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Sample records for applications predicting drug-target

  1. Prediction of potential drug targets based on simple sequence properties

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    Lai Luhua

    2007-09-01

    Full Text Available Abstract Background During the past decades, research and development in drug discovery have attracted much attention and efforts. However, only 324 drug targets are known for clinical drugs up to now. Identifying potential drug targets is the first step in the process of modern drug discovery for developing novel therapeutic agents. Therefore, the identification and validation of new and effective drug targets are of great value for drug discovery in both academia and pharmaceutical industry. If a protein can be predicted in advance for its potential application as a drug target, the drug discovery process targeting this protein will be greatly speeded up. In the current study, based on the properties of known drug targets, we have developed a sequence-based drug target prediction method for fast identification of novel drug targets. Results Based on simple physicochemical properties extracted from protein sequences of known drug targets, several support vector machine models have been constructed in this study. The best model can distinguish currently known drug targets from non drug targets at an accuracy of 84%. Using this model, potential protein drug targets of human origin from Swiss-Prot were predicted, some of which have already attracted much attention as potential drug targets in pharmaceutical research. Conclusion We have developed a drug target prediction method based solely on protein sequence information without the knowledge of family/domain annotation, or the protein 3D structure. This method can be applied in novel drug target identification and validation, as well as genome scale drug target predictions.

  2. A Review of Computational Methods for Predicting Drug Targets.

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    Huang, Guohua; Yan, Fengxia; Tan, Duoduo

    2016-11-14

    Drug discovery and development is not only a time-consuming and labor-intensive process but also full of risk. Identifying targets of small molecules helps evaluate safety of drugs and find new therapeutic applications. The biotechnology measures a wide variety of properties related to drug and targets from different perspectives, thus generating a large body of data. This undoubtedly provides a solid foundation to explore relationships between drugs and targets. A large number of computational techniques have recently been developed for drug target prediction. In this paper, we summarize these computational methods and classify them into structure-based, molecular activity-based, side-effect-based and multi-omics-based predictions according to the used data for inference. The multi-omics-based methods are further grouped into two types: classifier-based and network-based predictions. Furthermore,the advantages and limitations of each type of methods are discussed. Finally, we point out the future directions of computational predictions for drug targets.

  3. Deep-Learning-Based Drug-Target Interaction Prediction.

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    Wen, Ming; Zhang, Zhimin; Niu, Shaoyu; Sha, Haozhi; Yang, Ruihan; Yun, Yonghuan; Lu, Hongmei

    2017-03-13

    Identifying interactions between known drugs and targets is a major challenge in drug repositioning. In silico prediction of drug-target interaction (DTI) can speed up the expensive and time-consuming experimental work by providing the most potent DTIs. In silico prediction of DTI can also provide insights about the potential drug-drug interaction and promote the exploration of drug side effects. Traditionally, the performance of DTI prediction depends heavily on the descriptors used to represent the drugs and the target proteins. In this paper, to accurately predict new DTIs between approved drugs and targets without separating the targets into different classes, we developed a deep-learning-based algorithmic framework named DeepDTIs. It first abstracts representations from raw input descriptors using unsupervised pretraining and then applies known label pairs of interaction to build a classification model. Compared with other methods, it is found that DeepDTIs reaches or outperforms other state-of-the-art methods. The DeepDTIs can be further used to predict whether a new drug targets to some existing targets or whether a new target interacts with some existing drugs.

  4. Mining predicted essential genes of Brugia malayi for nematode drug targets.

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    Sanjay Kumar

    Full Text Available We report results from the first genome-wide application of a rational drug target selection methodology to a metazoan pathogen genome, the completed draft sequence of Brugia malayi, a parasitic nematode responsible for human lymphatic filariasis. More than 1.5 billion people worldwide are at risk of contracting lymphatic filariasis and onchocerciasis, a related filarial disease. Drug treatments for filariasis have not changed significantly in over 20 years, and with the risk of resistance rising, there is an urgent need for the development of new anti-filarial drug therapies. The recent publication of the draft genomic sequence for B. malayi enables a genome-wide search for new drug targets. However, there is no functional genomics data in B. malayi to guide the selection of potential drug targets. To circumvent this problem, we have utilized the free-living model nematode Caenorhabditis elegans as a surrogate for B. malayi. Sequence comparisons between the two genomes allow us to map C. elegans orthologs to B. malayi genes. Using these orthology mappings and by incorporating the extensive genomic and functional genomic data, including genome-wide RNAi screens, that already exist for C. elegans, we identify potentially essential genes in B. malayi. Further incorporation of human host genome sequence data and a custom algorithm for prioritization enables us to collect and rank nearly 600 drug target candidates. Previously identified potential drug targets cluster near the top of our prioritized list, lending credibility to our methodology. Over-represented Gene Ontology terms, predicted InterPro domains, and RNAi phenotypes of C. elegans orthologs associated with the potential target pool are identified. By virtue of the selection procedure, the potential B. malayi drug targets highlight components of key processes in nematode biology such as central metabolism, molting and regulation of gene expression.

  5. Mining predicted essential genes of Brugia malayi for nematode drug targets.

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    Kumar, Sanjay; Chaudhary, Kshitiz; Foster, Jeremy M; Novelli, Jacopo F; Zhang, Yinhua; Wang, Shiliang; Spiro, David; Ghedin, Elodie; Carlow, Clotilde K S

    2007-01-01

    We report results from the first genome-wide application of a rational drug target selection methodology to a metazoan pathogen genome, the completed draft sequence of Brugia malayi, a parasitic nematode responsible for human lymphatic filariasis. More than 1.5 billion people worldwide are at risk of contracting lymphatic filariasis and onchocerciasis, a related filarial disease. Drug treatments for filariasis have not changed significantly in over 20 years, and with the risk of resistance rising, there is an urgent need for the development of new anti-filarial drug therapies. The recent publication of the draft genomic sequence for B. malayi enables a genome-wide search for new drug targets. However, there is no functional genomics data in B. malayi to guide the selection of potential drug targets. To circumvent this problem, we have utilized the free-living model nematode Caenorhabditis elegans as a surrogate for B. malayi. Sequence comparisons between the two genomes allow us to map C. elegans orthologs to B. malayi genes. Using these orthology mappings and by incorporating the extensive genomic and functional genomic data, including genome-wide RNAi screens, that already exist for C. elegans, we identify potentially essential genes in B. malayi. Further incorporation of human host genome sequence data and a custom algorithm for prioritization enables us to collect and rank nearly 600 drug target candidates. Previously identified potential drug targets cluster near the top of our prioritized list, lending credibility to our methodology. Over-represented Gene Ontology terms, predicted InterPro domains, and RNAi phenotypes of C. elegans orthologs associated with the potential target pool are identified. By virtue of the selection procedure, the potential B. malayi drug targets highlight components of key processes in nematode biology such as central metabolism, molting and regulation of gene expression.

  6. Drug target prediction and prioritization: using orthology to predict essentiality in parasite genomes

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    Hall Ross S

    2010-04-01

    Full Text Available Abstract Background New drug targets are urgently needed for parasites of socio-economic importance. Genes that are essential for parasite survival are highly desirable targets, but information on these genes is lacking, as gene knockouts or knockdowns are difficult to perform in many species of parasites. We examined the applicability of large-scale essentiality information from four model eukaryotes, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Saccharomyces cerevisiae, to discover essential genes in each of their genomes. Parasite genes that lack orthologues in their host are desirable as selective targets, so we also examined prediction of essential genes within this subset. Results Cross-species analyses showed that the evolutionary conservation of genes and the presence of essential orthologues are each strong predictors of essentiality in eukaryotes. Absence of paralogues was also found to be a general predictor of increased relative essentiality. By combining several orthology and essentiality criteria one can select gene sets with up to a five-fold enrichment in essential genes compared with a random selection. We show how quantitative application of such criteria can be used to predict a ranked list of potential drug targets from Ancylostoma caninum and Haemonchus contortus - two blood-feeding strongylid nematodes, for which there are presently limited sequence data but no functional genomic tools. Conclusions The present study demonstrates the utility of using orthology information from multiple, diverse eukaryotes to predict essential genes. The data also emphasize the challenge of identifying essential genes among those in a parasite that are absent from its host.

  7. Drug-target interaction prediction by random walk on the heterogeneous network.

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    Chen, Xing; Liu, Ming-Xi; Yan, Gui-Ying

    2012-07-01

    Predicting potential drug-target interactions from heterogeneous biological data is critical not only for better understanding of the various interactions and biological processes, but also for the development of novel drugs and the improvement of human medicines. In this paper, the method of Network-based Random Walk with Restart on the Heterogeneous network (NRWRH) is developed to predict potential drug-target interactions on a large scale under the hypothesis that similar drugs often target similar target proteins and the framework of Random Walk. Compared with traditional supervised or semi-supervised methods, NRWRH makes full use of the tool of the network for data integration to predict drug-target associations. It integrates three different networks (protein-protein similarity network, drug-drug similarity network, and known drug-target interaction networks) into a heterogeneous network by known drug-target interactions and implements the random walk on this heterogeneous network. When applied to four classes of important drug-target interactions including enzymes, ion channels, GPCRs and nuclear receptors, NRWRH significantly improves previous methods in terms of cross-validation and potential drug-target interaction prediction. Excellent performance enables us to suggest a number of new potential drug-target interactions for drug development.

  8. Drug-target interaction prediction: databases, web servers and computational models.

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    Chen, Xing; Yan, Chenggang Clarence; Zhang, Xiaotian; Zhang, Xu; Dai, Feng; Yin, Jian; Zhang, Yongdong

    2016-07-01

    Identification of drug-target interactions is an important process in drug discovery. Although high-throughput screening and other biological assays are becoming available, experimental methods for drug-target interaction identification remain to be extremely costly, time-consuming and challenging even nowadays. Therefore, various computational models have been developed to predict potential drug-target associations on a large scale. In this review, databases and web servers involved in drug-target identification and drug discovery are summarized. In addition, we mainly introduced some state-of-the-art computational models for drug-target interactions prediction, including network-based method, machine learning-based method and so on. Specially, for the machine learning-based method, much attention was paid to supervised and semi-supervised models, which have essential difference in the adoption of negative samples. Although significant improvements for drug-target interaction prediction have been obtained by many effective computational models, both network-based and machine learning-based methods have their disadvantages, respectively. Furthermore, we discuss the future directions of the network-based drug discovery and network approach for personalized drug discovery based on personalized medicine, genome sequencing, tumor clone-based network and cancer hallmark-based network. Finally, we discussed the new evaluation validation framework and the formulation of drug-target interactions prediction problem by more realistic regression formulation based on quantitative bioactivity data.

  9. Optimized shapes of magnetic arrays for drug targeting applications

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    Barnsley, Lester C.; Carugo, Dario; Stride, Eleanor

    2016-06-01

    Arrays of permanent magnet elements have been utilized as light-weight, inexpensive sources for applying external magnetic fields in magnetic drug targeting applications, but they are extremely limited in the range of depths over which they can apply useful magnetic forces. In this paper, designs for optimized magnet arrays are presented, which were generated using an optimization routine to maximize the magnetic force available from an arbitrary arrangement of magnetized elements, depending on a set of design parameters including the depth of targeting (up to 50 mm from the magnet) and direction of force required. A method for assembling arrays in practice is considered, quantifying the difficulty of assembly and suggesting a means for easing this difficulty without a significant compromise to the applied field or force. Finite element simulations of in vitro magnetic retention experiments were run to demonstrate the capability of a subset of arrays to retain magnetic microparticles against flow. The results suggest that, depending on the choice of array, a useful proportion of particles (more than 10% ) could be retained at flow velocities up to 100 mm s-1 or to depths as far as 50 mm from the magnet. Finally, the optimization routine was used to generate a design for a Halbach array optimized to deliver magnetic force to a depth of 50 mm inside the brain.

  10. Large-scale prediction of drug-target interactions using protein sequences and drug topological structures

    Energy Technology Data Exchange (ETDEWEB)

    Cao Dongsheng [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Liu Shao [Xiangya Hospital, Central South University, Changsha 410008 (China); Xu Qingsong [School of Mathematical Sciences and Computing Technology, Central South University, Changsha 410083 (China); Lu Hongmei; Huang Jianhua [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China); Hu Qiannan [Key Laboratory of Combinatorial Biosynthesis and Drug Discovery (Wuhan University), Ministry of Education, and Wuhan University School of Pharmaceutical Sciences, Wuhan 430071 (China); Liang Yizeng, E-mail: yizeng_liang@263.net [Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083 (China)

    2012-11-08

    Highlights: Black-Right-Pointing-Pointer Drug-target interactions are predicted using an extended SAR methodology. Black-Right-Pointing-Pointer A drug-target interaction is regarded as an event triggered by many factors. Black-Right-Pointing-Pointer Molecular fingerprint and CTD descriptors are used to represent drugs and proteins. Black-Right-Pointing-Pointer Our approach shows compatibility between the new scheme and current SAR methodology. - Abstract: The identification of interactions between drugs and target proteins plays a key role in the process of genomic drug discovery. It is both consuming and costly to determine drug-target interactions by experiments alone. Therefore, there is an urgent need to develop new in silico prediction approaches capable of identifying these potential drug-target interactions in a timely manner. In this article, we aim at extending current structure-activity relationship (SAR) methodology to fulfill such requirements. In some sense, a drug-target interaction can be regarded as an event or property triggered by many influence factors from drugs and target proteins. Thus, each interaction pair can be represented theoretically by using these factors which are based on the structural and physicochemical properties simultaneously from drugs and proteins. To realize this, drug molecules are encoded with MACCS substructure fingerings representing existence of certain functional groups or fragments; and proteins are encoded with some biochemical and physicochemical properties. Four classes of drug-target interaction networks in humans involving enzymes, ion channels, G-protein-coupled receptors (GPCRs) and nuclear receptors, are independently used for establishing predictive models with support vector machines (SVMs). The SVM models gave prediction accuracy of 90.31%, 88.91%, 84.68% and 83.74% for four datasets, respectively. In conclusion, the results demonstrate the ability of our proposed method to predict the drug-target

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

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    Hao, Ming; Wang, Yanli, E-mail: ywang@ncbi.nlm.nih.gov; Bryant, Stephen H., E-mail: bryant@ncbi.nlm.nih.gov

    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. - Graphical abstract: Flowchart of the proposed RLS-KF algorithm for drug-target interaction predictions. - Highlights: • A nonlinear kernel fusion algorithm is proposed to perform drug-target interaction predictions. • Performance can further be improved by using the recalculated kernel. • Top predictions can be validated by experimental data.

  12. Application of RNAi to Genomic Drug Target Validation in Schistosomes.

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    Alessandra Guidi

    2015-05-01

    Full Text Available Concerns over the possibility of resistance developing to praziquantel (PZQ, has stimulated efforts to develop new drugs for schistosomiasis. In addition to the development of improved whole organism screens, the success of RNA interference (RNAi in schistosomes offers great promise for the identification of potential drug targets to initiate drug discovery. In this study we set out to contribute to RNAi based validation of putative drug targets. Initially a list of 24 target candidates was compiled based on the identification of putative essential genes in schistosomes orthologous of C. elegans essential genes. Knockdown of Calmodulin (Smp_026560.2 (Sm-Calm, that topped this list, produced a phenotype characterised by waves of contraction in adult worms but no phenotype in schistosomula. Knockdown of the atypical Protein Kinase C (Smp_096310 (Sm-aPKC resulted in loss of viability in both schistosomula and adults and led us to focus our attention on other kinase genes that were identified in the above list and through whole organism screening of known kinase inhibitor sets followed by chemogenomic evaluation. RNAi knockdown of these kinase genes failed to affect adult worm viability but, like Sm-aPKC, knockdown of Polo-like kinase 1, Sm-PLK1 (Smp_009600 and p38-MAPK, Sm-MAPK p38 (Smp_133020 resulted in an increased mortality of schistosomula after 2-3 weeks, an effect more marked in the presence of human red blood cells (hRBC. For Sm-PLK-1 the same effects were seen with the specific inhibitor, BI2536, which also affected viable egg production in adult worms. For Sm-PLK-1 and Sm-aPKC the in vitro effects were reflected in lower recoveries in vivo. We conclude that the use of RNAi combined with culture with hRBC is a reliable method for evaluating genes important for larval development. However, in view of the slow manifestation of the effects of Sm-aPKC knockdown in adults and the lack of effects of Sm-PLK-1 and Sm-MAPK p38 on adult viability

  13. Using compound similarity and functional domain composition for prediction of drug-target interaction networks.

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    Chen, Lei; He, Zhi-Song; Huang, Tao; Cai, Yu-Dong

    2010-11-01

    Study of interactions between drugs and target proteins is an essential step in genomic drug discovery. It is very hard to determine the compound-protein interactions or drug-target interactions by experiment alone. As supplementary, effective prediction model using machine learning or data mining methods can provide much help. In this study, a prediction method based on Nearest Neighbor Algorithm and a novel metric, which was obtained by combining compound similarity and functional domain composition, was proposed. The target proteins were divided into the following groups: enzymes, ion channels, G protein-coupled receptors, and nuclear receptors. As a result, four predictors with the optimal parameters were established. The overall prediction accuracies, evaluated by jackknife cross-validation test, for four groups of target proteins are 90.23%, 94.74%, 97.80%, and 97.51%, respectively, indicating that compound similarity and functional domain composition are very effective to predict drug-target interaction networks.

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

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    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.

  15. A systematic prediction of multiple drug-target interactions from chemical, genomic, and pharmacological data.

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    Hua Yu

    Full Text Available In silico prediction of drug-target interactions from heterogeneous biological data can advance our system-level search for drug molecules and therapeutic targets, which efforts have not yet reached full fruition. In this work, we report a systematic approach that efficiently integrates the chemical, genomic, and pharmacological information for drug targeting and discovery on a large scale, based on two powerful methods of Random Forest (RF and Support Vector Machine (SVM. The performance of the derived models was evaluated and verified with internally five-fold cross-validation and four external independent validations. The optimal models show impressive performance of prediction for drug-target interactions, with a concordance of 82.83%, a sensitivity of 81.33%, and a specificity of 93.62%, respectively. The consistence of the performances of the RF and SVM models demonstrates the reliability and robustness of the obtained models. In addition, the validated models were employed to systematically predict known/unknown drugs and targets involving the enzymes, ion channels, GPCRs, and nuclear receptors, which can be further mapped to functional ontologies such as target-disease associations and target-target interaction networks. This approach is expected to help fill the existing gap between chemical genomics and network pharmacology and thus accelerate the drug discovery processes.

  16. Predict potential drug targets from the ion channel proteins based on SVM.

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    Huang, Chen; Zhang, Ruijie; Chen, Zhiqiang; Jiang, Yongshuai; Shang, Zhenwei; Sun, Peng; Zhang, Xuehong; Li, Xia

    2010-02-21

    The identification of molecular targets is a critical step in the drug discovery and development process. Ion channel proteins represent highly attractive drug targets implicated in a diverse range of disorders, in particular in the cardiovascular and central nervous systems. Due to the limits of experimental technique and low-throughput nature of patch-clamp electrophysiology, they remain a target class waiting to be exploited. In our study, we combined three types of protein features, primary sequence, secondary structure and subcellular localization to predict potential drug targets from ion channel proteins applying classical support vector machine (SVM) method. In addition, our prediction comprised two stages. In stage 1, we predicted ion channel target proteins based on whole-genome target protein characteristics. Firstly, we performed feature selection by Mann-Whitney U test, then made predictions to identify potential ion channel targets by SVM and designed a new evaluating indicator Q to prioritize results. In stage 2, we made a prediction based on known ion channel target protein characteristics. Genetic algorithm was used to select features and SVM was used to predict ion channel targets. Then, we integrated results of two stages, and found that five ion channel proteins appeared in both prediction results including CGMP-gated cation channel beta subunit and Gamma-aminobutyric acid receptor subunit alpha-5, etc., and four of which were relative to some nerve diseases. It suggests that these five proteins are potential targets for drug discovery and our prediction strategies are effective.

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

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    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. PMID:28192537

  18. SimBoost: A Read-Across Approach for Drug-Target Interaction Prediction Using Gradient Boosting Machines

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    He, Tong

    2016-01-01

    Computational prediction of the interaction between drugs and targets is a standing challenge in drug discovery. High performance on binary drug-target benchmark datasets was reported for a number of methods. A possible drawback of binary data is that missing values and non-interacting drug-target pairs are not differentiated. In this paper, we present a method called SimBoost that predicts the continuous binding affinities of drugs and targets and thus incorporates the whole interaction spec...

  19. The application of antitumor drug-targeting models on liver cancer.

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    Yan, Yan; Chen, Ningbo; Wang, Yunbing; Wang, Ke

    2016-06-01

    Hepatocarcinoma animal models, such as the induced tumor model, transplanted tumor model, gene animal model, are significant experimental tools for the evaluation of targeting drug delivery system as well as the pre-clinical studies of liver cancer. The application of antitumor drug-targeting models not only furnishes similar biological characteristics to human liver cancer but also offers guarantee of pharmacokinetic indicators of the liver-targeting preparations. In this article, we have reviewed some kinds of antitumor drug-targeting models of hepatoma and speculated that the research on this field would be capable of attaining a deeper level and expecting a superior achievement in the future.

  20. Prediction of drug-target interaction by label propagation with mutual interaction information derived from heterogeneous network.

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    Yan, Xiao-Ying; Zhang, Shao-Wu; Zhang, Song-Yao

    2016-02-01

    The identification of potential drug-target interaction pairs is very important, which is useful not only for providing greater understanding of protein function, but also for enhancing drug research, especially for drug function repositioning. Recently, numerous machine learning-based algorithms (e.g. kernel-based, matrix factorization-based and network-based inference methods) have been developed for predicting drug-target interactions. All these methods implicitly utilize the assumption that similar drugs tend to target similar proteins and yield better results for predicting interactions between drugs and target proteins. To further improve the accuracy of prediction, a new method of network-based label propagation with mutual interaction information derived from heterogeneous networks, namely LPMIHN, is proposed to infer the potential drug-target interactions. LPMIHN separately performs label propagation on drug and target similarity networks, but the initial label information of the target (or drug) network comes from the drug (or target) label network and the known drug-target interaction bipartite network. The independent label propagation on each similarity network explores the cluster structure in its network, and the label information from the other network is used to capture mutual interactions (bicluster structures) between the nodes in each pair of the similarity networks. As compared to other recent state-of-the-art methods on the four popular benchmark datasets of binary drug-target interactions and two quantitative kinase bioactivity datasets, LPMIHN achieves the best results in terms of AUC and AUPR. In addition, many of the promising drug-target pairs predicted from LPMIHN are also confirmed on the latest publicly available drug-target databases such as ChEMBL, KEGG, SuperTarget and Drugbank. These results demonstrate the effectiveness of our LPMIHN method, indicating that LPMIHN has a great potential for predicting drug-target interactions.

  1. Prediction of drug-target interactions for drug repositioning only based on genomic expression similarity.

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    Kejian Wang

    Full Text Available Small drug molecules usually bind to multiple protein targets or even unintended off-targets. Such drug promiscuity has often led to unwanted or unexplained drug reactions, resulting in side effects or drug repositioning opportunities. So it is always an important issue in pharmacology to identify potential drug-target interactions (DTI. However, DTI discovery by experiment remains a challenging task, due to high expense of time and resources. Many computational methods are therefore developed to predict DTI with high throughput biological and clinical data. Here, we initiatively demonstrate that the on-target and off-target effects could be characterized by drug-induced in vitro genomic expression changes, e.g. the data in Connectivity Map (CMap. Thus, unknown ligands of a certain target can be found from the compounds showing high gene-expression similarity to the known ligands. Then to clarify the detailed practice of CMap based DTI prediction, we objectively evaluate how well each target is characterized by CMap. The results suggest that (1 some targets are better characterized than others, so the prediction models specific to these well characterized targets would be more accurate and reliable; (2 in some cases, a family of ligands for the same target tend to interact with common off-targets, which may help increase the efficiency of DTI discovery and explain the mechanisms of complicated drug actions. In the present study, CMap expression similarity is proposed as a novel indicator of drug-target interactions. The detailed strategies of improving data quality by decreasing the batch effect and building prediction models are also effectively established. We believe the success in CMap can be further translated into other public and commercial data of genomic expression, thus increasing research productivity towards valid drug repositioning and minimal side effects.

  2. Novel Methods for Drug-Target Interaction Prediction using Graph Mining

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    Ba Alawi, Wail

    2016-08-31

    The problem of developing drugs that can be used to cure diseases is important and requires a careful approach. Since pursuing the wrong candidate drug for a particular disease could be very costly in terms of time and money, there is a strong interest in minimizing such risks. Drug repositioning has become a hot topic of research, as it helps reduce these risks significantly at the early stages of drug development by reusing an approved drug for the treatment of a different disease. Still, finding new usage for a drug is non-trivial, as it is necessary to find out strong supporting evidence that the proposed new uses of drugs are plausible. Many computational approaches were developed to narrow the list of possible candidate drug-target interactions (DTIs) before any experiments are done. However, many of these approaches suffer from unacceptable levels of false positives. We developed two novel methods based on graph mining networks of drugs and targets. The first method (DASPfind) finds all non-cyclic paths that connect a drug and a target, and using a function that we define, calculates a score from all the paths. This score describes our confidence that DTI is correct. We show that DASPfind significantly outperforms other state-of-the-art methods in predicting the top ranked target for each drug. We demonstrate the utility of DASPfind by predicting 15 novel DTIs over a set of ion channel proteins, and confirming 12 out of these 15 DTIs through experimental evidence reported in literature and online drug databases. The second method (DASPfind+) modifies DASPfind in order to increase the confidence and reliability of the resultant predictions. Based on the structure of the drug-target interaction (DTI) networks, we introduced an optimization scheme that incrementally alters the network structure locally for each drug to achieve more robust top 1 ranked predictions. Moreover, we explored effects of several similarity measures between the targets on the prediction

  3. Antimalarial drug targets in Plasmodium falciparum predicted by stage-specific metabolic network analysis

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    Huthmacher Carola

    2010-08-01

    Full Text Available Abstract Background Despite enormous efforts to combat malaria the disease still afflicts up to half a billion people each year of which more than one million die. Currently no approved vaccine is available and resistances to antimalarials are widely spread. Hence, new antimalarial drugs are urgently needed. Results Here, we present a computational analysis of the metabolism of Plasmodium falciparum, the deadliest malaria pathogen. We assembled a compartmentalized metabolic model and predicted life cycle stage specific metabolism with the help of a flux balance approach that integrates gene expression data. Predicted metabolite exchanges between parasite and host were found to be in good accordance with experimental findings when the parasite's metabolic network was embedded into that of its host (erythrocyte. Knock-out simulations identified 307 indispensable metabolic reactions within the parasite. 35 out of 57 experimentally demonstrated essential enzymes were recovered and another 16 enzymes, if additionally the assumption was made that nutrient uptake from the host cell is limited and all reactions catalyzed by the inhibited enzyme are blocked. This predicted set of putative drug targets, shown to be enriched with true targets by a factor of at least 2.75, was further analyzed with respect to homology to human enzymes, functional similarity to therapeutic targets in other organisms and their predicted potency for prophylaxis and disease treatment. Conclusions The results suggest that the set of essential enzymes predicted by our flux balance approach represents a promising starting point for further drug development.

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

    Science.gov (United States)

    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.

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

    Science.gov (United States)

    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 .

  6. Predicted essential proteins ofPlasmodium falciparum for potential drug targets

    Institute of Scientific and Technical Information of China (English)

    Qing-Feng He; Li Deng; Qin-Ying Xu; Zheng Shao

    2012-01-01

    ABSTRACT Objective:To identify novel drug targets for treatment ofPlasmodium falciparum.Methods:LocalBLASTP were used to find the proteins non-homologous to human essential proteins as novel drug targets. Functional domains of novel drug targets were identified by InterPro and Pfam,3D structures of potential drug targets were predicated by theSWISS-MODELworkspace. Ligands and ligand-binding sites of the proteins were searched byEf-seek.Results:Three essential proteins were identified that might be considered as potential drug targets.AAN37254.1 belonged to1-deoxy-D-xylulose5-phosphate reductoisomerase,CAD50499.1 belonged to chorismate synthase,CAD51220.1 belonged toFAD binging3 family, but the function of CAD51220.1 was unknown. The3D structures, ligands and ligand-binding sites ofAAN37254.1 andCAD50499.1 were successfully predicated.Conclusions:Two of these potential drug targets are key enzymes in2-C-methyl-d-erythritol4-phosphate pathway and shikimate pathway, which are absent in humans, so these two essential proteins are good potential drug targets. The function and3D structures ofCAD50499.1 is still unknown, it still need further study.

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

    Science.gov (United States)

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

    2016-11-14

    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 drug-target 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-the-art 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.

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

    Science.gov (United States)

    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.

  9. Magnetic microgels for drug targeting applications: Physical–chemical properties and cytotoxicity evaluation

    Energy Technology Data Exchange (ETDEWEB)

    Turcu, Rodica, E-mail: rodica.turcu@itim-cj.ro [National Institute for Research and Development of Isotopic and Molecular Technologies, 65-103 Donath Street, 400293 Cluj-Napoca (Romania); Craciunescu, Izabell [National Institute for Research and Development of Isotopic and Molecular Technologies, 65-103 Donath Street, 400293 Cluj-Napoca (Romania); Garamus, Vasil M. [Helmholtz-Zentrum Geesthacht, Zentrum für Material- und Küstenforschung GmbH, 21502 Geesthacht (Germany); Janko, Christina; Lyer, Stefan; Tietze, Rainer; Alexiou, Christoph [ENT-Department, Else Kröner-Fresenius Stiftung-Professorship, Section for Experimental Oncology and Nanomedicine (SEON), University Hospital Erlangen (Germany); Vekas, Ladislau, E-mail: vekas@acad-tim.tm.edu.ro [Romanian Academy-Timisoara Branch, CFATR, Laboratory of Magnetic Fluids, Mihai Viteazul Street 24, 300223 Timisoara (Romania)

    2015-04-15

    Magnetoresponsive microgels with high saturation magnetization values have been obtained by a strategy based on the miniemulsion method using high colloidal stability organic carrier ferrofluid as primary material. Hydrophobic nanoparticles Fe{sub 3}O{sub 4}/oleic acid are densely packed into well-defined spherical nanoparticle clusters coated with polymers with sizes in the range 40–350 nm. Physical–chemical characteristics of magnetic microgels were investigated by TEM, SAXS, XPS and VSM measurements with the focus on the structure–properties relationship. The impact of magnetic microgels loaded with anticancer drug mitoxantrone (MTO) on the non-adherent human T cell leukemia line Jurkat was investigated in multiparameter flow cytometry. We showed that both MTO and microgel-loaded MTO penetrate into cells and both induce apoptosis and later secondary necrosis in a time- and dose dependent manner. In contrast, microgels without MTO are not cytotoxic in the corresponding concentrations. Our results show that MTO-loaded microgels are promising structures for application in magnetic drug targeting. - Highlights: • Densely packed spherical clusters of magnetic nanoparticles were obtained. • High magnetization microgels with superparamagnetic behavior are reported. • The facile and reproducible synthesis procedure applied is easy to be up-scaled. • The toxicity tests show that magnetic microgels are not cytotoxic. • We show that mitoxantrone loaded microgels induce death of Jurkat cells.

  10. Secretome Prediction of Two M. tuberculosis Clinical Isolates Reveals Their High Antigenic Density and Potential Drug Targets

    Science.gov (United States)

    Cornejo-Granados, Fernanda; Zatarain-Barrón, Zyanya L.; Cantu-Robles, Vito A.; Mendoza-Vargas, Alfredo; Molina-Romero, Camilo; Sánchez, Filiberto; Del Pozo-Yauner, Luis; Hernández-Pando, Rogelio; Ochoa-Leyva, Adrián

    2017-01-01

    druggability analysis of the secretomes, we found potential drug targets such as cytochrome P450, thiol peroxidase, the Ag85C, and Ribonucleoside Reductase in the secreted proteins that could be used as drug targets for novel treatments against Tuberculosis. PMID:28223967

  11. Evaluation and validation of drug targets

    Institute of Scientific and Technical Information of China (English)

    Guan-huaDU

    2004-01-01

    Drug target is one of the key factors for discovering and developing new drugs. To find and validate drug targets is a crucial technique required in drug discovery by the strategy of high throughput screening. Based on the knowledge of molecular biology, human genomics and proteomics, it has been predicted that 5000 to 10000 drug targets exist in human. So, it is important orocedure to evaluate and validate the drug targets.

  12. Ligand efficiency-based support vector regression models for predicting bioactivities of ligands to drug target proteins.

    Science.gov (United States)

    Sugaya, Nobuyoshi

    2014-10-27

    The concept of ligand efficiency (LE) indices is widely accepted throughout the drug design community and is frequently used in a retrospective manner in the process of drug development. For example, LE indices are used to investigate LE optimization processes of already-approved drugs and to re-evaluate hit compounds obtained from structure-based virtual screening methods and/or high-throughput experimental assays. However, LE indices could also be applied in a prospective manner to explore drug candidates. Here, we describe the construction of machine learning-based regression models in which LE indices are adopted as an end point and show that LE-based regression models can outperform regression models based on pIC50 values. In addition to pIC50 values traditionally used in machine learning studies based on chemogenomics data, three representative LE indices (ligand lipophilicity efficiency (LLE), binding efficiency index (BEI), and surface efficiency index (SEI)) were adopted, then used to create four types of training data. We constructed regression models by applying a support vector regression (SVR) method to the training data. In cross-validation tests of the SVR models, the LE-based SVR models showed higher correlations between the observed and predicted values than the pIC50-based models. Application tests to new data displayed that, generally, the predictive performance of SVR models follows the order SEI > BEI > LLE > pIC50. Close examination of the distributions of the activity values (pIC50, LLE, BEI, and SEI) in the training and validation data implied that the performance order of the SVR models may be ascribed to the much higher diversity of the LE-based training and validation data. In the application tests, the LE-based SVR models can offer better predictive performance of compound-protein pairs with a wider range of ligand potencies than the pIC50-based models. This finding strongly suggests that LE-based SVR models are better than pIC50-based

  13. Network output controllability-based method for drug target identification.

    Science.gov (United States)

    Wu, Lin; Shen, Yichao; Li, Min; Wu, Fang-Xiang

    2015-03-01

    Biomolecules do not perform their functions alone, but interactively with one another to form so called biomolecular networks. It is well known that a complex disease stems from the malfunctions of corresponding biomolecular networks. Therefore, one of important tasks is to identify drug targets from biomolecular networks. In this study, the drug target identification is formulated as a problem of finding steering nodes in biomolecular networks while the concept of network output controllability is applied to the problem of drug target identification. By applying control signals to these steering nodes, the biomolecular networks are expected to be transited from one state to another. A graph-theoretic algorithm has been proposed to find a minimum set of steering nodes in biomolecular networks which can be a potential set of drug targets. Application results of the method to real biomolecular networks show that identified potential drug targets are in agreement with existing research results. This indicates that the method can generate testable predictions and provide insights into experimental design of drug discovery.

  14. Molecular dynamic simulation and inhibitor prediction of cysteine synthase structured model as a potential drug target for trichomoniasis.

    Science.gov (United States)

    Singh, Satendra; Sablok, Gaurav; Farmer, Rohit; Singh, Atul Kumar; Gautam, Budhayash; Kumar, Sunil

    2013-01-01

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

  15. Kinetic modeling of tricarboxylic acid cycle and glyoxylate bypass in Mycobacterium tuberculosis, and its application to assessment of drug targets

    Directory of Open Access Journals (Sweden)

    Ghosh Indira

    2006-08-01

    Full Text Available Abstract Background Targeting persistent tubercule bacilli has become an important challenge in the development of anti-tuberculous drugs. As the glyoxylate bypass is essential for persistent bacilli, interference with it holds the potential for designing new antibacterial drugs. We have developed kinetic models of the tricarboxylic acid cycle and glyoxylate bypass in Escherichia coli and Mycobacterium tuberculosis, and studied the effects of inhibition of various enzymes in the M. tuberculosis model. Results We used E. coli to validate the pathway-modeling protocol and showed that changes in metabolic flux can be estimated from gene expression data. The M. tuberculosis model reproduced the observation that deletion of one of the two isocitrate lyase genes has little effect on bacterial growth in macrophages, but deletion of both genes leads to the elimination of the bacilli from the lungs. It also substantiated the inhibition of isocitrate lyases by 3-nitropropionate. On the basis of our simulation studies, we propose that: (i fractional inactivation of both isocitrate dehydrogenase 1 and isocitrate dehydrogenase 2 is required for a flux through the glyoxylate bypass in persistent mycobacteria; and (ii increasing the amount of active isocitrate dehydrogenases can stop the flux through the glyoxylate bypass, so the kinase that inactivates isocitrate dehydrogenase 1 and/or the proposed inactivator of isocitrate dehydrogenase 2 is a potential target for drugs against persistent mycobacteria. In addition, competitive inhibition of isocitrate lyases along with a reduction in the inactivation of isocitrate dehydrogenases appears to be a feasible strategy for targeting persistent mycobacteria. Conclusion We used kinetic modeling of biochemical pathways to assess various potential anti-tuberculous drug targets that interfere with the glyoxylate bypass flux, and indicated the type of inhibition needed to eliminate the pathogen. The advantage of such an

  16. Mathematical modelling of polyamine metabolism in bloodstream-form Trypanosoma brucei: an application to drug target identification.

    Directory of Open Access Journals (Sweden)

    Xu Gu

    Full Text Available We present the first computational kinetic model of polyamine metabolism in bloodstream-form Trypanosoma brucei, the causative agent of human African trypanosomiasis. We systematically extracted the polyamine pathway from the complete metabolic network while still maintaining the predictive capability of the pathway. The kinetic model is constructed on the basis of information gleaned from the experimental biology literature and defined as a set of ordinary differential equations. We applied Michaelis-Menten kinetics featuring regulatory factors to describe enzymatic activities that are well defined. Uncharacterised enzyme kinetics were approximated and justified with available physiological properties of the system. Optimisation-based dynamic simulations were performed to train the model with experimental data and inconsistent predictions prompted an iterative procedure of model refinement. Good agreement between simulation results and measured data reported in various experimental conditions shows that the model has good applicability in spite of there being gaps in the required data. With this kinetic model, the relative importance of the individual pathway enzymes was assessed. We observed that, at low-to-moderate levels of inhibition, enzymes catalysing reactions of de novo AdoMet (MAT and ornithine production (OrnPt have more efficient inhibitory effect on total trypanothione content in comparison to other enzymes in the pathway. In our model, prozyme and TSHSyn (the production catalyst of total trypanothione were also found to exhibit potent control on total trypanothione content but only when they were strongly inhibited. Different chemotherapeutic strategies against T. brucei were investigated using this model and interruption of polyamine synthesis via joint inhibition of MAT or OrnPt together with other polyamine enzymes was identified as an optimal therapeutic strategy.

  17. Properties of protein drug target classes.

    Directory of Open Access Journals (Sweden)

    Simon C Bull

    Full Text Available Accurate identification of drug targets is a crucial part of any drug development program. We mined the human proteome to discover properties of proteins that may be important in determining their suitability for pharmaceutical modulation. Data was gathered concerning each protein's sequence, post-translational modifications, secondary structure, germline variants, expression profile and drug target status. The data was then analysed to determine features for which the target and non-target proteins had significantly different values. This analysis was repeated for subsets of the proteome consisting of all G-protein coupled receptors, ion channels, kinases and proteases, as well as proteins that are implicated in cancer. Machine learning was used to quantify the proteins in each dataset in terms of their potential to serve as a drug target. This was accomplished by first inducing a random forest that could distinguish between its targets and non-targets, and then using the random forest to quantify the drug target likeness of the non-targets. The properties that can best differentiate targets from non-targets were primarily those that are directly related to a protein's sequence (e.g. secondary structure. Germline variants, expression levels and interactions between proteins had minimal discriminative power. Overall, the best indicators of drug target likeness were found to be the proteins' hydrophobicities, in vivo half-lives, propensity for being membrane bound and the fraction of non-polar amino acids in their sequences. In terms of predicting potential targets, datasets of proteases, ion channels and cancer proteins were able to induce random forests that were highly capable of distinguishing between targets and non-targets. The non-target proteins predicted to be targets by these random forests comprise the set of the most suitable potential future drug targets, and should therefore be prioritised when building a drug development programme.

  18. Aquaporins as potential drug targets

    Institute of Scientific and Technical Information of China (English)

    Fang WANG; Xue-chao FENG; Yong-ming LI; Hong YANG; Tong-hui MA

    2006-01-01

    The aquaporins (AQP) are a family of integral membrane proteins that selectively transport water and,in some cases,small neutral solutes such as glycerol and urea.Thirteen mammalian AQP have been molecularly identified and localized to various epithelial,endothelial and other tissues.Phenotype studies of transgenic mouse models of AQP knockout,mutation,and in some cases humans with AQP mutations have demonstrated essential roles for AQP in mammalian physiology and pathophysiology,including urinary concentrating function,exocrine glandular fluid secretion,brain edema formation,regulation of intracranial and intraocular pressure,skin hydration,fat metabolism,tumor angiogenesis and cell migration.These studies suggest that AQP may be potential drug targets for not only new diuretic reagents for various forms of pathological water retention,but also targets for novel therapy of brain edema,inflammatory disease,glaucoma,obesity,and cancer.However,potent AQP modulators for in vivo application remain to be discovered.

