thus, high-throughput genetics can be used to identify a high-confidence subset of the sequence-based regulatory predictions.« less

Visuospatial information processing load and the ratio between parietal cue and target P3 amplitudes in the Attentional Network Test.

PubMed

Abramov, Dimitri M; Pontes, Monique; Pontes, Adailton T; Mourao-Junior, Carlos A; Vieira, Juliana; Quero Cunha, Carla; Tamborino, Tiago; Galhanone, Paulo R; deAzevedo, Leonardo C; Lazarev, Vladimir V

2017-04-24

In ERP studies of cognitive processes during attentional tasks, the cue signals containing information about the target can increase the amplitude of the parietal cue P3 in relation to the 'neutral' temporal cue, and reduce the subsequent target P3 when this information is valid, i.e. corresponds to the target's attributes. The present study compared the cue-to-target P3 ratios in neutral and visuospatial cueing, in order to estimate the contribution of valid visuospatial information from the cue to target stages of the task performance, in terms of cognitive load. The P3 characteristics were also correlated with the results of individuals' performance of the visuospatial tasks, in order to estimate the relationship of the observed ERP with spatial reasoning. In 20 typically developing boys, aged 10-13 years (11.3±0.86), the intelligence quotient (I.Q.) was estimated by the Block Design and Vocabulary subtests from the WISC-III. The subjects performed the Attentional Network Test (ANT) accompanied by EEG recording. The cued two-choice task had three equiprobable cue conditions: No cue, with no information about the target; Neutral (temporal) cue, with an asterisk in the center of the visual field, predicting the target onset; and Spatial cues, with an asterisk in the upper or lower hemifield, predicting the onset and corresponding location of the target. The ERPs were estimated for the mid-frontal (Fz) and mid-parietal (Pz) scalp derivations. In the Pz, the Neutral cue P3 had a lower amplitude than the Spatial cue P3; whereas for the target ERPs, the P3 of the Neutral cue condition was larger than that of the Spatial cue condition. However, the sums of the magnitudes of the cue and target P3 were equal in the spatial and neutral cueing, probably indicating that in both cases the equivalent information processing load is included in either the cue or the target reaction, respectively. Meantime, in the Fz, the analog ERP components for both the cue and target

Decomposing experience-driven attention: Opposite attentional effects of previously predictive cues.

PubMed

Lin, Zhicheng; Lu, Zhong-Lin; He, Sheng

2016-10-01

A central function of the brain is to track the dynamic statistical regularities in the environment - such as what predicts what over time. How does this statistical learning process alter sensory and attentional processes? Drawing upon animal conditioning and predictive coding, we developed a learning procedure that revealed two distinct components through which prior learning-experience controls attention. During learning, a visual search task was used in which the target randomly appeared at one of several locations but always inside an encloser of a particular color - the learned color served to direct attention to the target location. During test, the color no longer predicted the target location. When the same search task was used in the subsequent test, we found that the learned color continued to attract attention despite the behavior being counterproductive for the task and despite the presence of a completely predictive cue. However, when tested with a flanker task that had minimal location uncertainty - the target was at the fixation surrounded by a distractor - participants were better at ignoring distractors in the learned color than other colors. Evidently, previously predictive cues capture attention in the same search task but can be better suppressed in a flanker task. These results demonstrate opposing components - capture and inhibition - in experience-driven attention, with their manifestations crucially dependent on task context. We conclude that associative learning enhances context-sensitive top-down modulation while it reduces bottom-up sensory drive and facilitates suppression, supporting a learning-based predictive coding account.

Decomposing experience-driven attention: opposite attentional effects of previously predictive cues

PubMed Central

Lin, Zhicheng; Lu, Zhong-Lin; He, Sheng

2016-01-01

A central function of the brain is to track the dynamic statistical regularities in the environment—such as what predicts what over time. How does this statistical learning process alter sensory and attentional processes? Drawing upon animal conditioning and predictive coding, we developed a learning procedure that revealed two distinct components through which prior learning-experience controls attention. During learning, a visual search task was used in which the target randomly appeared at one of several locations but always inside an encloser of a particular color—the learned color served to direct attention to the target location. During test, the color no longer predicted the target location. When the same search task was used in the subsequent test, we found that the learned color continued to attract attention despite the behavior being counterproductive for the task and despite the presence of a completely predictive cue. However, when tested with a flanker task that had minimal location uncertainty—the target was at the fixation surrounded by a distractor—participants were better at ignoring distractors in the learned color than other colors. Evidently, previously predictive cues capture attention in the same search task but can be better suppressed in a flanker task. These results demonstrate opposing components—capture and inhibition—in experience-driven attention, with their manifestations crucially dependent on task context. We conclude that associative learning enhances context-sensitive top-down modulation while reduces bottom-up sensory drive and facilitates suppression, supporting a learning-based predictive coding account. PMID:27068051

Whole-Genome Thermodynamic Analysis Reduces siRNA Off-Target Effects

PubMed Central

Chen, Xi; Liu, Peng; Chou, Hui-Hsien

2013-01-01

Small interfering RNAs (siRNAs) are important tools for knocking down targeted genes, and have been widely applied to biological and biomedical research. To design siRNAs, two important aspects must be considered: the potency in knocking down target genes and the off-target effect on any nontarget genes. Although many studies have produced useful tools to design potent siRNAs, off-target prevention has mostly been delegated to sequence-level alignment tools such as BLAST. We hypothesize that whole-genome thermodynamic analysis can identify potential off-targets with higher precision and help us avoid siRNAs that may have strong off-target effects. To validate this hypothesis, two siRNA sets were designed to target three human genes IDH1, ITPR2 and TRIM28. They were selected from the output of two popular siRNA design tools, siDirect and siDesign. Both siRNA design tools have incorporated sequence-level screening to avoid off-targets, thus their output is believed to be optimal. However, one of the sets we tested has off-target genes predicted by Picky, a whole-genome thermodynamic analysis tool. Picky can identify off-target genes that may hybridize to a siRNA within a user-specified melting temperature range. Our experiments validated that some off-target genes predicted by Picky can indeed be inhibited by siRNAs. Similar experiments were performed using commercially available siRNAs and a few off-target genes were also found to be inhibited as predicted by Picky. In summary, we demonstrate that whole-genome thermodynamic analysis can identify off-target genes that are missed in sequence-level screening. Because Picky prediction is deterministic according to thermodynamics, if a siRNA candidate has no Picky predicted off-targets, it is unlikely to cause off-target effects. Therefore, we recommend including Picky as an additional screening step in siRNA design. PMID:23484018

Tuberculosis Screening and Targeted Testing of College and University Students

ERIC Educational Resources Information Center

Journal of American College Health, 2011

2011-01-01

Screening and targeted testing for tuberculosis (TB) is a key strategy for controlling and preventing infection on college and university campuses. Early detection provides an opportunity to promote the health of affected individuals through prompt diagnosis and treatment while preventing potential spread to others. Implementation of a screening…

Human amygdala engagement moderated by early life stress exposure is a biobehavioral target for predicting recovery on antidepressants.

PubMed

Goldstein-Piekarski, Andrea N; Korgaonkar, Mayuresh S; Green, Erin; Suppes, Trisha; Schatzberg, Alan F; Hastie, Trevor; Nemeroff, Charles B; Williams, Leanne M

2016-10-18

Amygdala circuitry and early life stress (ELS) are both strongly and independently implicated in the neurobiology of depression. Importantly, animal models have revealed that the contribution of ELS to the development and maintenance of depression is likely a consequence of structural and physiological changes in amygdala circuitry in response to stress hormones. Despite these mechanistic foundations, amygdala engagement and ELS have not been investigated as biobehavioral targets for predicting functional remission in translational human studies of depression. Addressing this question, we integrated human neuroimaging and measurement of ELS within a controlled trial of antidepressant outcomes. Here we demonstrate that the interaction between amygdala activation engaged by emotional stimuli and ELS predicts functional remission on antidepressants with a greater than 80% cross-validated accuracy. Our model suggests that in depressed people with high ELS, the likelihood of remission is highest with greater amygdala reactivity to socially rewarding stimuli, whereas for those with low-ELS exposure, remission is associated with lower amygdala reactivity to both rewarding and threat-related stimuli. This full model predicted functional remission over and above the contribution of demographics, symptom severity, ELS, and amygdala reactivity alone. These findings identify a human target for elucidating the mechanisms of antidepressant functional remission and offer a target for developing novel therapeutics. The results also offer a proof-of-concept for using neuroimaging as a target for guiding neuroscience-informed intervention decisions at the level of the individual person.