  19. iDrug-Target: predicting the interactions between drug compounds and target proteins in cellular networking via benchmark dataset optimization approach.

    Science.gov (United States)

    Xiao, Xuan; Min, Jian-Liang; Lin, Wei-Zhong; Liu, Zi; Cheng, Xiang; Chou, Kuo-Chen

    2015-01-01

    Information about the interactions of drug compounds with proteins in cellular networking is very important for drug development. Unfortunately, all the existing predictors for identifying drug-protein interactions were trained by a skewed benchmark data-set where the number of non-interactive drug-protein pairs is overwhelmingly larger than that of the interactive ones. Using this kind of highly unbalanced benchmark data-set to train predictors would lead to the outcome that many interactive drug-protein pairs might be mispredicted as non-interactive. Since the minority interactive pairs often contain the most important information for drug design, it is necessary to minimize this kind of misprediction. In this study, we adopted the neighborhood cleaning rule and synthetic minority over-sampling technique to treat the skewed benchmark datasets and balance the positive and negative subsets. The new benchmark datasets thus obtained are called the optimized benchmark datasets, based on which a new predictor called iDrug-Target was developed that contains four sub-predictors: iDrug-GPCR, iDrug-Chl, iDrug-Ezy, and iDrug-NR, specialized for identifying the interactions of drug compounds with GPCRs (G-protein-coupled receptors), ion channels, enzymes, and NR (nuclear receptors), respectively. Rigorous cross-validations on a set of experiment-confirmed datasets have indicated that these new predictors remarkably outperformed the existing ones for the same purpose. To maximize users' convenience, a public accessible Web server for iDrug-Target has been established at http://www.jci-bioinfo.cn/iDrug-Target/ , by which users can easily get their desired results. It has not escaped our notice that the aforementioned strategy can be widely used in many other areas as well.

  20. 利用支持向量机预测G蛋白偶联受体中潜在的药物靶点%Predicting Potential Drug Targets of G-protein Coupled Receptors Based on SVM

    Institute of Scientific and Technical Information of China (English)

    王春丽; 张世强

    2013-01-01

    在已知的药物靶点中,G蛋白偶联受体(G protein-coupled receptor,GPCR)占绝大多数,它与高血压、哮喘、疼痛、神经和免疫紊乱等多种疾病有着密切联系.但由于GPCR的七次跨膜构象较复杂,其空间结构很难从实验中获取,因此,它们的功能就更难确定了.作者通过对已知蛋白质数据库中数据的分析,构建了两个不同的数据集,并利用蛋白质一级结构、基本理化性质及拓扑描述等特征,训练两组SVM分类器,预测GPCR中潜在的药物靶点.综合分析两组分类器的结果发现,其中有141个GPCR同时被这两组分类器预测为药物靶点.在这141个GPCR中,有39个同时存在于TTD数据库中.%The most of the known drug targets are the G protein-coupled receptors (GPCR).They are closely linked with hypertension,asthma,pain,nerve and immune disorders and many other kinds of diseases.Because of the complex seven transmembrane conformation of GPCR,the spatial structures are difficult to obtain from the experiments.Therefore,their functions are difficult to determine.According to the two different kinds of datasets from the known protein databases,the authors trained two groups of SVM classifiers to predict the GPCR potential drug targets by using the characteristics of protein primary structures,basic physical and chemical properties and topological descriptions.According to comprehensive analysis of the results of two kinds of segments,there are 141 GPCR predicted as drug targets in two kinds of segments.In the 141 GPCR,there are 39 GPCR exist in TTD database.

  1. Chemical proteomics: terra incognita for novel drug target profiling

    Institute of Scientific and Technical Information of China (English)

    Fuqiang Huang; Boya Zhang; Shengtao Zhou; Xia Zhao; Ce Bian; Yuquan Wei

    2012-01-01

    The growing demand for new therapeutic strategies in the medical and pharmaceutic fields has resulted in a pressing need for novel druggable targets.Paradoxically,however,the targets of certain drugs that are already widely used in clinical practice have largely not been annotated.Because the pharmacologic effects of a drug can only be appreciated when its interactions with cellular components are clearly delineated,an integrated deconvolution of drug-target interactions for each drug is necessary.The emerging field of chemical proteomics represents a powerful mass spectrometry (MS)-based affinity chromatography approach for identifying proteome-wide small molecule-protein interactions and mapping these interactions to signaling and metabolic pathways.This technique could comprehensively characterize drug targets,profile the toxicity of known drugs,and identify possible off-target activities.With the use of this technique,candidate drug molecules could be optimized,and predictable side effects might consequently be avoided.Herein,we provide a holistic overview of the major chemical proteomic approaches and highlight recent advances in this area as well as its potential applications in drug discovery.

  2. Fluid mechanics aspects of magnetic drug targeting.

    Science.gov (United States)

    Odenbach, Stefan

    2015-10-01

    Experiments and numerical simulations using a flow phantom for magnetic drug targeting have been undertaken. The flow phantom is a half y-branched tube configuration where the main tube represents an artery from which a tumour-supplying artery, which is simulated by the side branch of the flow phantom, branches off. In the experiments a quantification of the amount of magnetic particles targeted towards the branch by a magnetic field applied via a permanent magnet is achieved by impedance measurement using sensor coils. Measuring the targeting efficiency, i.e. the relative amount of particles targeted to the side branch, for different field configurations one obtains targeting maps which combine the targeting efficiency with the magnetic force densities in characteristic points in the flow phantom. It could be shown that targeting efficiency depends strongly on the magnetic field configuration. A corresponding numerical model has been set up, which allows the simulation of targeting efficiency for variable field configuration. With this simulation good agreement of targeting efficiency with experimental data has been found. Thus, the basis has been laid for future calculations of optimal field configurations in clinical applications of magnetic drug targeting. Moreover, the numerical model allows the variation of additional parameters of the drug targeting process and thus an estimation of the influence, e.g. of the fluid properties on the targeting efficiency. Corresponding calculations have shown that the non-Newtonian behaviour of the fluid will significantly influence the targeting process, an aspect which has to be taken into account, especially recalling the fact that the viscosity of magnetic suspensions depends strongly on the magnetic field strength and the mechanical load.

  3. Antibiotic drugs targeting bacterial RNAs

    Directory of Open Access Journals (Sweden)

    Weiling Hong

    2014-08-01

    Full Text Available RNAs have diverse structures that include bulges and internal loops able to form tertiary contacts or serve as ligand binding sites. The recent increase in structural and functional information related to RNAs has put them in the limelight as a drug target for small molecule therapy. In addition, the recognition of the marked difference between prokaryotic and eukaryotic rRNA has led to the development of antibiotics that specifically target bacterial rRNA, reduce protein translation and thereby inhibit bacterial growth. To facilitate the development of new antibiotics targeting RNA, we here review the literature concerning such antibiotics, mRNA, riboswitch and tRNA and the key methodologies used for their screening.

  4. NEW DRUG TARGETING TREATMENT - GLIVEC

    Institute of Scientific and Technical Information of China (English)

    SUN Xue-mei(孙雪梅); BRADY Ben

    2003-01-01

    This review evaluates the role of Glivec in the treatment of chronic myelogenous leukemia and other malignant tumors. Preclinical and clinical evidence showed that Glivec demonstrated a potent and specific inhibition on BCR-ABL positive leukemias and other malignant tumors in which overexpression of c-kit and PDGFR-β played a major role in their pathogenesis. Glivec has induced complete hematologic responses in up to 98% of patients evaluated in clinical trials. It's a very successful drug that supported the idea of targeted therapy through inhibition of tyrosine kinases. Although it's still in the early stages of clinical development and the resistance to Glivec remains to be a problem needed further study, a great deal has been learned from these research and observation. And with the increasing data, molecular targeting therapy will play much more important role in the treatment of malignant tumors. With the better understanding of the pathogenesis of malignant tumors, well-designed drugs targeting the specific molecular abnormalities with higher efficacy and lower side effect will benefit numerous patients with malignant tumors.

  5. Drug targeting using solid lipid nanoparticles.

    Science.gov (United States)

    Rostami, Elham; Kashanian, Soheila; Azandaryani, Abbas H; Faramarzi, Hossain; Dolatabadi, Jafar Ezzati Nazhad; Omidfar, Kobra

    2014-07-01

    The present review aims to show the features of solid lipid nanoparticles (SLNs) which are at the forefront of the rapidly developing field of nanotechnology with several potential applications in drug delivery and research. Because of some unique features of SLNs such as their unique size dependent properties it offers possibility to develop new therapeutics. A common denominator of all these SLN-based platforms is to deliver drugs into specific tissues or cells in a pathological setting with minimal adverse effects on bystander cells. SLNs are capable to incorporate drugs into nanocarriers which lead to a new prototype in drug delivery which maybe used for drug targeting. Hence solid lipid nanoparticles hold great promise for reaching the goal of controlled and site specific drug delivery and hence attracted wide attention of researchers. This review presents a broad treatment of targeted solid lipid nanoparticles discussing their types such as antibody SLN, magnetic SLN, pH sensitive SLN and cationic SLN.

  6. PBIT: pipeline builder for identification of drug targets for infectious diseases.

    Science.gov (United States)

    Shende, Gauri; Haldankar, Harshala; Barai, Ram Shankar; Bharmal, Mohammed Husain; Shetty, Vinit; Idicula-Thomas, Susan

    2016-12-30

    PBIT (Pipeline Builder for Identification of drug Targets) is an online webserver that has been developed for screening of microbial proteomes for critical features of human drug targets such as being non-homologous to human proteome as well as the human gut microbiota, essential for the pathogen's survival, participation in pathogen-specific pathways etc. The tool has been validated by analyzing 57 putative targets of Candida albicans documented in literature. PBIT integrates various in silico approaches known for drug target identification and will facilitate high-throughput prediction of drug targets for infectious diseases, including multi-pathogenic infections.

  7. Discovering drug targets through the web.

    Science.gov (United States)

    Wishart, David S

    2007-03-01

    Traditionally, drug-target discovery is a "wet-bench" experimental process, depending on carefully designed genetic screens, biochemical tests and cellular assays to identify proteins and genes that are associated with a particular disease or condition. However, recent advances in DNA sequencing, transcript profiling, protein identification and protein quantification are leading to a flood of genomic and proteomic data that is, or potentially could be, linked to disease data. The quantity of data generated by these high throughput methods is forcing scientists to re-think the way they do traditional drug-target discovery. In particular it is leading them more and more towards identifying potential drug targets using computers. In fact, drug-target identification is now being done as much on the desk-top as on the bench-top. This review focuses on describing how drug-target discovery can be done in silico (i.e. via computer) using a variety of bioinformatic resources that are freely available on the web. Specifically, it highlights a number of web-accessible sequence databases, automated genome annotation tools, text mining tools; and integrated drug/sequence databases that can be used to identify drug targets for both endogenous (genetic and epigenetic) diseases as well as exogenous (infectious) diseases.

  8. Assessing drug target association using semantic linked data.

    Directory of Open Access Journals (Sweden)

    Bin Chen

    Full Text Available The rapidly increasing amount of public data in chemistry and biology provides new opportunities for large-scale data mining for drug discovery. Systematic integration of these heterogeneous sets and provision of algorithms to data mine the integrated sets would permit investigation of complex mechanisms of action of drugs. In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges. We developed a statistical model to assess the association of drug target pairs based on their relation with other linked objects. Validation experiments demonstrate the model can correctly identify known direct drug target pairs with high precision. Indirect drug target pairs (for example drugs which change gene expression level are also identified but not as strongly as direct pairs. We further calculated the association scores for 157 drugs from 10 disease areas against 1683 human targets, and measured their similarity using a [Formula: see text] score matrix. The similarity network indicates that drugs from the same disease area tend to cluster together in ways that are not captured by structural similarity, with several potential new drug pairings being identified. This work thus provides a novel, validated alternative to existing drug target prediction algorithms. The web service is freely available at: http://chem2bio2rdf.org/slap.

  9. Assessing drug target association using semantic linked data.

    Science.gov (United States)

    Chen, Bin; Ding, Ying; Wild, David J

    2012-01-01

    The rapidly increasing amount of public data in chemistry and biology provides new opportunities for large-scale data mining for drug discovery. Systematic integration of these heterogeneous sets and provision of algorithms to data mine the integrated sets would permit investigation of complex mechanisms of action of drugs. In this work we integrated and annotated data from public datasets relating to drugs, chemical compounds, protein targets, diseases, side effects and pathways, building a semantic linked network consisting of over 290,000 nodes and 720,000 edges. We developed a statistical model to assess the association of drug target pairs based on their relation with other linked objects. Validation experiments demonstrate the model can correctly identify known direct drug target pairs with high precision. Indirect drug target pairs (for example drugs which change gene expression level) are also identified but not as strongly as direct pairs. We further calculated the association scores for 157 drugs from 10 disease areas against 1683 human targets, and measured their similarity using a [Formula: see text] score matrix. The similarity network indicates that drugs from the same disease area tend to cluster together in ways that are not captured by structural similarity, with several potential new drug pairings being identified. This work thus provides a novel, validated alternative to existing drug target prediction algorithms. The web service is freely available at: http://chem2bio2rdf.org/slap.

  10. Automated High Throughput Drug Target Crystallography

    Energy Technology Data Exchange (ETDEWEB)

    Rupp, B

    2005-02-18

    The molecular structures of drug target proteins and receptors form the basis for 'rational' or structure guided drug design. The majority of target structures are experimentally determined by protein X-ray crystallography, which as evolved into a highly automated, high throughput drug discovery and screening tool. Process automation has accelerated tasks from parallel protein expression, fully automated crystallization, and rapid data collection to highly efficient structure determination methods. A thoroughly designed automation technology platform supported by a powerful informatics infrastructure forms the basis for optimal workflow implementation and the data mining and analysis tools to generate new leads from experimental protein drug target structures.

  11. Emerging migraine treatments and drug targets

    DEFF Research Database (Denmark)

    Olesen, Jes; Ashina, Messoud

    2011-01-01

    . Tonabersat, a cortical spreading depression inhibitor, has shown efficacy in the prophylaxis of migraine with aura. Several new drug targets such as nitric oxide synthase, the 5-HT(1D) receptor, the prostanoid receptors EP(2) and EP(4), and the pituitary adenylate cyclase receptor PAC1 await development...

  12. The drug-target residence time model: a 10-year retrospective.

    Science.gov (United States)

    Copeland, Robert A

    2016-02-01

    The drug-target residence time model was first introduced in 2006 and has been broadly adopted across the chemical biology, biotechnology and pharmaceutical communities. While traditional in vitro methods view drug-target interactions exclusively in terms of equilibrium affinity, the residence time model takes into account the conformational dynamics of target macromolecules that affect drug binding and dissociation. The key tenet of this model is that the lifetime (or residence time) of the binary drug-target complex, and not the binding affinity per se, dictates much of the in vivo pharmacological activity. Here, this model is revisited and key applications of it over the past 10 years are highlighted.

  13. Mining nematode genome data for novel drug targets.

    Science.gov (United States)

    Foster, Jeremy M; Zhang, Yinhua; Kumar, Sanjay; Carlow, Clotilde K S

    2005-03-01

    Expressed sequence tag projects have currently produced over 400 000 partial gene sequences from more than 30 nematode species and the full genomic sequences of selected nematodes are being determined. In addition, functional analyses in the model nematode Caenorhabditis elegans have addressed the role of almost all genes predicted by the genome sequence. This recent explosion in the amount of available nematode DNA sequences, coupled with new gene function data, provides an unprecedented opportunity to identify pre-validated drug targets through efficient mining of nematode genomic databases. This article describes the various information sources available and strategies that can expedite this process.

  14. Malaria heat shock proteins: drug targets that chaperone other drug targets.

    Science.gov (United States)

    Pesce, E-R; Cockburn, I L; Goble, J L; Stephens, L L; Blatch, G L

    2010-06-01

    Ongoing research into the chaperone systems of malaria parasites, and particularly of Plasmodium falciparum, suggests that heat shock proteins (Hsps) could potentially be an excellent class of drug targets. The P. falciparum genome encodes a vast range and large number of chaperones, including 43 Hsp40, six Hsp70, and three Hsp90 proteins (PfHsp40s, PfHsp70s and PfHsp90s), which are involved in a number of fundamental cellular processes including protein folding and assembly, protein translocation, signal transduction and the cellular stress response. Despite the fact that Hsps are relatively conserved across different species, PfHsps do exhibit a considerable number of unique structural and functional features. One PfHsp90 is thought to be sufficiently different to human Hsp90 to allow for selective targeting. PfHsp70s could potentially be used as drug targets in two ways: either by the specific inhibition of Hsp70s by small molecule modulators, as well as disruption of the interactions between Hsp70s and co-chaperones such as the Hsp70/Hsp90 organising protein (Hop) and Hsp40s. Of the many PfHsp40s present on the parasite, there are certain unique or essential members which are considered to have good potential as drug targets. This review critically evaluates the potential of Hsps as malaria drug targets, as well as the use of chaperones as aids in the heterologous expression of other potential malarial drug targets.

  15. Amphotericin B formulations and drug targeting.

    Science.gov (United States)

    Torrado, J J; Espada, R; Ballesteros, M P; Torrado-Santiago, S

    2008-07-01

    Amphotericin B is a low-soluble polyene antibiotic which is able to self-aggregate. The aggregation state can modify its activity and pharmacokinetical characteristics. In spite of its high toxicity it is still widely employed for the treatment of systemic fungal infections and parasitic disease and different formulations are marketed. Some of these formulations, such as liposomal formulations, can be considered as classical examples of drug targeting. The pharmacokinetics, toxicity and activity are clearly dependent on the type of amphotericin B formulation. New drug delivery systems such as liposomes, nanospheres and microspheres can result in higher concentrations of AMB in the liver and spleen, but lower concentrations in kidney and lungs, so decreasing its toxicity. Moreover, the administration of these drug delivery systems can enhance the drug accessibility to organs and tissues (e.g., bone marrow) otherwise inaccessible to the free drug. During the last few years, new AMB formulations (AmBisome, Abelcet, and Amphotec) with an improved efficacy/toxicity ratio have been marketed. This review compares the different formulations of amphotericin B in terms of pharmacokinetics, toxicity and activity and discusses the possible drug targeting effect of some of these new formulations.

  16. Emerging migraine treatments and drug targets

    DEFF Research Database (Denmark)

    Olesen, Jes; Ashina, Messoud

    2011-01-01

    Migraine has a 1-year prevalence of 10% and high socioeconomic costs. Despite recent drug developments, there is a huge unmet need for better pharmacotherapy. In this review we discuss promising anti-migraine strategies such as calcitonin gene-related peptide (CGRP) receptor antagonists and 5......-hydroxytrypamine (5-HT)(1F) receptor agonists, which are in late-stage development. Nitric oxide antagonists are also in development. New forms of administration of sumatriptan might improve efficacy and reduce side effects. Botulinum toxin A has recently been approved for the prophylaxis of chronic migraine....... Tonabersat, a cortical spreading depression inhibitor, has shown efficacy in the prophylaxis of migraine with aura. Several new drug targets such as nitric oxide synthase, the 5-HT(1D) receptor, the prostanoid receptors EP(2) and EP(4), and the pituitary adenylate cyclase receptor PAC1 await development...

  17. Drug target identification using side-effect similarity

    DEFF Research Database (Denmark)

    Campillos, Monica; Kuhn, Michael; Gavin, Anne-Claude;

    2008-01-01

    Targets for drugs have so far been predicted on the basis of molecular or cellular features, for example, by exploiting similarity in chemical structure or in activity across cell lines. We used phenotypic side-effect similarities to infer whether two drugs share a target. Applied to 746 marketed...... drugs, a network of 1018 side effect-driven drug-drug relations became apparent, 261 of which are formed by chemically dissimilar drugs from different therapeutic indications. We experimentally tested 20 of these unexpected drug-drug relations and validated 13 implied drug-target relations by in vitro...... binding assays, of which 11 reveal inhibition constants equal to less than 10 micromolar. Nine of these were tested and confirmed in cell assays, documenting the feasibility of using phenotypic information to infer molecular interactions and hinting at new uses of marketed drugs....

  18. Identifying drug-target proteins based on network features

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    Proteins rarely function in isolation inside and outside cells, but operate as part of a highly intercon- nected cellular network called the interaction network. Therefore, the analysis of the properties of drug-target proteins in the biological network is especially helpful for understanding the mechanism of drug action in terms of informatics. At present, no detailed characterization and description of the topological features of drug-target proteins have been available in the human protein-protein interac- tion network. In this work, by mapping the drug-targets in DrugBank onto the interaction network of human proteins, five topological indices of drug-targets were analyzed and compared with those of the whole protein interactome set and the non-drug-target set. The experimental results showed that drug-target proteins have higher connectivity and quicker communication with each other in the PPI network. Based on these features, all proteins in the interaction network were ranked. The results showed that, of the top 100 proteins, 48 are covered by DrugBank; of the remaining 52 proteins, 9 are drug-target proteins covered by the TTD, Matador and other databases, while others have been dem- onstrated to be drug-target proteins in the literature.

  19. Identifying drug-target proteins based on network features

    Institute of Scientific and Technical Information of China (English)

    ZHU MingZhu; GAO Lei; LI Xia; LIU ZhiCheng

    2009-01-01

    Proteins rarely function in isolation Inside and outside cells, but operate as part of a highly Intercon-nected cellular network called the interaction network. Therefore, the analysis of the properties of drug-target proteins in the biological network is especially helpful for understanding the mechanism of drug action In terms of informatice. At present, no detailed characterization and description of the topological features of drug-target proteins have been available in the human protein-protein interac-tion network. In this work, by mapping the drug-targets in DrugBank onto the interaction network of human proteins, five topological indices of drug-targets were analyzed and compared with those of the whole protein interactome set and the non-drug-target set. The experimental results showed that drug-target proteins have higher connectivity and quicker communication with each other in the PPI network. Based on these features, all proteins In the interaction network were ranked. The results showed that, of the top 100 proteins, 48 are covered by DrugBank; of the remaining 52 proteins, 9 are drug-target proteins covered by the TTD, Matador and other databases, while others have been dem-onstrated to be drug-target proteins in the literature.

  20. Prediction of network drug target based on improved model of bipartite graph valuation%基于二分图评价模型的网络药物靶标预测改进方法

    Institute of Scientific and Technical Information of China (English)

    刘西; 卢朋; 左晓晗; 陈建新; 杨洪军; 杨一平; 高一波

    2012-01-01

    网络药理学作为新药研发领域中新的发展方向,受到越来越多的学者关注,而基因组药物发现研究中的一个关键问题就是如何识别药物与靶标蛋白质间新的交互作用.本研究即希望根据已知交互作用建立模型预测新的交互作用,以达到发现新靶标的目的.作者针对前人提出的二分图建模方法中存在的不足,提出了一种新的有监督的基于二分图评价模型的融合算法,根据已知的药物-靶标交互作用构建二分图网络,并建立药物-靶标蛋白质对的关联性评价模型,依此模型预测新的药物-靶标蛋白质交互作用,即预测新靶标.在已知交互作用数据集上做测试,本研究所提出的基于二分图评价模型的融合算法在性能上优于其他3种预测算法.基于二分图评价模型的融合算法集成化学空间、疗效空间和基因空间,构建药物候选化合物-靶标候选蛋白质交互网络,并建立交互作用预测模型,能预测出新的药物-靶标蛋白质交互作用,进而预测药物靶标,效果良好.%Network pharmacology, as a new developmental direction of drag discovery, was generating attention of more and more researchers. The key problem in drug discovery was how to identify the new interactions between drugs and target proteins. Prediction of new interaction was made to find potential targets based on the predicting model constructed by the known drug-protein interactions. According to the deficiencies of existing predicting algorithm based bipartite graph, a supervised learning integration method of bipartite graph was proposed in this paper. Firstly, the bipartite graph network was constructed based on the known interactions between drugs and target proteins. Secondly, the evaluation model for association between drugs and target proteins was created. Thirdly, the model was used to predict the new interactions between drugs and target proteins and confirm the new predicted targets

  1. Using Click Chemistry to Identify Potential Drug Targets in Plasmodium

    Science.gov (United States)

    2015-04-01

    AWARD NUMBER: W81XWH-13-1-0429 TITLE: Using "Click Chemistry" to Identify Potential Drug Targets in Plasmodium PRINCIPAL INVESTIGATOR: Dr. Purnima...SUBTITLE Sa. CONTRACT NUMBER W81XWH-1 3-1-0429 Using "Click Chemistry" to Identify Potential Drug Targets in Plasmodium 5b. GRANT NUMBER 5c. PROGRAM...Release; Distribution Unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT Sporozo ite infection of the liver is the first obl igate step of the Plasmodium

  2. Using Click Chemistry to Identify Potential Drug Targets in Plasmodium

    Science.gov (United States)

    2016-06-01

    AWARD NUMBER: W81XWH-13-1-0429 TITLE: Using "Click Chemistry " to Identify Potential Drug Targets in Plasmodium PRINCIPAL INVESTIGATOR...29Mar2016 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER W81XWH-13-1-0429 Using click chemistry to identify potential drug targets in Plasmodium 5b...Al-Tsp derivatives begins. Two classes of Tsp derivatives (Al-Tsp) are appropriate for click chemistry (Fig. 1). Class I derivatives carry a

  3. Adipokines as drug targets in diabetes and underlying disturbances.

    Science.gov (United States)

    Andrade-Oliveira, Vinícius; Câmara, Niels O S; Moraes-Vieira, Pedro M

    2015-01-01

    Diabetes and obesity are worldwide health problems. White fat dynamically participates in hormonal and inflammatory regulation. White adipose tissue is recognized as a multifactorial organ that secretes several adipose-derived factors that have been collectively termed "adipokines." Adipokines are pleiotropic molecules that gather factors such as leptin, adiponectin, visfatin, apelin, vaspin, hepcidin, RBP4, and inflammatory cytokines, including TNF and IL-1β, among others. Multiple roles in metabolic and inflammatory responses have been assigned to these molecules. Several adipokines contribute to the self-styled "low-grade inflammatory state" of obese and insulin-resistant subjects, inducing the accumulation of metabolic anomalies within these individuals, including autoimmune and inflammatory diseases. Thus, adipokines are an interesting drug target to treat autoimmune diseases, obesity, insulin resistance, and adipose tissue inflammation. The aim of this review is to present an overview of the roles of adipokines in different immune and nonimmune cells, which will contribute to diabetes as well as to adipose tissue inflammation and insulin resistance development. We describe how adipokines regulate inflammation in these diseases and their therapeutic implications. We also survey current attempts to exploit adipokines for clinical applications, which hold potential as novel approaches to drug development in several immune-mediated diseases.

  4. Mitochondrial drug targets in neurodegenerative diseases.

    Science.gov (United States)

    Lee, Jiyoun

    2016-02-01

    Growing evidence suggests that mitochondrial dysfunction is the main culprit in neurodegenerative diseases. Given the fact that mitochondria participate in diverse cellular processes, including energetics, metabolism, and death, the consequences of mitochondrial dysfunction in neuronal cells are inevitable. In fact, new strategies targeting mitochondrial dysfunction are emerging as potential alternatives to current treatment options for neurodegenerative diseases. In this review, we focus on mitochondrial proteins that are directly associated with mitochondrial dysfunction. We also examine recently identified small molecule modulators of these mitochondrial targets and assess their potential in research and therapeutic applications.

  5. Comparative genomics study for identification of putative drug targets in Salmonella typhi Ty2.

    Science.gov (United States)

    Batool, Nisha; Waqar, Maleeha; Batool, Sidra

    2016-01-15

    Typhoid presents a major health concern in developing countries with an estimated annual infection rate of 21 million. The disease is caused by Salmonella typhi, a pathogenic bacterium acquiring multiple drug resistance. We aim to identify proteins that could prove to be putative drug targets in the genome of S. typhi str. Ty2. We employed comparative and subtractive genomics to identify targets that are absent in humans and are essential to S. typhi Ty2. We concluded that 46 proteins essential to pathogen are absent in the host genome. Filtration on the basis of drug target prioritization singled out 20 potentially therapeutic targets. Their absence in the host and specificity to S. typhi Ty2 makes them ideal targets for treating typhoid in Homo sapiens. 3D structures of two of the final target enzymes, MurA and MurB have been predicted via homology modeling which are then used for a docking study.

  6. Enhanced molecular dynamics sampling of drug target conformations.

    Science.gov (United States)

    Rodriguez-Bussey, Isela G; Doshi, Urmi; Hamelberg, Donald

    2016-01-01

    Computational docking and virtual screening are two main important methods employed in structure-based drug design. Unlike the traditional approach that allows docking of a flexible ligand against a handful of receptor structures, receptor flexibility has now been appreciated and increasingly incorporated in computer-aided docking. Using a diverse set of receptor conformations increases the chances of finding potential drugs and inhibitors. Molecular dynamics (MD) is greatly useful to generate various receptor conformations. However, the diversity of the structures of the receptor, which is usually much larger than the ligand, depends on the sampling efficiency of MD. Enhanced sampling methods based on accelerated molecular dynamics (aMD) can alleviate the sampling limitation of conventional MD and aid in representation of the phase space to a much greater extent. RaMD-db, a variant of aMD that applies boost potential to the rotatable dihedrals and non-bonded diffusive degrees of freedom has been proven to reproduce the equilibrium properties more accurately and efficiently than aMD. Here, we discuss recent advances in the aMD methodology and review the applicability of RaMD-db as an enhanced sampling method. RaMD-db is shown to be able to generate a broad distribution of structures of a drug target, Cyclophilin A. These structures that have never been observed previously in very long conventional MD can be further used for structure-based computer-aided drug discovery, and docking, and thus, in the identification and design of potential novel inhibitors.

  7. Discovery of cancer drug targets by CRISPR-Cas9 screening of protein domains.

    Science.gov (United States)

    Shi, Junwei; Wang, Eric; Milazzo, Joseph P; Wang, Zihua; Kinney, Justin B; Vakoc, Christopher R

    2015-06-01

    CRISPR-Cas9 genome editing technology holds great promise for discovering therapeutic targets in cancer and other diseases. Current screening strategies target CRISPR-Cas9-induced mutations to the 5' exons of candidate genes, but this approach often produces in-frame variants that retain functionality, which can obscure even strong genetic dependencies. Here we overcome this limitation by targeting CRISPR-Cas9 mutagenesis to exons encoding functional protein domains. This generates a higher proportion of null mutations and substantially increases the potency of negative selection. We also show that the magnitude of negative selection can be used to infer the functional importance of individual protein domains of interest. A screen of 192 chromatin regulatory domains in murine acute myeloid leukemia cells identifies six known drug targets and 19 additional dependencies. A broader application of this approach may allow comprehensive identification of protein domains that sustain cancer cells and are suitable for drug targeting.

  8. Crowd sourcing a new paradigm for interactome driven drug target identification in Mycobacterium tuberculosis.

    Directory of Open Access Journals (Sweden)

    Rohit Vashisht

    Full Text Available A decade since the availability of Mycobacterium tuberculosis (Mtb genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative 'Connect to Decode' (C2D to generate the first and largest manually curated interactome of Mtb termed 'interactome pathway' (IPW, encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

  9. Development and evaluation of colloidal modified nanolipid carrier: application to topical delivery of tacrolimus, Part II--in vivo assessment, drug targeting, efficacy, and safety in treatment for atopic dermatitis.

    Science.gov (United States)

    Pople, Pallavi V; Singh, Kamalinder K

    2013-05-01

    In atopic dermatitis (AD), topical anti-inflammatory therapy with skin barrier restoration to prevent repeated inflammatory episodes leads to long-term therapeutic success. Tacrolimus, although effective against AD, is a challenging molecule due to low solubility, low-penetration, poor-bioavailability, and toxicity. Part I of this paper, reported novel modified nanolipid carrier system for topical delivery of tacrolimus (T-MNLC), offering great opportunity to load low-solubility drug with improved entrapment efficiency, enhanced stability and improved skin deposition. Present investigation focused on restoration of skin barrier, site-specific delivery, therapeutic effectiveness, and safety of novel T-MNLC. T-MNLC greatly enhanced occlusive properties, skin hydration potential and reduced transepidermal water loss. This might help to reduce the number of flares and better control the disease. Cutaneous uptake and drug deposition in albino rats by HPLC and confocal laser scanning microscopy revealed prominently elevated drug levels in all skin strata with T-MNLC as compared to reference. T-MNLC demonstrated efficient suppression of inflammatory responses in BALB/c mice model of AD. Safety assessment by acute and repeated-dose dermal toxicity demonstrated mild keratosis and collagenous mass infiltration at the treatment area with repeated application of reference. Interestingly, T-MNLC showed no evident toxicity exhibiting safe drug delivery. Thus, novel T-MNLC would be a safe, effective, and esthetically appealing alternative to conventional vehicles for treatment for AD.

  10. Identification of putative drug targets in Vancomycin-resistant Staphylococcus aureus (VRSA) using computer aided protein data analysis.

    Science.gov (United States)

    Hasan, Md Anayet; Khan, Md Arif; Sharmin, Tahmina; Hasan Mazumder, Md Habibul; Chowdhury, Afrin Sultana

    2016-01-01

    Vancomycin-resistant Staphylococcus aureus (VRSA) is a Gram-positive, facultative aerobic bacterium which is evolved from the extensive exposure of Vancomycin to Methicillin resistant S. aureus (MRSA) that had become the most common cause of hospital and community-acquired infections. Due to the emergence of different antibiotic resistance strains, there is an exigency to develop novel drug targets to address the provocation of multidrug-resistant bacteria. In this study, in-silico genome subtraction methodology was used to design potential and pathogen specific drug targets against VRSA. Our study divulged 1987 proteins from the proteome of 34,549 proteins, which have no homologues in human genome after sequential analysis through CD-HIT and BLASTp. The high stringency analysis of the remaining proteins against database of essential genes (DEG) resulted in 169 proteins which are essential for S. aureus. Metabolic pathway analysis of human host and pathogen by KAAS at the KEGG server sorted out 19 proteins involved in unique metabolic pathways. 26 human non-homologous membrane-bound essential proteins including 4 which were also involved in unique metabolic pathway were deduced through PSORTb, CELLO v.2.5, ngLOC. Functional classification of uncharacterized proteins through SVMprot derived 7 human non-homologous membrane-bound hypothetical essential proteins. Study of potential drug target against Drug Bank revealed pbpA-penicillin-binding protein 1 and hypothetical protein MQW_01796 as the best drug target candidate. 2D structure was predicted by PRED-TMBB, 3D structure and functional analysis was also performed. Protein-protein interaction network of potential drug target proteins was analyzed by using STRING. The identified drug targets are expected to have great potential for designing novel drugs against VRSA infections and further screening of the compounds against these new targets may result in the discovery of novel therapeutic compounds that can be

  11. PDTD: a web-accessible protein database for drug target identification

    Directory of Open Access Journals (Sweden)

    Gao Zhenting

    2008-02-01

    unique repository of drug targets. Integrated with TarFisDock, PDTD is a useful resource to identify binding proteins for active compounds or existing drugs. Its potential applications include in silico drug target identification, virtual screening, and the discovery of the secondary effects of an old drug (i.e. new pharmacological usage or an existing target (i.e. new pharmacological or toxic relevance, thus it may be a valuable platform for the pharmaceutical researchers. PDTD is available online at http://www.dddc.ac.cn/pdtd/.

  12. Efficient Data Mining Algorithms for Screening Potential Proteins of Drug Target

    Directory of Open Access Journals (Sweden)

    Qi Wang

    2017-01-01

    Full Text Available The past few decades have witnessed the boom in pharmacology as well as the dilemma of drug development. Playing a crucial role in drug design, the screening of potential human proteins of drug targets from open access database with well-measured physical and chemical properties is a task of challenge but significance. In this paper, the screening of potential drug target proteins (DTPs from a fine collected dataset containing 5376 unlabeled proteins and 517 known DTPs was researched. Our objective is to screen potential DTPs from the 5376 proteins. Here we proposed two strategies assisting the construction of dataset of reliable nondrug target proteins (NDTPs and then bagging of decision trees method was employed in the final prediction. Such two-stage algorithms have shown their effectiveness and superior performance on the testing set. Both of the algorithms maintained higher recall ratios of DTPs, respectively, 93.5% and 97.4%. In one turn of experiments, strategy1-based bagging of decision trees algorithm screened about 558 possible DTPs while 1782 potential DTPs were predicted in the second algorithm. Besides, two strategy-based algorithms showed the consensus of the predictions in the results, with approximately 442 potential DTPs in common. These selected DTPs provide reliable choices for further verification based on biomedical experiments.

  13. Drug targeting systems for cancer therapy: nanotechnological approach.

    Science.gov (United States)

    Tigli Aydin, R Seda

    2015-01-01

    Progress in cancer treatment remains challenging because of the great nature of tumor cells to be drug resistant. However, advances in the field of nanotechnology have enabled the delivery of drugs for cancer treatment by passively and actively targeting to tumor cells with nanoparticles. Dramatic improvements in nanotherapeutics, as applied to cancer, have rapidly accelerated clinical investigations. In this review, drug-targeting systems using nanotechnology and approved and clinically investigated nanoparticles for cancer therapy are discussed. In addition, the rationale for a nanotechnological approach to cancer therapy is emphasized because of its promising advances in the treatment of cancer patients.

  14. The drug target genes show higher evolutionary conservation than non-target genes.

    Science.gov (United States)

    Lv, Wenhua; Xu, Yongdeng; Guo, Yiying; Yu, Ziqi; Feng, Guanglong; Liu, Panpan; Luan, Meiwei; Zhu, Hongjie; Liu, Guiyou; Zhang, Mingming; Lv, Hongchao; Duan, Lian; Shang, Zhenwei; Li, Jin; Jiang, Yongshuai; Zhang, Ruijie

    2016-01-26

    Although evidence indicates that drug target genes share some common evolutionary features, there have been few studies analyzing evolutionary features of drug targets from an overall level. Therefore, we conducted an analysis which aimed to investigate the evolutionary characteristics of drug target genes. We compared the evolutionary conservation between human drug target genes and non-target genes by combining both the evolutionary features and network topological properties in human protein-protein interaction network. The evolution rate, conservation score and the percentage of orthologous genes of 21 species were included in our study. Meanwhile, four topological features including the average shortest path length, betweenness centrality, clustering coefficient and degree were considered for comparison analysis. Then we got four results as following: compared with non-drug target genes, 1) drug target genes had lower evolutionary rates; 2) drug target genes had higher conservation scores; 3) drug target genes had higher percentages of orthologous genes and 4) drug target genes had a tighter network structure including higher degrees, betweenness centrality, clustering coefficients and lower average shortest path lengths. These results demonstrate that drug target genes are more evolutionarily conserved than non-drug target genes. We hope that our study will provide valuable information for other researchers who are interested in evolutionary conservation of drug targets.

  15. Integral membrane pyrophosphatases: a novel drug target for human pathogens?

    Directory of Open Access Journals (Sweden)

    Henri Xhaard

    2016-03-01

    Full Text Available Membrane-integral pyrophosphatases (mPPases are found in several human pathogens, including Plasmodium species, the protozoan parasites that cause malaria. These enzymes hydrolyze pyrophosphate and couple this to the pumping of ions (H+ and/or Na+ across a membrane to generate an electrochemical gradient. mPPases play an important role in stress tolerance in plants, protozoan parasites, and bacteria. The solved structures of mPPases from Vigna radiata and Thermotoga maritima open the possibility of using structure-based drug design to generate novel molecules or repurpose known molecules against this enzyme. Here, we review the current state of knowledge regarding mPPases, focusing on their structure, the proposed mechanism of action, and their role in human pathogens. We also summarize different methodologies in structure-based drug design and propose an example region on the mPPase structure that can be exploited by these structure-based methods for drug targeting. Since mPPases are not found in animals and humans, this enzyme is a promising potential drug target against livestock and human pathogens.

  16. Spherons as a drug target in Alzheimer's disease.

    Science.gov (United States)

    Averback, P

    1998-10-01

    Spherons are unique brain entities that are causally linked to the amyloid plaques (SPs [senile plaques]) of Alzheimer's disease (AD). SPs are the quantitatively major tissue abnormality of AD. Spherons increase in size (but not in number) gradually throughout life until they reach a size range where they burst and form SPs. Drugs targeted at attenuating the process of spheron transformation into SPs are a logical approach to AD therapy. There are 20 criteria of validity for an SP causal entity that are satisfied by spherons-and no more than a few of these 20 criteria are satisfied by any other known hypothesis. These criteria of validity are reviewed, in addition to common difficulties in understanding spheron theory and a number of common-sense considerations in AD therapeutic research. Spheron-based drug therapy in AD potentially can retard the process of spheron bursting and subsequent plaque formation by: 1) blocking the formation of SPs; 2) reducing the size of SPs; 3) delaying spheron breakdown; and 4) retarding spheron growth. Isolated spherons from human brain are intact human drug targets and can be used as human in vitro or in vivo screening targets. The paramount importance of spherons as a target for drug therapy in AD is emphasized by considering that regardless of any other type of real or potential therapy, there still already exists in every middle-aged adult a full population of spherons in the brain, filled with more than enough amyloid to bring about full-blown AD.