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

NASA Astrophysics Data System (ADS)

Baqersad, Javad; Niezrecki, Christopher; Avitabile, Peter

2015-10-01

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

SEURAT: Safety Evaluation Ultimately Replacing Animal Testing--recommendations for future research in the field of predictive toxicology.

PubMed

Daston, George; Knight, Derek J; Schwarz, Michael; Gocht, Tilman; Thomas, Russell S; Mahony, Catherine; Whelan, Maurice

2015-01-01

The development of non-animal methodology to evaluate the potential for a chemical to cause systemic toxicity is one of the grand challenges of modern science. The European research programme SEURAT is active in this field and will conclude its first phase, SEURAT-1, in December 2015. Drawing on the experience gained in SEURAT-1 and appreciating international advancement in both basic and regulatory science, we reflect here on how SEURAT should evolve and propose that further research and development should be directed along two complementary and interconnecting work streams. The first work stream would focus on developing new 'paradigm' approaches for regulatory science. The goal here is the identification of 'critical biological targets' relevant for toxicity and to test their suitability to be used as anchors for predicting toxicity. The second work stream would focus on integration and application of new approach methods for hazard (and risk) assessment within the current regulatory 'paradigm', aiming for acceptance of animal-free testing strategies by regulatory authorities (i.e. translating scientific achievements into regulation). Components for both work streams are discussed and may provide a structure for a future research programme in the field of predictive toxicology.

Predictive genetic testing for complex diseases: a public health perspective

PubMed Central

Marzuillo, C.; De Vito, C.; D’Andrea, E.; Rosso, A.

2014-01-01

From a public health perspective, systematic, evidence-based technology assessments and economic evaluations are needed to guide the incorporation of genomics into clinical and public health practice. However, scientific evidence on the effectiveness of predictive genetic tests is difficult to obtain. This review first highlights the similarities and differences between traditional screening tests and predictive genetic testing for complex diseases and goes on to describe frameworks for the evaluation of genetic testing that have been developed in recent years providing some evidence that currently genetic tests are not used in an appropriate way. Nevertheless, evidence-based recommendations are already available for some genomic applications that can reduce morbidity and mortality and many more are expected to emerge over the next decade. The time is now ripe for the introduction of a range of genetic tests into healthcare practice, but this will require the development of specific health policies, proper public health evaluations, organizational changes within the healthcare systems, capacity building among the healthcare workforce and the education of the public. PMID:24049051

Rheumatoid arthritis patient perceptions on the value of predictive testing for treatments: a qualitative study.

PubMed

Kumar, Kanta; Peters, Sarah; Barton, Anne

2016-11-08

Rheumatoid arthritis (RA) is a long term condition that requires early treatment to control symptoms and improve long-term outcomes. Lack of response to RA treatments is not only a waste of healthcare resources, but also causes disability and distress to patients. Identifying biomarkers predictive of treatment response offers an opportunity to improve clinical decisions about which treatment to recommend in patients and could ultimately lead to better patient outcomes. The aim of this study was to explore the understanding of and factors affecting Rheumatoid Arthritis (RA) patients' decisions around predictive treatment testing. A qualitative study was conducted with a purposive sample of 16 patients with RA from three major UK cities. Four focus groups explored patient perceptions of the use of biomarker tests to predict response to treatments. Interviews were audio-recorded, transcribed verbatim and analysed using thematic analysis by three researchers. Data were organised within three interlinking themes: [1] Perceptions of predictive tests and patient preference of tests; [2] Utility of the test to manage expectations; [3] The influence of the disease duration on take up of predictive testing. During consultations for predictive testing, patients felt they would need, first, careful explanations detailing the consequences of untreated RA and delayed treatment response and, second, support to balance the risks of tests, which might be invasive and/or only moderately accurate, with the potential benefits of better management of symptoms. This study provides important insights into predictive testing. Besides supporting clinical decision making, the development of predictive testing in RA is largely supported by patients. Developing strategies which communicate risk information about predictive testing effectively while reducing the psychological burden associated with this information will be essential to maximise uptake.

Testing the Predictive Power of Coulomb Stress on Aftershock Sequences

NASA Astrophysics Data System (ADS)

Woessner, J.; Lombardi, A.; Werner, M. J.; Marzocchi, W.

2009-12-01

Empirical and statistical models of clustered seismicity are usually strongly stochastic and perceived to be uninformative in their forecasts, since only marginal distributions are used, such as the Omori-Utsu and Gutenberg-Richter laws. In contrast, so-called physics-based aftershock models, based on seismic rate changes calculated from Coulomb stress changes and rate-and-state friction, make more specific predictions: anisotropic stress shadows and multiplicative rate changes. We test the predictive power of models based on Coulomb stress changes against statistical models, including the popular Short Term Earthquake Probabilities and Epidemic-Type Aftershock Sequences models: We score and compare retrospective forecasts on the aftershock sequences of the 1992 Landers, USA, the 1997 Colfiorito, Italy, and the 2008 Selfoss, Iceland, earthquakes. To quantify predictability, we use likelihood-based metrics that test the consistency of the forecasts with the data, including modified and existing tests used in prospective forecast experiments within the Collaboratory for the Study of Earthquake Predictability (CSEP). Our results indicate that a statistical model performs best. Moreover, two Coulomb model classes seem unable to compete: Models based on deterministic Coulomb stress changes calculated from a given fault-slip model, and those based on fixed receiver faults. One model of Coulomb stress changes does perform well and sometimes outperforms the statistical models, but its predictive information is diluted, because of uncertainties included in the fault-slip model. Our results suggest that models based on Coulomb stress changes need to incorporate stochastic features that represent model and data uncertainty.

Economic evaluation of targeted cancer interventions: critical review and recommendations.

PubMed

Elkin, Elena B; Marshall, Deborah A; Kulin, Nathalie A; Ferrusi, Ilia L; Hassett, Michael J; Ladabaum, Uri; Phillips, Kathryn A

2011-10-01

Scientific advances have improved our ability to target cancer interventions to individuals who will benefit most and spare the risks and costs to those who will derive little benefit or even be harmed. Several approaches are currently used for targeting interventions for cancer risk reduction, screening, and treatment, including risk prediction algorithms for identifying high-risk subgroups and diagnostic tests for tumor markers and germline genetic mutations. Economic evaluation can inform decisions about the use of targeted interventions, which may be more costly than traditional strategies. However, assessing the impact of a targeted intervention on costs and health outcomes requires explicit consideration of the method of targeting. In this study, we describe the importance of this principle by reviewing published cost-effectiveness analyses of targeted interventions in breast cancer. Few studies we identified explicitly evaluated the relationships among the method of targeting, the accuracy of the targeting test, and outcomes of the targeted intervention. Those that did found that characteristics of targeting tests had a substantial impact on outcomes. We posit that the method of targeting and the outcomes of a targeted intervention are inextricably linked and recommend that cost-effectiveness analyses of targeted interventions explicitly consider costs and outcomes of the method of targeting.

Proof-test-based life prediction of high-toughness pressure vessels

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

Panontin, T.L.; Hill, M.R.

1996-02-01

The paper examines the problems associated with applying proof-test-based life prediction to vessels made of high-toughness metals. Two A106 Gr B pipe specimens containing long, through-wall circumferential flaws were tested. One failed during hydrostatic testing and the other during tension-tension cycling following a hydrostatic test. Quantitative fractography was used to verify experimentally obtained fatigue crack growth rates and a variety of LEFM and EPFM techniques were used to analyze the experimental results. The results show that: plastic collapse analysis provides accurate predictions of screened (initial) crack size when the flow stress is determined experimentally; LEFM analysis underestimates the crack sizemore » screened by the proof test and overpredicts the subsequent fatigue life of the vessel when retardation effects are small (i.e., low proof levels); and, at a high proof-test level (2.4 {times} operating pressure), the large retardation effect on fatigue crack growth due to the overload overwhelmed the deleterious effect on fatigue life from stable tearing during the proof test and alleviated the problem of screening only long cracks due to the high toughness of the metal.« less

Lay perceptions of predictive testing for diabetes based on DNA test results versus family history assessment: a focus group study.

PubMed

Wijdenes-Pijl, Miranda; Dondorp, Wybo J; Timmermans, Danielle Rm; Cornel, Martina C; Henneman, Lidewij

2011-07-05

This study assessed lay perceptions of issues related to predictive genetic testing for multifactorial diseases. These perceived issues may differ from the "classic" issues, e.g. autonomy, discrimination, and psychological harm that are considered important in predictive testing for monogenic disorders. In this study, type 2 diabetes was used as an example, and perceptions with regard to predictive testing based on DNA test results and family history assessment were compared. Eight focus group interviews were held with 45 individuals aged 35-70 years with (n = 3) and without (n = 1) a family history of diabetes, mixed groups of these two (n = 2), and diabetes patients (n = 2). All interviews were transcribed and analysed using Atlas-ti. Most participants believed in the ability of a predictive test to identify people at risk for diabetes and to motivate preventive behaviour. Different reasons underlying motivation were considered when comparing DNA test results and a family history risk assessment. A perceived drawback of DNA testing was that diabetes was considered not severe enough for this type of risk assessment. In addition, diabetes family history assessment was not considered useful by some participants, since there are also other risk factors involved, not everyone has a diabetes family history or knows their family history, and it might have a negative influence on family relations. Respect for autonomy of individuals was emphasized more with regard to DNA testing than family history assessment. Other issues such as psychological harm, discrimination, and privacy were only briefly mentioned for both tests. The results suggest that most participants believe a predictive genetic test could be used in the prevention of multifactorial disorders, such as diabetes, but indicate points to consider before both these tests are applied. These considerations differ with regard to the method of assessment (DNA test or obtaining family history) and also differ from

Predicting Work Activities with Divergent Thinking Tests: A Longitudinal Study

ERIC Educational Resources Information Center

Clapham, Maria M.; Cowdery, Edwina M.; King, Kelly E.; Montang, Melissa A.

2005-01-01

This study examined whether divergent thinking test scores obtained from engineering students during college predicted creative work activities fifteen years later. Results showed that a subscore of the "Owens Creativity Test", which assesses divergent thinking about mechanical objects, correlated significantly with self-ratings of…

Target switching in curved human arm movements is predicted by changing a single control parameter.

PubMed

Hoffmann, Heiko

2011-01-01

Straight-line movements have been studied extensively in the human motor-control literature, but little is known about how to generate curved movements and how to adjust them in a dynamic environment. The present work studied, for the first time to my knowledge, how humans adjust curved hand movements to a target that switches location. Subjects (n = 8) sat in front of a drawing tablet and looked at a screen. They moved a cursor on a curved trajectory (spiral or oval shaped) toward a goal point. In half of the trials, this goal switched 200 ms after movement onset to either one of two alternative positions, and subjects smoothly adjusted their movements to the new goal. To explain this adjustment, we compared three computational models: a superposition of curved and minimum-jerk movements (Flash and Henis in J Cogn Neurosci 3(3):220-230, 1991), Vector Planning (Gordon et al. in Exp Brain Res 99(1):97-111, 1994) adapted to curved movements (Rescale), and a nonlinear dynamical system, which could generate arbitrarily curved smooth movements and had a point attractor at the goal. For each model, we predicted the trajectory adjustment to the target switch by changing only the goal position in the model. As result, the dynamical model could explain the observed switch behavior significantly better than the two alternative models (spiral: P = 0.0002 vs. Flash, P = 0.002 vs. Rescale; oval: P = 0.04 vs. Flash; P values obtained from Wilcoxon test on R (2) values). We conclude that generalizing arbitrary hand trajectories to new targets may be explained by switching a single control command, without the need to re-plan or re-optimize the whole movement or superimpose movements.

Public interest in predictive genetic testing, including direct-to-consumer testing, for susceptibility to major depression: preliminary findings.

PubMed

Wilde, Alex; Meiser, Bettina; Mitchell, Philip B; Schofield, Peter R

2010-01-01

The past decade has seen rapid advances in the identification of associations between candidate genes and a range of common multifactorial disorders. This paper evaluates public attitudes towards the complexity of genetic risk prediction in psychiatry involving susceptibility genes, uncertain penetrance and gene-environment interactions on which successful molecular-based mental health interventions will depend. A qualitative approach was taken to enable the exploration of the views of the public. Four structured focus groups were conducted with a total of 36 participants. The majority of participants indicated interest in having a genetic test for susceptibility to major depression, if it was available. Having a family history of mental illness was cited as a major reason. After discussion of perceived positive and negative implications of predictive genetic testing, nine of 24 participants initially interested in having such a test changed their mind. Fear of genetic discrimination and privacy issues predominantly influenced change of attitude. All participants still interested in having a predictive genetic test for risk for depression reported they would only do so through trusted medical professionals. Participants were unanimously against direct-to-consumer genetic testing marketed through the Internet, although some would consider it if there was suitable protection against discrimination. The study highlights the importance of general practitioner and public education about psychiatric genetics, and the availability of appropriate treatment and support services prior to implementation of future predictive genetic testing services.

Selective pressures for accurate altruism targeting: evidence from digital evolution for difficult-to-test aspects of inclusive fitness theory.

PubMed

Clune, Jeff; Goldsby, Heather J; Ofria, Charles; Pennock, Robert T

2011-03-07

Inclusive fitness theory predicts that natural selection will favour altruist genes that are more accurate in targeting altruism only to copies of themselves. In this paper, we provide evidence from digital evolution in support of this prediction by competing multiple altruist-targeting mechanisms that vary in their accuracy in determining whether a potential target for altruism carries a copy of the altruist gene. We compete altruism-targeting mechanisms based on (i) kinship (kin targeting), (ii) genetic similarity at a level greater than that expected of kin (similarity targeting), and (iii) perfect knowledge of the presence of an altruist gene (green beard targeting). Natural selection always favoured the most accurate targeting mechanism available. Our investigations also revealed that evolution did not increase the altruism level when all green beard altruists used the same phenotypic marker. The green beard altruism levels stably increased only when mutations that changed the altruism level also changed the marker (e.g. beard colour), such that beard colour reliably indicated the altruism level. For kin- and similarity-targeting mechanisms, we found that evolution was able to stably adjust altruism levels. Our results confirm that natural selection favours altruist genes that are increasingly accurate in targeting altruism to only their copies. Our work also emphasizes that the concept of targeting accuracy must include both the presence of an altruist gene and the level of altruism it produces.

Large scale RNAi screen in Tribolium reveals novel target genes for pest control and the proteasome as prime target.

PubMed

Ulrich, Julia; Dao, Van Anh; Majumdar, Upalparna; Schmitt-Engel, Christian; Schwirz, Jonas; Schultheis, Dorothea; Ströhlein, Nadi; Troelenberg, Nicole; Grossmann, Daniela; Richter, Tobias; Dönitz, Jürgen; Gerischer, Lizzy; Leboulle, Gérard; Vilcinskas, Andreas; Stanke, Mario; Bucher, Gregor

2015-09-03

Insect pest control is challenged by insecticide resistance and negative impact on ecology and health. One promising pest specific alternative is the generation of transgenic plants, which express double stranded RNAs targeting essential genes of a pest species. Upon feeding, the dsRNA induces gene silencing in the pest resulting in its death. However, the identification of efficient RNAi target genes remains a major challenge as genomic tools and breeding capacity is limited in most pest insects impeding whole-animal-high-throughput-screening. We use the red flour beetle Tribolium castaneum as a screening platform in order to identify the most efficient RNAi target genes. From about 5,000 randomly screened genes of the iBeetle RNAi screen we identify 11 novel and highly efficient RNAi targets. Our data allowed us to determine GO term combinations that are predictive for efficient RNAi target genes with proteasomal genes being most predictive. Finally, we show that RNAi target genes do not appear to act synergistically and that protein sequence conservation does not correlate with the number of potential off target sites. Our results will aid the identification of RNAi target genes in many pest species by providing a manageable number of excellent candidate genes to be tested and the proteasome as prime target. Further, the identified GO term combinations will help to identify efficient target genes from organ specific transcriptomes. Our off target analysis is relevant for the sequence selection used in transgenic plants.

Clinical history and biologic age predicted falls better than objective functional tests.