  17. The Validation of Nematode-Specific Acetylcholine-Gated Chloride Channels as Potential Anthelmintic Drug Targets.

    Science.gov (United States)

    Wever, Claudia M; Farrington, Danielle; Dent, Joseph A

    2015-01-01

    New compounds are needed to treat parasitic nematode infections in humans, livestock and plants. Small molecule anthelmintics are the primary means of nematode parasite control in animals; however, widespread resistance to the currently available drug classes means control will be impossible without the introduction of new compounds. Adverse environmental effects associated with nematocides used to control plant parasitic species are also motivating the search for safer, more effective compounds. Discovery of new anthelmintic drugs in particular has been a serious challenge due to the difficulty of obtaining and culturing target parasites for high-throughput screens and the lack of functional genomic techniques to validate potential drug targets in these pathogens. We present here a novel strategy for target validation that employs the free-living nematode Caenorhabditis elegans to demonstrate the value of new ligand-gated ion channels as targets for anthelmintic discovery. Many successful anthelmintics, including ivermectin, levamisole and monepantel, are agonists of pentameric ligand-gated ion channels, suggesting that the unexploited pentameric ion channels encoded in parasite genomes may be suitable drug targets. We validated five members of the nematode-specific family of acetylcholine-gated chloride channels as targets of agonists with anthelmintic properties by ectopically expressing an ivermectin-gated chloride channel, AVR-15, in tissues that endogenously express the acetylcholine-gated chloride channels and using the effects of ivermectin to predict the effects of an acetylcholine-gated chloride channel agonist. In principle, our strategy can be applied to validate any ion channel as a putative anti-parasitic drug target.

  18. The Validation of Nematode-Specific Acetylcholine-Gated Chloride Channels as Potential Anthelmintic Drug Targets.

    Directory of Open Access Journals (Sweden)

    Claudia M Wever

    Full Text Available New compounds are needed to treat parasitic nematode infections in humans, livestock and plants. Small molecule anthelmintics are the primary means of nematode parasite control in animals; however, widespread resistance to the currently available drug classes means control will be impossible without the introduction of new compounds. Adverse environmental effects associated with nematocides used to control plant parasitic species are also motivating the search for safer, more effective compounds. Discovery of new anthelmintic drugs in particular has been a serious challenge due to the difficulty of obtaining and culturing target parasites for high-throughput screens and the lack of functional genomic techniques to validate potential drug targets in these pathogens. We present here a novel strategy for target validation that employs the free-living nematode Caenorhabditis elegans to demonstrate the value of new ligand-gated ion channels as targets for anthelmintic discovery. Many successful anthelmintics, including ivermectin, levamisole and monepantel, are agonists of pentameric ligand-gated ion channels, suggesting that the unexploited pentameric ion channels encoded in parasite genomes may be suitable drug targets. We validated five members of the nematode-specific family of acetylcholine-gated chloride channels as targets of agonists with anthelmintic properties by ectopically expressing an ivermectin-gated chloride channel, AVR-15, in tissues that endogenously express the acetylcholine-gated chloride channels and using the effects of ivermectin to predict the effects of an acetylcholine-gated chloride channel agonist. In principle, our strategy can be applied to validate any ion channel as a putative anti-parasitic drug target.

  19. The Validation of Nematode-Specific Acetylcholine-Gated Chloride Channels as Potential Anthelmintic Drug Targets

    Science.gov (United States)

    Wever, Claudia M.; Farrington, Danielle; Dent, Joseph A.

    2015-01-01

    New compounds are needed to treat parasitic nematode infections in humans, livestock and plants. Small molecule anthelmintics are the primary means of nematode parasite control in animals; however, widespread resistance to the currently available drug classes means control will be impossible without the introduction of new compounds. Adverse environmental effects associated with nematocides used to control plant parasitic species are also motivating the search for safer, more effective compounds. Discovery of new anthelmintic drugs in particular has been a serious challenge due to the difficulty of obtaining and culturing target parasites for high-throughput screens and the lack of functional genomic techniques to validate potential drug targets in these pathogens. We present here a novel strategy for target validation that employs the free-living nematode Caenorhabditis elegans to demonstrate the value of new ligand-gated ion channels as targets for anthelmintic discovery. Many successful anthelmintics, including ivermectin, levamisole and monepantel, are agonists of pentameric ligand-gated ion channels, suggesting that the unexploited pentameric ion channels encoded in parasite genomes may be suitable drug targets. We validated five members of the nematode-specific family of acetylcholine-gated chloride channels as targets of agonists with anthelmintic properties by ectopically expressing an ivermectin-gated chloride channel, AVR-15, in tissues that endogenously express the acetylcholine-gated chloride channels and using the effects of ivermectin to predict the effects of an acetylcholine-gated chloride channel agonist. In principle, our strategy can be applied to validate any ion channel as a putative anti-parasitic drug target. PMID:26393923

  20. p21-activated kinase family: promising new drug targets

    Directory of Open Access Journals (Sweden)

    Huynh N

    2015-05-01

    Full Text Available Nhi Huynh, Hong He Department of Surgery, University of Melbourne, Austin Health, Melbourne, VIC, Australia Abstract: The p21-activated kinase (PAK family of serine/threonine protein kinases are downstream effectors of the Rho family of GTPases. PAKs are frequently upregulated in human diseases, including various cancers, and their overexpression correlates with disease progression. Current research findings have validated important roles for PAKs in cell proliferation, survival, gene transcription, transformation, and cytoskeletal remodeling. PAKs are shown to act as a converging node for many signaling pathways that regulate these cellular processes. Therefore, PAKs have emerged as attractive targets for treatment of disease. This review discusses the physiological and pathological roles of PAKs, validation of PAKs as new promising drug targets, and current challenges and advances in the development of PAK-targeted anticancer therapy, with a focus on PAKs and human cancers. Keywords: p21-activated kinase, cancer, inhibitor

  1. Wzy-dependent bacterial capsules as potential drug targets.

    Science.gov (United States)

    Ericsson, Daniel J; Standish, Alistair; Kobe, Bostjan; Morona, Renato

    2012-10-01

    The bacterial capsule is a recognized virulence factor in pathogenic bacteria. It likely works as an antiphagocytic barrier by minimizing complement deposition on the bacterial surface. With the continual rise of bacterial pathogens resistant to multiple antibiotics, there is an increasing need for novel drugs. In the Wzy-dependent pathway, the biosynthesis of capsular polysaccharide (CPS) is regulated by a phosphoregulatory system, whose main components consist of bacterial-tyrosine kinases (BY-kinases) and their cognate phosphatases. The ability to regulate capsule biosynthesis has been shown to be vital for pathogenicity, because different stages of infection require a shift in capsule thickness, making the phosphoregulatory proteins suitable as drug targets. Here, we review the role of regulatory proteins focusing on Streptococcus pneumoniae, Staphylococcus aureus, and Escherichia coli and discuss their suitability as targets in structure-based drug design.

  2. Polymeric micelles with stimuli-triggering systems for advanced cancer drug targeting.

    Science.gov (United States)

    Nakayama, Masamichi; Akimoto, Jun; Okano, Teruo

    2014-08-01

    Since the 1990s, nanoscale drug carriers have played a pivotal role in cancer chemotherapy, acting through passive drug delivery mechanisms and subsequent pharmaceutical action at tumor tissues with reduction of adverse effects. Polymeric micelles, as supramolecular assemblies of amphiphilic polymers, have been considerably developed as promising drug carrier candidates, and a number of clinical studies of anticancer drug-loaded polymeric micelle carriers for cancer chemotherapy applications are now in progress. However, these systems still face several issues; at present, the simultaneous control of target-selective delivery and release of incorporated drugs remains difficult. To resolve these points, the introduction of stimuli-responsive mechanisms to drug carrier systems is believed to be a promising approach to provide better solutions for future tumor drug targeting strategies. As possible trigger signals, biological acidic pH, light, heating/cooling and ultrasound actively play significant roles in signal-triggering drug release and carrier interaction with target cells. This review article summarizes several molecular designs for stimuli-responsive polymeric micelles in response to variation of pH, light and temperature and discusses their potentials as next-generation tumor drug targeting systems.

  3. Leptin signaling molecular actions and drug target in hepatocellular carcinoma

    Directory of Open Access Journals (Sweden)

    Jiang N

    2014-11-01

    leptin and Ob-R in cancer cells compared to normal cells, makes leptin an ideal drug target for the prevention and treatment of HCC, especially in obese patients. Keywords: hepatocellular carcinoma, leptin, leptin antagonist, leptin signaling, tumor angiogenesis, drug target

  4. Color prediction in textile application

    Science.gov (United States)

    De Lucia, Maurizio; Buonopane, Massimo

    2004-09-01

    Nowadays production systems of fancy yarns for knits allow the creation of extremely complex products in which many effects are obtained by means of color alteration. Current production technique consists in defining type and quantity of fibers by making preliminary samples. This samples are then compared with a reference one. This comparison is based on operator experience. Many samples are required in order to achieve a sample similar to the reference one. This work requires time and then additional costs for a textile manufacturer. In addition, the methodology is subjective. Nowadays, spectrophotometers are the only devices that seem to be able to provide objective indications. They are based on a spectral analysis of the light reflected by the knit material. In this paper the study of a new method for color evaluation of a mix of wool fibers with different colors is presented. First of all fiber characterization were carried out through scattering and absorption coefficients using the Kubelka-Munk theory. Then the estimated color was compared with a reference item, in order to define conformity by means of objective parameters. Finally, theoretical characterization was compared with the measured quantity. This allowed estimation of prediction quality.

  5. Quantification of biodegradable PLGA nanoparticles for drug targeting

    Directory of Open Access Journals (Sweden)

    Nadira Ibrišimović

    2010-11-01

    Full Text Available Objective. The aim of this work was the development of appropriate analytical methods and assays for determining and monitoring composition and degradation of nanoparticles built from PLGA (poly D, L-lactid-co-glycolid, which can be reloaded with different drugs. A sensitive and precise method for monitoring of nanoparticle degradation in vitro was developed and optimized. Nanoparticles allow a selective enrichment of different drugs and knowledge of the nature and type of their degradation is essential for characterization and control of drug release and dosage. Materials and methods. The first method developed during this work to quantify the PLGA polymer matrix use advantage of the chemical reaction of aliphatic carboxylic acids with ferric chloride (FeCl3 thus quantifying both degradation products of PLGA, lactic and glycol acids, at the same time. A second assay method of choice was to react to the polymer hydrolysate with lactate dehydrogenase, thus assaying selectively the lactic acid part. Results. During development of both of described methods was possible to determine dynamic range for PLGA matrix and nanoparticles, as well as to characterize impact of Pluronic F-68 and glycolic acid on lactate dehydrogenase activity. Conclusion. During our work we were able to develop two sensitive methods for monitoring of biodegradation of polymers which are consecutively used as a nanoparticle matrix in drug targeting.

  6. Drug resistance mechanisms and novel drug targets for tuberculosis therapy.

    Science.gov (United States)

    Islam, Md Mahmudul; Hameed, H M Adnan; Mugweru, Julius; Chhotaray, Chiranjibi; Wang, Changwei; Tan, Yaoju; Liu, Jianxiong; Li, Xinjie; Tan, Shouyong; Ojima, Iwao; Yew, Wing Wai; Nuermberger, Eric; Lamichhane, Gyanu; Zhang, Tianyu

    2017-01-20

    Drug-resistant tuberculosis (TB) poses a significant challenge to the successful treatment and control of TB worldwide. Resistance to anti-TB drugs has existed since the beginning of the chemotherapy era. New insights into the resistant mechanisms of anti-TB drugs have been provided. Better understanding of drug resistance mechanisms helps in the development of new tools for the rapid diagnosis of drug-resistant TB. There is also a pressing need in the development of new drugs with novel targets to improve the current treatment of TB and to prevent the emergence of drug resistance in Mycobacterium tuberculosis. This review summarizes the anti-TB drug resistance mechanisms, furnishes some possible novel drug targets in the development of new agents for TB therapy and discusses the usefulness using known targets to develop new anti-TB drugs. Whole genome sequencing is currently an advanced technology to uncover drug resistance mechanisms in M. tuberculosis. However, further research is required to unravel the significance of some newly discovered gene mutations in their contribution to drug resistance.

  7. New alginic acid–atenolol microparticles for inhalatory drug targeting

    Energy Technology Data Exchange (ETDEWEB)

    Ceschan, Nazareth Eliana; Bucalá, Verónica [Planta Piloto de Ingeniería Química (PLAPIQUI), CONICET, Universidad Nacional del Sur (UNS), Camino La Carrindanga Km 7, 8000 Bahía Blanca (Argentina); Departamento de Ingeniería Química, UNS, Avenida Alem 1253, 8000 Bahía Blanca (Argentina); Ramírez-Rigo, María Verónica, E-mail: vrrigo@plapiqui.edu.ar [Planta Piloto de Ingeniería Química (PLAPIQUI), CONICET, Universidad Nacional del Sur (UNS), Camino La Carrindanga Km 7, 8000 Bahía Blanca (Argentina); Departamento de Biología, Bioquímica y Farmacia, UNS, San Juan 670, 8000 Bahía Blanca (Argentina)

    2014-08-01

    The inhalatory route allows drug delivery for local or systemic treatments in a noninvasively way. The current tendency of inhalable systems is oriented to dry powder inhalers due to their advantages in terms of stability and efficiency. In this work, microparticles of atenolol (AT, basic antihypertensive drug) and alginic acid (AA, acid biocompatible polyelectrolyte) were obtained by spray drying. Several formulations, varying the relative composition AT/AA and the total solid content of the atomized dispersions, were tested. The powders were characterized by: Fourier Transform Infrared Spectroscopy, Differential Scanning Calorimetry and Powder X-ray Diffraction, while also the following properties were measured: drug load efficiency, flow properties, particles size and density, moisture content, hygroscopicity and morphology. The ionic interaction between AA and AT was demonstrated, then the new chemical entity could improve the drug targeting to the respiratory membrane and increase its time residence due to the mucoadhesive properties of the AA polymeric chains. Powders exhibited high load efficiencies, low moisture contents, adequate mean aerodynamic diameters and high cumulative fraction of respirable particles (lower than 10 μm). - Highlights: • Novel particulate material to target atenolol to the respiratory membrane was developed. • Crumbled microparticles were obtained by spray drying of alginic–atenolol dispersions. • Ionic interaction between alginic acid and atenolol was demonstrated in the product. • Amorphous solids with low moisture content and high load efficiency were produced. • Relationships between the feed formulation and the product characteristics were found.

  8. Metabotropic glutamate receptors and interacting proteins: evolving drug targets.

    Science.gov (United States)

    Enz, Ralf

    2012-01-01

    The correct targeting, localization, regulation and signaling of metabotropic glutamate receptors (mGluRs) represent major mechanisms underlying the complex function of neuronal networks. These tasks are accomplished by the formation of synaptic signal complexes that integrate functionally related proteins such as neurotransmitter receptors, enzymes and scaffold proteins. By these means, proteins interacting with mGluRs are important regulators of glutamatergic neurotransmission. Most described mGluR interaction partners bind to the intracellular C-termini of the receptors. These domains are extensively spliced and phosphorylated, resulting in a high variability of binding surfaces offered to interacting proteins. Malfunction of mGluRs and associated proteins are linked to neurodegenerative and neuropsychiatric disorders including addiction, depression, epilepsy, schizophrenia, Alzheimer's, Huntington's and Parkinson's disease. MGluR associated signal complexes are dynamic structures that assemble and disassemble in response to the neuronal fate. This, in principle, allows therapeutic intervention, defining mGluRs and interacting proteins as promising drug targets. In the last years, several studies elucidated the geometry of mGluRs in contact with regulatory proteins, providing a solid fundament for the development of new therapeutic strategies. Here, I will give an overview of human disorders directly associated with mGluR malfunction, provide an up-to-date summary of mGluR interacting proteins and highlight recently described structures of mGluR domains in contact with binding partners.

  9. TRPV1: A Potential Drug Target for Treating Various Diseases

    Directory of Open Access Journals (Sweden)

    Rafael Brito

    2014-05-01

    Full Text Available Transient receptor potential vanilloid 1 (TRPV1 is an ion channel present on sensory neurons which is activated by heat, protons, capsaicin and a variety of endogenous lipids termed endovanilloids. As such, TRPV1 serves as a multimodal sensor of noxious stimuli which could trigger counteractive measures to avoid pain and injury. Activation of TRPV1 has been linked to chronic inflammatory pain conditions and peripheral neuropathy, as observed in diabetes. Expression of TRPV1 is also observed in non-neuronal sites such as the epithelium of bladder and lungs and in hair cells of the cochlea. At these sites, activation of TRPV1 has been implicated in the pathophysiology of diseases such as cystitis, asthma and hearing loss. Therefore, drugs which could modulate TRPV1 channel activity could be useful for the treatment of conditions ranging from chronic pain to hearing loss. This review describes the roles of TRPV1 in the normal physiology and pathophysiology of selected organs of the body and highlights how drugs targeting this channel could be important clinically.

  10. Iontophoresis of minoxidil sulphate loaded microparticles, a strategy for follicular drug targeting?

    Science.gov (United States)

    Gelfuso, Guilherme M; Barros, M Angélica de Oliveira; Delgado-Charro, M Begoña; Guy, Richard H; Lopez, Renata F V

    2015-10-01

    The feasibility of targeting drugs to hair follicles by a combination of microencapsulation and iontophoresis has been evaluated. Minoxidil sulphate (MXS), which is used in the treatment of alopecia, was selected as a relevant drug with respect to follicular penetration. The skin permeation and disposition of MXS encapsulated in chitosan microparticles (MXS-MP) was evaluated in vitro after passive and iontophoretic delivery. Uptake of MXS was quantified at different exposure times in the stratum corneum (SC) and hair follicles. Microencapsulation resulted in increased (6-fold) drug accumulation in the hair follicles relative to delivery from a simple MXS solution. Application of iontophoresis enhanced follicular delivery for both the solution and the microparticle formulations. It appears, therefore, that microencapsulation and iontophoresis can act synergistically to enhance topical drug targeting to hair follicles.

  11. Discovery: an interactive resource for the rational selection and comparison of putative drug target proteins in malaria

    Directory of Open Access Journals (Sweden)

    Odendaal Christiaan J

    2009-07-01

    Full Text Available Abstract Background Up to half a billion human clinical cases of malaria are reported each year, resulting in about 2.7 million deaths, most of which occur in sub-Saharan Africa. Due to the over-and misuse of anti-malarials, widespread resistance to all the known drugs is increasing at an alarming rate. Rational methods to select new drug target proteins and lead compounds are urgently needed. The Discovery system provides data mining functionality on extensive annotations of five malaria species together with the human and mosquito hosts, enabling the selection of new targets based on multiple protein and ligand properties. Methods A web-based system was developed where researchers are able to mine information on malaria proteins and predicted ligands, as well as perform comparisons to the human and mosquito host characteristics. Protein features used include: domains, motifs, EC numbers, GO terms, orthologs, protein-protein interactions, protein-ligand interactions and host-pathogen interactions among others. Searching by chemical structure is also available. Results An in silico system for the selection of putative drug targets and lead compounds is presented, together with an example study on the bifunctional DHFR-TS from Plasmodium falciparum. Conclusion The Discovery system allows for the identification of putative drug targets and lead compounds in Plasmodium species based on the filtering of protein and chemical properties.

  12. Legionella pneumophila Carbonic Anhydrases: Underexplored Antibacterial Drug Targets

    Directory of Open Access Journals (Sweden)

    Claudiu T. Supuran

    2016-06-01

    Full Text Available Carbonic anhydrases (CAs, EC 4.2.1.1 are metalloenzymes which catalyze the hydration of carbon dioxide to bicarbonate and protons. Many pathogenic bacteria encode such enzymes belonging to the α-, β-, and/or γ-CA families. In the last decade, enzymes from some of these pathogens, including Legionella pneumophila, have been cloned and characterized in detail. These enzymes were shown to be efficient catalysts for CO2 hydration, with kcat values in the range of (3.4–8.3 × 105 s−1 and kcat/KM values of (4.7–8.5 × 107 M−1·s−1. In vitro inhibition studies with various classes of inhibitors, such as anions, sulfonamides and sulfamates, were also reported for the two β-CAs from this pathogen, LpCA1 and LpCA2. Inorganic anions were millimolar inhibitors, whereas diethyldithiocarbamate, sulfamate, sulfamide, phenylboronic acid, and phenylarsonic acid were micromolar ones. The best LpCA1 inhibitors were aminobenzolamide and structurally similar sulfonylated aromatic sulfonamides, as well as acetazolamide and ethoxzolamide (KIs in the range of 40.3–90.5 nM. The best LpCA2 inhibitors belonged to the same class of sulfonylated sulfonamides, together with acetazolamide, methazolamide, and dichlorophenamide (KIs in the range of 25.2–88.5 nM. Considering such preliminary results, the two bacterial CAs from this pathogen represent promising yet underexplored targets for obtaining antibacterials devoid of the resistance problems common to most of the clinically used antibiotics, but further studies are needed to validate them in vivo as drug targets.

  13. An Algorithm of Predictability and Its Application

    Institute of Scientific and Technical Information of China (English)

    ZhijieCAI; JiongRUAN; 等

    1999-01-01

    Recently,the neural network has been infiltrated into many fields,In this paper,we introduce its application in signal analysis of the Heart Rate Variability(HRV) Not only we give the algorithm of predictability,but the analysis result of time series given by Shanghai No.1 People's Hospital is listed as well.The result shows that the series of HRV have typical nonlinear character and the predictability can be a rater accurate parameter to analyze and diagnose cardiovascular diseases in clinics.

  14. Collaborative development of predictive toxicology applications

    Directory of Open Access Journals (Sweden)

    Hardy Barry

    2010-08-01

    Full Text Available Abstract OpenTox provides an interoperable, standards-based Framework for the support of predictive toxicology data management, algorithms, modelling, validation and reporting. It is relevant to satisfying the chemical safety assessment requirements of the REACH legislation as it supports access to experimental data, (Quantitative Structure-Activity Relationship models, and toxicological information through an integrating platform that adheres to regulatory requirements and OECD validation principles. Initial research defined the essential components of the Framework including the approach to data access, schema and management, use of controlled vocabularies and ontologies, architecture, web service and communications protocols, and selection and integration of algorithms for predictive modelling. OpenTox provides end-user oriented tools to non-computational specialists, risk assessors, and toxicological experts in addition to Application Programming Interfaces (APIs for developers of new applications. OpenTox actively supports public standards for data representation, interfaces, vocabularies and ontologies, Open Source approaches to core platform components, and community-based collaboration approaches, so as to progress system interoperability goals. The OpenTox Framework includes APIs and services for compounds, datasets, features, algorithms, models, ontologies, tasks, validation, and reporting which may be combined into multiple applications satisfying a variety of different user needs. OpenTox applications are based on a set of distributed, interoperable OpenTox API-compliant REST web services. The OpenTox approach to ontology allows for efficient mapping of complementary data coming from different datasets into a unifying structure having a shared terminology and representation. Two initial OpenTox applications are presented as an illustration of the potential impact of OpenTox for high-quality and consistent structure

  15. Identification and Characterization of Genes Involved in Leishmania Pathogenesis: The Potential for Drug Target Selection

    Directory of Open Access Journals (Sweden)

    Robert Duncan

    2011-01-01

    Full Text Available Identifying and characterizing Leishmania donovani genes and the proteins they encode for their role in pathogenesis can reveal the value of this approach for finding new drug targets. Effective drug targets are likely to be proteins differentially expressed or required in the amastigote life cycle stage found in the patient. Several examples and their potential for chemotherapeutic disruption are presented. A pathway nearly ubiquitous in living cells targeted by anticancer drugs, the ubiquitin system, is examined. New findings in ubiquitin and ubiquitin-like modifiers in Leishmania show how disruption of those pathways could point to additional drug targets. The programmed cell death pathway, now recognized among protozoan parasites, is reviewed for some of its components and evidence that suggests they could be targeted for antiparasitic drug therapy. Finally, the endoplasmic reticulum quality control system is involved in secretion of many virulence factors. How disruptions in this pathway reduce virulence as evidence for potential drug targets is presented.

  16. Evaluation of Giardia lamblia thioredoxin reductase as drug activating enzyme and as drug target

    Directory of Open Access Journals (Sweden)

    David Leitsch

    2016-12-01

    Our results constitute first direct evidence for the notion that TrxR is an activator of metronidazole and furazolidone but rather question that it is a relevant drug target of presently used antigiardial drugs.

  17. Serine Proteases of Malaria Parasite Plasmodium falciparum: Potential as Antimalarial Drug Targets

    OpenAIRE

    Asrar Alam

    2014-01-01

    Malaria is a major global parasitic disease and a cause of enormous mortality and morbidity. Widespread drug resistance against currently available antimalarials warrants the identification of novel drug targets and development of new drugs. Malarial proteases are a group of molecules that serve as potential drug targets because of their essentiality for parasite life cycle stages and feasibility of designing specific inhibitors against them. Proteases belonging to various mechanistic classes...

  18. Local Fuel Rod Crud Prediction Tool Applications

    Energy Technology Data Exchange (ETDEWEB)

    Krammen, Michael A.; Karoutas, Zeses E.; Wang, Guoqiang; Young, Michael Y

    2009-06-15

    A code system with attendant methods has been developed for modeling local fuel rod crud. This tool is used to perform the Crud Induced Localized Corrosion (CILC) risk assessment recommended by the EPRI crud and corrosion guidelines, which were developed in response to the INPO zero fuel failures by 2010 initiatives. The methodology is in production use. This paper will describe the range of problems the methodology has already been applied to and the especial pertinence to low duty fuel applications. The methodology begins with Computational Fluid Dynamics (CFD) computations over a fuel assembly grid span. The CFD results provide detailed relative variations in local heat transfer coefficient over the grid span. These very local relative variations are used to determine very local thermal hydraulic conditions over the entire axial length of every fuel rod in a reactor core over the life of the rod in reactor. The expansion using the local relative variations is currently accomplished with the HIDUTYDRV code. The very local thermal hydraulic conditions are combined with reactor coolant crud concentrations derived from EPRI BOA analysis as input to models for predicting very local fuel rod crud deposition. The reactor coolant crud concentrations are determined over each reactor cycle by reactor system wide crud mass balance calculations. The reactor coolant crud concentrations are used to calculate local crud thickness using mass transfer models which are a function of the local thermal conditions. The advanced crud deposition models also include models for calculating local crud dryout. Local crud deposition and crud dryout are strongly dependent on very local boiling or steaming, which are predicted through the translation of the CFD results. The local crud thickness and degree of local crud dryout are key factors in determining the margin or risk for local fuel rod cladding crud induced fuel failure. The development and first application of these methods was in

  19. In silico exploration of novel phytoligands against probable drug target of Clostridium tetani.

    Science.gov (United States)

    Skariyachan, Sinosh; Prakash, Nisha; Bharadwaj, Navya

    2012-12-01

    Though tetanus is an old disease with well known medicines, its complications are still a serious issue worldwide. Tetanus is mainly due to a powerful neurotoxin, tetanolysin-O, produced by a Gram positive anaerobic bacterium, Clostridium tetani. The toxin has a thiol-activated cytolysin which causes lysis of human platelets, lysosomes and a variety of subcellular membranes. The existing therapy seems to have challenged as available vaccines are not so effective and the bacteria developed resistance to many drugs. Computer aided approach is a novel platform to screen drug targets and design potential inhibitors. The three dimensional structure of the toxin is essential for structure based drug design. But the structure of tetanolysin-O is not available in its native form. Moreover, the interaction and pharmacological activities of current drugs against tetanolysin-O is not clear. Hence, there is need for three dimensional model of the toxin. The model was generated by homology modeling using crystal structure of perfringolysin-O, chain-A (PDB ID: 1PFO) as the template. The modeled structure has 22.7% α helices, 27.51% β sheets and 41.75% random coils. A thiol-activated cytolysin was predicted in the region of 105 to 1579, which acts as a functional domain of the toxin. The hypothetical model showed the backbone root mean square deviation (RMSD) value of 0.6 Å and the model was validated by ProCheck. The Ramachandran plot of the model accounts for 92.3% residues in the most allowed region. The model was further refined by various tools and deposited to Protein Model Database (PMDB ID: PM0077550). The model was used as the drug target and the interaction of various lead molecules with protein was studied by molecular docking. We have selected phytoligands based on literatures and pharmacophoric studies. The efficiency of herbal compounds and chemical leads was compared. Our study concluded that herbal derivatives such as berberine (7, 8, 13, 13a-tetradehydro-9

  20. A new approach for potential drug target discovery through in silico metabolic pathway analysis using Trypanosoma cruzi genome information

    Directory of Open Access Journals (Sweden)

    Marcelo Alves-Ferreira

    2009-12-01

    Full Text Available The current drug options for the treatment of chronic Chagas disease have not been sufficient and high hopes have been placed on the use of genomic data from the human parasite Trypanosoma cruzi to identify new drug targets and develop appropriate treatments for both acute and chronic Chagas disease. However, the lack of a complete assembly of the genomic sequence and the presence of many predicted proteins with unknown or unsure functions has hampered our complete view of the parasite's metabolic pathways. Moreover, pinpointing new drug targets has proven to be more complex than anticipated and has revealed large holes in our understanding of metabolic pathways and their integrated regulation, not only for this parasite, but for many other similar pathogens. Using an in silicocomparative study on pathway annotation and searching for analogous and specific enzymes, we have been able to predict a considerable number of additional enzymatic functions in T. cruzi. Here we focus on the energetic pathways, such as glycolysis, the pentose phosphate shunt, the Krebs cycle and lipid metabolism. We point out many enzymes that are analogous to those of the human host, which could be potential new therapeutic targets.

  1. Green Toxicology – Application of predictive toxicology

    DEFF Research Database (Denmark)

    Vinggaard, Anne Marie; Wedebye, Eva Bay; Taxvig, Camilla

    2014-01-01

    Humans are constantly challenged by exposure to a cocktail of chemicals that can have negative health effects, and fetuses and young children are particularly vulnerable. Therefore, we need safer chemicals in order to reduce any potential environmental and human hazards. A solid framework to design...... safer chemicals and to identify problematic compounds already in use such as industrial compounds, drugs, pesticides and cosmetics, is required. Green toxicology is the application of predictive toxicology to the production of chemicals with the specific intent of improving their design for hazard...

  2. Finding new drug targets for the treatment of migraine attacks

    DEFF Research Database (Denmark)

    Olesen, J; Olesen, Jes; Tfelt-Hansen, P

    2009-01-01

    No new preventive drugs specific to migraine have appeared for the last 20 years and existing acute therapies need improvement. Unfortunately, no animal models can predict the efficacy of new therapies for migraine. Because migraine attacks are fully reversible and can be aborted by therapy...

  3. Identifying New Drug Targets for Potent Phospholipase D Inhibitors: Combining Sequence Alignment, Molecular Docking, and Enzyme Activity/Binding Assays.

    Science.gov (United States)

    Djakpa, Helene; Kulkarni, Aditya; Barrows-Murphy, Scheneque; Miller, Greg; Zhou, Weihong; Cho, Hyejin; Török, Béla; Stieglitz, Kimberly

    2016-05-01

    Phospholipase D enzymes cleave phospholipid substrates generating choline and phosphatidic acid. Phospholipase D from Streptomyces chromofuscus is a non-HKD (histidine, lysine, and aspartic acid) phospholipase D as the enzyme is more similar to members of the diverse family of metallo-phosphodiesterase/phosphatase enzymes than phospholipase D enzymes with active site HKD repeats. A highly efficient library of phospholipase D inhibitors based on 1,3-disubstituted-4-amino-pyrazolopyrimidine core structure was utilized to evaluate the inhibition of purified S. chromofuscus phospholipase D. The molecules exhibited inhibition of phospholipase D activity (IC50 ) in the nanomolar range with monomeric substrate diC4 PC and micromolar range with phospholipid micelles and vesicles. Binding studies with vesicle substrate and phospholipase D strongly indicate that these inhibitors directly block enzyme vesicle binding. Following these compelling results as a starting point, sequence searches and alignments with S. chromofuscus phospholipase D have identified potential new drug targets. Using AutoDock, inhibitors were docked into the enzymes selected from sequence searches and alignments (when 3D co-ordinates were available) and results analyzed to develop next-generation inhibitors for new targets. In vitro enzyme activity assays with several human phosphatases demonstrated that the predictive protocol was accurate. The strategy of combining sequence comparison, docking, and high-throughput screening assays has helped to identify new drug targets and provided some insight into how to make potential inhibitors more specific to desired targets.

  4. Editorial: Current status and perspective on drug targets in tubercle bacilli and drug design of antituberculous agents based on structure-activity relationship.

    Science.gov (United States)

    Tomioka, Haruaki

    2014-01-01

    present status of global research on novel drug targets related to the Toll-like receptor in the MTB pathogen, with special reference to mycobacterial virulence factors that cross-talk and interfere with signaling pathways of host macrophages [6]. The following four review articles deal with drug design of novel anti-TB agents employing QSAR techniques. Firstly, Drs. Nidhi and Mohammad Imran Siddiqi review 2D and 3D QSAR approaches and the recent trends of these methods integrated with virtual screening using the 3D pharmacophore and molecular docking approaches for the identification and design of novel antituberculous agents, by presenting a comprehensive overview of QSAR studies reported for newer antituberculous agents [7]. Secondly, Drs. Filomena Martins, Cristina Ventura, Susana Santos, and Miguel Viveiros review the current status of different QSAR-based strategies for the design of novel anti-TB drugs based upon the most active anti-TB agent, isoniazid, from the viewpoint of the development of promising derivatives that are active against isoniazid- resistant strains with katG mutations [8]. Thirdly, Drs. Sanchaita Rajkhowa and Ramesh C. Deka review current studies concerning 2D and 3D QSAR models that contain density-functional theory (DFT)-based descriptors as their parameters [9]. Notably, DFT-based descriptors such as atomic charges, molecular orbital energies, frontier orbital densities, and atom-atom polarizabilities are very useful in predicting the reactivity of atoms in molecules. Fourthly, Drs. Renata V. Bueno, Rodolpho C. Braga, Natanael D. Segretti, Elizabeth I. Ferreira, Gustavo H. G. Trossini, and Carolina H. Andrade review the current progress and applications of QSAR analysis for the discovery of innovative tuberculostatic agents as inhibitors of ribonucleotide reductase, DNA gyrase, ATP synthase, and thymidylate kinase enzymes, highlighting present challenges and new opportunities in TB drug design [10]. The aim of this issue is to address the

  5. A review of recent patents on the protozoan parasite HSP90 as a drug target.

    Science.gov (United States)

    Angel, Sergio O; Matrajt, Mariana; Echeverria, Pablo C

    2013-04-01

    Diseases caused by protozoan parasites are still an important health problem. These parasites can cause a wide spectrum of diseases, some of which are severe and have high morbidity or mortality if untreated. Since they are still uncontrolled, it is important to find novel drug targets and develop new therapies to decrease their remarkable social and economic impact on human societies. In the past years, human HSP90 has become an interesting drug target that has led to a large number of investigations both at state organizations and pharmaceutical companies, followed by clinical trials. The finding that HSP90 has important biological roles in some protozoan parasites like Plasmodium spp, Toxoplasma gondii and trypanosomatids has allowed the expansion of the results obtained in human cancer to these infections. This review summarizes the latest important findings showing protozoan HSP90 as a drug target and presents three patents targeting T. gondii, P. falciparum and trypanosomatids HSP90.

  6. Serine Proteases of Malaria Parasite Plasmodium falciparum: Potential as Antimalarial Drug Targets

    Directory of Open Access Journals (Sweden)

    Asrar Alam

    2014-01-01

    Full Text Available Malaria is a major global parasitic disease and a cause of enormous mortality and morbidity. Widespread drug resistance against currently available antimalarials warrants the identification of novel drug targets and development of new drugs. Malarial proteases are a group of molecules that serve as potential drug targets because of their essentiality for parasite life cycle stages and feasibility of designing specific inhibitors against them. Proteases belonging to various mechanistic classes are found in P. falciparum, of which serine proteases are of particular interest due to their involvement in parasite-specific processes of egress and invasion. In P. falciparum, a number of serine proteases belonging to chymotrypsin, subtilisin, and rhomboid clans are found. This review focuses on the potential of P. falciparum serine proteases as antimalarial drug targets.

  7. NIOSOMES- A NOVEL DRUG CARRIER FOR DRUG TARGETING

    Directory of Open Access Journals (Sweden)

    A.KRISHNA SAILAJA

    2016-02-01

    Full Text Available Niosomes are vesicular drug delivery systems made of cholesterol and non ionic surfactants. Various novel drug delivery systems available for targeting of drugs include liposomes, nanoparticles, and resealed erythrocytes. Because of the instability and higher cost liposomes are less preferred over niosomes. The application of vesicular systems in cosmetics and for therapeutic purpose may offer several advantages for niosomes. They improve the therapeutic performance of the drug molecules by delayed clearance from the circulation, protecting the drug from biological environment and restricting effects to target cells. Niosomes have great drug delivery potential for targeted delivery of anti-cancer, anti-infective agents. Drug delivery potential of niosome can enhance by using novel concepts like proniosomes and aspasome. Niosomes also serve better aid in diagnostic imaging and as a vaccine adjuvant. Thus these areas need further exploration and research so as to bring out commercially available niosomal preparation. This article mainly focuses on the significance, advantages over other drug delivery systems, manufacturing methods, characterization methods, current research in niosomal drug delivery and their limitations.

  8. A global comparison of the human and T. brucei degradomes gives insights about possible parasite drug targets.

    Directory of Open Access Journals (Sweden)

    Susan T Mashiyama

    Full Text Available We performed a genome-level computational study of sequence and structure similarity, the latter using crystal structures and models, of the proteases of Homo sapiens and the human parasite Trypanosoma brucei. Using sequence and structure similarity networks to summarize the results, we constructed global views that show visually the relative abundance and variety of proteases in the degradome landscapes of these two species, and provide insights into evolutionary relationships between proteases. The results also indicate how broadly these sequence sets are covered by three-dimensional structures. These views facilitate cross-species comparisons and offer clues for drug design from knowledge about the sequences and structures of potential drug targets and their homologs. Two protease groups ("M32" and "C51" that are very different in sequence from human proteases are examined in structural detail, illustrating the application of this global approach in mining new pathogen genomes for potential drug targets. Based on our analyses, a human ACE2 inhibitor was selected for experimental testing on one of these parasite proteases, TbM32, and was shown to inhibit it. These sequence and structure data, along with interactive versions of the protein similarity networks generated in this study, are available at http://babbittlab.ucsf.edu/resources.html.

  9. Finding potential drug targets against Shigella flexneri through druggable proteome exploration.

    Directory of Open Access Journals (Sweden)

    Mohammad Uzzal Hossain

    2016-11-01

    Full Text Available Abstract:Background: Shigella flexneri is a gram negative bacteria that causes the infectious disease ‘shigellosis’. Shigella flexneri (S. flexneri is responsible for developing diarrhea, fever and stomach cramps in human. Antibiotics are mostly given to patients infected with shigella. Resistance to antibiotics can hinder its treatment significantly. Upon identification of essential therapeutic targets, vaccine and drug could be effective therapy for the treatment of shigellosis. Methods: The study was designed for the identification and qualitative characterization for potential drug targets from S. flexneri by using the subtractive genome analysis. A set of computational tools were used to identify essential proteins those are required for the survival of S. flexneri. Total proteome (13503 proteins of S. flexneri was retrieved from NCBI and further analyzed by subtractive channel analysis. After identification of the metabolic proteins we have also performed its qualitative characterization to pave the way for the identification of promising drug targets. Results: Subtractive analysis revealed that a list of 53 targets of S. flexneri were human non-homologous essential metabolic proteins that might be used for potential drug targets. We have also found that 11 drug targets are involved in unique pathway. Most of these proteins are cytoplasmic, can be used as broad spectrum drug targets, can interact with other proteins and show the druggable properties. The functionality and drug binding site analysis suggest a promising effective way to design the new drugs against S. flexneri. Conclusion: We have identified 13 potential novel drug and one vaccine target(s against S. flexneri. The outcome might also be used as module as well as circuit design in systems biology. Keywords: S. flexneri, drug target, therapeutics, metabolic proteins, proteome

  10. Implying Analytic Measures for Unravelling Rheumatoid Arthritis Significant Proteins Through Drug-Target Interaction.