PubMed

Gerdhem, Paul; Ringsberg, Karin A M; Akesson, Kristina; Obrant, Karl J

2005-03-01

Fall risk assessment is important because the consequences, such as a fracture, may be devastating. The objective of this study was to find the test or tests that best predicted falls in a population-based sample of elderly women. The fall-predictive ability of a questionnaire, a subjective estimate of biologic age and objective functional tests (gait, balance [Romberg and sway test], thigh muscle strength, and visual acuity) were compared in 984 randomly selected women, all 75 years of age. A recalled fall was the most important predictor for future falls. Only recalled falls and intake of psycho-active drugs independently predicted future falls. Women with at least five of the most important fall predictors (previous falls, conditions affecting the balance, tendency to fall, intake of psychoactive medication, inability to stand on one leg, high biologic age) had an odds ratio of 11.27 (95% confidence interval 4.61-27.60) for a fall (sensitivity 70%, specificity 79%). The more time-consuming objective functional tests were of limited importance for fall prediction. A simple clinical history, the inability to stand on one leg, and a subjective estimate of biologic age were more important as part of the fall risk assessment.

Improved prediction of drug-target interactions using regularized least squares integrating with kernel fusion technique.

PubMed

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

2016-02-25

Identification of drug-target interactions (DTI) is a central task in drug discovery processes. In this work, a simple but effective regularized least squares integrating with nonlinear kernel fusion (RLS-KF) algorithm is proposed to perform DTI predictions. Using benchmark DTI datasets, our proposed algorithm achieves the state-of-the-art results with area under precision-recall curve (AUPR) of 0.915, 0.925, 0.853 and 0.909 for enzymes, ion channels (IC), G protein-coupled receptors (GPCR) and nuclear receptors (NR) based on 10 fold cross-validation. The performance can further be improved by using a recalculated kernel matrix, especially for the small set of nuclear receptors with AUPR of 0.945. Importantly, most of the top ranked interaction predictions can be validated by experimental data reported in the literature, bioassay results in the PubChem BioAssay database, as well as other previous studies. Our analysis suggests that the proposed RLS-KF is helpful for studying DTI, drug repositioning as well as polypharmacology, and may help to accelerate drug discovery by identifying novel drug targets. Published by Elsevier B.V.

Study on the measurement system of the target polarization characteristics and test

NASA Astrophysics Data System (ADS)

Fu, Qiang; Zhu, Yong; Zhang, Su; Duan, Jin; Yang, Di; Zhan, Juntong; Wang, Xiaoman; Jiang, Hui-Lin

2015-10-01

The polarization imaging detection technology increased the polarization information on the basis of the intensity imaging, which is extensive application in the military and civil and other fields, the research on the polarization characteristics of target is particularly important. The research of the polarization reflection model was introduced in this paper, which describes the scattering vector light energy distribution in reflecting hemisphere polarization characteristics, the target polarization characteristics test system solutions was put forward, by the irradiation light source, measuring turntable and camera, etc, which illuminate light source shall direct light source, with laser light sources and xenon lamp light source, light source can be replaced according to the test need; Hemispherical structure is used in measuring circumarotate placed near its base material sample, equipped with azimuth and pitching rotation mechanism, the manual in order to adjust the azimuth Angle and high Angle observation; Measuring camera pump works, through the different in the way of motor control polaroid polarization test, to ensure the accuracy of measurement and imaging resolution. The test platform has set up by existing laboratory equipment, the laser is 532 nm, line polaroid camera, at the same time also set the sending and receiving optical system. According to the different materials such as wood, metal, plastic, azimuth Angle and zenith Angle in different observation conditions, measurement of target in the polarization scattering properties of different exposure conditions, implementation of hemisphere space pBRDF measurement.

Artificial testing targets with controllable blur for adaptive optics microscopes

NASA Astrophysics Data System (ADS)

Hattori, Masayuki; Tamada, Yosuke; Murata, Takashi; Oya, Shin; Hasebe, Mitsuyasu; Hayano, Yutaka; Kamei, Yasuhiro

2017-08-01

This letter proposes a method of configuring a testing target to evaluate the performance of adaptive optics microscopes. In this method, a testing slide with fluorescent beads is used to simultaneously determine the point spread function and the field of view. The point spread function is reproduced to simulate actual biological samples by etching a microstructure on the cover glass. The fabrication process is simplified to facilitate an onsite preparation. The artificial tissue consists of solid materials and silicone oil and is stable for use in repetitive experiments.

Opto-mechanical system design of test system for near-infrared and visible target

NASA Astrophysics Data System (ADS)

Wang, Chunyan; Zhu, Guodong; Wang, Yuchao

2014-12-01

Guidance precision is the key indexes of the guided weapon shooting. The factors of guidance precision including: information processing precision, control system accuracy, laser irradiation accuracy and so on. The laser irradiation precision is an important factor. This paper aimed at the demand of the precision test of laser irradiator，and developed the laser precision test system. The system consists of modified cassegrain system, the wide range CCD camera, tracking turntable and industrial PC, and makes visible light and near infrared target imaging at the same time with a Near IR camera. Through the analysis of the design results, when it exposures the target of 1000 meters that the system measurement precision is43mm, fully meet the needs of the laser precision test.

Development of a forensic skin colour predictive test.

PubMed

Maroñas, Olalla; Phillips, Chris; Söchtig, Jens; Gomez-Tato, Antonio; Cruz, Raquel; Alvarez-Dios, José; de Cal, María Casares; Ruiz, Yarimar; Fondevila, Manuel; Carracedo, Ángel; Lareu, María V

2014-11-01

There is growing interest in skin colour prediction in the forensic field. However, a lack of consensus approaches for recording skin colour phenotype plus the complicating factors of epistatic effects, environmental influences such as exposure to the sun and unidentified genetic variants, present difficulties for the development of a forensic skin colour predictive test centred on the most strongly associated SNPs. Previous studies have analysed skin colour variation in single unadmixed population groups, including South Asians (Stokowski et al., 2007, Am. J. Hum. Genet, 81: 1119-32) and Europeans (Jacobs et al., 2013, Hum Genet. 132: 147-58). Nevertheless, a major challenge lies in the analysis of skin colour in admixed individuals, where co-ancestry proportions do not necessarily dictate any one person's skin colour. Our study sought to analyse genetic differences between African, European and admixed African-European subjects where direct spectrometric measurements and photographs of skin colour were made in parallel. We identified strong associations to skin colour variation in the subjects studied from a pigmentation SNP discovery panel of 59 markers and developed a forensic online classifier based on naïve Bayes analysis of the SNP profiles made. A skin colour predictive test is described using the ten most strongly associated SNPs in 8 genes linked to skin pigmentation variation. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Attentional Control via Parallel Target-Templates in Dual-Target Search

PubMed Central

Barrett, Doug J. K.; Zobay, Oliver

2014-01-01

Simultaneous search for two targets has been shown to be slower and less accurate than independent searches for the same two targets. Recent research suggests this ‘dual-target cost’ may be attributable to a limit in the number of target-templates than can guide search at any one time. The current study investigated this possibility by comparing behavioural responses during single- and dual-target searches for targets defined by their orientation. The results revealed an increase in reaction times for dual- compared to single-target searches that was largely independent of the number of items in the display. Response accuracy also decreased on dual- compared to single-target searches: dual-target accuracy was higher than predicted by a model restricting search guidance to a single target-template and lower than predicted by a model simulating two independent single-target searches. These results are consistent with a parallel model of dual-target search in which attentional control is exerted by more than one target-template at a time. The requirement to maintain two target-templates simultaneously, however, appears to impose a reduction in the specificity of the memory representation that guides search for each target. PMID:24489793

Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction.

PubMed

Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

2007-11-01

Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

Automatically detect and track infrared small targets with kernel Fukunaga-Koontz transform and Kalman prediction

NASA Astrophysics Data System (ADS)

Liu, Ruiming; Liu, Erqi; Yang, Jie; Zeng, Yong; Wang, Fanglin; Cao, Yuan

2007-11-01

Fukunaga-Koontz transform (FKT), stemming from principal component analysis (PCA), is used in many pattern recognition and image-processing fields. It cannot capture the higher-order statistical property of natural images, so its detection performance is not satisfying. PCA has been extended into kernel PCA in order to capture the higher-order statistics. However, thus far there have been no researchers who have definitely proposed kernel FKT (KFKT) and researched its detection performance. For accurately detecting potential small targets from infrared images, we first extend FKT into KFKT to capture the higher-order statistical properties of images. Then a framework based on Kalman prediction and KFKT, which can automatically detect and track small targets, is developed. Results of experiments show that KFKT outperforms FKT and the proposed framework is competent to automatically detect and track infrared point targets.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Blood test could predict risk of heart attack and subsequent death.

PubMed

2017-01-18

A high-sensitivity blood test, known as a troponin test, could predict the risk of heart attack and death and patients' response to statins, say researchers from the Universities of Edinburgh and Glasgow.

Testing light dark matter coannihilation with fixed-target experiments

DOE PAGES

Izaguirre, Eder; Kahn, Yonatan; Krnjaic, Gordan; ...

2017-09-01

In this paper, we introduce a novel program of fixed-target searches for thermal-origin Dark Matter (DM), which couples inelastically to the Standard Model. Since the DM only interacts by transitioning to a heavier state, freeze-out proceeds via coannihilation and the unstable heavier state is depleted at later times. For sufficiently large mass splittings, direct detection is kinematically forbidden and indirect detection is impossible, so this scenario can only be tested with accelerators. Here we propose new searches at proton and electron beam fixed-target experiments to probe sub-GeV coannihilation, exploiting the distinctive signals of up- and downscattering as well as decaymore » of the excited state inside the detector volume. We focus on a representative model in which DM is a pseudo-Dirac fermion coupled to a hidden gauge field (dark photon), which kinetically mixes with the visible photon. We define theoretical targets in this framework and determine the existing bounds by reanalyzing results from previous experiments. We find that LSND, E137, and BaBar data already place strong constraints on the parameter space consistent with a thermal freeze-out origin, and that future searches at Belle II and MiniBooNE, as well as recently-proposed fixed-target experiments such as LDMX and BDX, can cover nearly all remaining gaps. We also briefly comment on the discovery potential for proposed beam dump and neutrino experiments which operate at much higher beam energies.« less

Testing light dark matter coannihilation with fixed-target experiments

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

Izaguirre, Eder; Kahn, Yonatan; Krnjaic, Gordan

In this paper, we introduce a novel program of fixed-target searches for thermal-origin Dark Matter (DM), which couples inelastically to the Standard Model. Since the DM only interacts by transitioning to a heavier state, freeze-out proceeds via coannihilation and the unstable heavier state is depleted at later times. For sufficiently large mass splittings, direct detection is kinematically forbidden and indirect detection is impossible, so this scenario can only be tested with accelerators. Here we propose new searches at proton and electron beam fixed-target experiments to probe sub-GeV coannihilation, exploiting the distinctive signals of up- and downscattering as well as decaymore » of the excited state inside the detector volume. We focus on a representative model in which DM is a pseudo-Dirac fermion coupled to a hidden gauge field (dark photon), which kinetically mixes with the visible photon. We define theoretical targets in this framework and determine the existing bounds by reanalyzing results from previous experiments. We find that LSND, E137, and BaBar data already place strong constraints on the parameter space consistent with a thermal freeze-out origin, and that future searches at Belle II and MiniBooNE, as well as recently-proposed fixed-target experiments such as LDMX and BDX, can cover nearly all remaining gaps. We also briefly comment on the discovery potential for proposed beam dump and neutrino experiments which operate at much higher beam energies.« less

Testing light dark matter coannihilation with fixed-target experiments

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

Izaguirre, Eder; Kahn, Yonatan; Krnjaic, Gordan

In this paper, we introduce a novel program of fixed-target searches for thermal-origin Dark Matter (DM), which couples inelastically to the Standard Model. Since the DM only interacts by transitioning to a heavier state, freeze-out proceeds via coannihilation and the unstable heavier state is depleted at later times. For sufficiently large mass splittings, direct detection is kinematically forbidden and indirect detection is impossible, so this scenario can only be tested with accelerators. Here we propose new searches at proton and electron beam fixed-target experiments to probe sub-GeV coannihilation, exploiting the distinctive signals of up- and down-scattering as well as decaymore » of the excited state inside the detector volume. We focus on a representative model in which DM is a pseudo-Dirac fermion coupled to a hidden gauge field (dark photon), which kinetically mixes with the visible photon. We define theoretical targets in this framework and determine the existing bounds by reanalyzing results from previous experiments. We find that LSND, E137, and BaBar data already place strong constraints on the parameter space consistent with a thermal freeze-out origin, and that future searches at Belle II and MiniBooNE, as well as recently-proposed fixed-target experiments such as LDMX and BDX, can cover nearly all remaining gaps. We also briefly comment on the discovery potential for proposed beam dump and neutrino experiments which operate at much higher beam energies.« less

Two-Speed Gearbox Dynamic Simulation Predictions and Test Validation

NASA Technical Reports Server (NTRS)

Lewicki, David G.; DeSmidt, Hans; Smith, Edward C.; Bauman, Steven W.

2010-01-01

Dynamic simulations and experimental validation tests were performed on a two-stage, two-speed gearbox as part of the drive system research activities of the NASA Fundamental Aeronautics Subsonics Rotary Wing Project. The gearbox was driven by two electromagnetic motors and had two electromagnetic, multi-disk clutches to control output speed. A dynamic model of the system was created which included a direct current electric motor with proportional-integral-derivative (PID) speed control, a two-speed gearbox with dual electromagnetically actuated clutches, and an eddy current dynamometer. A six degree-of-freedom model of the gearbox accounted for the system torsional dynamics and included gear, clutch, shaft, and load inertias as well as shaft flexibilities and a dry clutch stick-slip friction model. Experimental validation tests were performed on the gearbox in the NASA Glenn gear noise test facility. Gearbox output speed and torque as well as drive motor speed and current were compared to those from the analytical predictions. The experiments correlate very well with the predictions, thus validating the dynamic simulation methodologies.

Predicting the Effects of Test Media in Ground-Based Propulsion Testing

NASA Technical Reports Server (NTRS)

Drummond, J. Philip; Danehy, Paul M.; Bivolaru, Daniel; Gaffney, Richard L.; Parker, Peter A.; Chelliah, Harsha K.; Cutler, Andrew D.; Givi, Peyman; Hassan, Hassan, A.

2006-01-01

This paper discusses the progress of work which began in mid-2004 sponsored by the Office of the Secretary of Defense (OSD) Test & Evaluation/Science & Technology (T&E/S&T) Program. The purpose of the work is to improve the state of the art of CFD capabilities for predicting the effects of the test media on the flameholding characteristics in scramjet engines. The program has several components including the development of advance algorithms and models for simulating engine flowpaths as well as a fundamental experimental and diagnostic development effort to support the formulation and validation of the mathematical models. The paper will provide details of current work involving the development of phenomenological models for Reynolds averaged Navier-Stokes codes, large-eddy simulation techniques and reduced-kinetics models. Experiments that will provide data for the modeling efforts will also be described, along with with the associated nonintrusive diagnostics used to collect the data.

Epidermal Growth Factor Receptor Mutation (EGFR) Testing for Prediction of Response to EGFR-Targeting Tyrosine Kinase Inhibitor (TKI) Drugs in Patients with Advanced Non-Small-Cell Lung Cancer: An Evidence-Based Analysis.

PubMed

2010-01-01

In February 2010, the Medical Advisory Secretariat (MAS) began work on evidence-based reviews of the literature surrounding three pharmacogenomic tests. This project came about when Cancer Care Ontario (CCO) asked MAS to provide evidence-based analyses on the effectiveness and cost-effectiveness of three oncology pharmacogenomic tests currently in use in Ontario.Evidence-based analyses have been prepared for each of these technologies. These have been completed in conjunction with internal and external stakeholders, including a Provincial Expert Panel on Pharmacogenetics (PEPP). Within the PEPP, subgroup committees were developed for each disease area. For each technology, an economic analysis was also completed by the Toronto Health Economics and Technology Assessment Collaborative (THETA) and is summarized within the reports.THE FOLLOWING REPORTS CAN BE PUBLICLY ACCESSED AT THE MAS WEBSITE AT: http://www.health.gov.on.ca/mas or at www.health.gov.on.ca/english/providers/program/mas/mas_about.htmlGENE EXPRESSION PROFILING FOR GUIDING ADJUVANT CHEMOTHERAPY DECISIONS IN WOMEN WITH EARLY BREAST CANCER: An Evidence-Based AnalysisEpidermal Growth Factor Receptor Mutation (EGFR) Testing for Prediction of Response to EGFR-Targeting Tyrosine Kinase Inhibitor (TKI) Drugs in Patients with Advanced Non-Small-Cell Lung Cancer: an Evidence-Based AnalysisK-RAS testing in Treatment Decisions for Advanced Colorectal Cancer: an Evidence-Based Analysis The Medical Advisory Secretariat undertook a systematic review of the evidence on the clinical effectiveness and cost-effectiveness of epidermal growth factor receptor (EGFR) mutation testing compared with no EGFR mutation testing to predict response to tyrosine kinase inhibitors (TKIs), gefitinib (Iressa(®)) or erlotinib (Tarceva(®)) in patients with advanced non-small cell lung cancer (NSCLC). TARGET POPULATION AND CONDITION With an estimated 7,800 new cases and 7,000 deaths last year, lung cancer is the leading cause of cancer

Online Control of Prehension Predicts Performance on a Standardized Motor Assessment Test in 8- to 12-Year-Old Children

PubMed Central

Blanchard, Caroline C. V.; McGlashan, Hannah L.; French, Blandine; Sperring, Rachel J.; Petrocochino, Bianca; Holmes, Nicholas P.