    Science.gov (United States)

    Singh, Sachidanand; Vennila, J Jannet; Snijesh, V P; George, Gincy; Sunny, Chinnu

    2016-06-01

    Rheumatoid arthritis (RA) is a systemic autoimmune and inflammatory disease that mainly alters the synovial joints and ultimately leads to their destruction. The involvement of the immune system and its related cells is a basic trademark of autoimmune-associated diseases. The present work focuses on network analysis and its functional characterization to predict novel targets for RA. The interactive model called as rheumatoid arthritis drug-target-protein (RA-DTP) is built of 1727 nodes and 7954 edges followed the power-law distribution. RA-DTP comprised of 20 islands, 55 modules and 123 submodules. Good interactome coverage of target-protein was detected in island 2 (Q-Score 0.875) which includes 673 molecules with 20 modules and 68 submodules. The biological landscape of these modules was examined based on the participation molecules in specific cellular localization, molecular function and biological pathway with favourable p value. Functional characterization and pathway analysis through KEGG, Biocarta and Reactome also showed their involvement in relation to the immune system and inflammatory processes and biological processes such as cell signalling and communication, glucosamine metabolic process, renin-angiotensin system, BCR signals, galactose metabolism, MAPK signalling, complement and coagulation system and NGF signalling pathways. Traffic values and centrality parameters were applied as the selection criteria for identifying potential targets from the important hubs which resulted into FOS, KNG1, PTGDS, HSP90AA1, REN, POMC, FCER1G, IL6, ICAM1, SGK1, NOS3 and PLA2G4A. This approach provides an insight into experimental validation of these associations of potential targets for clinical value to find their effect on animal studies.

  11. In Silico Characterization of Endotoxin: A Future Drug Target for Neisseria meningitidis

    Directory of Open Access Journals (Sweden)

    Satish K. Kyatam

    2015-02-01

    Full Text Available Neissaria maningitidis is a gram negative bacterium, approximately 5000 people per year suffering from maningitidis disease out of that 21% are dying in India. Lipopolysaccharide is a component of the outermost membrane of N. maningitidis, which composed of hydrophobic domain known as lipid A, non-repeating core oligosaccharide and a distal polysaccharide O-antigen. Lipooligosaccharide and lipopolysaccharide have conserved inner cores composed of heptose and 3-deoxy-D-manno-octulosonic acids (kdo which are anchored in outer membrane by lipid A, which acts as an endotoxin. Kdo transferase is an enzyme encoded by the kdtA gene, catalyzes the addition of kdo residues using cyclic monophosphate-kdo (cmp-kdo. The purpose of this investigation is to carry out in silico characterization of kdo transferase. Primary protein sequence analysis reveals that the pI value was 9.19, the total number of negatively charged residues (Asp+Glu was 44 and the total number of positively charged residues (Arg+Lys was 55. SOPMA was used to predict the secondary structure of protein which contains alpha helix 52.72%; extended strand 12.53% and random coil 27.66%. Homology modeling of kdo transferase was done using 2xci A as a template by SWISS MODEL and the model quality 92.7% was determined by PROCHECK. The functional domains contain KdtA and PRK05749 as a multi-domain and belong to Glycos_transf_N superfamily. Inhibition of kdo transferase which causes no addition of kdo residue to lipid A, this ultimately leads to the blocking of endotoxin pathway. Thus kdo transferase serves as a potential drug target for the treatment of maningitidis disease.

  12. Thiamin (Vitamin B1 Biosynthesis and Regulation: A Rich Source of Antimicrobial Drug Targets?

    Directory of Open Access Journals (Sweden)

    Qinglin Du, Honghai Wang, Jianping Xie

    2011-01-01

    Full Text Available Drug resistance of pathogens has necessitated the identification of novel targets for antibiotics. Thiamin (vitamin B1 is an essential cofactor for all organisms in its active form thiamin diphosphate (ThDP. Therefore, its metabolic pathways might be one largely untapped source of antibiotics targets. This review describes bacterial thiamin biosynthetic, salvage, and transport pathways. Essential thiamin synthetic enzymes such as Dxs and ThiE are proposed as promising drug targets. The regulation mechanism of thiamin biosynthesis by ThDP riboswitch is also discussed. As drug targets of existing antimicrobial compound pyrithiamin, the ThDP riboswitch might serves as alternative targets for more antibiotics.

  13. Genome-wide identification of structural variants in genes encoding drug targets

    DEFF Research Database (Denmark)

    Rasmussen, Henrik Berg; Dahmcke, Christina Mackeprang

    2012-01-01

    The objective of the present study was to identify structural variants of drug target-encoding genes on a genome-wide scale. We also aimed at identifying drugs that are potentially amenable for individualization of treatments based on knowledge about structural variation in the genes encoding the...

  14. Identification of New Drug Targets in Multi-Drug Resistant Bacterial Infections

    Science.gov (United States)

    2012-10-01

    COVERED 26 September 2011 25 September 2012 4. TITLE AND SUBTITLE 5a. CONTRACT NUMBER Identification of New Drug Targets in Multi-Drug Resistant...will be necessary for the fragment based screening and subsequent design of new drug lead compounds. To accompany and validate the structural studies

  15. ROCK1 is a potential combinatorial drug target for BRAF mutant melanoma

    NARCIS (Netherlands)

    Smit, Marjon A; Maddalo, Gianluca; Greig, Kylie; Raaijmakers, Linsey M; Possik, Patricia A; van Breukelen, Bas; Cappadona, Salvatore; Heck, Albert Jr; Altelaar, Adrianus; Peeper, Daniel S

    2014-01-01

    Treatment of BRAF mutant melanomas with specific BRAF inhibitors leads to tumor remission. However, most patients eventually relapse due to drug resistance. Therefore, we designed an integrated strategy using (phospho)proteomic and functional genomic platforms to identify drug targets whose inhibiti

  16. Drug Target Identification and Elucidation of Natural Inhibitors for Bordetella petrii: An In Silico Study

    Science.gov (United States)

    Ray, Manisha; Pattnaik, Animesh; Pradhan, Sukanta Kumar

    2016-01-01

    Environmental microbes like Bordetella petrii has been established as a causative agent for various infectious diseases in human. Again, development of drug resistance in B. petrii challenged to combat against the infection. Identification of potential drug target and proposing a novel lead compound against the pathogen has a great aid and value. In this study, bioinformatics tools and technology have been applied to suggest a potential drug target by screening the proteome information of B. petrii DSM 12804 (accession No. PRJNA28135) from genome database of National Centre for Biotechnology information. In this regards, the inhibitory effect of nine natural compounds like ajoene (Allium sativum), allicin (A. sativum), cinnamaldehyde (Cinnamomum cassia), curcumin (Curcuma longa), gallotannin (active component of green tea and red wine), isoorientin (Anthopterus wardii), isovitexin (A. wardii), neral (Melissa officinalis), and vitexin (A. wardii) have been acknowledged with anti-bacterial properties and hence tested against identified drug target of B. petrii by implicating computational approach. The in silico studies revealed the hypothesis that lpxD could be a potential drug target and with recommendation of a strong inhibitory effect of selected natural compounds against infection caused due to B. petrii, would be further validated through in vitro experiments. PMID:28154518

  17. Rho-kinase as a drug target for the treatment of airway hyperresponsiveness in asthma

    NARCIS (Netherlands)

    Gosens, R; Schaafsma, D; Nelemans, SA; Halayko, AJ

    2006-01-01

    In asthma, inflammatory and structural cells contribute to increased bronchoconstriction acutely and more chronically to airway remodelling. Current asthma therapy doesn't inhibit these features satisfactorily. This review discusses Rho-kinase as a potential drug target, since increasing evidence su

  18. A conceptual framework for the identification of candidate drugs and drug targets in acute promyelocytic leukemia

    DEFF Research Database (Denmark)

    Marstrand, T T; Borup, R; Willer, A

    2010-01-01

    regulation, and (ii) the identification of candidate drugs and drug targets for therapeutic interventions. Significantly, our study provides a conceptual framework that can be applied to any subtype of AML and cancer in general to uncover novel information from published microarray data sets at low cost...

  19. PCR-based ordered genomic libraries: a new approach to drug target identification for Streptococcus pneumoniae.

    Science.gov (United States)

    Belanger, Aimee E; Lai, Angel; Brackman, Marcia A; LeBlanc, Donald J

    2002-08-01

    Described here are the development and validation of a novel approach to identify genes encoding drug targets in Streptococcus pneumoniae. The method relies on the use of an ordered genomic library composed of PCR amplicons that were generated under error-prone conditions so as to introduce random mutations into the DNA. Since some of the mutations occur in drug target-encoding genes and subsequently affect the binding of the drug to its respective cellular target, amplicons containing drug targets can be identified as those producing drug-resistant colonies when transformed into S. pneumoniae. Examination of the genetic content of the amplicon giving resistance coupled with bioinformatics and additional genetic approaches could be used to rapidly identify candidate drug target genes. The utility of this approach was verified by using a number of known antibiotics. For drugs with single protein targets, amplicons were identified that rendered S. pneumoniae drug resistant. Assessment of amplicon composition revealed that each of the relevant amplicons contained the gene encoding the known target for the particular drug tested. Fusidic acid-resistant mutants that resulted from the transformation of S. pneumoniae with amplicons containing fusA were further characterized by sequence analysis. A single mutation was found to occur in a region of the S. pneumoniae elongation factor G protein that is analogous to that already implicated in other bacteria as being associated with fusidic acid resistance. Thus, in addition to facilitating the identification of genes encoding drug targets, this method could provide strains that aid future mechanistic studies.

  20. Core-shell magnetite nanoparticles surface encapsulated with smart stimuli-responsive polymer: synthesis, characterization, and LCST of viable drug-targeting delivery system.

    Science.gov (United States)

    Zhang, J L; Srivastava, R S; Misra, R D K

    2007-05-22

    We describe here the synthesis of a novel magnetic drug-targeting carrier characterized by a core-shell structure. The core-shell carrier combines the advantages of a magnetic core and the stimuli-responsive property of the thermosensitive biodegradable polymer shell (e.g., an on-off mechanism responsive to external temperature change). The composite nanoparticles are approximately 8 nm in diameter with approximately 3 nm shell. The lower critical solution temperature (LCST) is approximately 38 degrees C as determined by UV-vis absorption spectroscopy. The carrier is composed of cross-linked dextran grafted with a poly(N-isopropylacrylamide-co-N,N-dimethylacrylamide) [dextran-g-poly(NIPAAm-co-DMAAm)] shell and superparamagnetic Fe3O4 core. Fourier transform infrared spectroscopy (FTIR) confirmed the composition of the carrier. The synthesized magnetic carrier system has potential applications in magnetic drug-targeting delivery and magnetic resonance imaging.

  1. Applications of Neural Networks in Spinning Prediction

    Institute of Scientific and Technical Information of China (English)

    程文红; 陆凯

    2003-01-01

    The neural network spinning prediction model (BP and RBF Networks) trained by data from the mill can predict yarn qualities and spinning performance. The input parameters of the model are as follows: yarn count, diameter, hauteur, bundle strength, spinning draft, spinning speed, traveler number and twist.And the output parameters are: yarn evenness, thin places, tenacity and elongation, ends-down.Predicting results match the testing data well.

  2. Conformal prediction for reliable machine learning theory, adaptations and applications

    CERN Document Server

    Balasubramanian, Vineeth; Vovk, Vladimir

    2014-01-01

    The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detecti

  3. Mechanistic models enable the rational use of in vitro drug-target binding kinetics for better drug effects in patients.

    NARCIS (Netherlands)

    Witte, W.E.; Wong, Y.C.; Nederpelt, I.; Heitman, L.H.; Danhof, M.; Graaf, van der P.H.; Gilissen, R.A.; de, Lange E.C.

    2016-01-01

    INTRODUCTION Drug-target binding kinetics are major determinants of the time course of drug action for several drugs, as clearly described for the irreversible binders omeprazole and aspirin. This supports the increasing interest to incorporate newly developed high-throughput assays for drug-target

  4. Identification of drug targets by chemogenomic and metabolomic profiling in yeast

    KAUST Repository

    Wu, Manhong

    2012-12-01

    OBJECTIVE: To advance our understanding of disease biology, the characterization of the molecular target for clinically proven or new drugs is very important. Because of its simplicity and the availability of strains with individual deletions in all of its genes, chemogenomic profiling in yeast has been used to identify drug targets. As measurement of drug-induced changes in cellular metabolites can yield considerable information about the effects of a drug, we investigated whether combining chemogenomic and metabolomic profiling in yeast could improve the characterization of drug targets. BASIC METHODS: We used chemogenomic and metabolomic profiling in yeast to characterize the target for five drugs acting on two biologically important pathways. A novel computational method that uses a curated metabolic network was also developed, and it was used to identify the genes that are likely to be responsible for the metabolomic differences found. RESULTS AND CONCLUSION: The combination of metabolomic and chemogenomic profiling, along with data analyses carried out using a novel computational method, could robustly identify the enzymes targeted by five drugs. Moreover, this novel computational method has the potential to identify genes that are causative of metabolomic differences or drug targets. © 2012 Wolters Kluwer Health | Lippincott Williams & Wilkins.

  5. New approaches for the identification of drug targets in protozoan parasites.

    Science.gov (United States)

    Müller, Joachim; Hemphill, Andrew

    2013-01-01

    Antiparasitic chemotherapy is an important issue for drug development. Traditionally, novel compounds with antiprotozoan activities have been identified by screening of compound libraries in high-throughput systems. More recently developed approaches employ target-based drug design supported by genomics and proteomics of protozoan parasites. In this chapter, the drug targets in protozoan parasites are reviewed. The gene-expression machinery has been among the first targets for antiparasitic drugs and is still under investigation as a target for novel compounds. Other targets include cytoskeletal proteins, proteins involved in intracellular signaling, membranes, and enzymes participating in intermediary metabolism. In apicomplexan parasites, the apicoplast is a suitable target for established and novel drugs. Some drugs act on multiple subcellular targets. Drugs with nitro groups generate free radicals under anaerobic growth conditions, and drugs with peroxide groups generate radicals under aerobic growth conditions, both affecting multiple cellular pathways. Mefloquine and thiazolides are presented as examples for antiprotozoan compounds with multiple (side) effects. The classic approach of drug discovery employing high-throughput physiological screenings followed by identification of drug targets has yielded the mainstream of current antiprotozoal drugs. Target-based drug design supported by genomics and proteomics of protozoan parasites has not produced any antiparasitic drug so far. The reason for this is discussed and a synthesis of both methods is proposed.

  6. Predictive microbiology in food packaging applications

    Science.gov (United States)

    Predictive microbiology including growth, inactivation, surface transfer (or cross-contamination), and survival, plays important roles in understanding microbial food safety. Growth models may involve the growth potential of a specified pathogen under different stresses, e.g., temperature, pH, wate...

  7. Application of optimal prediction to molecular dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Barber, IV, John Letherman [Univ. of California, Berkeley, CA (United States)

    2004-12-01

    Optimal prediction is a general system reduction technique for large sets of differential equations. In this method, which was devised by Chorin, Hald, Kast, Kupferman, and Levy, a projection operator formalism is used to construct a smaller system of equations governing the dynamics of a subset of the original degrees of freedom. This reduced system consists of an effective Hamiltonian dynamics, augmented by an integral memory term and a random noise term. Molecular dynamics is a method for simulating large systems of interacting fluid particles. In this thesis, I construct a formalism for applying optimal prediction to molecular dynamics, producing reduced systems from which the properties of the original system can be recovered. These reduced systems require significantly less computational time than the original system. I initially consider first-order optimal prediction, in which the memory and noise terms are neglected. I construct a pair approximation to the renormalized potential, and ignore three-particle and higher interactions. This produces a reduced system that correctly reproduces static properties of the original system, such as energy and pressure, at low-to-moderate densities. However, it fails to capture dynamical quantities, such as autocorrelation functions. I next derive a short-memory approximation, in which the memory term is represented as a linear frictional force with configuration-dependent coefficients. This allows the use of a Fokker-Planck equation to show that, in this regime, the noise is δ-correlated in time. This linear friction model reproduces not only the static properties of the original system, but also the autocorrelation functions of dynamical variables.

  8. Erosive Burning and its Applications for Performance Prediction

    Directory of Open Access Journals (Sweden)

    A. R. Kulkarni

    1993-04-01

    Full Text Available A modified method for prediction of performance of large motors based on erosion constant obtained by partial burning technique is discussed. Erosion constants for two different double base compositions have been determined by partial burning technique. These constraints have been used to predict the performance of the large scale motors developed for Defence applications. The predicted performance compares well with the experimental values.

  9. Artificial Neural Networks: A New Approach to Predicting Application Behavior.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    2002-01-01

    Applied the technique of artificial neural networks to predict which students were likely to apply to one research university. Compared the results to the traditional analysis tool, logistic regression modeling. Found that the addition of artificial intelligence models was a useful new tool for predicting student application behavior. (EV)

  10. Implant-assisted magnetic drug targeting in permeable microvessels: Comparison of two-fluid statistical transport model with experiment

    Science.gov (United States)

    ChiBin, Zhang; XiaoHui, Lin; ZhaoMin, Wang; ChangBao, Wang

    2017-03-01

    In experiments and theoretical analyses, this study examines the capture efficiency (CE) of magnetic drug carrier particles (MDCPs) for implant-assisted magnetic drug targeting (IA-MDT) in microvessels. It also proposes a three-dimensional statistical transport model of MDCPs for IA-MDT in permeable microvessels, which describes blood flow by the two-fluid (Casson and Newtonian) model. The model accounts for the permeable effect of the microvessel wall and the coupling effect between the blood flow and tissue fluid flow. The MDCPs move randomly through the microvessel, and their transport state is described by the Boltzmann equation. The regulated changes and factors affecting the CE of the MDCPs in the assisted magnetic targeting were obtained by solving the theoretical model and by experimental testing. The CE was negatively correlated with the blood flow velocity, and positively correlated with the external magnetic field intensity and microvessel permeability. The predicted CEs of the MDCPs were consistent with the experimental results. Additionally, under the same external magnetic field, the predicted CE was 5-8% higher in the IA-MDT model than in the model ignoring the permeability effect of the microvessel wall.

  11. Downburst Prediction Applications of Meteorological Geostationary Satellites

    CERN Document Server

    Pryor, Kenneth L

    2014-01-01

    A suite of products has been developed and evaluated to assess hazards presented by convective storm downbursts derived from the current generation of Geostationary Operational Environmental Satellite (GOES) (13-15). The existing suite of GOES downburst prediction products employs the GOES sounder to calculate risk based on conceptual models of favorable environmental profiles for convective downburst generation. A diagnostic nowcasting product, the Microburst Windspeed Potential Index (MWPI), is designed to infer attributes of a favorable downburst environment: 1) the presence of large convective available potential energy (CAPE), and 2) the presence of a surface-based or elevated mixed layer with a steep temperature lapse rate and vertical relative humidity gradient. These conditions foster intense convective downdrafts upon the interaction of sub-saturated air in the elevated or sub-cloud mixed layer with the storm precipitation core. This paper provides an updated assessment of the MWPI algorithm, present...

  12. Identification of attractive drug targets in neglected-disease pathogens using an in silico approach.

    Directory of Open Access Journals (Sweden)

    Gregory J Crowther

    Full Text Available BACKGROUND: The increased sequencing of pathogen genomes and the subsequent availability of genome-scale functional datasets are expected to guide the experimental work necessary for target-based drug discovery. However, a major bottleneck in this has been the difficulty of capturing and integrating relevant information in an easily accessible format for identifying and prioritizing potential targets. The open-access resource TDRtargets.org facilitates drug target prioritization for major tropical disease pathogens such as the mycobacteria Mycobacterium leprae and Mycobacterium tuberculosis; the kinetoplastid protozoans Leishmania major, Trypanosoma brucei, and Trypanosoma cruzi; the apicomplexan protozoans Plasmodium falciparum, Plasmodium vivax, and Toxoplasma gondii; and the helminths Brugia malayi and Schistosoma mansoni. METHODOLOGY/PRINCIPAL FINDINGS: Here we present strategies to prioritize pathogen proteins based on whether their properties meet criteria considered desirable in a drug target. These criteria are based upon both sequence-derived information (e.g., molecular mass and functional data on expression, essentiality, phenotypes, metabolic pathways, assayability, and druggability. This approach also highlights the fact that data for many relevant criteria are lacking in less-studied pathogens (e.g., helminths, and we demonstrate how this can be partially overcome by mapping data from homologous genes in well-studied organisms. We also show how individual users can easily upload external datasets and integrate them with existing data in TDRtargets.org to generate highly customized ranked lists of potential targets. CONCLUSIONS/SIGNIFICANCE: Using the datasets and the tools available in TDRtargets.org, we have generated illustrative lists of potential drug targets in seven tropical disease pathogens. While these lists are broadly consistent with the research community's current interest in certain specific proteins, and suggest

  13. Avionics Applications on a Time-Predictable Chip-Multiprocessor

    DEFF Research Database (Denmark)

    Rocha, André; Silva, Cláudio; Sørensen, Rasmus Bo;

    2016-01-01

    Avionics applications need to be certified for the highest criticality standard. This certification includes schedulability analysis and worst-case execution time (WCET) analysis. WCET analysis is only possible when the software is written to be WCET analyzable and when the platform is time......-predictable. In this paper we present prototype avionics applications that have been ported to the time-predictable T-CREST platform. The applications are WCET analyzable, and T-CREST is supported by the aiT WCET analyzer. This combination allows us to provide WCET bounds of avionic tasks, even when executing on a multicore...

  14. Central nervous system myeloid cells as drug targets: current status and translational challenges.

    Science.gov (United States)

    Biber, Knut; Möller, Thomas; Boddeke, Erik; Prinz, Marco

    2016-02-01

    Myeloid cells of the central nervous system (CNS), which include parenchymal microglia, macrophages at CNS interfaces and monocytes recruited from the circulation during disease, are increasingly being recognized as targets for therapeutic intervention in neurological and psychiatric diseases. The origin of these cells in the immune system distinguishes them from ectodermal neurons and other glia and endows them with potential drug targets distinct from classical CNS target groups. However, despite the identification of several promising therapeutic approaches and molecular targets, no agents directly targeting these cells are currently available. Here, we assess strategies for targeting CNS myeloid cells and address key issues associated with their translation into the clinic.

  15. Adhesion molecules and the extracellular matrix as drug targets for glioma.

    Science.gov (United States)

    Shimizu, Toshihiko; Kurozumi, Kazuhiko; Ishida, Joji; Ichikawa, Tomotsugu; Date, Isao

    2016-04-01

    The formation of tumor vasculature and cell invasion along white matter tracts have pivotal roles in the development and progression of glioma. A better understanding of the mechanisms of angiogenesis and invasion in glioma will aid the development of novel therapeutic strategies. The processes of angiogenesis and invasion cause the production of an array of adhesion molecules and extracellular matrix (ECM) components. This review focuses on the role of adhesion molecules and the ECM in malignant glioma. The results of clinical trials using drugs targeted against adhesion molecules and the ECM for glioma are also discussed.

  16. Prioritizing drug targets in Clostridium botulinum with a computational systems biology approach.

    Science.gov (United States)

    Muhammad, Syed Aun; Ahmed, Safia; Ali, Amjad; Huang, Hui; Wu, Xiaogang; Yang, X Frank; Naz, Anam; Chen, Jake

    2014-07-01

    A computational and in silico system level framework was developed to identify and prioritize the antibacterial drug targets in Clostridium botulinum (Clb), the causative agent of flaccid paralysis in humans that can be fatal in 5 to 10% of cases. This disease is difficult to control due to the emergence of drug-resistant pathogenic strains and the only available treatment antitoxin which can target the neurotoxin at the extracellular level and cannot reverse the paralysis. This study framework is based on comprehensive systems-scale analysis of genomic sequence homology and phylogenetic relationships among Clostridium, other infectious bacteria, host and human gut flora. First, the entire 2628-annotated genes of this bacterial genome were categorized into essential, non-essential and virulence genes. The results obtained showed that 39% of essential proteins that functionally interact with virulence proteins were identified, which could be a key to new interventions that may kill the bacteria and minimize the host damage caused by the virulence factors. Second, a comprehensive comparative COGs and blast sequence analysis of these proteins and host proteins to minimize the risks of side effects was carried out. This revealed that 47% of a set of C. botulinum proteins were evolutionary related with Homo sapiens proteins to sort out the non-human homologs. Third, orthology analysis with other infectious bacteria to assess broad-spectrum effects was executed and COGs were mostly found in Clostridia, Bacilli (Firmicutes), and in alpha and beta Proteobacteria. Fourth, a comparative phylogenetic analysis was performed with human microbiota to filter out drug targets that may also affect human gut flora. This reduced the list of candidate proteins down to 131. Finally, the role of these putative drug targets in clostridial biological pathways was studied while subcellular localization of these candidate proteins in bacterial cellular system exhibited that 68% of the

  17. An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions

    Directory of Open Access Journals (Sweden)

    Xin Deng

    2015-07-01

    Full Text Available Protein disordered regions are segments of a protein chain that do not adopt a stable structure. Thus far, a variety of protein disorder prediction methods have been developed and have been widely used, not only in traditional bioinformatics domains, including protein structure prediction, protein structure determination and function annotation, but also in many other biomedical fields. The relationship between intrinsically-disordered proteins and some human diseases has played a significant role in disorder prediction in disease identification and epidemiological investigations. Disordered proteins can also serve as potential targets for drug discovery with an emphasis on the disordered-to-ordered transition in the disordered binding regions, and this has led to substantial research in drug discovery or design based on protein disordered region prediction. Furthermore, protein disorder prediction has also been applied to healthcare by predicting the disease risk of mutations in patients and studying the mechanistic basis of diseases. As the applications of disorder prediction increase, so too does the need to make quick and accurate predictions. To fill this need, we also present a new approach to predict protein residue disorder using wide sequence windows that is applicable on the genomic scale.

  18. Identification of potential drug targets by subtractive genome analysis of Escherichia coli O157:H7: an in silico approach

    Directory of Open Access Journals (Sweden)

    Mondal SI

    2015-12-01

    Full Text Available Shakhinur Islam Mondal,1,6,* Sabiha Ferdous,1,* Nurnabi Azad Jewel,1 Arzuba Akter,2,6 Zabed Mahmud,1 Md Muzahidul Islam,1 Tanzila Afrin,3 Nurul Karim4,5 1Genetic Engineering and Biotechnology Department, Shahjalal University of Science and Technology, Sylhet, Bangladesh; 2Biochemistry and Molecular Biology Department, Shahjalal University of Science and Technology, Sylhet, Bangladesh; 3Department of Pharmacy, East West University, Aftabnagar, Bangladesh; 4Biochemistry and Molecular Biology Department, Jahangirnagar University, Savar, Bangladesh; 5Division of Parasitology, 6Division of Microbiology, Department of Infectious Diseases, Faculty of Medicine, University of Miyazaki, Miyazaki, Japan *These authors contributed equally to this work Abstract: Bacterial enteric infections resulting in diarrhea, dysentery, or enteric fever constitute a huge public health problem, with more than a billion episodes of disease annually in developing and developed countries. In this study, the deadly agent of hemorrhagic diarrhea and hemolytic uremic syndrome, Escherichia coli O157:H7 was investigated with extensive computational approaches aimed at identifying novel and broad-spectrum antibiotic targets. A systematic in silico workflow consisting of comparative genomics, metabolic pathways analysis, and additional drug prioritizing parameters was used to identify novel drug targets that were essential for the pathogen’s survival but absent in its human host. Comparative genomic analysis of Kyoto Encyclopedia of Genes and Genomes annotated metabolic pathways identified 350 putative target proteins in E. coli O157:H7 which showed no similarity to human proteins. Further bioinformatic approaches including prediction of subcellular localization, calculation of molecular weight, and web-based investigation of 3D structural characteristics greatly aided in filtering the potential drug targets from 350 to 120. Ultimately, 44 non-homologous essential proteins of E

  19. Leishmaniasis:Current status of available drugs and new potential drug targets

    Institute of Scientific and Technical Information of China (English)

    Nisha Singh; Manish Kumar; Rakesh Kumar Singh

    2012-01-01

    The control ofLeishmania infection relies primarily on chemotherapy till date. Resistance to pentavalent antimonials, which have been the recommended drugs to treat cutaneous and visceral leishmaniasis, is now widespread in Indian subcontinents. New drug formulations like amphotericinB, its lipid formulations, and miltefosine have shown great efficacy to treat leishmaniasis but their high cost and therapeutic complications limit their usefulness. In addition, irregular and inappropriate uses of these second line drugs in endemic regions like state of Bihar, India threaten resistance development in the parasite. In context to the limited drug options and unavailability of either preventive or prophylactic candidates, there is a pressing need to develop true antileishmanial drugs to reduce the disease burden of this debilitating endemic disease. Notwithstanding significant progress of leishmanial research during last few decades, identification and characterization of novel drugs and drug targets are far from satisfactory. This review will initially describe current drug regimens and later will provide an overview on few important biochemical and enzymatic machineries that could be utilized as putative drug targets for generation of true antileishmanial drugs.

  20. Controllability in cancer metabolic networks according to drug targets as driver nodes.

    Science.gov (United States)

    Asgari, Yazdan; Salehzadeh-Yazdi, Ali; Schreiber, Falk; Masoudi-Nejad, Ali

    2013-01-01

    Networks are employed to represent many nonlinear complex systems in the real world. The topological aspects and relationships between the structure and function of biological networks have been widely studied in the past few decades. However dynamic and control features of complex networks have not been widely researched, in comparison to topological network features. In this study, we explore the relationship between network controllability, topological parameters, and network medicine (metabolic drug targets). Considering the assumption that targets of approved anticancer metabolic drugs are driver nodes (which control cancer metabolic networks), we have applied topological analysis to genome-scale metabolic models of 15 normal and corresponding cancer cell types. The results show that besides primary network parameters, more complex network metrics such as motifs and clusters may also be appropriate for controlling the systems providing the controllability relationship between topological parameters and drug targets. Consequently, this study reveals the possibilities of following a set of driver nodes in network clusters instead of considering them individually according to their centralities. This outcome suggests considering distributed control systems instead of nodal control for cancer metabolic networks, leading to a new strategy in the field of network medicine.

  1. Application of GIS in Mineral Resource Prediction of Synthetic Information

    Institute of Scientific and Technical Information of China (English)

    Ye Shuisheng; Wang Shicheng; Li Deqiong

    2003-01-01

    This paper introduces the formation mechanism and synthetic information prediction of large and superlarge deposits in Shandong Province by analyzing and studying on the GIS platform. The authors established a prospecting model of synthetic information from large and superlarge gold deposit concentration region, and the multi-source spatial database from concentration region of deposits and anomalies. On the basis of the spatial database, a target map layer, a model map layer and a predictive map layer were set up. Based on these map layers, geological variables of the model unit and predictive unit were extracted, then launched location and quantitative prediction of the gold deposit concentration region. The achievement of predicting large and superlarge deposits by the GIS platform has enabled the authors to design automation (or semi-automatic) interpretation subsystems, namely geophysics, geochemistry, geologic prospecting and comprehensive prognosis, and a set of the applicable GIS softwarefor mineral resources prognosis of synthetic information.

  2. Time-series prediction and applications a machine intelligence approach

    CERN Document Server

    Konar, Amit

    2017-01-01

    This book presents machine learning and type-2 fuzzy sets for the prediction of time-series with a particular focus on business forecasting applications. It also proposes new uncertainty management techniques in an economic time-series using type-2 fuzzy sets for prediction of the time-series at a given time point from its preceding value in fluctuating business environments. It employs machine learning to determine repetitively occurring similar structural patterns in the time-series and uses stochastic automaton to predict the most probabilistic structure at a given partition of the time-series. Such predictions help in determining probabilistic moves in a stock index time-series Primarily written for graduate students and researchers in computer science, the book is equally useful for researchers/professionals in business intelligence and stock index prediction. A background of undergraduate level mathematics is presumed, although not mandatory, for most of the sections. Exercises with tips are provided at...

  3. Inhibition of Glutamine Synthetase: A Potential Drug Target in Mycobacterium tuberculosis

    Directory of Open Access Journals (Sweden)

    Sherry L. Mowbray

    2014-08-01

    Full Text Available Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis. Globally, tuberculosis is second only to AIDS in mortality and the disease is responsible for over 1.3 million deaths each year. The impractically long treatment schedules (generally 6–9 months and unpleasant side effects of the current drugs often lead to poor patient compliance, which in turn has resulted in the emergence of multi-, extensively- and totally-drug resistant strains. The development of new classes of anti-tuberculosis drugs and new drug targets is of global importance, since attacking the bacterium using multiple strategies provides the best means to prevent resistance. This review presents an overview of the various strategies and compounds utilized to inhibit glutamine synthetase, a promising target for the development of drugs for TB therapy.

  4. Metabolic network analysis-based identification of antimicrobial drug targets in category A bioterrorism agents.

    Directory of Open Access Journals (Sweden)

    Yong-Yeol Ahn

    Full Text Available The 2001 anthrax mail attacks in the United States demonstrated the potential threat of bioterrorism, hence driving the need to develop sophisticated treatment and diagnostic protocols to counter biological warfare. Here, by performing flux balance analyses on the fully-annotated metabolic networks of multiple, whole genome-sequenced bacterial strains, we have identified a large number of metabolic enzymes as potential drug targets for each of the three Category A-designated bioterrorism agents including Bacillus anthracis, Francisella tularensis and Yersinia pestis. Nine metabolic enzymes- belonging to the coenzyme A, folate, phosphatidyl-ethanolamine and nucleic acid pathways common to all strains across the three distinct genera were identified as targets. Antimicrobial agents against some of these enzymes are available. Thus, a combination of cross species-specific antibiotics and common antimicrobials against shared targets may represent a useful combinatorial therapeutic approach against all Category A bioterrorism agents.

  5. Pharmaceutical formulation of HSA hybrid coated iron oxide nanoparticles for magnetic drug targeting.

    Science.gov (United States)

    Zaloga, Jan; Pöttler, Marina; Leitinger, Gerd; Friedrich, Ralf P; Almer, Gunter; Lyer, Stefan; Baum, Eva; Tietze, Rainer; Heimke-Brinck, Ralph; Mangge, Harald; Dörje, Frank; Lee, Geoffrey; Alexiou, Christoph

    2016-04-01

    In this work we present a new formulation of superparamagnetic iron oxide nanoparticles (SPIONs) for magnetic drug targeting. The particles were reproducibly synthesized from current good manufacturing practice (cGMP) - grade substances. They were surface coated using fatty acids as anchoring molecules for human serum albumin. We comprehensively characterized the physicochemical core-shell structure of the particles using sophisticated methods. We investigated biocompatibility and cellular uptake of the particles using an established flow cytometric method in combination with microwave-plasma assisted atomic emission spectroscopy (MP-AES). The cytotoxic drug mitoxantrone was adsorbed on the protein shell and we showed that even in complex media it is slowly released with a close to zero order kinetics. We also describe an in vitro proof-of-concept assay in which we clearly showed that local enrichment of this SPION-drug conjugate with a magnet allows site-specific therapeutic effects.

  6. Comparative genomics of NAD(P) biosynthesis and novel antibiotic drug targets.

    Science.gov (United States)

    Bi, Jicai; Wang, Honghai; Xie, Jianping

    2011-02-01

    NAD(P) is an indispensable cofactor for all organisms and its biosynthetic pathways are proposed as promising novel antibiotics targets against pathogens such as Mycobacterium tuberculosis. Six NAD(P) biosynthetic pathways were reconstructed by comparative genomics: de novo pathway (Asp), de novo pathway (Try), NmR pathway I (RNK-dependent), NmR pathway II (RNK-independent), Niacin salvage, and Niacin recycling. Three enzymes pivotal to the key reactions of NAD(P) biosynthesis are shared by almost all organisms, that is, NMN/NaMN adenylyltransferase (NMN/NaMNAT), NAD synthetase (NADS), and NAD kinase (NADK). They might serve as ideal broad spectrum antibiotic targets. Studies in M. tuberculosis have in part tested such hypothesis. Three regulatory factors NadR, NiaR, and NrtR, which regulate NAD biosynthesis, have been identified. M. tuberculosis NAD(P) metabolism and regulation thereof, potential drug targets and drug development are summarized in this paper.

  7. Metabolic network analysis-based identification of antimicrobial drug targets in category A bioterrorism agents.

    Science.gov (United States)

    Ahn, Yong-Yeol; Lee, Deok-Sun; Burd, Henry; Blank, William; Kapatral, Vinayak

    2014-01-01

    The 2001 anthrax mail attacks in the United States demonstrated the potential threat of bioterrorism, hence driving the need to develop sophisticated treatment and diagnostic protocols to counter biological warfare. Here, by performing flux balance analyses on the fully-annotated metabolic networks of multiple, whole genome-sequenced bacterial strains, we have identified a large number of metabolic enzymes as potential drug targets for each of the three Category A-designated bioterrorism agents including Bacillus anthracis, Francisella tularensis and Yersinia pestis. Nine metabolic enzymes- belonging to the coenzyme A, folate, phosphatidyl-ethanolamine and nucleic acid pathways common to all strains across the three distinct genera were identified as targets. Antimicrobial agents against some of these enzymes are available. Thus, a combination of cross species-specific antibiotics and common antimicrobials against shared targets may represent a useful combinatorial therapeutic approach against all Category A bioterrorism agents.

  8. Important biology events and pathways in Brucella infection and implications for novel antibiotic drug targets.

    Science.gov (United States)

    Gao, Guangjun; Xu, Jie

    2013-01-01

    Brucellosis caused by Brucella spp. is a common zoonosis in many parts of the world. Humans are infected through contact with infected animals or their dirty products. Many mechanisms are needed for this successful infection, although the mechanisms are still unclear. Host immune response and some signaling molecules play an important role in the infection event. Bacterial pathogens operate by attacking crucial intracellular pathways or some important molecules in each of these pathways for survival in their hosts. The crucial components (molecules) of immunity or pathway play a critical role in the whole process of Brucella infection. Here we summarize the findings of the Brucella-host interactions' immune system and signaling molecular cascades involved in the TLR-initiated immune response to Brucella spp. infection. The paper serves to deepen our understanding of this complex process and to provide some clues regarding the discovery of drug targets for prevention and control.

  9. Identification of pyruvate kinase in methicillin-resistant Staphylococcus aureus as a novel antimicrobial drug target.

    Science.gov (United States)

    Zoraghi, Roya; See, Raymond H; Axerio-Cilies, Peter; Kumar, Nag S; Gong, Huansheng; Moreau, Anne; Hsing, Michael; Kaur, Sukhbir; Swayze, Richard D; Worrall, Liam; Amandoron, Emily; Lian, Tian; Jackson, Linda; Jiang, Jihong; Thorson, Lisa; Labriere, Christophe; Foster, Leonard; Brunham, Robert C; McMaster, William R; Finlay, B Brett; Strynadka, Natalie C; Cherkasov, Artem; Young, Robert N; Reiner, Neil E

    2011-05-01

    Novel classes of antimicrobials are needed to address the challenge of multidrug-resistant bacteria such as methicillin-resistant Staphylococcus aureus (MRSA). Using the architecture of the MRSA interactome, we identified pyruvate kinase (PK) as a potential novel drug target based upon it being a highly connected, essential hub in the MRSA interactome. Structural modeling, including X-ray crystallography, revealed discrete features of PK in MRSA, which appeared suitable for the selective targeting of the bacterial enzyme. In silico library screening combined with functional enzymatic assays identified an acyl hydrazone-based compound (IS-130) as a potent MRSA PK inhibitor (50% inhibitory concentration [IC50] of 0.1 μM) with >1,000-fold selectivity over human PK isoforms. Medicinal chemistry around the IS-130 scaffold identified analogs that more potently and selectively inhibited MRSA PK enzymatic activity and S. aureus growth in vitro (MIC of 1 to 5 μg/ml). These novel anti-PK compounds were found to possess antistaphylococcal activity, including both MRSA and multidrug-resistant S. aureus (MDRSA) strains. These compounds also exhibited exceptional antibacterial activities against other Gram-positive genera, including enterococci and streptococci. PK lead compounds were found to be noncompetitive inhibitors and were bactericidal. In addition, mutants with significant increases in MICs were not isolated after 25 bacterial passages in culture, indicating that resistance may be slow to emerge. These findings validate the principles of network science as a powerful approach to identify novel antibacterial drug targets. They also provide a proof of principle, based upon PK in MRSA, for a research platform aimed at discovering and optimizing selective inhibitors of novel bacterial targets where human orthologs exist, as leads for anti-infective drug development.