2017-01-01

Goal-directed hand movements are guided by sensory information and may be adjusted ‘online,’ during the movement. If the target of a movement unexpectedly changes position, trajectory corrections can be initiated in as little as 100 ms in adults. This rapid visual online control is impaired in children with developmental coordination disorder (DCD), and potentially in other neurodevelopmental conditions. We investigated the visual control of hand movements in children in a ‘center-out’ double-step reaching and grasping task, and examined how parameters of this visuomotor control co-vary with performance on standardized motor tests often used with typically and atypically developing children. Two groups of children aged 8–12 years were asked to reach and grasp an illuminated central ball on a vertically oriented board. On a proportion of trials, and at movement onset, the illumination switched unpredictably to one of four other balls in a center-out configuration (left, right, up, or down). When the target moved, all but one of the children were able to correct their movements before reaching the initial target, at least on some trials, but the latencies to initiate these corrections were longer than those typically reported in the adult literature, ranging from 211 to 581 ms. These later corrections may be due to less developed motor skills in children, or to the increased cognitive and biomechanical complexity of switching movements in four directions. In the first group (n = 187), reaching and grasping parameters significantly predicted standardized movement scores on the MABC-2, most strongly for the aiming and catching component. In the second group (n = 85), these same parameters did not significantly predict scores on the DCDQ′07 parent questionnaire. Our reaching and grasping task provides a sensitive and continuous measure of movement skill that predicts scores on standardized movement tasks used to screen for DCD. PMID:28360874

Can quantitative sensory testing predict responses to analgesic treatment?

PubMed

Grosen, K; Fischer, I W D; Olesen, A E; Drewes, A M

2013-10-01

The role of quantitative sensory testing (QST) in prediction of analgesic effect in humans is scarcely investigated. This updated review assesses the effectiveness in predicting analgesic effects in healthy volunteers, surgical patients and patients with chronic pain. A systematic review of English written, peer-reviewed articles was conducted using PubMed and Embase (1980-2013). Additional studies were identified by chain searching. Search terms included 'quantitative sensory testing', 'sensory testing' and 'analgesics'. Studies on the relationship between QST and response to analgesic treatment in human adults were included. Appraisal of the methodological quality of the included studies was based on evaluative criteria for prognostic studies. Fourteen studies (including 720 individuals) met the inclusion criteria. Significant correlations were observed between responses to analgesics and several QST parameters including (1) heat pain threshold in experimental human pain, (2) electrical and heat pain thresholds, pressure pain tolerance and suprathreshold heat pain in surgical patients, and (3) electrical and heat pain threshold and conditioned pain modulation in patients with chronic pain. Heterogeneity among studies was observed especially with regard to application of QST and type and use of analgesics. Although promising, the current evidence is not sufficiently robust to recommend the use of any specific QST parameter in predicting analgesic response. Future studies should focus on a range of different experimental pain modalities rather than a single static pain stimulation paradigm. © 2013 European Federation of International Association for the Study of Pain Chapters.

Can somatosensory and visual evoked potentials predict neurological outcome during targeted temperature management in post cardiac arrest patients?

PubMed

Choi, Seung Pill; Park, Kyu Nam; Wee, Jung Hee; Park, Jeong Ho; Youn, Chun Song; Kim, Han Joon; Oh, Sang Hoon; Oh, Yoon Sang; Kim, Soo Hyun; Oh, Joo Suk

2017-10-01

In cardiac arrest patients treated with targeted temperature management (TTM), it is not certain if somatosensory evoked potentials (SEPs) and visual evoked potentials (VEPs) can predict neurological outcomes during TTM. The aim of this study was to investigate the prognostic value of SEPs and VEPs during TTM and after rewarming. This retrospective cohort study included comatose patients resuscitated from cardiac arrest and treated with TTM between March 2007 and July 2015. SEPs and VEPs were recorded during TTM and after rewarming in these patients. Neurological outcome was assessed at discharge by the Cerebral Performance Category (CPC) Scale. In total, 115 patients were included. A total of 175 SEPs and 150 VEPs were performed. Five SEPs during treated with TTM and nine SEPs after rewarming were excluded from outcome prediction by SEPs due to an indeterminable N20 response because of technical error. Using 80 SEPs and 85 VEPs during treated with TTM, absent SEPs yielded a sensitivity of 58% and a specificity of 100% for poor outcome (CPC 3-5), and absent VEPs predicted poor neurological outcome with a sensitivity of 44% and a specificity of 96%. The AUC of combination of SEPs and VEPs was superior to either test alone (0.788 for absent SEPs and 0.713 for absent VEPs compared with 0.838 for the combination). After rewarming, absent SEPs and absent VEPs predicted poor neurological outcome with a specificity of 100%. When SEPs and VEPs were combined, VEPs slightly increased the prognostic accuracy of SEPs alone. Although one patient with absent VEP during treated with TTM had a good neurological outcome, none of the patients with good neurological outcome had an absent VEP after rewarming. Absent SEPs could predict poor neurological outcome during TTM as well as after rewarming. Absent VEPs may predict poor neurological outcome in both periods and VEPs may provide additional prognostic value in outcome prediction. Copyright © 2017 Elsevier B.V. All rights reserved.

Testing the Predictive Validity of the Hendrich II Fall Risk Model.

PubMed

Jung, Hyesil; Park, Hyeoun-Ae

2018-03-01

Cumulative data on patient fall risk have been compiled in electronic medical records systems, and it is possible to test the validity of fall-risk assessment tools using these data between the times of admission and occurrence of a fall. The Hendrich II Fall Risk Model scores assessed during three time points of hospital stays were extracted and used for testing the predictive validity: (a) upon admission, (b) when the maximum fall-risk score from admission to falling or discharge, and (c) immediately before falling or discharge. Predictive validity was examined using seven predictive indicators. In addition, logistic regression analysis was used to identify factors that significantly affect the occurrence of a fall. Among the different time points, the maximum fall-risk score assessed between admission and falling or discharge showed the best predictive performance. Confusion or disorientation and having a poor ability to rise from a sitting position were significant risk factors for a fall.

In vitro Perturbations of Targets in Cancer Hallmark Processes Predict Rodent Chemical Carcinogenesis

EPA Science Inventory

Thousands of untested chemicals in the environment require efficient characterization of carcinogenic potential in humans. A proposed solution is rapid testing of chemicals using in vitro high-throughput screening (HTS) assays for targets in pathways linked to disease processes ...

Comparison of Predicted and Measured Attenuation of Turbine Noise from a Static Engine Test

NASA Technical Reports Server (NTRS)

Chien, Eugene W.; Ruiz, Marta; Yu, Jia; Morin, Bruce L.; Cicon, Dennis; Schwieger, Paul S.; Nark, Douglas M.

2007-01-01

Aircraft noise has become an increasing concern for commercial airlines. Worldwide demand for quieter aircraft is increasing, making the prediction of engine noise suppression one of the most important fields of research. The Low-Pressure Turbine (LPT) can be an important noise source during the approach condition for commercial aircraft. The National Aeronautics and Space Administration (NASA), Pratt & Whitney (P&W), and Goodrich Aerostructures (Goodrich) conducted a joint program to validate a method for predicting turbine noise attenuation. The method includes noise-source estimation, acoustic treatment impedance prediction, and in-duct noise propagation analysis. Two noise propagation prediction codes, Eversman Finite Element Method (FEM) code [1] and the CDUCT-LaRC [2] code, were used in this study to compare the predicted and the measured turbine noise attenuation from a static engine test. In this paper, the test setup, test configurations and test results are detailed in Section II. A description of the input parameters, including estimated noise modal content (in terms of acoustic potential), and acoustic treatment impedance values are provided in Section III. The prediction-to-test correlation study results are illustrated and discussed in Section IV and V for the FEM and the CDUCT-LaRC codes, respectively, and a summary of the results is presented in Section VI.