  10. Functional expression of parasite drug targets and their human orthologs in yeast.

    Directory of Open Access Journals (Sweden)

    Elizabeth Bilsland

    2011-10-01

    Full Text Available BACKGROUND: The exacting nutritional requirements and complicated life cycles of parasites mean that they are not always amenable to high-throughput drug screening using automated procedures. Therefore, we have engineered the yeast Saccharomyces cerevisiae to act as a surrogate for expressing anti-parasitic targets from a range of biomedically important pathogens, to facilitate the rapid identification of new therapeutic agents. METHODOLOGY/PRINCIPAL FINDINGS: Using pyrimethamine/dihydrofolate reductase (DHFR as a model parasite drug/drug target system, we explore the potential of engineered yeast strains (expressing DHFR enzymes from Plasmodium falciparum, P. vivax, Homo sapiens, Schistosoma mansoni, Leishmania major, Trypanosoma brucei and T. cruzi to exhibit appropriate differential sensitivity to pyrimethamine. Here, we demonstrate that yeast strains (lacking the major drug efflux pump, Pdr5p expressing yeast ((ScDFR1, human ((HsDHFR, Schistosoma ((SmDHFR, and Trypanosoma ((TbDHFR and (TcDHFR DHFRs are insensitive to pyrimethamine treatment, whereas yeast strains producing Plasmodium ((PfDHFR and (PvDHFR DHFRs are hypersensitive. Reassuringly, yeast strains expressing field-verified, drug-resistant mutants of P. falciparum DHFR ((Pfdhfr(51I,59R,108N are completely insensitive to pyrimethamine, further validating our approach to drug screening. We further show the versatility of the approach by replacing yeast essential genes with other potential drug targets, namely phosphoglycerate kinases (PGKs and N-myristoyl transferases (NMTs. CONCLUSIONS/SIGNIFICANCE: We have generated a number of yeast strains that can be successfully harnessed for the rapid and selective identification of urgently needed anti-parasitic agents.

  11. In vitro study of magnetic nanoparticles as the implant for implant assisted magnetic drug targeting

    Energy Technology Data Exchange (ETDEWEB)

    Mangual, Jan O.; Aviles, Misael O.; Ebner, Armin D. [Department of Chemical Engineering, University of South Carolina, Columbia, SC 29208 (United States); Ritter, James A., E-mail: ritter@cec.sc.ed [Department of Chemical Engineering, University of South Carolina, Columbia, SC 29208 (United States)

    2011-07-15

    Magnetic nanoparticle (MNP) seeds were studied in vitro for use as an implant in implant assisted-magnetic drug targeting (IA-MDT). The magnetite seeds were captured in a porous polymer, mimicking capillary tissue, with an external magnetic field (70 mT) and then used subsequently to capture magnetic drug carrier particles (MDCPs) (0.87 {mu}m diameter) with the same magnetic field. The effects of the MNP seed diameter (10, 50 and 100 nm), MNP seed concentration (0.25-2.0 mg/mL), and fluid velocity (0.03-0.15 cm/s) on the capture efficiency (CE) of both the MNP seeds and the MDCPs were studied. The CE of the 10 nm MNP seeds was never more than 30%, while those of the 50 and 100 nm MNP seeds was always greater than 80% and in many cases exceeded 90%. Only the MNP seed concentration affected its CE. The 10 nm MNP seeds did not increase the MDCP CE over that obtained in the absence of the MNP seeds, while the 50 and 100 nm MNP seeds increased significantly, typically by more than a factor of two. The 50 and 100 nm MNP seeds also exhibited similar abilities to capture the MDCPs, with the MDCP CE always increasing with decreasing fluid velocity and generally increasing with increasing MNP seed concentration. The MNP seed size, magnetic properties, and capacity to self-agglomerate and form clusters were key properties that make them a viable implant in IA-MDT. - Highlights: 50-100 nm magnetite nanoparticles can be retained in a porous scaffold using a 70 mT field. Their presence improves the collection efficiency of 0.87 {mu}m magnetic drug carrier particles. Magnetic nanoparticles can thus be used as the implant in implant assisted-magnetic drug targeting.

  12. Perspective of microsomal prostaglandin E2 synthase-1 as drug target in inflammation-related disorders.

    Science.gov (United States)

    Koeberle, Andreas; Werz, Oliver

    2015-11-01

    Prostaglandin (PG)E2 encompasses crucial roles in pain, fever, inflammation and diseases with inflammatory component, such as cancer, but is also essential for gastric, renal, cardiovascular and immune homeostasis. Cyclooxygenases (COX) convert arachidonic acid to the intermediate PGH2 which is isomerized to PGE2 by at least three different PGE2 synthases. Inhibitors of COX - non-steroidal anti-inflammatory drugs (NSAIDs) - are currently the only available therapeutics that target PGE2 biosynthesis. Due to adverse effects of COX inhibitors on the cardiovascular system (COX-2-selective), stomach and kidney (COX-1/2-unselective), novel pharmacological strategies are in demand. The inducible microsomal PGE2 synthase (mPGES)-1 is considered mainly responsible for the excessive PGE2 synthesis during inflammation and was suggested as promising drug target for suppressing PGE2 biosynthesis. However, 15 years after intensive research on the biology and pharmacology of mPGES-1, the therapeutic value of mPGES-1 as drug target is still vague and mPGES-1 inhibitors did not enter the market so far. This commentary will first shed light on the structure, mechanism and regulation of mPGES-1 and will then discuss its biological function and the consequence of its inhibition for the dynamic network of eicosanoids. Moreover, we (i) present current strategies for interfering with mPGES-1-mediated PGE2 synthesis, (ii) summarize bioanalytical approaches for mPGES-1 drug discovery and (iii) describe preclinical test systems for the characterization of mPGES-1 inhibitors. The pharmacological potential of selective mPGES-1 inhibitor classes as well as dual mPGES-1/5-lipoxygenase inhibitors is reviewed and pitfalls in their development, including species discrepancies and loss of in vivo activity, are discussed.

  13. Cos-Seq for high-throughput identification of drug target and resistance mechanisms in the protozoan parasite Leishmania.

    Science.gov (United States)

    Gazanion, Élodie; Fernández-Prada, Christopher; Papadopoulou, Barbara; Leprohon, Philippe; Ouellette, Marc

    2016-05-24

    Innovative strategies are needed to accelerate the identification of antimicrobial drug targets and resistance mechanisms. Here we develop a sensitive method, which we term Cosmid Sequencing (or "Cos-Seq"), based on functional cloning coupled to next-generation sequencing. Cos-Seq identified >60 loci in the Leishmania genome that were enriched via drug selection with methotrexate and five major antileishmanials (antimony, miltefosine, paromomycin, amphotericin B, and pentamidine). Functional validation highlighted both known and previously unidentified drug targets and resistance genes, including novel roles for phosphatases in resistance to methotrexate and antimony, for ergosterol and phospholipid metabolism genes in resistance to miltefosine, and for hypothetical proteins in resistance to paromomycin, amphothericin B, and pentamidine. Several genes/loci were also found to confer resistance to two or more antileishmanials. This screening method will expedite the discovery of drug targets and resistance mechanisms and is easily adaptable to other microorganisms.

  14. Are Pharmaceuticals with Evolutionary Conserved Molecular Drug Targets More Potent to Cause Toxic Effects in Non-Target Organisms?

    Science.gov (United States)

    Furuhagen, Sara; Fuchs, Anne; Lundström Belleza, Elin; Breitholtz, Magnus; Gorokhova, Elena

    2014-01-01

    The ubiquitous use of pharmaceuticals has resulted in a continuous discharge into wastewater and pharmaceuticals and their metabolites are found in the environment. Due to their design towards specific drug targets, pharmaceuticals may be therapeutically active already at low environmental concentrations. Several human drug targets are evolutionary conserved in aquatic organisms, raising concerns about effects of these pharmaceuticals in non-target organisms. In this study, we hypothesized that the toxicity of a pharmaceutical towards a non-target invertebrate depends on the presence of the human drug target orthologs in this species. This was tested by assessing toxicity of pharmaceuticals with (miconazole and promethazine) and without (levonorgestrel) identified drug target orthologs in the cladoceran Daphnia magna. The toxicity was evaluated using general toxicity endpoints at individual (immobility, reproduction and development), biochemical (RNA and DNA content) and molecular (gene expression) levels. The results provide evidence for higher toxicity of miconazole and promethazine, i.e. the drugs with identified drug target orthologs. At the individual level, miconazole had the lowest effect concentrations for immobility and reproduction (0.3 and 0.022 mg L−1, respectively) followed by promethazine (1.6 and 0.18 mg L−1, respectively). At the biochemical level, individual RNA content was affected by miconazole and promethazine already at 0.0023 and 0.059 mg L−1, respectively. At the molecular level, gene expression for cuticle protein was significantly suppressed by exposure to both miconazole and promethazine; moreover, daphnids exposed to miconazole had significantly lower vitellogenin expression. Levonorgestrel did not have any effects on any endpoints in the concentrations tested. These results highlight the importance of considering drug target conservation in environmental risk assessments of pharmaceuticals. PMID:25140792

  15. Are pharmaceuticals with evolutionary conserved molecular drug targets more potent to cause toxic effects in non-target organisms?

    Science.gov (United States)

    Furuhagen, Sara; Fuchs, Anne; Lundström Belleza, Elin; Breitholtz, Magnus; Gorokhova, Elena

    2014-01-01

    The ubiquitous use of pharmaceuticals has resulted in a continuous discharge into wastewater and pharmaceuticals and their metabolites are found in the environment. Due to their design towards specific drug targets, pharmaceuticals may be therapeutically active already at low environmental concentrations. Several human drug targets are evolutionary conserved in aquatic organisms, raising concerns about effects of these pharmaceuticals in non-target organisms. In this study, we hypothesized that the toxicity of a pharmaceutical towards a non-target invertebrate depends on the presence of the human drug target orthologs in this species. This was tested by assessing toxicity of pharmaceuticals with (miconazole and promethazine) and without (levonorgestrel) identified drug target orthologs in the cladoceran Daphnia magna. The toxicity was evaluated using general toxicity endpoints at individual (immobility, reproduction and development), biochemical (RNA and DNA content) and molecular (gene expression) levels. The results provide evidence for higher toxicity of miconazole and promethazine, i.e. the drugs with identified drug target orthologs. At the individual level, miconazole had the lowest effect concentrations for immobility and reproduction (0.3 and 0.022 mg L-1, respectively) followed by promethazine (1.6 and 0.18 mg L-1, respectively). At the biochemical level, individual RNA content was affected by miconazole and promethazine already at 0.0023 and 0.059 mg L-1, respectively. At the molecular level, gene expression for cuticle protein was significantly suppressed by exposure to both miconazole and promethazine; moreover, daphnids exposed to miconazole had significantly lower vitellogenin expression. Levonorgestrel did not have any effects on any endpoints in the concentrations tested. These results highlight the importance of considering drug target conservation in environmental risk assessments of pharmaceuticals.

  16. Are pharmaceuticals with evolutionary conserved molecular drug targets more potent to cause toxic effects in non-target organisms?

    Directory of Open Access Journals (Sweden)

    Sara Furuhagen

    Full Text Available The ubiquitous use of pharmaceuticals has resulted in a continuous discharge into wastewater and pharmaceuticals and their metabolites are found in the environment. Due to their design towards specific drug targets, pharmaceuticals may be therapeutically active already at low environmental concentrations. Several human drug targets are evolutionary conserved in aquatic organisms, raising concerns about effects of these pharmaceuticals in non-target organisms. In this study, we hypothesized that the toxicity of a pharmaceutical towards a non-target invertebrate depends on the presence of the human drug target orthologs in this species. This was tested by assessing toxicity of pharmaceuticals with (miconazole and promethazine and without (levonorgestrel identified drug target orthologs in the cladoceran Daphnia magna. The toxicity was evaluated using general toxicity endpoints at individual (immobility, reproduction and development, biochemical (RNA and DNA content and molecular (gene expression levels. The results provide evidence for higher toxicity of miconazole and promethazine, i.e. the drugs with identified drug target orthologs. At the individual level, miconazole had the lowest effect concentrations for immobility and reproduction (0.3 and 0.022 mg L-1, respectively followed by promethazine (1.6 and 0.18 mg L-1, respectively. At the biochemical level, individual RNA content was affected by miconazole and promethazine already at 0.0023 and 0.059 mg L-1, respectively. At the molecular level, gene expression for cuticle protein was significantly suppressed by exposure to both miconazole and promethazine; moreover, daphnids exposed to miconazole had significantly lower vitellogenin expression. Levonorgestrel did not have any effects on any endpoints in the concentrations tested. These results highlight the importance of considering drug target conservation in environmental risk assessments of pharmaceuticals.

  17. A target repurposing approach identifies N-myristoyltransferase as a new candidate drug target in filarial nematodes.

    Directory of Open Access Journals (Sweden)

    Brendan D Galvin

    2014-09-01

    Full Text Available Myristoylation is a lipid modification involving the addition of a 14-carbon unsaturated fatty acid, myristic acid, to the N-terminal glycine of a subset of proteins, a modification that promotes their binding to cell membranes for varied biological functions. The process is catalyzed by myristoyl-CoA:protein N-myristoyltransferase (NMT, an enzyme which has been validated as a drug target in human cancers, and for infectious diseases caused by fungi, viruses and protozoan parasites. We purified Caenorhabditis elegans and Brugia malayi NMTs as active recombinant proteins and carried out kinetic analyses with their essential fatty acid donor, myristoyl-CoA and peptide substrates. Biochemical and structural analyses both revealed that the nematode enzymes are canonical NMTs, sharing a high degree of conservation with protozoan NMT enzymes. Inhibitory compounds that target NMT in protozoan species inhibited the nematode NMTs with IC50 values of 2.5-10 nM, and were active against B. malayi microfilariae and adult worms at 12.5 µM and 50 µM respectively, and C. elegans (25 µM in culture. RNA interference and gene deletion in C. elegans further showed that NMT is essential for nematode viability. The effects observed are likely due to disruption of the function of several downstream target proteins. Potential substrates of NMT in B. malayi are predicted using bioinformatic analysis. Our genetic and chemical studies highlight the importance of myristoylation in the synthesis of functional proteins in nematodes and have shown for the first time that NMT is required for viability in parasitic nematodes. These results suggest that targeting NMT could be a valid approach for the development of chemotherapeutic agents against nematode diseases including filariasis.

  18. A target repurposing approach identifies N-myristoyltransferase as a new candidate drug target in filarial nematodes.

    Science.gov (United States)

    Galvin, Brendan D; Li, Zhiru; Villemaine, Estelle; Poole, Catherine B; Chapman, Melissa S; Pollastri, Michael P; Wyatt, Paul G; Carlow, Clotilde K S

    2014-09-01

    Myristoylation is a lipid modification involving the addition of a 14-carbon unsaturated fatty acid, myristic acid, to the N-terminal glycine of a subset of proteins, a modification that promotes their binding to cell membranes for varied biological functions. The process is catalyzed by myristoyl-CoA:protein N-myristoyltransferase (NMT), an enzyme which has been validated as a drug target in human cancers, and for infectious diseases caused by fungi, viruses and protozoan parasites. We purified Caenorhabditis elegans and Brugia malayi NMTs as active recombinant proteins and carried out kinetic analyses with their essential fatty acid donor, myristoyl-CoA and peptide substrates. Biochemical and structural analyses both revealed that the nematode enzymes are canonical NMTs, sharing a high degree of conservation with protozoan NMT enzymes. Inhibitory compounds that target NMT in protozoan species inhibited the nematode NMTs with IC50 values of 2.5-10 nM, and were active against B. malayi microfilariae and adult worms at 12.5 µM and 50 µM respectively, and C. elegans (25 µM) in culture. RNA interference and gene deletion in C. elegans further showed that NMT is essential for nematode viability. The effects observed are likely due to disruption of the function of several downstream target proteins. Potential substrates of NMT in B. malayi are predicted using bioinformatic analysis. Our genetic and chemical studies highlight the importance of myristoylation in the synthesis of functional proteins in nematodes and have shown for the first time that NMT is required for viability in parasitic nematodes. These results suggest that targeting NMT could be a valid approach for the development of chemotherapeutic agents against nematode diseases including filariasis.

  19. Determining the prediction limits of models and classifiers with applications for disruption prediction in JET

    Science.gov (United States)

    Murari, A.; Peluso, E.; Vega, J.; Gelfusa, M.; Lungaroni, M.; Gaudio, P.; Martínez, F. J.; Contributors, JET

    2017-01-01

    Understanding the many aspects of tokamak physics requires the development of quite sophisticated models. Moreover, in the operation of the devices, prediction of the future evolution of discharges can be of crucial importance, particularly in the case of the prediction of disruptions, which can cause serious damage to various parts of the machine. The determination of the limits of predictability is therefore an important issue for modelling, classifying and forecasting. In all these cases, once a certain level of performance has been reached, the question typically arises as to whether all the information available in the data has been exploited, or whether there are still margins for improvement of the tools being developed. In this paper, a theoretical information approach is proposed to address this issue. The excellent properties of the developed indicator, called the prediction factor (PF), have been proved with the help of a series of numerical tests. Its application to some typical behaviour relating to macroscopic instabilities in tokamaks has shown very positive results. The prediction factor has also been used to assess the performance of disruption predictors running in real time in the JET system, including the one systematically deployed in the feedback loop for mitigation purposes. The main conclusion is that the most advanced predictors basically exploit all the information contained in the locked mode signal on which they are based. Therefore, qualitative improvements in disruption prediction performance in JET would need the processing of additional signals, probably profiles.

  20. LIBP-Pred: web server for lipid binding proteins using structural network parameters; PDB mining of human cancer biomarkers and drug targets in parasites and bacteria.

    Science.gov (United States)

    González-Díaz, Humberto; Munteanu, Cristian R; Postelnicu, Lucian; Prado-Prado, Francisco; Gestal, Marcos; Pazos, Alejandro

    2012-03-01

    Lipid-Binding Proteins (LIBPs) or Fatty Acid-Binding Proteins (FABPs) play an important role in many diseases such as different types of cancer, kidney injury, atherosclerosis, diabetes, intestinal ischemia and parasitic infections. Thus, the computational methods that can predict LIBPs based on 3D structure parameters became a goal of major importance for drug-target discovery, vaccine design and biomarker selection. In addition, the Protein Data Bank (PDB) contains 3000+ protein 3D structures with unknown function. This list, as well as new experimental outcomes in proteomics research, is a very interesting source to discover relevant proteins, including LIBPs. However, to the best of our knowledge, there are no general models to predict new LIBPs based on 3D structures. We developed new Quantitative Structure-Activity Relationship (QSAR) models based on 3D electrostatic parameters of 1801 different proteins, including 801 LIBPs. We calculated these electrostatic parameters with the MARCH-INSIDE software and they correspond to the entire protein or to specific protein regions named core, inner, middle, and surface. We used these parameters as inputs to develop a simple Linear Discriminant Analysis (LDA) classifier to discriminate 3D structure of LIBPs from other proteins. We implemented this predictor in the web server named LIBP-Pred, freely available at , along with other important web servers of the Bio-AIMS portal. The users can carry out an automatic retrieval of protein structures from PDB or upload their custom protein structural models from their disk created with LOMETS server. We demonstrated the PDB mining option performing a predictive study of 2000+ proteins with unknown function. Interesting results regarding the discovery of new Cancer Biomarkers in humans or drug targets in parasites have been discussed here in this sense.

  1. Application of MD simulations to predict membrane properties of MOFs

    OpenAIRE

    Elda Adatoz; Seda Keskin

    2015-01-01

    Research Article Application of MD Simulations to Predict Membrane Properties of MOFs Elda Adatoz and Seda Keskin Chemical and Biological Engineering, Koc University, Rumelifeneri Yolu, Sariyer, 34450 Istanbul, Turkey Correspondence should be addressed to Seda Keskin; Received 22 May 2015; Accepted 13 July 2015 Academic Editor: Yanlin Song Copyright © 2015 E. Adatoz and S. Keskin. This is an open access article distributed under the Creative Commons A...

  2. Giardia fatty acyl-CoA synthetases as potential drug targets

    Directory of Open Access Journals (Sweden)

    Fengguang eGuo

    2015-07-01

    Full Text Available Giardiasis caused by Giardia intestinalis (syn. G. lamblia, G. duodenalis is one of the leading causes of diarrheal parasitic diseases worldwide. Although limited drugs to treat giardiasis are available, there are concerns regarding toxicity in some patients and the emerging drug resistance. By data-mining genome sequences, we observed that G. intestinalis is incapable of synthesizing fatty acids de novo. However, this parasite has five long-chain fatty acyl-CoA synthetases (GiACS1 to GiACS5 to activate fatty acids scavenged from the host. ACS is an essential enzyme because fatty acids need to be activated to form acyl-CoA thioesters before they can enter subsequent metabolism. In the present study, we performed experiments to explore whether some GiACS enzymes could serve as drug targets in Giardia. Based on the high-throughput datasets and protein modeling analyses, we initially studied the GiACS1 and GiACS2, because genes encoding these two enzymes were found to be more consistently expressed in varied parasite life cycle stages and when interacting with host cells based on previously reported transcriptome data. These two proteins were cloned and expressed as recombinant proteins. Biochemical analysis revealed that both had apparent substrate preference towards palmitic acid (C16:0 and myristic acid (C14:0, and allosteric or Michaelis-Menten kinetics on palmitic acid or ATP. The ACS inhibitor triacsin C inhibited the activity of both enzymes (IC50 = 1.56 µM, Ki = 0.18 µM for GiACS1 and IC50 = 2.28 µM, Ki = 0.23 µM for GiACS2, respectively and the growth of G. intestinalis in vitro (IC50 = 0.8 µM. As expected from giardial evolutionary characteristics, both GiACSs displayed differences in overall folding structure as compared with their human counterparts. These observations support the notion that some of the GiACS enzymes may be explored as drug targets in this parasite.

  3. Improved drug targeting of cancer cells by utilizing actively targetable folic acid-conjugated albumin nanospheres.

    Science.gov (United States)

    Shen, Zheyu; Li, Yan; Kohama, Kazuhiro; Oneill, Brian; Bi, Jingxiu

    2011-01-01

    Folic acid-conjugated albumin nanospheres (FA-AN) have been developed to provide an actively targetable drug delivery system for improved drug targeting of cancer cells with reduced side effects. The nanospheres were prepared by conjugating folic acid onto the surface of albumin nanospheres using 1-ethyl-3-(3-dimethylaminopropyl) carbodiimide (EDAC) as a catalyst. To test the efficacy of these nanospheres as a potential delivery platform, doxorubicin-loaded albumin nanospheres (DOX-AN) and doxorubicin-loaded FA-AN (FA-DOX-AN) were prepared by entrapping DOX (an anthracycline, antibiotic drug widely used in cancer chemotherapy that works by intercalating DNA) into AN and FA-AN nanoparticles. Cell uptake of the DOX was then measured. The results show that FA-AN was incorporated into HeLa cells (tumor cells) only after 2.0h incubation, whereas HeLa cells failed to incorporate albumin nanospheres without conjugated folic acid after 4.0h incubation. When HeLa cells were treated with the DOX-AN, FA-DOX-AN nanoparticles or free DOX, cell viability decreased with increasing culture time (i.e. cell death increases with time) over a 70h period. Cell viability was always the lowest for free DOX followed by FA-DOX-AN4 and then DOX-AN. In a second set of experiments, HeLa cells washed to remove excess DOX after an initial incubation for 2h were incubated for 70h. The corresponding cell viability was slightly higher when the cells were treated with FA-DOX-AN or free DOX whilst cells treated with DOX-AN nanoparticles remained viable. The above experiments were repeated for non-cancerous, aortic smooth muscle cells (AoSMC). As expected, cell viability of the HeLa cells (with FA receptor alpha, FRα) and AoSMC cells (without FRα) decreased rapidly with time in the presence of free DOX, but treatment with FA-DOX-AN resulted in selective killing of the tumor cells. These results indicated that FA-AN may be used as a promising actively targetable drug delivery system to improve drug

  4. Comparative genomics allowed the identification of drug targets against human fungal pathogens

    Directory of Open Access Journals (Sweden)

    Martins Natalia F

    2011-01-01

    Full Text Available Abstract Background The prevalence of invasive fungal infections (IFIs has increased steadily worldwide in the last few decades. Particularly, there has been a global rise in the number of infections among immunosuppressed people. These patients present severe clinical forms of the infections, which are commonly fatal, and they are more susceptible to opportunistic fungal infections than non-immunocompromised people. IFIs have historically been associated with high morbidity and mortality, partly because of the limitations of available antifungal therapies, including side effects, toxicities, drug interactions and antifungal resistance. Thus, the search for alternative therapies and/or the development of more specific drugs is a challenge that needs to be met. Genomics has created new ways of examining genes, which open new strategies for drug development and control of human diseases. Results In silico analyses and manual mining selected initially 57 potential drug targets, based on 55 genes experimentally confirmed as essential for Candida albicans or Aspergillus fumigatus and other 2 genes (kre2 and erg6 relevant for fungal survival within the host. Orthologs for those 57 potential targets were also identified in eight human fungal pathogens (C. albicans, A. fumigatus, Blastomyces dermatitidis, Paracoccidioides brasiliensis, Paracoccidioides lutzii, Coccidioides immitis, Cryptococcus neoformans and Histoplasma capsulatum. Of those, 10 genes were present in all pathogenic fungi analyzed and absent in the human genome. We focused on four candidates: trr1 that encodes for thioredoxin reductase, rim8 that encodes for a protein involved in the proteolytic activation of a transcriptional factor in response to alkaline pH, kre2 that encodes for α-1,2-mannosyltransferase and erg6 that encodes for Δ(24-sterol C-methyltransferase. Conclusions Our data show that the comparative genomics analysis of eight fungal pathogens enabled the identification of

  5. Application of Model Predictive Control to BESS for Microgrid Control

    Directory of Open Access Journals (Sweden)

    Thai-Thanh Nguyen

    2015-08-01

    Full Text Available Battery energy storage systems (BESSs have been widely used for microgrid control. Generally, BESS control systems are based on proportional-integral (PI control techniques with the outer and inner control loops based on PI regulators. Recently, model predictive control (MPC has attracted attention for application to future energy processing and control systems because it can easily deal with multivariable cases, system constraints, and nonlinearities. This study considers the application of MPC-based BESSs to microgrid control. Two types of MPC are presented in this study: MPC based on predictive power control (PPC and MPC based on PI control in the outer and predictive current control (PCC in the inner control loops. In particular, the effective application of MPC for microgrids with multiple BESSs should be considered because of the differences in their control performance. In this study, microgrids with two BESSs based on two MPC techniques are considered as an example. The control performance of the MPC used for the control microgrid is compared to that of the PI control. The proposed control strategy is investigated through simulations using MATLAB/Simulink software. The simulation results show that the response time, power and voltage ripples, and frequency spectrum could be improved significantly by using MPC.

  6. An abundance of rare functional variants in 202 drug target genes sequenced in 14.002 people

    DEFF Research Database (Denmark)

    Nelson, Matthew R.; Wegmann, Daniel; Ehm, Margaret G.;

    2012-01-01

    Rare genetic variants contribute to complex disease risk; however, the abundance of rare variants in human populations remains unknown. We explored this spectrum of variation by sequencing 202 genes encoding drug targets in 14,002 individuals. We find rare variants are abundant (1 every 17 bases)...

  7. Streptococcus pneumoniae TIGR4 Flavodoxin: Structural and Biophysical Characterization of a Novel Drug Target

    Science.gov (United States)

    Rodríguez-Cárdenas, Ángela; Rojas, Adriana L.; Conde-Giménez, María; Velázquez-Campoy, Adrián; Hurtado-Guerrero, Ramón; Sancho, Javier

    2016-01-01

    Streptococcus pneumoniae (Sp) strain TIGR4 is a virulent, encapsulated serotype that causes bacteremia, otitis media, meningitis and pneumonia. Increased bacterial resistance and limited efficacy of the available vaccine to some serotypes complicate the treatment of diseases associated to this microorganism. Flavodoxins are bacterial proteins involved in several important metabolic pathways. The Sp flavodoxin (Spfld) gene was recently reported to be essential for the establishment of meningitis in a rat model, which makes SpFld a potential drug target. To facilitate future pharmacological studies, we have cloned and expressed SpFld in E. coli and we have performed an extensive structural and biochemical characterization of both the apo form and its active complex with the FMN cofactor. SpFld is a short-chain flavodoxin containing 146 residues. Unlike the well-characterized long-chain apoflavodoxins, the Sp apoprotein displays a simple two-state thermal unfolding equilibrium and binds FMN with moderate affinity. The X-ray structures of the apo and holo forms of SpFld differ at the FMN binding site, where substantial rearrangement of residues at the 91–100 loop occurs to permit cofactor binding. This work will set up the basis for future studies aiming at discovering new potential drugs to treat S. pneumoniae diseases through the inhibition of SpFld. PMID:27649488

  8. Complementary Approaches to Existing Target Based Drug Discovery for Identifying Novel Drug Targets

    Directory of Open Access Journals (Sweden)

    Suhas Vasaikar

    2016-11-01

    Full Text Available In the past decade, it was observed that the relationship between the emerging New Molecular Entities and the quantum of R&D investment has not been favorable. There might be numerous reasons but few studies stress the introduction of target based drug discovery approach as one of the factors. Although a number of drugs have been developed with an emphasis on a single protein target, yet identification of valid target is complex. The approach focuses on an in vitro single target, which overlooks the complexity of cell and makes process of validation drug targets uncertain. Thus, it is imperative to search for alternatives rather than looking at success stories of target-based drug discovery. It would be beneficial if the drugs were developed to target multiple components. New approaches like reverse engineering and translational research need to take into account both system and target-based approach. This review evaluates the strengths and limitations of known drug discovery approaches and proposes alternative approaches for increasing efficiency against treatment.

  9. Metabonomic analysis of potential biomarkers and drug targets involved in diabetic nephropathy mice.

    Science.gov (United States)

    Wei, Tingting; Zhao, Liangcai; Jia, Jianmin; Xia, Huanhuan; Du, Yao; Lin, Qiuting; Lin, Xiaodong; Ye, Xinjian; Yan, Zhihan; Gao, Hongchang

    2015-07-07

    Diabetic nephropathy (DN) is one of the lethal manifestations of diabetic systemic microvascular disease. Elucidation of characteristic metabolic alterations during diabetic progression is critical to understand its pathogenesis and identify potential biomarkers and drug targets involved in the disease. In this study, (1)H nuclear magnetic resonance ((1)H NMR)-based metabonomics with correlative analysis was performed to study the characteristic metabolites, as well as the related pathways in urine and kidney samples of db/db diabetic mice, compared with age-matched wildtype mice. The time trajectory plot of db/db mice revealed alterations, in an age-dependent manner, in urinary metabolic profiles along with progression of renal damage and dysfunction. Age-dependent and correlated metabolite analysis identified that cis-aconitate and allantoin could serve as biomarkers for the diagnosis of DN. Further correlative analysis revealed that the enzymes dimethylarginine dimethylaminohydrolase (DDAH), guanosine triphosphate cyclohydrolase I (GTPCH I), and 3-hydroxy-3-methylglutaryl-CoA lyase (HMG-CoA lyase) were involved in dimethylamine metabolism, ketogenesis and GTP metabolism pathways, respectively, and could be potential therapeutic targets for DN. Our results highlight that metabonomic analysis can be used as a tool to identify potential biomarkers and novel therapeutic targets to gain a better understanding of the mechanisms underlying the initiation and progression of diseases.

  10. Controllability analysis of the directed human protein interaction network identifies disease genes and drug targets.

    Science.gov (United States)

    Vinayagam, Arunachalam; Gibson, Travis E; Lee, Ho-Joon; Yilmazel, Bahar; Roesel, Charles; Hu, Yanhui; Kwon, Young; Sharma, Amitabh; Liu, Yang-Yu; Perrimon, Norbert; Barabási, Albert-László

    2016-05-03

    The protein-protein interaction (PPI) network is crucial for cellular information processing and decision-making. With suitable inputs, PPI networks drive the cells to diverse functional outcomes such as cell proliferation or cell death. Here, we characterize the structural controllability of a large directed human PPI network comprising 6,339 proteins and 34,813 interactions. This network allows us to classify proteins as "indispensable," "neutral," or "dispensable," which correlates to increasing, no effect, or decreasing the number of driver nodes in the network upon removal of that protein. We find that 21% of the proteins in the PPI network are indispensable. Interestingly, these indispensable proteins are the primary targets of disease-causing mutations, human viruses, and drugs, suggesting that altering a network's control property is critical for the transition between healthy and disease states. Furthermore, analyzing copy number alterations data from 1,547 cancer patients reveals that 56 genes that are frequently amplified or deleted in nine different cancers are indispensable. Among the 56 genes, 46 of them have not been previously associated with cancer. This suggests that controllability analysis is very useful in identifying novel disease genes and potential drug targets.

  11. Medicinal chemistry based approaches and nanotechnology-based systems to improve CNS drug targeting and delivery.

    Science.gov (United States)

    Vlieghe, Patrick; Khrestchatisky, Michel

    2013-05-01

    The central nervous system (CNS) is protected by various barriers, which regulate nervous tissue homeostasis and control the selective and specific uptake, efflux, and metabolism of endogenous and exogenous molecules. Among these barriers is the blood-brain barrier (BBB), a physical and physiological barrier that filters very efficiently and selectively the entry of compounds from the blood to the brain and protects nervous tissue from harmful substances and infectious agents present in the bloodstream. The BBB also prevents the entry of potential drugs. As a result, various drug targeting and delivery strategies are currently being developed to enhance the transport of drugs from the blood to the brain. Following a general introduction, we briefly overview in this review article the fundamental physiological properties of the BBB. Then, we describe current strategies to bypass the BBB (i.e., invasive methods, alternative approaches, and temporary opening) and to cross it (i.e., noninvasive approaches). This section is followed by a chapter addressing the chemical and technological solutions developed to cross the BBB. A special emphasis is given to prodrug-targeting approaches and targeted nanotechnology-based systems, two promising strategies for BBB targeting and delivery of drugs to the brain.

  12. DbMDR: a relational database for multidrug resistance genes as potential drug targets.

    Science.gov (United States)

    Gupta, Sanchita; Mishra, Manoj; Sen, Naresh; Parihar, Rashi; Dwivedi, Gaurav Raj; Khan, Feroz; Sharma, Ashok

    2011-10-01

    DbMDR is non-redundant reference database of multidrug resistance (MDR) genes and their orthologs acting as potential drug targets. Drug resistance is a common phenomenon of pathogens, creating a serious problem of inactivation of drugs and antibiotics resulting in occurrence of diseases. Apart from other factors, the MDR genes present in pathogens are shown to be responsible for multidrug resistance. Much of the unorganized information on MDR genes is scattered across the literature and other web resources. Thus, consolidation of such knowledge about MDR genes into one database will make the drug discovery research more efficient. Mining of text for MDR genes has resulted into a large number of publications but in scattered and unorganized form. This information was compiled into a database, which enables a user not only to look at a particular MDR gene but also to find out putative homologs based on sequence similarity, conserved domains, and motifs in proteins encoded by MDR genes more efficiently. At present, DbMDR database contains 2843 MDR genes characterized experimentally as well as functionally annotated with cross-referencing search support. The DbMDR database (http://203.190.147.116/dbmdr/) is a comprehensive resource for comparative study focused on MDR genes and metabolic pathway efflux pumps and intended to provide a platform for researchers for further research in drug resistance.

  13. Genomic profiling of murine mammary tumors identifies potential personalized drug targets for p53-deficient mammary cancers

    Directory of Open Access Journals (Sweden)

    Adam D. Pfefferle

    2016-07-01

    Full Text Available Targeted therapies against basal-like breast tumors, which are typically ‘triple-negative breast cancers (TNBCs’, remain an important unmet clinical need. Somatic TP53 mutations are the most common genetic event in basal-like breast tumors and TNBC. To identify additional drivers and possible drug targets of this subtype, a comparative study between human and murine tumors was performed by utilizing a murine Trp53-null mammary transplant tumor model. We show that two subsets of murine Trp53-null mammary transplant tumors resemble aspects of the human basal-like subtype. DNA-microarray, whole-genome and exome-based sequencing approaches were used to interrogate the secondary genetic aberrations of these tumors, which were then compared to human basal-like tumors to identify conserved somatic genetic features. DNA copy-number variation produced the largest number of conserved candidate personalized drug targets. These candidates were filtered using a DNA-RNA Pearson correlation cut-off and a requirement that the gene was deemed essential in at least 5% of human breast cancer cell lines from an RNA-mediated interference screen database. Five potential personalized drug target genes, which were spontaneously amplified loci in both murine and human basal-like tumors, were identified: Cul4a, Lamp1, Met, Pnpla6 and Tubgcp3. As a proof of concept, inhibition of Met using crizotinib caused Met-amplified murine tumors to initially undergo complete regression. This study identifies Met as a promising drug target in a subset of murine Trp53-null tumors, thus identifying a potential shared driver with a subset of human basal-like breast cancers. Our results also highlight the importance of comparative genomic studies for discovering personalized drug targets and for providing a preclinical model for further investigations of key tumor signaling pathways.

  14. Neuro-fuzzy predictive control for nonlinear application

    Institute of Scientific and Technical Information of China (English)

    CHEN Dong-xiang; WANG Gang; LV Shi-xia

    2008-01-01

    Aiming at the unsatisfactory dynamic performances of conventional model predictive control (MPC) in a highly nonlinear process, a scheme employed the fuzzy neural network to realize the nonlinear process is proposed. The neuro-fuzzy predictor has the capability of achieving high predictive accuracy due to its nonlinear mapping and interpolation features, and adaptively updating network parameters by a learning procedure to re-duce the model errors caused by changes of the process under control. To cope with the difficult problem of non-linear optimization, Pepanaqi method was applied to search the optimal or suboptimal solution. Comparisons were made among the objective function values of alternatives in initial space. The search was then confined to shrink the smaller region according to results of comparisons. The convergent point was finally approached to be considered as the optimal or suboptimal solution. Experimental results of the neuro-fuzzy predictive control for drier application reveal that the proposed control scheme has less tracking errors and can smooth control actions, which is applicable to changes of drying condition.

  15. Economic model predictive control theory, formulations and chemical process applications

    CERN Document Server

    Ellis, Matthew; Christofides, Panagiotis D

    2017-01-01

    This book presents general methods for the design of economic model predictive control (EMPC) systems for broad classes of nonlinear systems that address key theoretical and practical considerations including recursive feasibility, closed-loop stability, closed-loop performance, and computational efficiency. Specifically, the book proposes: Lyapunov-based EMPC methods for nonlinear systems; two-tier EMPC architectures that are highly computationally efficient; and EMPC schemes handling explicitly uncertainty, time-varying cost functions, time-delays and multiple-time-scale dynamics. The proposed methods employ a variety of tools ranging from nonlinear systems analysis, through Lyapunov-based control techniques to nonlinear dynamic optimization. The applicability and performance of the proposed methods are demonstrated through a number of chemical process examples. The book presents state-of-the-art methods for the design of economic model predictive control systems for chemical processes. In addition to being...