Contrast media enhancement reduction predicts tumor response to presurgical molecular-targeting therapy in patients with advanced renal cell carcinoma.

PubMed

Hosogoe, Shogo; Hatakeyama, Shingo; Kusaka, Ayumu; Hamano, Itsuto; Tanaka, Yoshimi; Hagiwara, Kazuhisa; Hirai, Hideaki; Morohashi, Satoko; Kijima, Hiroshi; Yamamoto, Hayato; Tobisawa, Yuki; Yoneyama, Tohru; Yoneyama, Takahiro; Hashimoto, Yasuhiro; Koie, Takuya; Ohyama, Chikara

2017-07-25

A quantitative tumor response evaluation to molecular-targeting agents in advanced renal cell carcinoma (RCC) is debatable. We aimed to evaluate the relationship between radiologic tumor response and pathological response in patients with advanced RCC who underwent presurgical therapy. Of 34 patients, 31 underwent scheduled radical nephrectomy. Presurgical therapy agents included axitinib (n = 26), everolimus (n = 3), sunitinib (n = 1), and axitinib followed by temsirolimus (n = 1). The major presurgical treatment-related adverse event was grade 2 or 3 hypertension (44%). The median radiologic tumor response by RECIST, Choi, and CMER were -19%, -24%, and -49%, respectively. Among the radiologic tumor response tests, CMER showed a higher association with tumor necrosis in surgical specimens than others. Ki67/MIB1 status was significantly decreased in surgical specimens than in biopsy specimens. The magnitude of the slope of the regression line associated with the tumor necrosis percentage was greater in CMER than in Choi and RECIST. Between March 2012 and December 2016, we prospectively enrolled 34 locally advanced and/or metastatic RCC who underwent presurgical molecular-targeting therapy followed by radical nephrectomy. Primary endpoint was comparison of radiologic tumor response among Response Evaluation Criteria in Solid Tumors (RECIST), Choi, and contrast media enhancement reduction (CMER). Secondary endpoint included pathological downstaging, treatment related adverse events, postoperative complications, Ki67/MIB1 status, and tumor necrosis. CMER may predict tumor response after presurgical molecular-targeting therapy. Larger prospective studies are needed to develop an optimal tumor response evaluation for molecular-targeting therapy.

Memory Binding Test Predicts Incident Amnestic Mild Cognitive Impairment.

PubMed

Mowrey, Wenzhu B; Lipton, Richard B; Katz, Mindy J; Ramratan, Wendy S; Loewenstein, David A; Zimmerman, Molly E; Buschke, Herman

2016-07-14

The Memory Binding Test (MBT), previously known as Memory Capacity Test, has demonstrated discriminative validity for distinguishing persons with amnestic mild cognitive impairment (aMCI) and dementia from cognitively normal elderly. We aimed to assess the predictive validity of the MBT for incident aMCI. In a longitudinal, community-based study of adults aged 70+, we administered the MBT to 246 cognitively normal elderly adults at baseline and followed them annually. Based on previous work, a subtle reduction in memory binding at baseline was defined by a Total Items in the Paired (TIP) condition score of ≤22 on the MBT. Cox proportional hazards models were used to assess the predictive validity of the MBT for incident aMCI accounting for the effects of covariates. The hazard ratio of incident aMCI was also assessed for different prediction time windows ranging from 4 to 7 years of follow-up, separately. Among 246 controls who were cognitively normal at baseline, 48 developed incident aMCI during follow-up. A baseline MBT reduction was associated with an increased risk for developing incident aMCI (hazard ratio (HR) = 2.44, 95% confidence interval: 1.30-4.56, p = 0.005). When varying the prediction window from 4-7 years, the MBT reduction remained significant for predicting incident aMCI (HR range: 2.33-3.12, p: 0.0007-0.04). Persons with poor performance on the MBT are at significantly greater risk for developing incident aMCI. High hazard ratios up to seven years of follow-up suggest that the MBT is sensitive to early disease.

Predicting Health Resilience in Pediatric Type 1 Diabetes: A Test of the Resilience Model Framework

PubMed Central

Huang, Bin; Pendley, Jennifer Shroff; Delamater, Alan; Dolan, Lawrence; Reeves, Grafton; Drotar, Dennis

2015-01-01

Objectives This research examined whether individual and family-level factors during the transition from late childhood to early adolescence protected individuals from an increased risk of poor glycemic control across time, which is a predictor of future diabetes-related complications (i.e., health resilience). Methods This longitudinal, multisite study included 239 patients with type 1 diabetes and their caregivers. Glycemic control was based on hemoglobin A1c. Individual and family-level factors included: demographic variables, youth behavioral regulation, adherence (frequency of blood glucose monitoring), diabetes self-management, level of parental support for diabetes autonomy, level of youth mastery and responsibility for diabetes management, and diabetes-related family conflict. Results Longitudinal mixed-effects logistic regression indicated that testing blood glucose more frequently, better self-management, and less diabetes-related family conflict were indicators of health resilience. Conclusions Multiple individual and family-level factors predicted risk for future health complications. Future research should develop interventions targeting specific individual and family-level factors to sustain glycemic control within recommended targets, which reduces the risk of developing future health complications during the transition to adolescence and adulthood. PMID:26152400

AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer's disease.

PubMed

Fang, Jiansong; Wang, Ling; Li, Yecheng; Lian, Wenwen; Pang, Xiaocong; Wang, Hong; Yuan, Dongsheng; Wang, Qi; Liu, Ai-Lin; Du, Guan-Hua

2017-01-01

Alzheimer's disease (AD) is a complicated progressive neurodegeneration disorder. To confront AD, scientists are searching for multi-target-directed ligands (MTDLs) to delay disease progression. The in silico prediction of chemical-protein interactions (CPI) can accelerate target identification and drug discovery. Previously, we developed 100 binary classifiers to predict the CPI for 25 key targets against AD using the multi-target quantitative structure-activity relationship (mt-QSAR) method. In this investigation, we aimed to apply the mt-QSAR method to enlarge the model library to predict CPI towards AD. Another 104 binary classifiers were further constructed to predict the CPI for 26 preclinical AD targets based on the naive Bayesian (NB) and recursive partitioning (RP) algorithms. The internal 5-fold cross-validation and external test set validation were applied to evaluate the performance of the training sets and test set, respectively. The area under the receiver operating characteristic curve (ROC) for the test sets ranged from 0.629 to 1.0, with an average of 0.903. In addition, we developed a web server named AlzhCPI to integrate the comprehensive information of approximately 204 binary classifiers, which has potential applications in network pharmacology and drug repositioning. AlzhCPI is available online at http://rcidm.org/AlzhCPI/index.html. To illustrate the applicability of AlzhCPI, the developed system was employed for the systems pharmacology-based investigation of shichangpu against AD to enhance the understanding of the mechanisms of action of shichangpu from a holistic perspective.

Predictive Testing

MedlinePlus

... Financial Planning Who Should I Tell? Genetic Testing & Counseling Compensation for Genetic Testing Whole Genome Sequencing Screening vs. Testing What Is Genetic Counseling? Participating in Research Disease Research Patient Privacy Clinical ...

Identification of MicroRNA Targets of Capsicum spp. Using MiRTrans—a Trans-Omics Approach

PubMed Central

Zhang, Lu; Qin, Cheng; Mei, Junpu; Chen, Xiaocui; Wu, Zhiming; Luo, Xirong; Cheng, Jiaowen; Tang, Xiangqun; Hu, Kailin; Li, Shuai C.