  16. Applications of Machine Learning in Cancer Prediction and Prognosis

    Directory of Open Access Journals (Sweden)

    Joseph A. Cruz

    2006-01-01

    Full Text Available Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25% improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

  17. Plasmodium falciparum glutamate dehydrogenase a is dispensable and not a drug target during erythrocytic development

    LENUS (Irish Health Repository)

    Storm, Janet

    2011-07-14

    Abstract Background Plasmodium falciparum contains three genes encoding potential glutamate dehydrogenases. The protein encoded by gdha has previously been biochemically and structurally characterized. It was suggested that it is important for the supply of reducing equivalents during intra-erythrocytic development of Plasmodium and, therefore, a suitable drug target. Methods The gene encoding the NADP(H)-dependent GDHa has been disrupted by reverse genetics in P. falciparum and the effect on the antioxidant and metabolic capacities of the resulting mutant parasites was investigated. Results No growth defect under low and elevated oxygen tension, no up- or down-regulation of a number of antioxidant and NADP(H)-generating proteins or mRNAs and no increased levels of GSH were detected in the D10Δgdha parasite lines. Further, the fate of the carbon skeleton of [13C] labelled glutamine was assessed by metabolomic studies, revealing no differences in the labelling of α-ketoglutarate and other TCA pathway intermediates between wild type and mutant parasites. Conclusions First, the data support the conclusion that D10Δgdha parasites are not experiencing enhanced oxidative stress and that GDHa function may not be the provision of NADP(H) for reductive reactions. Second, the results imply that the cytosolic, NADP(H)-dependent GDHa protein is not involved in the oxidative deamination of glutamate but that the protein may play a role in ammonia assimilation as has been described for other NADP(H)-dependent GDH from plants and fungi. The lack of an obvious phenotype in the absence of GDHa may point to a regulatory role of the protein providing glutamate (as nitrogen storage molecule) in situations where the parasites experience a limiting supply of carbon sources and, therefore, under in vitro conditions the enzyme is unlikely to be of significant importance. The data imply that the protein is not a suitable target for future drug development against intra

  18. Biological drugs targeting the immune response in the therapy of psoriasis

    Directory of Open Access Journals (Sweden)

    Saveria Pastore

    2008-08-01

    Full Text Available Saveria Pastore1, Emanuela Gubinelli2, Luca Leoni2, Desanka Raskovic2, Liudmila Korkina11Laboratory of Tissue Engineering and Cutaneous Physiopathology; 2Second Dermatology Unit, Istituto Dermopatico dell’Immacolata, IRCCS, Roma, ItalyAbstract: Chronic plaque psoriasis affects more than 2% of world population, has a chronic recurrent behavior, gives a heavy burden to the patients’ quality of life, and hence remains a huge medical and social problem. The clinical results of conventional therapies of psoriasis are not satisfactory. According to the current knowledge of the molecular and cellular basis of psoriasis, it is defined as an immune-mediated chronic inflammatory and hyperproliferative skin disease. A new generation of biological drugs, targeting molecules and cells involved into perturbed pro-inflammatory immune response in the psoriatic skin and joints, has been recently designed and applied clinically. These biological agents are bioengineered proteins such as chimeric and humanized antibodies and fusion proteins. In particular, they comprise the antitumor necrosis factor-α agents etanercept, infliximab, and adalimumab, with clinical efficacy in both moderate-severe psoriasis and psoriatic arthritis, and the anti-CD11a efalizumab with selective therapeutic action exclusively in the skin. Here, we overview recent findings on the molecular pathways relevant to the inflammatory response in psoriasis and present our clinical experience with the drugs currently employed in the dermatologic manifestations, namely etanercept, infliximab, and efalizumab. The growing body of clinical data on the efficacy and safety of antipsoriasis biological drugs is reviewed as well. Particular focus is given to long-term safety concerns and feasibility of combined therapeutic protocols to ameliorate clinical results.Keywords: psoriasis, immune-mediated inflammation, etanercept, infliximab, efalizumab

  19. Drug targets from human pathogenic amoebas: Entamoeba histolytica, Acanthamoeba polyphaga and Naegleria fowleri.

    Science.gov (United States)

    Ondarza, R N

    2007-09-01

    In this review we present our search for the presence of drug targets in several species of human pathogenic parasites, mainly the amoebas Entamoeba histolytica, Acanthamoeba polyphaga and Naegleria fowleri. We started with an analysis of the concepts of essentiality and validity of the targets and continue with a description of the main characteristics of pathogenicity of these amoebas. We then proceed to evaluate these targets arranged mainly in seven groups corresponding to: a) enzymes which are secreted by these parasites to invade the human host, for example proteinases, phospholipases and pore forming peptides, b) glycolytic enzymes from Entamoeba and Naegleria, like the PPi-dependent phospho-fructokinase that differ from the host enzyme, c) thiols and enzymes of redox metabolism, present only in trypanosomatids, Entamoeba and Naegleria, such as the trypanothione/trypanothione reductase that maintains the reducing environment within the cell, d) antioxidant enzymes to regulate the oxidative stress produced by the phagocytic cells of the host or by the parasite metabolism, like the trypanothione peroxidase in connection with the NADPH-dependent trypanothione/trypanothione reductase which maybe is present in Naegleria fowleri, and peroxiredoxin in E. histolytica, e) enzymes for the synthesis of trypanothione like the ornithine decarboxylase, spermidine synthase and trypanothione synthetase, f) some of the proteins that assemble the secretory vesicles with the cell membrane, like the synaptobrevins and finally, g) encystment pathways and cyst-wall assembly proteins. Some of the above new targets will need to be studied in a more detail, including crystallographic studies of the enzymes for rational drug design. As far as we know there are no advanced crystallographic studies being conducted on targets from these three amoebas, as has been the case for various targets from the trypanosomatids.

  20. Isolation, purification and characterization of pyruvate kinase from Staphylococcus aureus : a potential drug target

    Directory of Open Access Journals (Sweden)

    K. Venkatesh

    2014-04-01

    Full Text Available Background: With emergence of multidrug-resistant strains of Staphylococcus aureus, there is an urgent need for the development of new antimicrobials which are narrow and pathogen specific. In this context, pyruvate kinase (PK an important enzyme in the glycolysis, which catalyses the formation of pyruvate which is the key intersection in the network of metabolic pathways was isolated and purified from Staphylococcus aureus ATCC12600. Methods: Purification steps included 10%-20% ammonium sulphate fractionation, diethyl aminoethyl cellulose ion exchange chromatography followed by gel filtration on Sephadex G-100. The pure PK molecular weight was determined by sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE and Km and Vmax for the PK was demonstrated. Results: The pure PK obtained from Sephadex G-100 gel filtration column exhibited Km of 0.78+0.18 µM and Vmax 76.47+0.82 µM NADH/mg/min and molecular weight of 250 kDa in solution. However, in SDS-PAGE showed single band with a molecular weight of 63 kDa confirming the homotetramer nature. In all steps of purification the Km remained constant indicating presence of only one kind of enzyme. The PK gene searched in the genomic sequences of Staphylococcus aureus also confirmed the same. Interpretation and conclusions: In Staphylococcus aureus presence of only one kind of PK unlike in other Gram positive bacteria exhibiting distinct differences in enzyme kinetics. This enzyme also showed the functionality of PK is found to be different from its human host. Therefore, PK probably is regarded as an ideal drug target in the development of new potent antimicrobials.

  1. Transient receptor potential (TRP) channels as drug targets for diseases of the digestive system.

    Science.gov (United States)

    Holzer, Peter

    2011-07-01

    Approximately 20 of the 30 mammalian transient receptor potential (TRP) channel subunits are expressed by specific neurons and cells within the alimentary canal. They subserve important roles in taste, chemesthesis, mechanosensation, pain and hyperalgesia and contribute to the regulation of gastrointestinal motility, absorptive and secretory processes, blood flow, and mucosal homeostasis. In a cellular perspective, TRP channels operate either as primary detectors of chemical and physical stimuli, as secondary transducers of ionotropic or metabotropic receptors, or as ion transport channels. The polymodal sensory function of TRPA1, TRPM5, TRPM8, TRPP2, TRPV1, TRPV3 and TRPV4 enables the digestive system to survey its physical and chemical environment, which is relevant to all processes of digestion. TRPV5 and TRPV6 as well as TRPM6 and TRPM7 contribute to the absorption of Ca²⁺ and Mg²⁺, respectively. TRPM7 participates in intestinal pacemaker activity, and TRPC4 transduces muscarinic acetylcholine receptor activation to smooth muscle contraction. Changes in TRP channel expression or function are associated with a variety of diseases/disorders of the digestive system, notably gastro-esophageal reflux disease, inflammatory bowel disease, pain and hyperalgesia in heartburn, functional dyspepsia and irritable bowel syndrome, cholera, hypomagnesemia with secondary hypocalcemia, infantile hypertrophic pyloric stenosis, esophageal, gastrointestinal and pancreatic cancer, and polycystic liver disease. These implications identify TRP channels as promising drug targets for the management of a number of gastrointestinal pathologies. As a result, major efforts are put into the development of selective TRP channel agonists and antagonists and the assessment of their therapeutic potential.

  2. Identification of new drug targets and resistance mechanisms in Mycobacterium tuberculosis.

    Directory of Open Access Journals (Sweden)

    Thomas R Ioerger

    Full Text Available Identification of new drug targets is vital for the advancement of drug discovery against Mycobacterium tuberculosis, especially given the increase of resistance worldwide to first- and second-line drugs. Because traditional target-based screening has largely proven unsuccessful for antibiotic discovery, we have developed a scalable platform for target identification in M. tuberculosis that is based on whole-cell screening, coupled with whole-genome sequencing of resistant mutants and recombineering to confirm. The method yields targets paired with whole-cell active compounds, which can serve as novel scaffolds for drug development, molecular tools for validation, and/or as ligands for co-crystallization. It may also reveal other information about mechanisms of action, such as activation or efflux. Using this method, we identified resistance-linked genes for eight compounds with anti-tubercular activity. Four of the genes have previously been shown to be essential: AspS, aspartyl-tRNA synthetase, Pks13, a polyketide synthase involved in mycolic acid biosynthesis, MmpL3, a membrane transporter, and EccB3, a component of the ESX-3 type VII secretion system. AspS and Pks13 represent novel targets in protein translation and cell-wall biosynthesis. Both MmpL3 and EccB3 are involved in membrane transport. Pks13, AspS, and EccB3 represent novel candidates not targeted by existing TB drugs, and the availability of whole-cell active inhibitors greatly increases their potential for drug discovery.

  3. The tuberculosis drug discovery and development pipeline and emerging drug targets.

    Science.gov (United States)

    Mdluli, Khisimuzi; Kaneko, Takushi; Upton, Anna

    2015-01-29

    The recent accelerated approval for use in extensively drug-resistant and multidrug-resistant-tuberculosis (MDR-TB) of two first-in-class TB drugs, bedaquiline and delamanid, has reinvigorated the TB drug discovery and development field. However, although several promising clinical development programs are ongoing to evaluate new TB drugs and regimens, the number of novel series represented is few. The global early-development pipeline is also woefully thin. To have a chance of achieving the goal of better, shorter, safer TB drug regimens with utility against drug-sensitive and drug-resistant disease, a robust and diverse global TB drug discovery pipeline is key, including innovative approaches that make use of recently acquired knowledge on the biology of TB. Fortunately, drug discovery for TB has resurged in recent years, generating compounds with varying potential for progression into developable leads. In parallel, advances have been made in understanding TB pathogenesis. It is now possible to apply the lessons learned from recent TB hit generation efforts and newly validated TB drug targets to generate the next wave of TB drug leads. Use of currently underexploited sources of chemical matter and lead-optimization strategies may also improve the efficiency of future TB drug discovery. Novel TB drug regimens with shorter treatment durations must target all subpopulations of Mycobacterium tuberculosis existing in an infection, including those responsible for the protracted TB treatment duration. This review summarizes the current TB drug development pipeline and proposes strategies for generating improved hits and leads in the discovery phase that could help achieve this goal.

  4. Sphingolipids Are Dual Specific Drug Targets for the Management of Pulmonary Infections: Perspective

    Science.gov (United States)

    Sharma, Lalita; Prakash, Hridayesh

    2017-01-01

    bactericidal potential in macrophages for the control of TB. In this review, we have discussed and emphasized that sphingolipids may represent effective novel, yet dual specific drug targets for controlling pulmonary infections.

  5. Utilizing Chemical Genomics to Identify Cytochrome b as a Novel Drug Target for Chagas Disease.

    Directory of Open Access Journals (Sweden)

    Shilpi Khare

    2015-07-01

    Full Text Available Unbiased phenotypic screens enable identification of small molecules that inhibit pathogen growth by unanticipated mechanisms. These small molecules can be used as starting points for drug discovery programs that target such mechanisms. A major challenge of the approach is the identification of the cellular targets. Here we report GNF7686, a small molecule inhibitor of Trypanosoma cruzi, the causative agent of Chagas disease, and identification of cytochrome b as its target. Following discovery of GNF7686 in a parasite growth inhibition high throughput screen, we were able to evolve a GNF7686-resistant culture of T. cruzi epimastigotes. Clones from this culture bore a mutation coding for a substitution of leucine by phenylalanine at amino acid position 197 in cytochrome b. Cytochrome b is a component of complex III (cytochrome bc1 in the mitochondrial electron transport chain and catalyzes the transfer of electrons from ubiquinol to cytochrome c by a mechanism that utilizes two distinct catalytic sites, QN and QP. The L197F mutation is located in the QN site and confers resistance to GNF7686 in both parasite cell growth and biochemical cytochrome b assays. Additionally, the mutant cytochrome b confers resistance to antimycin A, another QN site inhibitor, but not to strobilurin or myxothiazol, which target the QP site. GNF7686 represents a promising starting point for Chagas disease drug discovery as it potently inhibits growth of intracellular T. cruzi amastigotes with a half maximal effective concentration (EC50 of 0.15 µM, and is highly specific for T. cruzi cytochrome b. No effect on the mammalian respiratory chain or mammalian cell proliferation was observed with up to 25 µM of GNF7686. Our approach, which combines T. cruzi chemical genetics with biochemical target validation, can be broadly applied to the discovery of additional novel drug targets and drug leads for Chagas disease.

  6. A Tale of Two Drug Targets: The Evolutionary History of BACE1 and BACE2

    Directory of Open Access Journals (Sweden)

    Christopher eSouthan

    2013-12-01

    Full Text Available The beta amyloid (APP cleaving enzyme (BACE1 has been a drug target for Alzheimer's Disease (AD since 1999 with lead inhibitors now entering clinical trials. In 2011, the paralogue, BACE2, became a new target for type II diabetes (T2DM having been identified as a TMEM27 secretase regulating pancreatic β cell function. However, the normal roles of both enzymes are unclear. This study outlines their evolutionary history and new opportunities for functional genomics. We identified 30 homologues (UrBACEs in basal phyla including Placozoans, Cnidarians, Choanoflagellates, Porifera, Echinoderms, Annelids, Mollusks and Ascidians (but not Ecdysozoans. UrBACEs are predominantly single copy, show 35% to 45% protein sequence identity with mammalian BACE1, are approximately 100 residues longer than cathepsin paralogues with an aspartyl protease domain flanked by a signal peptide and a C-terminal transmembrane domain. While multiple paralogues in Trichoplax and Monosiga pre-date the nervous system, duplication of the UrBACE in fish gave rise to BACE1 and BACE2 in the vertebrate lineage. The latter evolved more rapidly as the former maintained the emergent neuronal role. In mammals, Ka/Ks for BACE2 is higher than BACE1 but low ratios for both suggest purifying selection. The 5’ exons show higher Ka/Ks than the catalytic section. Model organism genomes show the absence of certain BACE human substrates when the UrBACE is present. Experiments could thus reveal undiscovered substrates and roles. The human protease double-target status means that evolutionary trajectories and functional shifts associated with different substrates will have implications for the development of clinical candidates for both AD and T2DM. A rational basis for inhibition specificity ratios and assessing target-related side effects will be facilitated by a more complete picture of BACE1 and BACE2 functions informed by their evolutionary context.

  7. Addressing Confounding in Predictive Models with an Application to Neuroimaging.

    Science.gov (United States)

    Linn, Kristin A; Gaonkar, Bilwaj; Doshi, Jimit; Davatzikos, Christos; Shinohara, Russell T

    2016-05-01

    Understanding structural changes in the brain that are caused by a particular disease is a major goal of neuroimaging research. Multivariate pattern analysis (MVPA) comprises a collection of tools that can be used to understand complex disease efxcfects across the brain. We discuss several important issues that must be considered when analyzing data from neuroimaging studies using MVPA. In particular, we focus on the consequences of confounding by non-imaging variables such as age and sex on the results of MVPA. After reviewing current practice to address confounding in neuroimaging studies, we propose an alternative approach based on inverse probability weighting. Although the proposed method is motivated by neuroimaging applications, it is broadly applicable to many problems in machine learning and predictive modeling. We demonstrate the advantages of our approach on simulated and real data examples.

  8. Complete genome-wide screening and subtractive genomic approach revealed new virulence factors, potential drug targets against bio-war pathogen Brucella melitensis 16M.

    Science.gov (United States)

    Pradeepkiran, Jangampalli Adi; Sainath, Sri Bhashyam; Kumar, Konidala Kranthi; Bhaskar, Matcha

    2015-01-01

    Brucella melitensis 16M is a Gram-negative coccobacillus that infects both animals and humans. It causes a disease known as brucellosis, which is characterized by acute febrile illness in humans and causes abortions in livestock. To prevent and control brucellosis, identification of putative drug targets is crucial. The present study aimed to identify drug targets in B. melitensis 16M by using a subtractive genomic approach. We used available database repositories (Database of Essential Genes, Kyoto Encyclopedia of Genes and Genomes Automatic Annotation Server, and Kyoto Encyclopedia of Genes and Genomes) to identify putative genes that are nonhomologous to humans and essential for pathogen B. melitensis 16M. The results revealed that among 3 Mb genome size of pathogen, 53 putative characterized and 13 uncharacterized hypothetical genes were identified; further, from Basic Local Alignment Search Tool protein analysis, one hypothetical protein showed a close resemblance (50%) to Silicibacter pomeroyi DUF1285 family protein (2RE3). A further homology model of the target was constructed using MODELLER 9.12 and optimized through variable target function method by molecular dynamics optimization with simulating annealing. The stereochemical quality of the restrained model was evaluated by PROCHECK, VERIFY-3D, ERRAT, and WHATIF servers. Furthermore, structure-based virtual screening was carried out against the predicted active site of the respective protein using the glycerol structural analogs from the PubChem database. We identified five best inhibitors with strong affinities, stable interactions, and also with reliable drug-like properties. Hence, these leads might be used as the most effective inhibitors of modeled protein. The outcome of the present work of virtual screening of putative gene targets might facilitate design of potential drugs for better treatment against brucellosis.

  9. Development of a Mobile Application for Building Energy Prediction Using Performance Prediction Model

    Directory of Open Access Journals (Sweden)

    Yu-Ri Kim

    2016-03-01

    Full Text Available Recently, the Korean government has enforced disclosure of building energy performance, so that such information can help owners and prospective buyers to make suitable investment plans. Such a building energy performance policy of the government makes it mandatory for the building owners to obtain engineering audits and thereby evaluate the energy performance levels of their buildings. However, to calculate energy performance levels (i.e., asset rating methodology, a qualified expert needs to have access to at least the full project documentation and/or conduct an on-site inspection of the buildings. Energy performance certification costs a lot of time and money. Moreover, the database of certified buildings is still actually quite small. A need, therefore, is increasing for a simplified and user-friendly energy performance prediction tool for non-specialists. Also, a database which allows building owners and users to compare best practices is required. In this regard, the current study developed a simplified performance prediction model through experimental design, energy simulations and ANOVA (analysis of variance. Furthermore, using the new prediction model, a related mobile application was also developed.

  10. Applicative limitations of sediment transport on predictive modeling in geomorphology

    Institute of Scientific and Technical Information of China (English)

    WEIXiang; LIZhanbin

    2004-01-01

    Sources of uncertainty or error that arise in attempting to scale up the results of laboratory-scale sediment transport studies for predictive modeling of geomorphic systems include: (i) model imperfection, (ii) omission of important processes, (iii) lack of knowledge of initial conditions, (iv) sensitivity to initial conditions, (v) unresolved heterogeneity, (vi) occurrence of external forcing, and (vii) inapplicability of the factor of safety concept. Sources of uncertainty that are unimportant or that can be controlled at small scales and over short time scales become important in large-scale applications and over long time scales. Control and repeatability, hallmarks of laboratory-scale experiments, are usually lacking at the large scales characteristic of geomorphology. Heterogeneity is an important concomitant of size, and tends to make large systems unique. Uniqueness implies that prediction cannot be based upon first-principles quantitative modeling alone, but must be a function of system history as well. Periodic data collection, feedback, and model updating are essential where site-specific prediction is required.

  11. Identification of Multiple Cryptococcal Fungicidal Drug Targets by Combined Gene Dosing and Drug Affinity Responsive Target Stability Screening

    Directory of Open Access Journals (Sweden)

    Yoon-Dong Park

    2016-08-01

    Full Text Available Cryptococcus neoformans is a pathogenic fungus that is responsible for up to half a million cases of meningitis globally, especially in immunocompromised individuals. Common fungistatic drugs, such as fluconazole, are less toxic for patients but have low efficacy for initial therapy of the disease. Effective therapy against the disease is provided by the fungicidal drug amphotericin B; however, due to its high toxicity and the difficulty in administering its intravenous formulation, it is imperative to find new therapies targeting the fungus. The antiparasitic drug bithionol has been recently identified as having potent fungicidal activity. In this study, we used a combined gene dosing and drug affinity responsive target stability (GD-DARTS screen as well as protein modeling to identify a common drug binding site of bithionol within multiple NAD-dependent dehydrogenase drug targets. This combination genetic and proteomic method thus provides a powerful method for identifying novel fungicidal drug targets for further development.

  12. Cos-Seq for high-throughput identification of drug target and resistance mechanisms in the protozoan parasite Leishmania

    OpenAIRE

    Gazanion, Élodie; Fernández-Prada, Christopher; Papadopoulou, Barbara; Leprohon, Philippe; Ouellette, Marc

    2016-01-01

    Gain-of-function screens using overexpression genomic libraries are powerful tools for discovering drug target/resistance genes, but several limitations make this technique less amenable to high-throughput screening. Using cosmid-based functional screening coupled to next-generation sequencing, an approach that we term Cosmid Sequencing (or “Cos-Seq”), we followed the dynamics of cosmid enrichment during drug pressure in Leishmania, the parasite responsible for leishmaniasis, a neglected trop...

  13. Association of hypertension drug target genes with blood pressure and hypertension in 86,588 individuals.

    Science.gov (United States)

    Johnson, Andrew D; Newton-Cheh, Christopher; Chasman, Daniel I; Ehret, Georg B; Johnson, Toby; Rose, Lynda; Rice, Kenneth; Verwoert, Germaine C; Launer, Lenore J; Gudnason, Vilmundur; Larson, Martin G; Chakravarti, Aravinda; Psaty, Bruce M; Caulfield, Mark; van Duijn, Cornelia M; Ridker, Paul M; Munroe, Patricia B; Levy, Daniel

    2011-05-01

    We previously conducted genome-wide association meta-analysis of systolic blood pressure, diastolic blood pressure, and hypertension in 29,136 people from 6 cohort studies in the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium. Here we examine associations of these traits with 30 gene regions encoding known antihypertensive drug targets. We find nominal evidence of association of ADRB1, ADRB2, AGT, CACNA1A, CACNA1C, and SLC12A3 polymorphisms with 1 or more BP traits in the Cohorts for Heart and Aging Research in Genomic Epidemiology genome-wide association meta-analysis. We attempted replication of the top meta-analysis single nucleotide polymorphisms for these genes in the Global BPgen Consortium (n=34,433) and the Women's Genome Health Study (n=23,019) and found significant results for rs1801253 in ADRB1 (Arg389Gly), with the Gly allele associated with a lower mean systolic blood pressure (β: 0.57 mm Hg; SE: 0.09 mm Hg; meta-analysis: P=4.7×10(-10)), diastolic blood pressure (β: 0.36 mm Hg; SE: 0.06 mm Hg; meta-analysis: P=9.5×10(-10)), and prevalence of hypertension (β: 0.06 mm Hg; SE: 0.02 mm Hg; meta-analysis: P=3.3×10(-4)). Variation in AGT (rs2004776) was associated with systolic blood pressure (β: 0.42 mm Hg; SE: 0.09 mm Hg; meta-analysis: P=3.8×10(-6)), as well as diastolic blood pressure (P=5.0×10(-8)) and hypertension (P=3.7×10(-7)). A polymorphism in ACE (rs4305) showed modest replication of association with increased hypertension (β: 0.06 mm Hg; SE: 0.01 mm Hg; meta-analysis: P=3.0×10(-5)). Two loci, ADRB1 and AGT, contain single nucleotide polymorphisms that reached a genome-wide significance threshold in meta-analysis for the first time. Our findings suggest that these genes warrant further studies of their genetic effects on blood pressure, including pharmacogenetic interactions.

  14. Explicit Nonlinear Model Predictive Control Theory and Applications

    CERN Document Server

    Grancharova, Alexandra

    2012-01-01

    Nonlinear Model Predictive Control (NMPC) has become the accepted methodology to solve complex control problems related to process industries. The main motivation behind explicit NMPC is that an explicit state feedback law avoids the need for executing a numerical optimization algorithm in real time. The benefits of an explicit solution, in addition to the efficient on-line computations, include also verifiability of the implementation and the possibility to design embedded control systems with low software and hardware complexity. This book considers the multi-parametric Nonlinear Programming (mp-NLP) approaches to explicit approximate NMPC of constrained nonlinear systems, developed by the authors, as well as their applications to various NMPC problem formulations and several case studies. The following types of nonlinear systems are considered, resulting in different NMPC problem formulations: Ø  Nonlinear systems described by first-principles models and nonlinear systems described by black-box models; �...

  15. Machine learning applications in cancer prognosis and prediction.

    Science.gov (United States)

    Kourou, Konstantina; Exarchos, Themis P; Exarchos, Konstantinos P; Karamouzis, Michalis V; Fotiadis, Dimitrios I

    2015-01-01

    Cancer has been characterized as a heterogeneous disease consisting of many different subtypes. The early diagnosis and prognosis of a cancer type have become a necessity in cancer research, as it can facilitate the subsequent clinical management of patients. The importance of classifying cancer patients into high or low risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. A variety of these techniques, including Artificial Neural Networks (ANNs), Bayesian Networks (BNs), Support Vector Machines (SVMs) and Decision Trees (DTs) have been widely applied in cancer research for the development of predictive models, resulting in effective and accurate decision making. Even though it is evident that the use of ML methods can improve our understanding of cancer progression, an appropriate level of validation is needed in order for these methods to be considered in the everyday clinical practice. In this work, we present a review of recent ML approaches employed in the modeling of cancer progression. The predictive models discussed here are based on various supervised ML techniques as well as on different input features and data samples. Given the growing trend on the application of ML methods in cancer research, we present here the most recent publications that employ these techniques as an aim to model cancer risk or patient outcomes.

  16. A Review of Predictive Analytic Applications of Bayesian Network

    Directory of Open Access Journals (Sweden)

    Mohammad Hafiz Mohd Yusof

    2016-12-01

    Full Text Available Malware can be defined as malicious software that infiltrates a network and computer host in a variety of ways, from software flaws to social engineering. Due to the polymorphic and stealth nature of malware attacks, a signature-based analysis that is done statically is no longer sufficient to solve such a problem. Therefore, a behavioral or anomalous analysis will provide a more dynamic approach for the solution. However recent studies have shown that current behavioral methods at the network-level have several issues such as the inability to predict zero-day attacks, high-level assumptions, non-inferential analysis and performance issues. Other than performance issues, this study has identified common scientific characteristics which are reduced parameter, θ and lack of priori information p(θ that causes the problems. Previous methods were proposed to address the problem however were still unable to resolve the stated scientific hitches. Due to the shortcomings, the Bayesian Network in terms of its probabilistic modelling would be the best method to deal with the stated scientific glitches which also have been proven in the area of Clinical Expert Systems, Artificial Intelligence and Pattern Recognition. This study will critically review the predictive analytic applications of Bayesian Network model in different research domain such as Clinical Expert Systems, Artificial Intelligence and Pattern Recognition and discover any potential approach available in the domain of Computer Networks. Based on the review, this paper has identified several Bayesian Network properties which have been used to overcome the abovementioned problems. Those properties will be applied in future studies to model the Behavioral Malware Predictive Analytics.

  17. Predicting indoor pollutant concentrations, and applications to air quality management

    Energy Technology Data Exchange (ETDEWEB)

    Lorenzetti, David M.

    2002-10-01

    Because most people spend more than 90% of their time indoors, predicting exposure to airborne pollutants requires models that incorporate the effect of buildings. Buildings affect the exposure of their occupants in a number of ways, both by design (for example, filters in ventilation systems remove particles) and incidentally (for example, sorption on walls can reduce peak concentrations, but prolong exposure to semivolatile organic compounds). Furthermore, building materials and occupant activities can generate pollutants. Indoor air quality depends not only on outdoor air quality, but also on the design, maintenance, and use of the building. For example, ''sick building'' symptoms such as respiratory problems and headaches have been related to the presence of air-conditioning systems, to carpeting, to low ventilation rates, and to high occupant density (1). The physical processes of interest apply even in simple structures such as homes. Indoor air quality models simulate the processes, such as ventilation and filtration, that control pollutant concentrations in a building. Section 2 describes the modeling approach, and the important transport processes in buildings. Because advection usually dominates among the transport processes, Sections 3 and 4 describe methods for predicting airflows. The concluding section summarizes the application of these models.

  18. Prediction of Silicon-Based Layered Structures for Optoelectronic Applications

    Science.gov (United States)

    Luo, Wei; Ma, Yanming; Gong, Xingao; Xiang, Hongjun; CCMG Team

    2015-03-01

    A method based on the particle swarm optimization (PSO) algorithm is presented to design quasi-two-dimensional (Q2D) materials. With this development, various single-layer and bi-layer materials in C, Si, Ge, Sn, and Pb were predicted. A new Si bi-layer structure is found to have a much-favored energy than the previously widely accepted configuration. Both single-layer and bi-layer Si materials have small band gaps, limiting their usages in optoelectronic applications. Hydrogenation has therefore been used to tune the electronic and optical properties of Si layers. We discover two hydrogenated materials of layered Si8H2andSi6H2 possessing quasi-direct band gaps of 0.75 eV and 1.59 eV, respectively. Their potential applications for light emitting diode and photovoltaics are proposed and discussed. Our study opened up the possibility of hydrogenated Si layered materials as next-generation optoelectronic devices.

  19. FUNCTIONAL GENOMICS IDENTIFIES TIS21-DEPENDENT MECHANISMS AND PUTATIVE CANCER DRUG TARGETS UNDERLYING MEDULLOBLASTOMA SHH-TYPE DEVELOPMENT

    Directory of Open Access Journals (Sweden)

    Giulia Gentile

    2016-11-01

    Full Text Available We have recently generated a novel medulloblastoma (MB mouse model with activation of the Shh pathway and lacking the MB suppressor Tis21 (Patched1+-Tis21KO.ts main phenotype is a defect of migration of the cerebellar granule precursor cells (GCPs. By genomic analysis of GCPs in vivo, we identified as drug target and major responsible of this defect the down-regulation of the promigratory chemokine Cxcl3. Consequently, the GCPs remain longer in the cerebellum proliferative area, and the MB frequency is enhanced. Here, we further analyzed the genes deregulated in a Tis21-dependent manner (Patched1+-is21 wild-type versus Ptch1+-Tis21 knockout, among which are a number of down-regulated tumor inhibitors and up-regulated tumor facilitators, focusing on pathways potentially involved in the tumorigenesis and on putative new drug targets.The data analysis using bioinformatic tools revealed: i a link between the Shh signaling and the Tis21-dependent impairment of the GCPs migration, through a Shh-dependent deregulation of the clathrin-mediated chemotaxis operating in the primary cilium through the Cxcl3-Cxcr2 axis; ii a possible lineage shift of Shh-type GCPs toward retinal precursor phenotype the neural cell type involved in group 3 MB; iii the identification of a subset of putative drug targets for MB, involved, among the others, in the regulation of Hippo signaling and centrosome assembly. Finally, our findings define also the role of Tis21 in the regulation of gene expression, through epigenetic and RNA processing mechanisms, influencing the fate of the GCPs.

  20. Application of MD Simulations to Predict Membrane Properties of MOFs

    Directory of Open Access Journals (Sweden)

    Elda Adatoz

    2015-01-01

    Full Text Available Metal organic frameworks (MOFs are a new group of nanomaterials that have been widely examined for various chemical applications. Gas separation using MOF membranes has become an increasingly important research field in the last years. Several experimental studies have shown that thin-film MOF membranes can outperform well known polymer and zeolite membranes due to their higher gas permeances and selectivities. Given the very large number of available MOF materials, it is impractical to fabricate and test the performance of every single MOF membrane using purely experimental techniques. In this study, we used molecular simulations, Monte Carlo and Molecular Dynamics, to estimate both single-gas and mixture permeances of MOF membranes. Predictions of molecular simulations were compared with the experimental gas permeance data of MOF membranes in order to validate the accuracy of our computational approach. Results show that computational methodology that we described in this work can be used to accurately estimate membrane properties of MOFs prior to extensive experimental efforts.

  1. Neuropeptides and neuropeptide receptors: drug targets, and peptide and non-peptide ligands: a tribute to Prof. Dieter Seebach.

    Science.gov (United States)

    Hoyer, Daniel; Bartfai, Tamas

    2012-11-01

    The number of neuropeptides and their corresponding receptors has increased steadily over the last fourty years: initially, peptides were isolated from gut or brain (e.g., Substance P, somatostatin), then by targeted mining in specific regions (e.g., cortistatin, orexin in the brain), or by deorphanization of G-protein-coupled receptors (GPCRs; orexin, ghrelin receptors) and through the completion the Human Genome Project. Neuropeptides (and their receptors) have regionally restricted distributions in the central and peripheral nervous system. The neuropeptide signaling is somewhat more distinct spatially than signaling with classical, low-molecular-weight neurotransmitters that are more widely expressed, and, therefore, one assumes that drugs acting at neuropeptide receptors may have more selective pharmacological actions with possibly fewer side effects than drugs acting on glutamatergic, GABAergic, monoaminergic, or cholinergic systems. Neuropeptide receptors, which may have a few or multiple subtypes and splice variants, belong almost exclusively to the GPCR family also known as seven-transmembrane receptors (7TM), a favorite class of drug targets in the pharmaceutical industry. Most neuropeptides are co-stored and co-released with classic neurotransmitters, albeit often only at higher frequencies of stimulation or at bursting activity, thus restricting the neuropeptide signaling to specific circumstances, another reason to assume that neuropeptide drug mimics may have less side effects. Neuropeptides possess a wide spectrum of functions from neurohormone, neurotransmitter to growth factor, but also as key inflammatory mediators. Neuropeptides become 'active' when the nervous system is challenged, e.g., by stress, injury, drug abuse, or neuropsychiatric disorders with genetic, epigenetic, and/or environmental components. The unsuspected number of true neuropeptides and their cognate receptors provides opportunities to identify novel targets for the treatment of

  2. Non-linear aeroelastic prediction for aircraft applications

    Science.gov (United States)

    de C. Henshaw, M. J.; Badcock, K. J.; Vio, G. A.; Allen, C. B.; Chamberlain, J.; Kaynes, I.; Dimitriadis, G.; Cooper, J. E.; Woodgate, M. A.; Rampurawala, A. M.; Jones, D.; Fenwick, C.; Gaitonde, A. L.; Taylor, N. V.; Amor, D. S.; Eccles, T. A.; Denley, C. J.

    2007-05-01

    Current industrial practice for the prediction and analysis of flutter relies heavily on linear methods and this has led to overly conservative design and envelope restrictions for aircraft. Although the methods have served the industry well, it is clear that for a number of reasons the inclusion of non-linearity in the mathematical and computational aeroelastic prediction tools is highly desirable. The increase in available and affordable computational resources, together with major advances in algorithms, mean that non-linear aeroelastic tools are now viable within the aircraft design and qualification environment. The Partnership for Unsteady Methods in Aerodynamics (PUMA) Defence and Aerospace Research Partnership (DARP) was sponsored in 2002 to conduct research into non-linear aeroelastic prediction methods and an academic, industry, and government consortium collaborated to address the following objectives: To develop useable methodologies to model and predict non-linear aeroelastic behaviour of complete aircraft. To evaluate the methodologies on real aircraft problems. To investigate the effect of non-linearities on aeroelastic behaviour and to determine which have the greatest effect on the flutter qualification process. These aims have been very effectively met during the course of the programme and the research outputs include: New methods available to industry for use in the flutter prediction process, together with the appropriate coaching of industry engineers. Interesting results in both linear and non-linear aeroelastics, with comprehensive comparison of methods and approaches for challenging problems. Additional embryonic techniques that, with further research, will further improve aeroelastics capability. This paper describes the methods that have been developed and how they are deployable within the industrial environment. We present a thorough review of the PUMA aeroelastics programme together with a comprehensive review of the relevant research

  3. Complete genome-wide screening and subtractive genomic approach revealed new virulence factors, potential drug targets against bio-war pathogen Brucella melitensis 16M

    Directory of Open Access Journals (Sweden)

    Pradeepkiran JA

    2015-03-01

    Full Text Available Jangampalli Adi Pradeepkiran,1* Sri Bhashyam Sainath,2,3* Konidala Kranthi Kumar,1 Matcha Bhaskar1 1Division of Animal Biotechnology, Department of Zoology, Sri Venkateswara University, Tirupati, India; 2CIMAR/CIIMAR, Centro Interdisciplinar de Investigação Marinha e Ambiental, Universidade do Porto, Rua dos Bragas, Porto, Portugal, 3Department of Biotechnology, Vikrama Simhapuri University, Nellore, Andhra Pradesh, India *These authors contributed equally to this work Abstract: Brucella melitensis 16M is a Gram-negative coccobacillus that infects both animals and humans. It causes a disease known as brucellosis, which is characterized by acute febrile illness in humans and causes abortions in livestock. To prevent and control brucellosis, identification of putative drug targets is crucial. The present study aimed to identify drug targets in B. melitensis 16M by using a subtractive genomic approach. We used available database repositories (Database of Essential Genes, Kyoto Encyclopedia of Genes and Genomes Automatic Annotation Server, and Kyoto Encyclopedia of Genes and Genomes to identify putative genes that are nonhomologous to humans and essential for pathogen B. melitensis 16M. The results revealed that among 3 Mb genome size of pathogen, 53 putative characterized and 13 uncharacterized hypothetical genes were identified; further, from Basic Local Alignment Search Tool protein analysis, one hypothetical protein showed a close resemblance (50% to Silicibacter pomeroyi DUF1285 family protein (2RE3. A further homology model of the target was constructed using MODELLER 9.12 and optimized through variable target function method by molecular dynamics optimization with simulating annealing. The stereochemical quality of the restrained model was evaluated by PROCHECK, VERIFY-3D, ERRAT, and WHATIF servers. Furthermore, structure-based virtual screening was carried out against the predicted active site of the respective protein using the

  4. A Review: The Current In Vivo Models for the Discovery and Utility of New Anti-leishmanial Drugs Targeting Cutaneous Leishmaniasis.

    Directory of Open Access Journals (Sweden)

    Emily Rose Mears

    Full Text Available The current in vivo models for the utility and discovery of new potential anti-leishmanial drugs targeting Cutaneous Leishmaniasis (CL differ vastly in their immunological responses to the disease and clinical presentation of symptoms. Animal models that show similarities to the human form of CL after infection with Leishmania should be more representative as to the effect of the parasite within a human. Thus, these models are used to evaluate the efficacy of new anti-leishmanial compounds before human clinical trials. Current animal models aim to investigate (i host-parasite interactions, (ii pathogenesis, (iii biochemical changes/pathways, (iv in vivo maintenance of parasites, and (v clinical evaluation of drug candidates. This review focuses on the trends of infection observed between Leishmania parasites, the predictability of different strains, and the determination of parasite load. These factors were used to investigate the overall effectiveness of the current animal models. The main aim was to assess the efficacy and limitations of the various CL models and their potential for drug discovery and evaluation. In conclusion, we found that the following models are the most suitable for the assessment of anti-leishmanial drugs: L. major-C57BL/6 mice (or-vervet monkey, or-rhesus monkeys, L. tropica-CsS-16 mice, L. amazonensis-CBA mice, L. braziliensis-golden hamster (or-rhesus monkey. We also provide in-depth guidance for which models are not suitable for these investigations.

  5. A Review: The Current In Vivo Models for the Discovery and Utility of New Anti-leishmanial Drugs Targeting Cutaneous Leishmaniasis.

    Science.gov (United States)

    Mears, Emily Rose; Modabber, Farrokh; Don, Robert; Johnson, George E

    2015-01-01

    The current in vivo models for the utility and discovery of new potential anti-leishmanial drugs targeting Cutaneous Leishmaniasis (CL) differ vastly in their immunological responses to the disease and clinical presentation of symptoms. Animal models that show similarities to the human form of CL after infection with Leishmania should be more representative as to the effect of the parasite within a human. Thus, these models are used to evaluate the efficacy of new anti-leishmanial compounds before human clinical trials. Current animal models aim to investigate (i) host-parasite interactions, (ii) pathogenesis, (iii) biochemical changes/pathways, (iv) in vivo maintenance of parasites, and (v) clinical evaluation of drug candidates. This review focuses on the trends of infection observed between Leishmania parasites, the predictability of different strains, and the determination of parasite load. These factors were used to investigate the overall effectiveness of the current animal models. The main aim was to assess the efficacy and limitations of the various CL models and their potential for drug discovery and evaluation. In conclusion, we found that the following models are the most suitable for the assessment of anti-leishmanial drugs: L. major-C57BL/6 mice (or-vervet monkey, or-rhesus monkeys), L. tropica-CsS-16 mice, L. amazonensis-CBA mice, L. braziliensis-golden hamster (or-rhesus monkey). We also provide in-depth guidance for which models are not suitable for these investigations.

  6. Structure of pyrR (Rv1379) from Mycobacterium tuberculosis: A persistence gene and protein drug target

    Energy Technology Data Exchange (ETDEWEB)

    Kantardjieff, K A; Vasquez, C; Castro, P; Warfel, N M; Rho, B; Lekin, T; Kim, C; Segelke, B W; Terwilliger, T C; Rupp, B

    2004-09-24

    The 1.9 {angstrom} native structure of pyrimidine biosynthesis regulatory protein encoded by the Mycobacterium tuberculosis pyrR gene (Rv1379) is reported. Because pyrimidine biosynthesis is an essential step in the progression of TB, pyrR is an attractive antitubercular drug target. The Mycobacterium tuberculosis pyrR gene (Rv1379) encodes a protein that regulates expression of pyrimidine nucleotide biosynthesis (pyr) genes in a UMP-dependent manner. Because pyrimidine biosynthesis is an essential step in the progression of TB, the gene product pyrR is an attractive antitubercular drug target. We report the 1.9 {angstrom} native structure of Mtb pyrR determined by the TB Structural Genomics Consortium facilities (PDB entry 1W30) in trigonal space group P3{sub 1}21, with cell dimensions at 120K of a = 66.64 {angstrom}, c = 154.72 {angstrom}, and two molecules in the asymmetric unit. The 3D structure and residual uracil phosphoribosyltransferase activity point to a common PRTase ancestor for pyrR. However, while PRPP and UMP binding sites have been retained in Mtb pyrR, a novel dimer interaction among subunits creates a deep, positively charged cleft capable of binding pyr mRNA. In silico screening of pyrimidine nucleoside analogs has revealed a number of potential leads compounds that, if bound to Mtb pyrR, could facilitate transcriptional attenuation, particularly cyclopentenyl nucleosides.