2017-01-01

The microRNA (miRNA) can regulate the transcripts that are involved in eukaryotic cell proliferation, differentiation, and metabolism. Especially for plants, our understanding of miRNA targets, is still limited. Early attempts of prediction on sequence alignments have been plagued by enormous false positives. It is helpful to improve target prediction specificity by incorporating the other data sources such as the dependency between miRNA and transcript expression or even cleaved transcripts by miRNA regulations, which are referred to as trans-omics data. In this paper, we developed MiRTrans (Prediction of MiRNA targets by Trans-omics data) to explore miRNA targets by incorporating miRNA sequencing, transcriptome sequencing, and degradome sequencing. MiRTrans consisted of three major steps. First, the target transcripts of miRNAs were predicted by scrutinizing their sequence characteristics and collected as an initial potential targets pool. Second, false positive targets were eliminated if the expression of miRNA and its targets were weakly correlated by lasso regression. Third, degradome sequencing was utilized to capture the miRNA targets by examining the cleaved transcripts that regulated by miRNAs. Finally, the predicted targets from the second and third step were combined by Fisher's combination test. MiRTrans was applied to identify the miRNA targets for Capsicum spp. (i.e., pepper). It can generate more functional miRNA targets than sequence-based predictions by evaluating functional enrichment. MiRTrans identified 58 miRNA-transcript pairs with high confidence from 18 miRNA families conserved in eudicots. Most of these targets were transcription factors; this lent support to the role of miRNA as key regulator in pepper. To our best knowledge, this work is the first attempt to investigate the miRNA targets of pepper, as well as their regulatory networks. Surprisingly, only a small proportion of miRNA-transcript pairs were shared between degradome sequencing

Target-D: a stratified individually randomized controlled trial of the diamond clinical prediction tool to triage and target treatment for depressive symptoms in general practice: study protocol for a randomized controlled trial.

PubMed

Gunn, Jane; Wachtler, Caroline; Fletcher, Susan; Davidson, Sandra; Mihalopoulos, Cathrine; Palmer, Victoria; Hegarty, Kelsey; Coe, Amy; Murray, Elizabeth; Dowrick, Christopher; Andrews, Gavin; Chondros, Patty

2017-07-20

Depression is a highly prevalent and costly disorder. Effective treatments are available but are not always delivered to the right person at the right time, with both under- and over-treatment a problem. Up to half the patients presenting to general practice report symptoms of depression, but general practitioners have no systematic way of efficiently identifying level of need and allocating treatment accordingly. Therefore, our team developed a new clinical prediction tool (CPT) to assist with this task. The CPT predicts depressive symptom severity in three months' time and based on these scores classifies individuals into three groups (minimal/mild, moderate, severe), then provides a matched treatment recommendation. This study aims to test whether using the CPT reduces depressive symptoms at three months compared with usual care. The Target-D study is an individually randomized controlled trial. Participants will be 1320 general practice patients with depressive symptoms who will be approached in the practice waiting room by a research assistant and invited to complete eligibility screening on an iPad. Eligible patients will provide informed consent and complete the CPT on a purpose-built website. A computer-generated allocation sequence stratified by practice and depressive symptom severity group, will randomly assign participants to intervention (treatment recommendation matched to predicted depressive symptom severity group) or comparison (usual care plus Target-D attention control) arms. Follow-up assessments will be completed online at three and 12 months. The primary outcome is depressive symptom severity at three months. Secondary outcomes include anxiety, mental health self-efficacy, quality of life, and cost-effectiveness. Intention-to-treat analyses will test for differences in outcome means between study arms overall and by depressive symptom severity group. To our knowledge, this is the first depressive symptom stratification tool designed for

Experience over fifteen years with a protocol for predictive testing for Huntington disease.

PubMed

Dufrasne, Suzanne; Roy, Madeleine; Galvez, Maria; Rosenblatt, David S

2011-04-01

To compile a comprehensive profile of the participants who had predictive testing from Huntington disease (HD) between 1994 and 2008 in Montreal, Canada. This is a retrospective cohort study. The predictive testing protocol consisted of a telephone interview to give information about predictive testing and collect demographic data; a psychological assessment and counseling session; a session focused on medical and family history of HD; a session reserved for genetic counseling; a session where results were given to participants; and a follow-up telephone interview. A total of 181 applicants requested presymptomatic testing. 135 applicants (77 women and 58 men) completed the protocol and received test results while 40 withdrew. Of the latter, 3 manifested symptoms of the disease and were referred to a neurologist or psychiatrist, and 3 had previously been tested by linkage analysis. Participants usually mentioned more than one reason for requesting predictive testing but the most frequent was to put an end to uncertainty concerning their risk of illness. The proportion of positive and negatives test results was 40% and 54.1% respectively, significantly different from the expected 50% (p<0.01). Prenatal testing was not frequently requested. All the participants expressed satisfaction regarding their decision to be tested. None to our knowledge had a catastrophic reaction (major depressive disorder or psychiatric hospitalization, declared suicide attempt or suicide). Our study highlights that preparation for receiving test results is a psychologically complex process for which appropriate support in a timely fashion is critical. We feel that a cautious and ethical case by case approach remains essential and that high standards of testing should be maintained because of the far reaching impact of test results. Copyright © 2010 Elsevier Inc. All rights reserved.

Contextual match and cue-independence of retrieval-induced forgetting: Testing the prediction of the model by Norman, Newman, and Detre (2007).

PubMed

Hanczakowski, Maciej; Mazzoni, Giuliana

2013-05-01

Retrieval-induced forgetting (RIF) is the finding of impaired memory performance for information stored in long-term memory due to retrieval of a related set of information. This phenomenon is often assigned to operations of a specialized mechanism recruited to resolve interference during retrieval by deactivating competing memory representations. This inhibitory account is supported by, among others, findings showing that RIF occurs with independent cues not used during retrieval practice. However, these findings are not always consistent. Recently, Norman, Newman, and Detre (2007) have proposed a model that aims at resolving discrepancies concerning cue-independence of RIF. The model predicts that RIF should be present with independent cues when episodic associations are created between independent cues and their targets in the same episodic context that is later used to cue memory during retrieval practice. In the present study we aimed to test this prediction. We associated studied items with semantically unrelated words during the main study phase of the retrieval practice paradigm, and we tested memory with both cues used during retrieval practice (Experiment 2) and episodic associates serving as independent cues (Experiments 3a and 3b). Although RIF was present when the same cues were used during retrieval practice and a final test, contrary to the prediction formulated by Norman et al., RIF failed to emerge when episodic associates were employed as independent cues.

Predicting Student Performance in Statewide High-Stakes Tests for Middle School Mathematics Using the Results from Third Party Testing Instruments

ERIC Educational Resources Information Center

Meylani, Rusen; Bitter, Gary G.; Castaneda, Rene

2014-01-01

In this study regression and neural networks based methods are used to predict statewide high-stakes test results for middle school mathematics using the scores obtained from third party tests throughout the school year. Such prediction is of utmost significance for school districts to live up to the state's educational standards mandated by the…

Visual performance on detection tasks with double-targets of the same and different difficulty.

PubMed

Chan, Alan H S; Courtney, Alan J; Ma, C W

2002-10-20

This paper reports a study of measurement of horizontal visual sensitivity limits for 16 subjects in single-target and double-targets detection tasks. Two phases of tests were conducted in the double-targets task; targets of the same difficulty were tested in phase one while targets of different difficulty were tested in phase two. The range of sensitivity for the double-targets test was found to be smaller than that for single-target in both the same and different target difficulty cases. The presence of another target was found to affect performance to a marked degree. Interference effect of the difficult target on detection of the easy one was greater than that of the easy one on the detection of the difficult one. Performance decrement was noted when correct percentage detection was plotted against eccentricity of target in both the single-target and double-targets tests. Nevertheless, the non-significant correlation found between the performance for the two tasks demonstrated that it was impossible to predict quantitatively ability for detection of double targets from the data for single targets. This indicated probable problems in generalizing data for single target visual lobes to those for multiple targets. Also lobe area values obtained from measurements using a single-target task cannot be applied in a mathematical model for situations with multiple occurrences of targets.

Chemical Structural Novelty: On-Targets and Off-Targets

PubMed Central

Yera, Emmanuel R.; Cleves, Ann. E.; Jain, Ajay N.

2011-01-01

Drug structures may be quantitatively compared based on 2D topological structural considerations and based on 3D characteristics directly related to binding. A framework for combining multiple similarity computations is presented along with its systematic application to 358 drugs with overlapping pharmacology. Given a new molecule along with a set of molecules sharing some biological effect, a single score based on comparison to the known set is produced, reflecting either 2D similarity, 3D similarity, or their combination. For prediction of primary targets, the benefit of 3D over 2D was relatively small, but for prediction of off-targets, the added benefit was large. In addition to assessing prediction, the relationship between chemical similarity and pharmacological novelty was studied. Drug pairs that shared high 3D similarity but low 2D similarity (i.e. a novel scaffold) were shown to be much more likely to exhibit pharmacologically relevant differences in terms of specific protein target modulation. PMID:21916467

[Current status of the predictive genetic testing for hereditary neurological diseases in Shinshu University Hospital].

PubMed