  7. Identification and Validation of Small-Gatekeeper Kinases as Drug Targets in Giardia lamblia

    OpenAIRE

    Hennessey, Kelly M.; Smith, Tess R.; Xu, Jennifer W.; Germain C. M. Alas; Ojo, Kayode K.; Merritt, Ethan A.; Paredez, Alexander R.

    2016-01-01

    Giardiasis is widely acknowledged to be a neglected disease in need of new therapeutics to address toxicity and resistance issues associated with the limited available treatment options. We examined seven protein kinases in the Giardia lamblia genome that are predicted to share an unusual structural feature in their active site. This feature, an expanded active site pocket resulting from an atypically small gatekeeper residue, confers sensitivity to “bumped” kinase inhibitors (BKIs), a class ...

  8. MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development.

    Science.gov (United States)

    Harati, Sahar; Cooper, Lee A D; Moran, Josue D; Giuste, Felipe O; Du, Yuhong; Ivanov, Andrei A; Johns, Margaret A; Khuri, Fadlo R; Fu, Haian; Moreno, Carlos S

    2017-01-01

    Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology. Here we introduce a computational method (MEDICI) to predict PPI essentiality by combining gene knockdown studies with network models of protein interaction pathways in an analytic framework. Our method uses network topology to model how gene silencing can disrupt PPIs, relating the unknown essentialities of individual PPIs to experimentally observed protein essentialities. This model is then deconvolved to recover the unknown essentialities of individual PPIs. We demonstrate the validity of our approach via prediction of sensitivities to compounds based on PPI essentiality and differences in essentiality based on genetic mutations. We further show that lung cancer patients have improved overall survival when specific PPIs are no longer present, suggesting that these PPIs may be potentially new targets for therapeutic development. Software is freely available at https://github.com/cooperlab/MEDICI. Datasets are available at https://ctd2.nci.nih.gov/dataPortal.

  9. Comparing Predictions and Outcomes : Theory and Application to Income Changes

    NARCIS (Netherlands)

    Das, J.W.M.; Dominitz, J.; van Soest, A.H.O.

    1997-01-01

    Household surveys often elicit respondents' intentions or predictions of future outcomes. The survey questions may ask respondents to choose among a selection of (ordered) response categories. If panel data or repeated cross-sections are available, predictions may be compared with realized outcomes.

  10. Application of wavelet transform to recursive prediction of vibration signals

    Institute of Scientific and Technical Information of China (English)

    SUN Zhen-ming; WANG Ri-xin; JIANG Xing-wei; XU Min-qiang

    2005-01-01

    This paper investigates the characteristics of a non-stationary time series, which exists in mechanical fault diagnosis. Combining the characteristics with predictive efficiency, the limitation of the ARIMA model prediction method is analyzed. This model often is applied in the prediction of a non-stationary times series in present. Thus, a wavelet prediction method is introduced to solve non-stationary problems. The Mallat method,often used in signal processing, results form the decimation or the retention of one out of every two samples. Its advantage is that just enough information is kept to allow the exact reconstruction of the input series, but the disadvantage is a time-varying series on line cannot be pursued. Therefore, the authors present another method,à Trous method, which can be applied for recursive prediction in real-time sampling procedure.

  11. Development and application of chronic disease risk prediction models.

    Science.gov (United States)

    Oh, Sun Min; Stefani, Katherine M; Kim, Hyeon Chang

    2014-07-01

    Currently, non-communicable chronic diseases are a major cause of morbidity and mortality worldwide, and a large proportion of chronic diseases are preventable through risk factor management. However, the prevention efficacy at the individual level is not yet satisfactory. Chronic disease prediction models have been developed to assist physicians and individuals in clinical decision-making. A chronic disease prediction model assesses multiple risk factors together and estimates an absolute disease risk for the individual. Accurate prediction of an individual's future risk for a certain disease enables the comparison of benefits and risks of treatment, the costs of alternative prevention strategies, and selection of the most efficient strategy for the individual. A large number of chronic disease prediction models, especially targeting cardiovascular diseases and cancers, have been suggested, and some of them have been adopted in the clinical practice guidelines and recommendations of many countries. Although few chronic disease prediction tools have been suggested in the Korean population, their clinical utility is not as high as expected. This article reviews methodologies that are commonly used for developing and evaluating a chronic disease prediction model and discusses the current status of chronic disease prediction in Korea.

  12. The application of modeling and prediction with MRA wavelet network

    Institute of Scientific and Technical Information of China (English)

    LU Shu-ping; YANG Xue-jing; ZHAO Xi-ren

    2004-01-01

    As there are lots of non-linear systems in the real engineering, it is very important to do more researches on the modeling and prediction of non-linear systems. Based on the multi-resolution analysis (MRA) of wavelet theory, this paper combined the wavelet theory with neural network and established a MRA wavelet network with the scaling function and wavelet function as its neurons. From the analysis in the frequency domain, the results indicated that MRA wavelet network was better than other wavelet networks in the ability of approaching to the signals. An essential research was carried out on modeling and prediction with MRA wavelet network in the non-linear system. Using the lengthwise sway data received from the experiment of ship model, a model of offline prediction was established and was applied to the short-time prediction of ship motion. The simulation results indicated that the forecasting model improved the prediction precision effectively, lengthened the forecasting time and had a better prediction results than that of AR linear model.The research indicates that it is feasible to use the MRA wavelet network in the short -time prediction of ship motion.

  13. The application of neural networks to comprehensive prediction by seismology prediction method

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    BP neural networks is used to mid-term earthquake prediction in this paper. Some usual prediction parameters of seismology are used as the import units of neural networks. And the export units of neural networks is called as the character parameter W0 describing enhancement of seismicity. We applied this method to space scanning of North China. The result shows that the mid-term anomalous zone of W0-value usually appeared obviously around the future epicenter 1~3 years before earthquake. It is effective to mid-term prediction.

  14. Drug target identification using network analysis: Taking active components in Sini decoction as an example

    Science.gov (United States)

    Chen, Si; Jiang, Hailong; Cao, Yan; Wang, Yun; Hu, Ziheng; Zhu, Zhenyu; Chai, Yifeng

    2016-04-01

    Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

  15. Drug target identification using network analysis: Taking active components in Sini decoction as an example.

    Science.gov (United States)

    Chen, Si; Jiang, Hailong; Cao, Yan; Wang, Yun; Hu, Ziheng; Zhu, Zhenyu; Chai, Yifeng

    2016-04-20

    Identifying the molecular targets for the beneficial effects of active small-molecule compounds simultaneously is an important and currently unmet challenge. In this study, we firstly proposed network analysis by integrating data from network pharmacology and metabolomics to identify targets of active components in sini decoction (SND) simultaneously against heart failure. To begin with, 48 potential active components in SND against heart failure were predicted by serum pharmacochemistry, text mining and similarity match. Then, we employed network pharmacology including text mining and molecular docking to identify the potential targets of these components. The key enriched processes, pathways and related diseases of these target proteins were analyzed by STRING database. At last, network analysis was conducted to identify most possible targets of components in SND. Among the 25 targets predicted by network analysis, tumor necrosis factor α (TNF-α) was firstly experimentally validated in molecular and cellular level. Results indicated that hypaconitine, mesaconitine, higenamine and quercetin in SND can directly bind to TNF-α, reduce the TNF-α-mediated cytotoxicity on L929 cells and exert anti-myocardial cell apoptosis effects. We envisage that network analysis will also be useful in target identification of a bioactive compound.

  16. On Predictive Understanding of Extreme Events: Pattern Recognition Approach; Prediction Algorithms; Applications to Disaster Preparedness

    Science.gov (United States)

    Keilis-Borok, V. I.; Soloviev, A.; Gabrielov, A.

    2011-12-01

    We describe a uniform approach to predicting different extreme events, also known as critical phenomena, disasters, or crises. The following types of such events are considered: strong earthquakes; economic recessions (their onset and termination); surges of unemployment; surges of crime; and electoral changes of the governing party. A uniform approach is possible due to the common feature of these events: each of them is generated by a certain hierarchical dissipative complex system. After a coarse-graining, such systems exhibit regular behavior patterns; we look among them for "premonitory patterns" that signal the approach of an extreme event. We introduce methodology, based on the optimal control theory, assisting disaster management in choosing optimal set of disaster preparedness measures undertaken in response to a prediction. Predictions with their currently realistic (limited) accuracy do allow preventing a considerable part of the damage by a hierarchy of preparedness measures. Accuracy of prediction should be known, but not necessarily high.

  17. Application of an immune algorithm to settlement prediction

    Institute of Scientific and Technical Information of China (English)

    Jia GUO; Jun-jie ZHENG; Yong LIU

    2009-01-01

    The settlement curve of the foundation endured the ramp load is an S-type curve, which is usually simulated via Poisson curve. Aimed at the difficulty of preferences in Poisson curve, an immune algorithm (IA) is used. IA is able to obtain a multiple quasi-optimum solution while maintaining the population diversity. In this paper, IA is used in an attempt to obtain accurate settlement prediction. The predicted settlements obtained by IA are compared with those predicted by the least squares fitting method (LSM), the Asaoka method and the genetic algorithm (GA). The results show that IA is a useful technique for predicting the settlement of foundations with an acceptable degree of accuracy and has much better performance than GA and the Asaoka methods.

  18. APPLICATION OF MACHINE LEARNING TO THE PREDICTION OF VEGETATION HEALTH

    Directory of Open Access Journals (Sweden)

    E. Burchfield

    2016-06-01

    Full Text Available This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI. The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern.

  19. Application of Machine Learning to the Prediction of Vegetation Health

    Science.gov (United States)

    Burchfield, Emily; Nay, John J.; Gilligan, Jonathan

    2016-06-01

    This project applies machine learning techniques to remotely sensed imagery to train and validate predictive models of vegetation health in Bangladesh and Sri Lanka. For both locations, we downloaded and processed eleven years of imagery from multiple MODIS datasets which were combined and transformed into two-dimensional matrices. We applied a gradient boosted machines model to the lagged dataset values to forecast future values of the Enhanced Vegetation Index (EVI). The predictive power of raw spectral data MODIS products were compared across time periods and land use categories. Our models have significantly more predictive power on held-out datasets than a baseline. Though the tool was built to increase capacity to monitor vegetation health in data scarce regions like South Asia, users may include ancillary spatiotemporal datasets relevant to their region of interest to increase predictive power and to facilitate interpretation of model results. The tool can automatically update predictions as new MODIS data is made available by NASA. The tool is particularly well-suited for decision makers interested in understanding and predicting vegetation health dynamics in countries in which environmental data is scarce and cloud cover is a significant concern.

  20. SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets.

    Science.gov (United States)

    Guo, Jing; Liu, Hui; Zheng, Jie

    2016-01-04

    Synthetic lethality (SL) is a type of genetic interaction between two genes such that simultaneous perturbations of the two genes result in cell death or a dramatic decrease of cell viability, while a perturbation of either gene alone is not lethal. SL reflects the biologically endogenous difference between cancer cells and normal cells, and thus the inhibition of SL partners of genes with cancer-specific mutations could selectively kill cancer cells but spare normal cells. Therefore, SL is emerging as a promising anticancer strategy that could potentially overcome the drawbacks of traditional chemotherapies by reducing severe side effects. Researchers have developed experimental technologies and computational prediction methods to identify SL gene pairs on human and a few model species. However, there has not been a comprehensive database dedicated to collecting SL pairs and related knowledge. In this paper, we propose a comprehensive database, SynLethDB (http://histone.sce.ntu.edu.sg/SynLethDB/), which contains SL pairs collected from biochemical assays, other related databases, computational predictions and text mining results on human and four model species, i.e. mouse, fruit fly, worm and yeast. For each SL pair, a confidence score was calculated by integrating individual scores derived from different evidence sources. We also developed a statistical analysis module to estimate the druggability and sensitivity of cancer cells upon drug treatments targeting human SL partners, based on large-scale genomic data, gene expression profiles and drug sensitivity profiles on more than 1000 cancer cell lines. To help users access and mine the wealth of the data, we developed other practical functionalities, such as search and filtering, orthology search, gene set enrichment analysis. Furthermore, a user-friendly web interface has been implemented to facilitate data analysis and interpretation. With the integrated data sets and analytics functionalities, SynLethDB would

  1. Computational 3D structures of drug-targeting proteins in the 2009-H1N1 influenza A virus

    Science.gov (United States)

    Du, Qi-Shi; Wang, Shu-Qing; Huang, Ri-Bo; Chou, Kuo-Chen

    2010-01-01

    The neuraminidase (NA) and M2 proton channel of influenza virus are the drug-targeting proteins, based on which several drugs were developed. However these once powerful drugs encountered drug-resistant problem to the H5N1 and H1N1 flu. To address this problem, the computational 3D structures of NA and M2 proteins of 2009-H1N1 influenza virus were built using the molecular modeling technique and computational chemistry method. Based on the models the structure features of NA and M2 proteins were analyzed, the docking structures of drug-protein complexes were computed, and the residue mutations were annotated. The results may help to solve the drug-resistant problem and stimulate designing more effective drugs against 2009-H1N1 influenza pandemic.

  2. Integrated gene co-expression network analysis in the growth phase of Mycobacterium tuberculosis reveals new potential drug targets.

    Science.gov (United States)

    Puniya, Bhanwar Lal; Kulshreshtha, Deepika; Verma, Srikant Prasad; Kumar, Sanjiv; Ramachandran, Srinivasan

    2013-11-01

    We have carried out weighted gene co-expression network analysis of Mycobacterium tuberculosis to gain insights into gene expression architecture during log phase growth. The differentially expressed genes between at least one pair of 11 different M. tuberculosis strains as source of biological variability were used for co-expression network analysis. This data included genes with highest coefficient of variation in expression. Five distinct modules were identified using topological overlap based clustering. All the modules together showed significant enrichment in biological processes: fatty acid biosynthesis, cell membrane, intracellular membrane bound organelle, DNA replication, Quinone biosynthesis, cell shape and peptidoglycan biosynthesis, ribosome and structural constituents of ribosome and transposition. We then extracted the co-expressed connections which were supported either by transcriptional regulatory network or STRING database or high edge weight of topological overlap. The genes trpC, nadC, pitA, Rv3404c, atpA, pknA, Rv0996, purB, Rv2106 and Rv0796 emerged as top hub genes. After overlaying this network on the iNJ661 metabolic network, the reactions catalyzed by 15 highly connected metabolic genes were knocked down in silico and evaluated by Flux Balance Analysis. The results showed that in 12 out of 15 cases, in 11 more than 50% of reactions catalyzed by genes connected through co-expressed connections also had altered fluxes. The modules 'Turquoise', 'Blue' and 'Red' also showed enrichment in essential genes. We could map 152 of the previously known or proposed drug targets in these modules and identified 15 new potential drug targets based on their high degree of co-expressed connections and strong correlation with module eigengenes.

  3. Application of support vector machine to synthetic earthquake prediction

    Institute of Scientific and Technical Information of China (English)

    Chun Jiang; Xueli Wei; Xiaofeng Cui; Dexiang You

    2009-01-01

    This paper introduces the method of support vector machine (SVM) into the field of synthetic earthquake prediction, which is a non-linear and complex seismogenic system. As an example, we apply this method to predict the largest annual magnitude for the North China area (30°E-42°E, 108°N-125°N) and the capital region (38°E-41.5°E, 114°N-120°N) on the basis of seismicity parameters and observed precursory data. The corresponding prediction rates for the North China area and the capital region are 64.1% and 75%, respectively, which shows that the method is feasible.

  4. Application of Nonlinear Predictive Control Based on RBF Network Predictive Model in MCFC Plant

    Institute of Scientific and Technical Information of China (English)

    CHEN Yue-hua; CAO Guang-yi; ZHU Xin-jian

    2007-01-01

    This paper described a nonlinear model predictive controller for regulating a molten carbonate fuel cell (MCFC). A detailed mechanism model of output voltage of a MCFC was presented at first. However, this model was too complicated to be used in a control system. Consequently, an off line radial basis function (RBF) network was introduced to build a nonlinear predictive model. And then, the optimal control sequences were obtained by applying golden mean method. The models and controller have been realized in the MATLAB environment. Simulation results indicate the proposed algorithm exhibits satisfying control effect even when the current densities vary largely.

  5. Application of neural networks for the prediction of multidirectional magnetostriction

    CERN Document Server

    Baumgartinger, N; Pfützner, H; Krismanic, G

    2000-01-01

    This paper describes attempts to use artificial neural networks (ANNs) for the prediction of magnetostriction (MS) characteristics of transformer core materials. In this first approach, the ANNs were trained with data from a rotational single-sheet tester to predict MS in rolling direction (r.d.) as a function of material grade, amplitude and shape of multidirectional magnetisation as well as the level of additional mechanical stress. It is shown that ANNs are able to forecast the corresponding relative MS changes in an approximate way.

  6. Application of ACO algorithm in protein structure prediction

    Institute of Scientific and Technical Information of China (English)

    TANG Hao-xuan; QU Yi

    2009-01-01

    The hydrophobic-polar (HP) lattice model is an important simplified model for studying protein folding. In this paper, we present an improved AGO algorithm for the protein structure prediction. In the algorithm, the "lone" ethod is applied to deal with the infeasible structures, and the "oint mutation and reconstruction"ethod is applied in local search phase. The empirical results show that the presented method is feasible and effective to solve the problem of protein structure prediction, and notable improvements in CPU time are obtained.

  7. Application of Grey System Theory to tree growth prediction

    Institute of Scientific and Technical Information of China (English)

    王晶; 侯月松; 李伟林; 成文惠

    2000-01-01

    Based on Grey System theory, tree growth prediction models are developed by using 202 temporary plots and 206 stem analysis trees of Dahurian larch (Larix gemlinii Rupr.) in 10 forestry bureaus of Yakeshi Forestry Administrative Bureau in Daxing'an Mountains of the Inner Mongolia Autonomous Region. By residual and posterior tests, their precisions are qualified. With several data, tree growth can be predicted using Grey System models. For DBH and volume, the fitting results of Grey System models are better than that of statistical models.

  8. Identification and Validation of Small-Gatekeeper Kinases as Drug Targets in Giardia lamblia

    Science.gov (United States)

    Hennessey, Kelly M.; Smith, Tess R.; Xu, Jennifer W.; Alas, Germain C. M.; Ojo, Kayode K.; Merritt, Ethan A.

    2016-01-01

    Giardiasis is widely acknowledged to be a neglected disease in need of new therapeutics to address toxicity and resistance issues associated with the limited available treatment options. We examined seven protein kinases in the Giardia lamblia genome that are predicted to share an unusual structural feature in their active site. This feature, an expanded active site pocket resulting from an atypically small gatekeeper residue, confers sensitivity to “bumped” kinase inhibitors (BKIs), a class of compounds that has previously shown good pharmacological properties and minimal toxicity. An initial phenotypic screen for biological activity using a subset of an in-house BKI library found that 5 of the 36 compounds tested reduced trophozoite growth by at least 50% at a concentration of 5 μM. The cellular localization and the relative expression levels of the seven protein kinases of interest were determined after endogenously tagging the kinases. Essentiality of these kinases for parasite growth and infectivity were evaluated genetically using morpholino knockdown of protein expression to establish those that could be attractive targets for drug design. Two of the kinases were critical for trophozoite growth and attachment. Therefore, recombinant enzymes were expressed, purified and screened against a BKI library of >400 compounds in thermal stability assays in order to identify high affinity compounds. Compounds with substantial thermal stabilization effects on recombinant protein were shown to have good inhibition of cell growth in wild-type G. lamblia and metronidazole-resistant strains of G. lamblia. Our data suggest that BKIs are a promising starting point for the development of new anti-giardiasis therapeutics that do not overlap in mechanism with current drugs. PMID:27806042

  9. Identification and Validation of Small-Gatekeeper Kinases as Drug Targets in Giardia lamblia.

    Science.gov (United States)

    Hennessey, Kelly M; Smith, Tess R; Xu, Jennifer W; Alas, Germain C M; Ojo, Kayode K; Merritt, Ethan A; Paredez, Alexander R

    2016-11-01

    Giardiasis is widely acknowledged to be a neglected disease in need of new therapeutics to address toxicity and resistance issues associated with the limited available treatment options. We examined seven protein kinases in the Giardia lamblia genome that are predicted to share an unusual structural feature in their active site. This feature, an expanded active site pocket resulting from an atypically small gatekeeper residue, confers sensitivity to "bumped" kinase inhibitors (BKIs), a class of compounds that has previously shown good pharmacological properties and minimal toxicity. An initial phenotypic screen for biological activity using a subset of an in-house BKI library found that 5 of the 36 compounds tested reduced trophozoite growth by at least 50% at a concentration of 5 μM. The cellular localization and the relative expression levels of the seven protein kinases of interest were determined after endogenously tagging the kinases. Essentiality of these kinases for parasite growth and infectivity were evaluated genetically using morpholino knockdown of protein expression to establish those that could be attractive targets for drug design. Two of the kinases were critical for trophozoite growth and attachment. Therefore, recombinant enzymes were expressed, purified and screened against a BKI library of >400 compounds in thermal stability assays in order to identify high affinity compounds. Compounds with substantial thermal stabilization effects on recombinant protein were shown to have good inhibition of cell growth in wild-type G. lamblia and metronidazole-resistant strains of G. lamblia. Our data suggest that BKIs are a promising starting point for the development of new anti-giardiasis therapeutics that do not overlap in mechanism with current drugs.

  10. Neospora caninum calcium-dependent protein kinase 1 is an effective drug target for neosporosis therapy.

    Directory of Open Access Journals (Sweden)

    Kayode K Ojo

    Full Text Available Despite the enormous economic importance of Neospora caninum related veterinary diseases, the number of effective therapeutic agents is relatively small. Development of new therapeutic strategies to combat the economic impact of neosporosis remains an important scientific endeavor. This study demonstrates molecular, structural and phenotypic evidence that N. caninum calcium-dependent protein kinase 1 (NcCDPK1 is a promising molecular target for neosporosis drug development. Recombinant NcCDPK1 was expressed, purified and screened against a select group of bumped kinase inhibitors (BKIs previously shown to have low IC50s against Toxoplasma gondii CDPK1 and T. gondii tachyzoites. NcCDPK1 was inhibited by low concentrations of BKIs. The three-dimensional structure of NcCDPK1 in complex with BKIs was studied crystallographically. The BKI-NcCDPK1 structures demonstrated the structural basis for potency and selectivity. Calcium-dependent conformational changes in solution as characterized by small-angle X-ray scattering are consistent with previous structures in low Calcium-state but different in the Calcium-bound active state than predicted by X-ray crystallography. BKIs effectively inhibited N. caninum tachyzoite proliferation in vitro. Electron microscopic analysis of N. caninum cells revealed ultra-structural changes in the presence of BKI compound 1294. BKI compound 1294 interfered with an early step in Neospora tachyzoite host cell invasion and egress. Prolonged incubation in the presence of 1294 interfered produced observable interference with viability and replication. Oral dosing of BKI compound 1294 at 50 mg/kg for 5 days in established murine neosporosis resulted in a 10-fold reduced cerebral parasite burden compared to untreated control. Further experiments are needed to determine the PK, optimal dosage, and duration for effective treatment in cattle and dogs, but these data demonstrate proof-of-concept for BKIs, and 1294 specifically, for

  11. Expression of drug targets in patients treated with sorafenib, carboplatin and paclitaxel.

    Directory of Open Access Journals (Sweden)

    Lucia B Jilaveanu

    Full Text Available Sorafenib, a multitarget kinase inhibitor, targets members of the mitogen-activated protein kinase (MAPK pathway and VEGFR kinases. Here we assessed the association between expression of sorafenib targets and biomarkers of taxane sensitivity and response to therapy in pre-treatment tumors from patients enrolled in ECOG 2603, a phase III comparing sorafenib, carboplatin and paclitaxel (SCP to carboplatin, paclitaxel and placebo (CP.Using a method of automated quantitative analysis (AQUA of in situ protein expression, we quantified expression of VEGF-R2, VEGF-R1, VEGF-R3, FGF-R1, PDGF-Rβ, c-Kit, B-Raf, C-Raf, MEK1, ERK1/2, STMN1, MAP2, EB1 and Bcl-2 in pretreatment specimens from 263 patients.An association was found between high FGF-R1 and VEGF-R1 and increased progression-free survival (PFS and overall survival (OS in our combined cohort (SCP and CP arms. Expression of FGF-R1 and VEGF-R1 was higher in patients who responded to therapy ((CR+PR vs. (SD+PD+ un-evaluable.In light of the absence of treatment effect associated with sorafenib, the association found between FGF-R1 and VEGF-R1 expression and OS, PFS and response might reflect a predictive biomarker signature for carboplatin/paclitaxel-based therapy. Seeing that carboplatin and pacitaxel are now widely used for this disease, corroboration in another cohort might enable us to improve the therapeutic ratio of this regimen.

  12. The Application of Neural Network in Lifetime Prediction of Concrete

    Institute of Scientific and Technical Information of China (English)

    2002-01-01

    There are many difficulties in concrete endurance prediction, especially in accurate predicting service life of concrete engineering. It is determined by the concentration of SO2-4/ Mg2+/Cl-/Ca2+, reaction areas, the cycles of freezing and dissolving, alternatives of dry and wet state, the kind of cement, etc.. In general, because of complexity itself and cognitive limitation, endurance prediction under sulphate erosion is still illegible and uncertain,so this paper adopts neural network technology to research this problem. Through analyzing, the paper sets up a 3-levels neural network and a 4-levels neural network to predict the endurance under sulphate erosion. The 3-levels neural network includes 13 inputting nodes, 7 outputting nodes and 34 hidden nodes. The 4-levels neural network also has 13 inputting nodes and 7 outputting nodes with two hidden levels which has 7 nodes and 8 nodes separately. In the end the paper give a example with laboratorial data and discussion the result and deviation. The paper shows that deviation results from some faults of training specimens:such as few training specimens and few distinctions among training specimens. So the more specimens should be collected to reduce data redundancy and improve the reliability of network analysis conclusion.

  13. HOMOLOGY MODELLING AND BINDING SITE IDENTIFICATION OF 1DEOXY D-XYLULOSE 5 PHOSPHATE REDUCTOISOMERASE OF PLASMODIUM FALCIPARUM: NEW DRUG TARGET FOR PLSMODIUM FALCIPARUM

    Directory of Open Access Journals (Sweden)

    JYOTSNA CHOUBEY

    2010-08-01

    Full Text Available Malaria is major global health problem. Malaria parasite had developed resistance to the drug being used till date. It implies the development of new effective drug with different mode of action. Apicoplast in malaria and related parasite offer various new target for drug therapy[1]. Apicoplast contains various metabolic pathways that differ from those of host thereby presenting ideal strategies for drug therapy. Plasmodium falciparum 1deoxy- Dxylulose 5- phosphate reductoisomerase (pfDXR is a potential target for antimalarial chemotherapy. The three dimentional model (3D of this enzyme was determined by means of homology modeling through multiplealignment followed by intensive optimization and validation. The comparative modeling of pfDXPR was performed by using comparative modeling program MODELLER, Swiss Model, 3Djigsaw, and Geno3D.The modelling of the three dimensional structure of pfDXPR shows that models generated by Modeller were more acceptable in comparison to that by 3Djigsaw, Geno3D and Swiss Model. The obtained models were verified with the structure validation programs like, PROCHECK & Swiss pdb viewer was used for energy refinement of the model. SelfOptimized Prediction Method with Alignment (SOPMA is employed for calculating the secondary structural features of pfDXR protein sequences considered for this study. The secondary structure indicates whether a given amino acid lies in a helix, strand or coil. The results revealed that alpha helix dominated among secondary structure elements followed by random coils, extended strand and beta turns for all sequences. Active site determination through CASTp suggests that this protein can acts as potential drug target.

  14. Downburst Prediction Applications of GOES over the Western United States

    CERN Document Server

    Pryor, Kenneth L

    2016-01-01

    Over the western United States, the hazards posed to aviation operations by convective storm-generated downbursts have been extensively documented. Other significant hazards posed by convective downbursts over the intermountain western U.S. include the rapid intensification and propagation of wildfires and the sudden generation of visibility-reducing dust storms (haboobs). The existing suite of GOES downburst prediction algorithms employs the GOES sounder to calculate potential of occurrence based on conceptual models of favorable environmental thermodynamic profiles for downburst generation. Previous research has demonstrated the effectiveness of the Dry Microburst Index (DMI) as a prediction tool for convectively generated high winds. A more recently-developed diagnostic nowcasting product, the Microburst Windspeed Potential Index (MWPI) is designed to diagnose attributes of a favorable downburst environment: 1) the presence of convective available potential energy (CAPE), and 2) the presence of a deep surf...

  15. Application of Predictive Control in District Heating Systems

    DEFF Research Database (Denmark)

    Palsson, Olafur Petur; Madsen, Henrik; Søgaard, Henning Tangen

    1993-01-01

    In district heating systems, and in particular if the heat production cakes place at a combined heat and power (CHP) plant, a reasonable control strategy is to keep the supply temperature from the district heating plant as low as possible. However, the control is subject to some restrictions....... A district heating system is an example of a non-stationary system, and the model parameters have to be time varying. Hence, the classical predictive control theory has to be modified. Simulation experiments are performed in order to study the performance of modified predictive controllers. The systems ape......, for example, that the total heat requirement for all consumers is supplied at any time and each individual consumer is guaranteed some minimum supply temperature at any time. A lower supply temperature implies lower heat loss from the transport and the distribution network, and lower production costs...

  16. Multistep prediction of physiological tremor for surgical robotics applications.

    Science.gov (United States)

    Veluvolu, Kalyana C; Tatinati, Sivanagaraja; Hong, Sun-Mog; Ang, Wei Tech

    2013-11-01

    Accurate canceling of physiological tremor is extremely important in robotics-assisted surgical instruments/procedures. The performance of robotics-based hand-held surgical devices degrades in real time due to the presence of phase delay in sensors (hardware) and filtering (software) processes. Effective tremor compensation requires zero-phase lag in filtering process so that the filtered tremor signal can be used to regenerate an opposing motion in real time. Delay as small as 20 ms degrades the performance of human-machine interference. To overcome this phase delay, we employ multistep prediction in this paper. Combined with the existing tremor estimation methods, the procedure improves the overall accuracy by 60% for tremor estimation compared to single-step prediction methods in the presence of phase delay. Experimental results with developed methods for 1-DOF tremor estimation highlight the improvement.

  17. Application of Improved Grey Prediction Model to Petroleum Cost Forecasting

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The grey theory is a multidisciplinary and generic theory that deals with systems that lack adequate information and/or have only poor information. In this paper, an improved grey model using step function was proposed.Petroleum cost forecast of the Henan oil field was used as the case study to test the efficiency and accuracy of the proposed method. According to the experimental results, the proposed method obviously could improve the prediction accuracy of the original grey model.

  18. Application of Artificial Neural Networks for Predicting Generated Wind Power

    OpenAIRE

    Vijendra Singh

    2016-01-01

    This paper addresses design and development of an artificial neural network based system for prediction of wind energy produced by wind turbines. Now in the last decade, renewable energy emerged as an additional alternative source for electrical power generation. We need to assess wind power generation capacity by wind turbines because of its non-exhaustible nature. The power generation by electric wind turbines depends on the speed of wind, flow direction, fluctuations, density of air, gener...

  19. A Study on Predictive Analytics Application to Ship Machinery Maintenance

    Science.gov (United States)

    2013-09-01

    VBA , and the codes can be found in Appendix. The baseline model is tested with data from Main Engine Cylinder 3. In this study, 70% of the data are... modeling of failures. There is a threshold limit of 520 degree Celsius for the EGT prior to the need for human intervention. With this knowledge, the use...of time series forecasting technique, to predict the crossing over of threshold, is appropriate to model the EGT as a function of its operating

  20. Application of Neural Network for Concrete Carbonation Depth Prediction

    OpenAIRE

    Luo, Daming; Niu, Ditao; Dong, Zhenping

    2014-01-01

    Concrete carbonation is one of the most significant causes of deterioration of reinforced concrete structures in atmospheric environment. However, current models based on the laboratory tests cannot predict carbonation depth accurately. In this paper, the BP neural network is optimized by the particle swarm optimization (PSO) algorithm to establish the model of the length of the partial carbonation zone for concrete. After simulation training, the improved model is applied to a concrete bridg...

  1. Economic decision making and the application of nonparametric prediction models

    Science.gov (United States)

    Attanasi, E.D.; Coburn, T.C.; Freeman, P.A.

    2008-01-01

    Sustained increases in energy prices have focused attention on gas resources in low-permeability shale or in coals that were previously considered economically marginal. Daily well deliverability is often relatively small, although the estimates of the total volumes of recoverable resources in these settings are often large. Planning and development decisions for extraction of such resources must be areawide because profitable extraction requires optimization of scale economies to minimize costs and reduce risk. For an individual firm, the decision to enter such plays depends on reconnaissance-level estimates of regional recoverable resources and on cost estimates to develop untested areas. This paper shows how simple nonparametric local regression models, used to predict technically recoverable resources at untested sites, can be combined with economic models to compute regional-scale cost functions. The context of the worked example is the Devonian Antrim-shale gas play in the Michigan basin. One finding relates to selection of the resource prediction model to be used with economic models. Models chosen because they can best predict aggregate volume over larger areas (many hundreds of sites) smooth out granularity in the distribution of predicted volumes at individual sites. This loss of detail affects the representation of economic cost functions and may affect economic decisions. Second, because some analysts consider unconventional resources to be ubiquitous, the selection and order of specific drilling sites may, in practice, be determined arbitrarily by extraneous factors. The analysis shows a 15-20% gain in gas volume when these simple models are applied to order drilling prospects strategically rather than to choose drilling locations randomly. Copyright ?? 2008 Society of Petroleum Engineers.

  2. RFI modeling and prediction approach for SATOP applications: RFI prediction models

    Science.gov (United States)

    Nguyen, Tien M.; Tran, Hien T.; Wang, Zhonghai; Coons, Amanda; Nguyen, Charles C.; Lane, Steven A.; Pham, Khanh D.; Chen, Genshe; Wang, Gang

    2016-05-01

    This paper describes a technical approach for the development of RFI prediction models using carrier synchronization loop when calculating Bit or Carrier SNR degradation due to interferences for (i) detecting narrow-band and wideband RFI signals, and (ii) estimating and predicting the behavior of the RFI signals. The paper presents analytical and simulation models and provides both analytical and simulation results on the performance of USB (Unified S-Band) waveforms in the presence of narrow-band and wideband RFI signals. The models presented in this paper will allow the future USB command systems to detect the RFI presence, estimate the RFI characteristics and predict the RFI behavior in real-time for accurate assessment of the impacts of RFI on the command Bit Error Rate (BER) performance. The command BER degradation model presented in this paper also allows the ground system operator to estimate the optimum transmitted SNR to maintain a required command BER level in the presence of both friendly and un-friendly RFI sources.

  3. Application of a predictive Bayesian model to environmental accounting.

    Science.gov (United States)

    Anex, R P; Englehardt, J D

    2001-03-30

    Environmental accounting techniques are intended to capture important environmental costs and benefits that are often overlooked in standard accounting practices. Environmental accounting methods themselves often ignore or inadequately represent large but highly uncertain environmental costs and costs conditioned by specific prior events. Use of a predictive Bayesian model is demonstrated for the assessment of such highly uncertain environmental and contingent costs. The predictive Bayesian approach presented generates probability distributions for the quantity of interest (rather than parameters thereof). A spreadsheet implementation of a previously proposed predictive Bayesian model, extended to represent contingent costs, is described and used to evaluate whether a firm should undertake an accelerated phase-out of its PCB containing transformers. Variability and uncertainty (due to lack of information) in transformer accident frequency and severity are assessed simultaneously using a combination of historical accident data, engineering model-based cost estimates, and subjective judgement. Model results are compared using several different risk measures. Use of the model for incorporation of environmental risk management into a company's overall risk management strategy is discussed.

  4. Application of Artificial Neural Networks for Predicting Generated Wind Power

    Directory of Open Access Journals (Sweden)

    Vijendra Singh

    2016-03-01

    Full Text Available This paper addresses design and development of an artificial neural network based system for prediction of wind energy produced by wind turbines. Now in the last decade, renewable energy emerged as an additional alternative source for electrical power generation. We need to assess wind power generation capacity by wind turbines because of its non-exhaustible nature. The power generation by electric wind turbines depends on the speed of wind, flow direction, fluctuations, density of air, generator hours, seasons of an area, and wind turbine position. During a particular season, wind power generation access can be increased. In such a case, wind energy generation prediction is crucial for transmission of generated wind energy to a power grid system. It is advisable for the wind power generation industry to predict wind power capacity to diagnose it. The present paper proposes an effort to apply artificial neural network technique for measurement of the wind energy generation capacity by wind farms in Harshnath, Sikar, Rajasthan, India.

  5. Cell-Based Selection Expands the Utility of DNA-Encoded Small-Molecule Library Technology to Cell Surface Drug Targets: Identification of Novel Antagonists of the NK3 Tachykinin Receptor.

    Science.gov (United States)

    Wu, Zining; Graybill, Todd L; Zeng, Xin; Platchek, Michael; Zhang, Jean; Bodmer, Vera Q; Wisnoski, David D; Deng, Jianghe; Coppo, Frank T; Yao, Gang; Tamburino, Alex; Scavello, Genaro; Franklin, G Joseph; Mataruse, Sibongile; Bedard, Katie L; Ding, Yun; Chai, Jing; Summerfield, Jennifer; Centrella, Paolo A; Messer, Jeffrey A; Pope, Andrew J; Israel, David I

    2015-12-14

    DNA-encoded small-molecule library technology has recently emerged as a new paradigm for identifying ligands against drug targets. To date, this technology has been used with soluble protein targets that are produced and used in a purified state. Here, we describe a cell-based method for identifying small-molecule ligands from DNA-encoded libraries against integral membrane protein targets. We use this method to identify novel, potent, and specific inhibitors of NK3, a member of the tachykinin family of G-protein coupled receptors (GPCRs). The method is simple and broadly applicable to other GPCRs and integral membrane proteins. We have extended the application of DNA-encoded library technology to membrane-associated targets and demonstrate the feasibility of selecting DNA-tagged, small-molecule ligands from complex combinatorial libraries against targets in a heterogeneous milieu, such as the surface of a cell.

  6. Fire occurrence prediction in the Mediterranean: Application to Southern France

    Science.gov (United States)

    Papakosta, Panagiota; Öster, Jan; Scherb, Anke; Straub, Daniel

    2013-04-01

    The areas that extend in the Mediterranean basin have a long fire history. The climatic conditions of wet winters and long hot drying summers support seasonal fire events, mainly ignited by humans. Extended land fragmentation hinders fire spread, but seasonal winds (e.g. Mistral in South France or Meltemia in Greece) can drive fire events to become uncontrollable fires with severe impacts to humans and the environment [1]. Prediction models in these areas should incorporate both natural and anthropogenic factors. Several indices have been developed worldwide to express fire weather conditions. The Canadian Fire Weather Index (FWI) is currently adapted by many countries in Europe due to the easily observable input weather parameters (temperature, wind speed, relative humidity, precipitation) and the easy-to-implement algorithms of the Canadian formulation describing fuel moisture relations [2],[3]. Human influence can be expressed directly by human presence (e.g. population density) or indirectly by proxy indicators (e.g. street density [4], land cover type). The random nature of fire occurrences and the uncertainties associated with the influencing factors motivate probabilistic prediction models. The aim of this study is to develop a prediction model of fire occurrence probability under natural and anthropogenic influence in Southern France and to compare it with earlier developed predictions in other Mediterranean areas [5]. Fire occurrence is modeled as a Poisson process. Two interpolation methods (Kriging and Inverse Distance Weighting) are used to interpolate daily weather observations from weather stations to a 1 km² spatial grid and their results are compared. Poisson regression estimates the parameters of the model and the resulting daily predictions are provided in terms of maps displaying fire occurrence rates. The model is applied to the regions Provence-Alpes-Côtes D'Azur und Languedoc-Roussillon in the South of France. Weather data are obtained from

  7. Life prediction of advanced materials for gas turbine application

    Energy Technology Data Exchange (ETDEWEB)

    Zamrik, S.Y.; Ray, A.; Koss, D.A. [Pennsylvania State Univ., University Park, PA (United States)

    1995-10-01

    Most of the studies on the low cycle fatigue life prediction have been reported under isothermal conditions where the deformation of the material is strain dependent. In the development of gas turbines, components such as blades and vanes are exposed to temperature variations in addition to strain cycling. As a result, the deformation process becomes temperature and strain dependent. Therefore, the life of the component becomes sensitive to temperature-strain cycling which produces a process known as {open_quotes}thermomechanical fatigue, or TMF{close_quotes}. The TMF fatigue failure phenomenon has been modeled using conventional fatigue life prediction methods, which are not sufficiently accurate to quantitatively establish an allowable design procedure. To add to the complexity of TMF life prediction, blade and vane substrates are normally coated with aluminide, overlay or thermal barrier type coatings (TBC) where the durability of the component is dominated by the coating/substrate constitutive response and by the fatigue behavior of the coating. A number of issues arise from TMF depending on the type of temperature/strain phase cycle: (1) time-dependent inelastic behavior can significantly affect the stress response. For example, creep relaxation during a tensile or compressive loading at elevated temperatures leads to a progressive increase in the mean stress level under cyclic loading. (2) the mismatch in elastic and thermal expansion properties between the coating and the substrate can lead to significant deviations in the coating stress levels due to changes in the elastic modulii. (3) the {open_quotes}dry{close_quotes} corrosion resistance coatings applied to the substrate may act as primary crack initiation sites. Crack initiation in the coating is a function of the coating composition, its mechanical properties, creep relaxation behavior, thermal strain range and the strain/temperature phase relationship.

  8. Applications of URANS on predicting unsteady turbulent separated flows

    Science.gov (United States)

    Xu, Jinglei; Ma, Huiyang

    2009-06-01

    Accurate prediction of unsteady separated turbulent flows remains one of the toughest tasks and a practical challenge for turbulence modeling. In this paper, a 2D flow past a circular cylinder at Reynolds number 3,900 is numerically investigated by using the technique of unsteady RANS (URANS). Some typical linear and nonlinear eddy viscosity turbulence models (LEVM and NLEVM) and a quadratic explicit algebraic stress model (EASM) are evaluated. Numerical results have shown that a high-performance cubic NLEVM, such as CLS, are superior to the others in simulating turbulent separated flows with unsteady vortex shedding.

  9. Applications of URANS on predicting unsteady turbulent separated flows

    Institute of Scientific and Technical Information of China (English)

    Jinglei Xu; Huiyang Ma

    2009-01-01

    Accurate prediction of unsteady separated turbu-lent flows remains one of the toughest tasks and a practi-cal challenge for turbulence modeling. In this paper, a 2D flow past a circular cylinder at Reynolds number 3,900 is numerically investigated by using the technique of unsteady RANS (URANS). Some typical linear and nonlinear eddy viscosity turbulence models (LEVM and NLEVM) and a quadratic explicit algebraic stress model (EASM) are evalu-ated. Numerical results have shown that a high-performance cubic NLEVM, such as CLS, are superior to the others in simulating turbulent separated flows with unsteady vortex shedding.

  10. Predicting Adolescent Sexual and Contraceptive Behavior: An Application and Test of the Fishbein Model.

    Science.gov (United States)

    Jorgensen, Stephen R.; Sonstegard, Janet S.

    1984-01-01

    Presents a test of the Fishbein model of behavior prediction applied to predict the pregnancy risk-taking behavior of adolescent females (N=244). Analyses of data showed that the Fishbein model of attitude-behavior consistency seems to be applicable to the fertility-related behavior of adolescent females. (LLL)

  11. Artificial Neural Networks: A New Approach for Predicting Application Behavior. AIR 2001 Annual Forum Paper.

    Science.gov (United States)

    Gonzalez, Julie M. Byers; DesJardins, Stephen L.

    This paper examines how predictive modeling can be used to study application behavior. A relatively new technique, artificial neural networks (ANNs), was applied to help predict which students were likely to get into a large Research I university. Data were obtained from a university in Iowa. Two cohorts were used, each containing approximately…

  12. The heme biosynthetic pathway of the obligate Wolbachia endosymbiont of Brugia malayi as a potential anti-filarial drug target.

    Directory of Open Access Journals (Sweden)

    Bo Wu

    Full Text Available BACKGROUND: Filarial parasites (e.g., Brugia malayi, Onchocerca volvulus, and Wuchereria bancrofti are causative agents of lymphatic filariasis and onchocerciasis, which are among the most disabling of neglected tropical diseases. There is an urgent need to develop macro-filaricidal drugs, as current anti-filarial chemotherapy (e.g., diethylcarbamazine [DEC], ivermectin and albendazole can interrupt transmission predominantly by killing microfilariae (mf larvae, but is less effective on adult worms, which can live for decades in the human host. All medically relevant human filarial parasites appear to contain an obligate endosymbiotic bacterium, Wolbachia. This alpha-proteobacterial mutualist has been recognized as a potential target for filarial nematode life cycle intervention, as antibiotic treatments of filarial worms harboring Wolbachia result in the loss of worm fertility and viability upon antibiotic treatments both in vitro and in vivo. Human trials have confirmed this approach, although the length of treatments, high doses required and medical counter-indications for young children and pregnant women warrant the identification of additional anti-Wolbachia drugs. METHODS AND FINDINGS: Genome sequence analysis indicated that enzymes involved in heme biosynthesis might constitute a potential anti-Wolbachia target set. We tested different heme biosynthetic pathway inhibitors in ex vivo B. malayi viability assays and report a specific effect of N-methyl mesoporphyrin (NMMP, which targets ferrochelatase (FC, the last step. Our phylogenetic analysis indicates evolutionarily significant divergence between Wolbachia heme genes and their human homologues. We therefore undertook the cloning, overexpression and analysis of several enzymes of this pathway alongside their human homologues, and prepared proteins for drug targeting. In vitro enzyme assays revealed a approximately 600-fold difference in drug sensitivities to succinyl acetone (SA between

  13. Evaluation of a convective downburst prediction application for India

    Science.gov (United States)

    Pryor, Kenneth L.; Johny, C. J.; Prasad, V. S.

    2016-05-01

    During the month of June 2015, the South Asian (or Southwest) monsoon advanced steadily from the southern to the northwestern states of India. The progression of the monsoon had an apparent effect on the relative strength of convective storm downbursts that occurred during June and July 2015. A convective downburst prediction algorithm, involving the Microburst Windspeed Potential Index (MWPI) and a satellite-derived three-band microburst risk product, and applied with meteorological geostationary satellite (KALPANA-1 VHRR and METEOSAT-7) and MODIS Aqua data, was evaluated and found to effectively indicate relative downburst intensity in both pre-monsoon and monsoon environments over various regions of India. The MWPI product, derived from T574L64 Global Forecast System (NGFS) model data, is being generated in real-time by National Center for Medium Range Weather Forecasting (NCMRWF), Ministry of Earth Sciences, India. The validation process entailed direct comparison of measured downburst-related wind gusts at airports and India Meteorological Department (IMD) observatories to adjacent MWPI values calculated from GFS and India NGFS model datasets. Favorable results include a statistically significant positive correlation between MWPI values and proximate measured downburst wind gusts with a confidence level near 100%. Case studies demonstrate the influence of the South Asian monsoon on convective storm environments and the response of the downburst prediction algorithm.

  14. Ortholog prediction of the Aspergillus genus applicable for synthetic biology

    DEFF Research Database (Denmark)

    Rasmussen, Jane Lind Nybo; Vesth, Tammi Camilla; Theobald, Sebastian

    The Aspergillus genus contains leading industrial microorganisms, excelling in producing bioactive compounds and enzymes. Using synthetic biology and bioinformatics, we aim to re-engineer these organisms for applications within human health, pharmaceuticals, environmental engineering, and food...... production. In this project, we compare the genomes of +300 species from the Aspergillus genus to generate a high-resolution pan-genomic map, representing genetic diversity spanning ~200 million years. We are identifying genes specific to species and clades to allow for guilt-by-association-based mapping......-directional hits. The result is orthologous protein families describing the genomic and functional features of individual species, clades and the core/pan genome of Aspergillus; and applicable to genotype-to-phenotype analyses in other microbial genera....

  15. Predictive control and identification: Applications to steering dynamics

    DEFF Research Database (Denmark)

    Hansen, Anca Daniela

    1996-01-01

    . The influence of wind, waves and currents on the chip motions are also discussed. Chapter 3 deals with the model reduction problem. Some basic concepts are explained, due to their role in the reduction of the dynamic models. Two model reductions techniques, based on singular values, are described....... The theoretical properties of these methods are studied and their performance is examined via simulation on a stochastic linear Mariner Class Vessel model. In Chapter 4, the attention is focused on the derivation of an extended GPC. This extended strategy implies a generalization of the model structure...... and of the loss function, which defines the optimality of the control. Some guidelines on how to choose the design parameters, depending on the type of process to be controlled and on the required control performance, are presented. A predictive track keeping system for a Mariner Class Vessel is formulated based...

  16. Application of generalized predictive control in networked control system

    Institute of Scientific and Technical Information of China (English)

    YANG Can; ZHU Sha-nan; KONG Wan-zeng; LU Li-ming

    2006-01-01

    A new framework for networked control system based on Generalized Predictive Control (GPC) is proposed in this paper. Clock-driven sensors, event-driven controller, and clock-driven actuators are required in this framework. A queuing strategy is proposed to overcome the network induced delay. Without redesigning, the proposed framework enables the existing GPC controller to be used in a network environment. It also does not require clock synchronization and is only slightly affected by bad network condition such as package loss. Various experiments are designed over the real network to test the proposed approach,which verify that the proposed approach can stabilize the Networked Control System (NCS) and is robust.

  17. Ranking beta sheet topologies with applications to protein structure prediction

    DEFF Research Database (Denmark)

    Fonseca, Rasmus; Helles, Glennie; Winter, Pawel

    2011-01-01

    One reason why ab initio protein structure predictors do not perform very well is their inability to reliably identify long-range interactions between amino acids. To achieve reliable long-range interactions, all potential pairings of ß-strands (ß-topologies) of a given protein are enumerated......, including the native ß-topology. Two very different ß-topology scoring methods from the literature are then used to rank all potential ß-topologies. This has not previously been attempted for any scoring method. The main result of this paper is a justification that one of the scoring methods, in particular...... of this paper is a method to deal with the inaccuracies of secondary structure predictors when enumerating potential ß-topologies. The results reported in this paper are highly relevant for ab initio protein structure prediction methods based on decoy generation. They indicate that decoy generation can...

  18. APPLICATION OF MODEL PREDICTIVE CONTROL TO BATCH POLYMERIZATION REACTOR

    Directory of Open Access Journals (Sweden)

    N.M. Ghasem

    2006-06-01

    Full Text Available The absence of a stable operational state in polymerization reactors that operates in batches is factor that determine the need of a special control system. In this study, advanced control methodology is implemented for controlling the operation of a batch polymerization reactor for polystyrene production utilizingmodel predictive control. By utilizing a model of the polymerization process, the necessary operational conditions were determined for producing the polymer within the desired characteristics. The maincontrol objective is to bring the reactor temperature to its target temperature as rapidly as possible with minimal temperature overshoot. Control performance for the proposed method is encouraging. It has been observed that temperature overshoot can be minimized by the proposed method with the use of both reactor and jacket energy balance for reactor temperature control.

  19. Data Mining Applications: A comparative Study for Predicting Student's performance

    CERN Document Server

    Yadav, Surjeet Kumar; Pal, Saurabh

    2012-01-01

    Knowledge Discovery and Data Mining (KDD) is a multidisciplinary area focusing upon methodologies for extracting useful knowledge from data and there are several useful KDD tools to extracting the knowledge. This knowledge can be used to increase the quality of education. But educational institution does not use any knowledge discovery process approach on these data. Data mining can be used for decision making in educational system. A decision tree classifier is one of the most widely used supervised learning methods used for data exploration based on divide & conquer technique. This paper discusses use of decision trees in educational data mining. Decision tree algorithms are applied on students' past performance data to generate the model and this model can be used to predict the students' performance. It helps earlier in identifying the dropouts and students who need special attention and allow the teacher to provide appropriate advising/counseling.

  20. Identification and Evaluation of Novel Drug Targets against the Human Fungal Pathogen Aspergillus fumigatus with Elaboration on the Possible Role of RNA-Binding Protein

    Science.gov (United States)

    Malekzadeh, Saeid; Sardari, Soroush; Azerang, Parisa; Khorasanizadeh, Dorsa; Amiri, Solmaz Agha; Azizi, Mohammad; Mohajerani, Nazanin; Khalaj, Vahid

    2017-01-01

    Bakground: Aspergillus fumigatus is an airborne opportunistic fungal pathogen that can cause fatal infections in immunocompromised patients. Although the current anti-fungal therapies are relatively efficient, some issues such as drug toxicity, drug interactions, and the emergence of drug-resistant fungi have promoted the intense research toward finding the novel drug targets. Methods: In search of new antifungal drug targets, we have used a bioinformatics approach to identify novel drug targets. We compared the whole proteome of this organism with yeast Saccharomyces cerevisiae to come up with 153 specific proteins. Further screening of these proteins revealed 50 potential molecular targets in A. fumigatus. Amongst them, RNA-binding protein (RBP) was selected for further examination. The aspergillus fumigatus RBP (AfuRBP), as a peptidylprolyl isomerase, was evaluated by homology modeling and bioinformatics tools. RBP-deficient mutant strains of A. fumigatus were generated and characterized. Furthermore, the susceptibility of these strains to known peptidylprolyl isomerase inhibitors was assessed. Results: AfuRBP-deficient mutants demonstrated a normal growth phenotype. MIC assay results using inhibitors of peptidylprolyl isomerase confirmed a higher sensitivity of these mutants compared to the wild type. Conclusion: Our bioinformatics approach revealed a number of fungal-specific proteins that may be considered as new targets for drug discovery purposes. Peptidylprolyl isomerase, as a possible drug target, was evaluated against two potential inhibitors, and the promising results were investigated mechanistically. Future studies would confirm the impact of such target on the antifungal discovery investigations PMID:28000798

  1. Proteome mining for the identification and in-silico characterization of putative drug targets of multi-drug resistant Clostridium difficile strain 630.

    Science.gov (United States)

    Lohani, Mohtashim; Dhasmana, Anupam; Haque, Shafiul; Wahid, Mohd; Jawed, Arshad; Dar, Sajad A; Mandal, Raju K; Areeshi, Mohammed Y; Khan, Saif

    2017-05-01

    Clostridium difficile is an enteric pathogen that causes approximately 20% to 30% of antibiotic-associated diarrhea. In recent years, there has been a substantial rise in the rate of C. difficile infections as well as the emergence of virulent and antibiotic resistant C. difficile strains. So, there is an urgent need for the identification of therapeutic potential targets and development of new drugs for the treatment and prevention of C. difficile infections. In the current study, we used a hybrid approach by combining sequence similarity-based approach and protein-protein interaction network topology-based approach to identify and characterize the potential drug targets of C. difficile. A total of 155 putative drug targets of C. difficile were identified and the metabolic pathway analysis of these putative drug targets using DAVID revealed that 46 of them are involved in 9 metabolic pathways. In-silico characterization of these proteins identified seven proteins involved in pathogen-specific peptidoglycan biosynthesis pathway. Three promising targets viz. homoserine dehydrogenase, aspartate-semialdehyde dehydrogenase and aspartokinase etc. were found to be involved in multiple enzymatic pathways of the pathogen. These 3 drug targets are of particular interest as they can be used for developing effective drugs against multi-drug resistant C. difficile strain 630 in the near future.

  2. Plausible Drug Targets in the Streptococcus mutans Quorum Sensing Pathways to Combat Dental Biofilms and Associated Risks.

    Science.gov (United States)

    Kaur, Gurmeet; Rajesh, Shrinidhi; Princy, S Adline

    2015-12-01

    Streptococcus mutans, a Gram positive facultative anaerobe, is one among the approximately seven hundred bacterial species to exist in human buccal cavity and cause dental caries. Quorum sensing (QS) is a cell-density dependent communication process that respond to the inter/intra-species signals and elicit responses to show behavioral changes in the bacteria to an aggressive forms. In accordance to this phenomenon, the S. mutans also harbors a Competing Stimulating Peptide (CSP)-mediated quorum sensing, ComCDE (Two-component regulatory system) to regulate several virulence-associated traits that includes the formation of the oral biofilm (dental plaque), genetic competence and acidogenicity. The QS-mediated response of S. mutans adherence on tooth surface (dental plaque) imparts antibiotic resistance to the bacterium and further progresses to lead a chronic state, known as periodontitis. In recent years, the oral streptococci, S. mutans are not only recognized for its cariogenic potential but also well known to worsen the infective endocarditis due to its inherent ability to colonize and form biofilm on heart valves. The review significantly appreciate the increasing complexity of the CSP-mediated quorum-sensing pathway with a special emphasis to identify the plausible drug targets within the system for the development of anti-quorum drugs to control biofilm formation and associated risks.

  3. Anti-cancer drug loaded iron-gold core-shell nanoparticles (Fe@Au) for magnetic drug targeting.

    Science.gov (United States)

    Kayal, Sibnath; Ramanujan, Raju Vijayaraghavan

    2010-09-01

    Magnetic drug targeting, using core-shell magnetic carrier particles loaded with anti-cancer drugs, is an emerging and significant method of cancer treatment. Gold shell-iron core nanoparticles (Fe@Au) were synthesized by the reverse micelle method with aqueous reactants, surfactant, co-surfactant and oil phase. XRD, XPS, TEM and magnetic property measurements were utilized to characterize these core-shell nanoparticles. Magnetic measurements showed that the particles were superparamagnetic at room temperature and that the saturation magnetization decreased with increasing gold concentration. The anti-cancer drug doxorubicin (DOX) was loaded onto these Fe@Au nanoparticle carriers and the drug release profiles showed that upto 25% of adsorbed drug was released in 80 h. It was found that the amine (-NH2) group of DOX binds to the gold shell. An in vitro apparatus simulating the human circulatory system was used to determine the retention of these nanoparticle carriers when exposed to an external magnetic field. A high percentage of magnetic carriers could be retained for physiologically relevant flow speeds of fluid. The present findings show that DOX loaded gold coated iron nanoparticles are promising for magnetically targeted drug delivery.

  4. Identification of Interaction Hot Spots in Structures of Drug Targets on the Basis of Three-Dimensional Activity Cliff Information.

    Science.gov (United States)

    Furtmann, Norbert; Hu, Ye; Gütschow, Michael; Bajorath, Jürgen

    2015-12-01

    Activity cliffs are defined as pairs or groups of structurally similar or analogous compounds that share the same specific activity but have large differences in potency. Although activity cliffs are mostly studied in medicinal chemistry at the level of molecular graphs, they can also be assessed by comparing compound binding modes. If such three-dimensional activity cliffs (3D-cliffs) are studied on the basis of X-ray complex structures, experimental ligand-target interaction details can be taken into account. Rapid growth in the number of 3D-cliffs that can be derived from X-ray complex structures has made it possible to identify targets for which a substantial body of 3D-cliff information is available. Activity cliffs are typically studied to identify structure-activity relationship determinants and aid in compound optimization. However, 3D-cliff information can also be used to search for interaction hot spots and key residues, as reported herein. For six of seven drug targets for which more than 20 3D-cliffs were available, series of 3D-cliffs were identified that were consistently involved in interactions with different hot spots. These 3D-cliffs often encoded chemical modifications resulting in interactions that were characteristic of highly potent compounds but absent in weakly potent ones, thus providing information for structure-based design.

  5. Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets

    Directory of Open Access Journals (Sweden)

    Chen Bor-Sen

    2008-06-01

    Full Text Available Abstract Background Cancer is caused by genetic abnormalities, such as mutations of oncogenes or tumor suppressor genes, which alter downstream signal transduction pathways and protein-protein interactions. Comparisons of the interactions of proteins in cancerous and normal cells can shed light on the mechanisms of carcinogenesis. Results We constructed initial networks of protein-protein interactions involved in the apoptosis of cancerous and normal cells by use of two human yeast two-hybrid data sets and four online databases. Next, we applied a nonlinear stochastic model, maximum likelihood parameter estimation, and Akaike Information Criteria (AIC to eliminate false-positive protein-protein interactions in our initial protein interaction networks by use of microarray data. Comparisons of the networks of apoptosis in HeLa (human cervical carcinoma cells and in normal primary lung fibroblasts provided insight into the mechanism of apoptosis and allowed identification of potential drug targets. The potential targets include BCL2, caspase-3 and TP53. Our comparison of cancerous and normal cells also allowed derivation of several party hubs and date hubs in the human protein-protein interaction networks involved in caspase activation. Conclusion Our method allows identification of cancer-perturbed protein-protein interactions involved in apoptosis and identification of potential molecular targets for development of anti-cancer drugs.

  6. FGF23-FGF Receptor/Klotho Pathway as a New Drug Target for Disorders of Bone and Mineral Metabolism.

    Science.gov (United States)

    Fukumoto, Seiji

    2016-04-01

    Fibroblast growth factor 23 (FGF23) is a phosphaturic hormone produced by bone and works by binding to Klotho-FGF receptor complex. Excessive and deficient actions of FGF23 result in hypophosphatemic and hyperphosphatemic diseases, respectively. Therefore, it is reasonable to think that modulating FGF23 activities may be a novel therapeutic measure for these diseases. Several preclinical reports indicate that the inhibition of FGF23 activities ameliorates hypophosphatemic rickets/osteomalacia caused by excessive actions of FGF23. In addition, phase I-II clinical trials of anti-FGF23 antibody in adult patients with X-linked hypophosphatemia rickets, the most prevalent cause of genetic FGF23-related hypophosphatemic rickets, indicated that the antibody enhances renal tubular phosphate reabsorption and increases serum phosphate. However, it is not known whether the inhibition of FGF23 activities actually brings clinical improvement of rickets and osteomalacia. Available data indicate that FGF23-FGF receptor/Klotho pathway can be a new drug target for disorders of phosphate and bone metabolism.

  7. Autonomous CaMKII Activity as a Drug Target for Histological and Functional Neuroprotection after Resuscitation from Cardiac Arrest

    Directory of Open Access Journals (Sweden)

    Guiying Deng

    2017-01-01

    Full Text Available The Ca2+/calmodulin-dependent protein kinase II (CaMKII is a major mediator of physiological glutamate signaling, but its role in pathological glutamate signaling (excitotoxicity remains less clear, with indications for both neuro-toxic and neuro-protective functions. Here, the role of CaMKII in ischemic injury is assessed utilizing our mouse model of cardiac arrest and cardiopulmonary resuscitation (CA/CPR. CaMKII inhibition (with tatCN21 or tatCN19o at clinically relevant time points (30 min after resuscitation greatly reduces neuronal injury. Importantly, CaMKII inhibition also works in combination with mild hypothermia, the current standard of care. The relevant drug target is specifically Ca2+-independent “autonomous” CaMKII activity generated by T286 autophosphorylation, as indicated by substantial reduction in injury in autonomy-incompetent T286A mutant mice. In addition to reducing cell death, tatCN19o also protects the surviving neurons from functional plasticity impairments and prevents behavioral learning deficits, even at extremely low doses (0.01 mg/kg, further highlighting the clinical potential of our findings.

  8. Autonomous CaMKII Activity as a Drug Target for Histological and Functional Neuroprotection after Resuscitation from Cardiac Arrest.

    Science.gov (United States)

    Deng, Guiying; Orfila, James E; Dietz, Robert M; Moreno-Garcia, Myriam; Rodgers, Krista M; Coultrap, Steve J; Quillinan, Nidia; Traystman, Richard J; Bayer, K Ulrich; Herson, Paco S

    2017-01-31

    The Ca(2+)/calmodulin-dependent protein kinase II (CaMKII) is a major mediator of physiological glutamate signaling, but its role in pathological glutamate signaling (excitotoxicity) remains less clear, with indications for both neuro-toxic and neuro-protective functions. Here, the role of CaMKII in ischemic injury is assessed utilizing our mouse model of cardiac arrest and cardiopulmonary resuscitation (CA/CPR). CaMKII inhibition (with tatCN21 or tatCN19o) at clinically relevant time points (30 min after resuscitation) greatly reduces neuronal injury. Importantly, CaMKII inhibition also works in combination with mild hypothermia, the current standard of care. The relevant drug target is specifically Ca(2+)-independent "autonomous" CaMKII activity generated by T286 autophosphorylation, as indicated by substantial reduction in injury in autonomy-incompetent T286A mutant mice. In addition to reducing cell death, tatCN19o also protects the surviving neurons from functional plasticity impairments and prevents behavioral learning deficits, even at extremely low doses (0.01 mg/kg), further highlighting the clinical potential of our findings.

  9. Phenotypic Screening of Small-Molecule Inhibitors: Implications for Therapeutic Discovery and Drug Target Development in Traumatic Brain Injury.

    Science.gov (United States)

    Al-Ali, Hassan; Lemmon, Vance P; Bixby, John L

    2016-01-01

    The inability of central nervous system (CNS) neurons to regenerate damaged axons and dendrites following traumatic brain injury (TBI) creates a substantial obstacle for functional recovery. Apoptotic cell death, deposition of scar tissue, and growth-repressive molecules produced by glia further complicate the problem and make it challenging for re-growing axons to extend across injury sites. To date, there are no approved drugs for the treatment of TBI, accentuating the need for relevant leads. Cell-based and organotypic bioassays can better mimic outcomes within the native CNS microenvironment than target-based screening methods and thus should speed the discovery of therapeutic agents that induce axon or dendrite regeneration. Additionally, when used to screen focused chemical libraries such as small-molecule protein kinase inhibitors, these assays can help elucidate molecular mechanisms involved in neurite outgrowth and regeneration as well as identify novel drug targets. Here, we describe a phenotypic cellular (high content) screening assay that utilizes brain-derived primary neurons for screening small-molecule chemical libraries.

  10. Associating Drugs, Targets and Clinical Outcomes into an Integrated Network Affords a New Platform for Computer-Aided Drug Repurposing.

    Science.gov (United States)

    Oprea, Tudor I; Nielsen, Sonny Kim; Ursu, Oleg; Yang, Jeremy J; Taboureau, Olivier; Mathias, Stephen L; Kouskoumvekaki, Lrene; Sklar, Larry A; Bologa, Cristian G

    2011-03-14

    Finding new uses for old drugs is a strategy embraced by the pharmaceutical industry, with increasing participation from the academic sector. Drug repurposing efforts focus on identifying novel modes of action, but not in a systematic manner. With intensive data mining and curation, we aim to apply bio- and cheminformatics tools using the DRUGS database, containing 3,837 unique small molecules annotated on 1,750 proteins. These are likely to serve as drug targets and antitargets (i.e., associated with side effects, SE). The academic community, the pharmaceutical sector and clinicians alike could benefit from an integrated, semantic-web compliant computer-aided drug repurposing (CADR) effort, one that would enable deep data mining of associations between approved drugs (D), targets (T), clinical outcomes (CO) and SE. We report preliminary results from text mining and multivariate statistics, based on 7,684 approved drug labels, ADL (Dailymed) via text mining. From the ADL corresponding to 988 unique drugs, the "adverse reactions" section was mapped onto 174 SE, then clustered via principal component analysis into a 5x5 self-organizing map that was integrated into a Cytoscape network of SE-D-T-CO. This type of data can be used to streamline drug repurposing and may result in novel insights that can lead to the identification of novel drug actions.

  11. Amidated pectin/sodium carboxymethylcellulose microspheres as a new carrier for colonic drug targeting: Development and optimization by factorial design.

    Science.gov (United States)

    Gadalla, Hytham H; El-Gibaly, Ibrahim; Soliman, Ghareb M; Mohamed, Fergany A; El-Sayed, Ahmed M

    2016-11-20

    The colon is a promising site for drug targeting owing to its long transit time and mild proteolytic activity. The aim of this study was to prepare new low methoxy amidated pectin/NaCMC microspheres cross-linked by a mixture of Zn(2+) and Al(3+) ions and test their potential for colonic targeting of progesterone. A 2(4) factorial design was carried out to optimize the preparation conditions. High drug entrapment efficiency (82-99%) was obtained and it increased with increasing drug concentration but decreased with increasing polymer concentration. Drug release rate was directly proportional to the microsphere drug content and inversely related to Al(3+) ion concentration. Drug release was minimal during the first 3h but was significantly improved in the presence of 1% rat caecal contents, confirming the microsphere potential for colonic delivery. The microspheres achieved >2.3-fold enhancement of colonic progesterone permeability. These results confirm the viability of the produced microspheres as colon-targeted drug delivery vehicle.

  12. CRISPR-Mediated Drug-Target Validation Reveals Selective Pharmacological Inhibition of the RNA Helicase, eIF4A

    Directory of Open Access Journals (Sweden)

    Jennifer Chu

    2016-06-01

    Full Text Available Targeting translation initiation is an emerging anti-neoplastic strategy that capitalizes on de-regulated upstream MAPK and PI3K-mTOR signaling pathways in cancers. A key regulator of translation that controls ribosome recruitment flux is eukaryotic initiation factor (eIF 4F, a hetero-trimeric complex composed of the cap binding protein eIF4E, the scaffolding protein eIF4G, and the RNA helicase eIF4A. Small molecule inhibitors targeting eIF4F display promising anti-neoplastic activity in preclinical settings. Among these are some rocaglate family members that are well tolerated in vivo, deplete eIF4F of its eIF4A helicase subunit, have shown activity as single agents in several xenograft models, and can reverse acquired resistance to MAPK and PI3K-mTOR targeted therapies. Herein, we highlight the power of using genetic complementation approaches and CRISPR/Cas9-mediated editing for drug-target validation ex vivo and in vivo, linking the anti-tumor properties of rocaglates to eIF4A inhibition.

  13. Assessment of Pseudomonas aeruginosa N5,N10-methylenetetrahydrofolate dehydrogenase-cyclohydrolase as a potential antibacterial drug target.

    Directory of Open Access Journals (Sweden)

    Thomas C Eadsforth

    Full Text Available The bifunctional enzyme methylenetetrahydrofolate dehydrogenase - cyclohydrolase (FolD is identified as a potential drug target in Gram-negative bacteria, in particular the troublesome Pseudomonas aeruginosa. In order to provide a comprehensive and realistic assessment of the potential of this target for drug discovery we generated a highly efficient recombinant protein production system and purification protocol, characterized the enzyme, carried out screening of two commercial compound libraries by differential scanning fluorimetry, developed a high-throughput enzyme assay and prosecuted a screening campaign against almost 80,000 compounds. The crystal structure of P. aeruginosa FolD was determined at 2.2 Å resolution and provided a template for an assessment of druggability and for modelling of ligand complexes as well as for comparisons with the human enzyme. New FolD inhibitors were identified and characterized but the weak levels of enzyme inhibition suggest that these compounds are not optimal starting points for future development. Furthermore, the close similarity of the bacterial and human enzyme structures suggest that selective inhibition might be difficult to attain. In conclusion, although the preliminary biological data indicates that FolD represents a valuable target for the development of new antibacterial drugs, indeed spurred us to investigate it, our screening results and structural data suggest that this would be a difficult enzyme to target with respect to developing the appropriate lead molecules required to underpin a serious drug discovery effort.

  14. On the possibility of the unification of drug targeting systems. Studies with liposome transport to the mixtures of target antigens.

    Science.gov (United States)

    Trubetskoy, V S; Berdichevsky, V R; Efremov, E E; Torchilin, V P

    1987-03-15

    In order to make the drug targeting system more effective, simple and technological, we suggest creation of drug-bearing conjugates capable of simultaneous binding with different antigenic components of the target via specific antibodies. It is supposed that the targeted therapy should include sequential administration of the mixture of modified antibodies (or other specific vectors) against different components of affected tissue and, upon antibody accumulation in the desired region, administration of modified drugs or drug carrying systems which can recognize and bind with the target via accumulated antibodies due to the interaction between vector modifier and carrier modifier. Using as a model system monolayers consisting of the mixture of extracellular antigens and appropriated antibodies, it was shown that the treatment of the target with the mixture of biotinylated antibodies against all target components and subsequent binding with the target of biotinylated liposomes via avidin permits high liposome accumulation on the monolayer. The binding achieved is always higher than in the case of the utilization of single antibody-bearing liposomes. Besides, the system suggested is very simple and its components can be easily obtained on technological scale in standardized conditions.

  15. Aggregate Interview Method of ranking orthopedic applicants predicts future performance.

    Science.gov (United States)

    Geissler, Jacqueline; VanHeest, Ann; Tatman, Penny; Gioe, Terence

    2013-07-01

    This article evaluates and describes a process of ranking orthopedic applicants using what the authors term the Aggregate Interview Method. The authors hypothesized that higher-ranking applicants using this method at their institution would perform better than those ranked lower using multiple measures of resident performance. A retrospective review of 115 orthopedic residents was performed at the authors' institution. Residents were grouped into 3 categories by matching rank numbers: 1-5, 6-14, and 15 or higher. Each rank group was compared with resident performance as measured by faculty evaluations, the Orthopaedic In-Training Examination (OITE), and American Board of Orthopaedic Surgery (ABOS) test results. Residents ranked 1-5 scored significantly better on patient care, behavior, and overall competence by faculty evaluation (Porthopedic resident candidates who scored highly on the Accreditation Council for Graduate Medical Education resident core competencies as measured by faculty evaluations, performed above the national average on the OITE, and passed the ABOS part 1 examination at rates exceeding the national average.

  16. Application of neural networks for the prediction of energy use in supermarket buildings

    Energy Technology Data Exchange (ETDEWEB)

    Suh, T.J.; Tassou, S.A.; Datta, D. [Brunel Univ., Uxbridge (United Kingdom); Marriott, D. [Safeway Stores 6 Millington, Middx (United Kingdom)

    1996-12-31

    This paper discusses the application of neural networks to predict energy consumption in commercial buildings. To date, many researchers have demonstrated that neural networks can be more reliable energy predictors than the traditional statistical approaches and can also form the basis for predictive controllers of HVAC equipment. This paper shows the preliminary results of research work at Brunel University for predicting the variation of electricity consumption in a supermarket building based on a neural network. A comparison of the prediction performance of the neural network and a traditional regression approach is presented.

  17. Application of Newtonian physics to predict the speed of a gravity racer

    Science.gov (United States)

    Driscoll, H. F.; Bullas, A. M.; King, C. E.; Senior, T.; Haake, S. J.; Hart, J.

    2016-07-01

    Gravity racing can be studied using numerical solutions to the equations of motion derived from Newton’s second law. This allows students to explore the physics of gravity racing and to understand how design and course selection influences vehicle speed. Using Euler’s method, we have developed a spreadsheet application that can be used to predict the speed of a gravity powered vehicle. The application includes the effects of air and rolling resistance. Examples of the use of the application for designing a gravity racer are presented and discussed. Predicted speeds are compared to the results of an official world record attempt.

  18. Application Natura 2000 Data For The Invasive Plants Spread Prediction*

    Directory of Open Access Journals (Sweden)

    Pěknicová J.

    2015-12-01

    Full Text Available The distribution of invasive plants depends on several environmental factors, e.g. on the distance from the vector of spreading, invaded community composition, land-use, etc. The species distribution models, a research tool for invasive plants spread prediction, involve the combination of environmental factors, occurrence data, and statistical approach. For the construction of the presented distribution model, the occurrence data on invasive plants (Solidago sp., Fallopia sp., Robinia pseudoaccacia, and Heracleum mantegazzianum and Natura 2000 habitat types from the Protected Landscape Area Kokořínsko have been intersected in ArcGIS and statistically analyzed. The data analysis was focused on (1 verification of the accuracy of the Natura 2000 habitat map layer, and the accordance with the habitats occupied by invasive species and (2 identification of a suitable scale of intersection between the habitat and species distribution. Data suitability was evaluated for the construction of the model on local scale. Based on the data, the invaded habitat types were described and the optimal scale grid was evaluated. The results show the suitability of Natura 2000 habitat types for modelling, however more input data (e.g. on soil types, elevation are needed.

  19. Predicting the reliability of polyisobutylene seal for photovoltaic application

    Science.gov (United States)

    Liu, Hua; Feng, Jie; Nicoli, Edoardo; López, Leonardo; Kauffmann, Keith; Yang, Kwanho; Ramesh, Narayan

    2012-10-01

    Polyisobutylene (PIB) or butyl rubber has been used widely in applications such as construction materials, adhesives and sealants, agricultural chemicals, medical devices, personal care products, and fuel additives. Due to the unique low gas permeability, flexibility, and excellent weathering resistance, PIB or PIB based materials are frequently employed in photovoltaic (PV) industry as sealant to protect the electrical assembly in the package as well as moisture sensitive PV cells from aggressive environments. Long term behavior of the PIB sealant within the operating temperature range of the PV devices thus becomes a critical factor to the reliability of the device. In this paper, an experimental study of the temperature dependent fatigue behavior of a PIB based joint is presented. A finite element model capturing the joint region geometry is developed and an approach to estimate lifetime is proposed.

  20. Bilingual nurse education program: applicant characteristics that predict success.

    Science.gov (United States)

    Bosch, Paul C; Doshier, Sally A; Gess-Newsome, Julie

    2012-01-01

    Nurses are in great demand across the United States, but those fluent in both Spanish and English are in particularly short supply. This study examined three cohorts of students that entered a Spanish-English nursing education program to determine characteristics of applicants that produced student success. Unlike many nursing programs, entrance requirements for this bilingual program did not include a minimal grade point average (GPA) or previous course completions. Logistic regression was used to analyze the relationship between five different characteristics of entering students and their later success in the program. Success was measured in terms of program persistence and performance on the NCLEX-PN and NCLEX-RN exams. Incoming students with relatively high GPAs (M = 3.2) were significantly more likely to persist through the entire nursing 0ronram and oass the NCLEX-RN exam (t < .05) than those with lower GPAs (M = 2.5).

  1. Core-Level Modeling and Frequency Prediction for DSP Applications on FPGAs

    Directory of Open Access Journals (Sweden)

    Gongyu Wang

    2015-01-01

    Full Text Available Field-programmable gate arrays (FPGAs provide a promising technology that can improve performance of many high-performance computing and embedded applications. However, unlike software design tools, the relatively immature state of FPGA tools significantly limits productivity and consequently prevents widespread adoption of the technology. For example, the lengthy design-translate-execute (DTE process often must be iterated to meet the application requirements. Previous works have enabled model-based, design-space exploration to reduce DTE iterations but are limited by a lack of accurate model-based prediction of key design parameters, the most important of which is clock frequency. In this paper, we present a core-level modeling and design (CMD methodology that enables modeling of FPGA applications at an abstract level and yet produces accurate predictions of parameters such as clock frequency, resource utilization (i.e., area, and latency. We evaluate CMD’s prediction methods using several high-performance DSP applications on various families of FPGAs and show an average clock-frequency prediction error of 3.6%, with a worst-case error of 20.4%, compared to the best of existing high-level prediction methods, 13.9% average error with 48.2% worst-case error. We also demonstrate how such prediction enables accurate design-space exploration without coding in a hardware-description language (HDL, significantly reducing the total design time.

  2. The Ascaris suum nicotinic receptor, ACR-16, as a drug target: Four novel negative allosteric modulators from virtual screening

    Directory of Open Access Journals (Sweden)

    Fudan Zheng

    2016-04-01

    Full Text Available Soil-transmitted helminth infections in humans and livestock cause significant debility, reduced productivity and economic losses globally. There are a limited number of effective anthelmintic drugs available for treating helminths infections, and their frequent use has led to the development of resistance in many parasite species. There is an urgent need for novel therapeutic drugs for treating these parasites. We have chosen the ACR-16 nicotinic acetylcholine receptor of Ascaris suum (Asu-ACR-16, as a drug target and have developed three-dimensional models of this transmembrane protein receptor to facilitate the search for new bioactive compounds. Using the human α7 nAChR chimeras and Torpedo marmorata nAChR for homology modeling, we defined orthosteric and allosteric binding sites on the Asu-ACR-16 receptor for virtual screening. We identified four ligands that bind to sites on Asu-ACR-16 and tested their activity using electrophysiological recording from Asu-ACR-16 receptors expressed in Xenopus oocytes. The four ligands were acetylcholine inhibitors (SB-277011-A, IC50, 3.12 ± 1.29 μM; (+-butaclamol Cl, IC50, 9.85 ± 2.37 μM; fmoc-1, IC50, 10.00 ± 1.38 μM; fmoc-2, IC50, 16.67 ± 1.95 μM that behaved like negative allosteric modulators. Our work illustrates a structure-based in silico screening method for seeking anthelmintic hits, which can then be tested electrophysiologically for further characterization.

  3. An application of numerical methods to the prediction of strata methane flow in longwall mining

    OpenAIRE

    Ediz, I.G.

    1991-01-01

    This research describes an application of numerical methods for the prediction of strata methane flow into mine workings around a longwall coal face employing methane drainage. This method of methane prediction was developed by solving the time-dependent gas flow equation using the finite element analysis. Having obtained the gas pressure distribution throughout the finite element mesh, a mass flow equation was derived to calculate methane flow rate for a given mining boundary. A computer pro...

  4. WAsP prediction errors due to site orography[Wind Atlas Analysis and Application Program

    Energy Technology Data Exchange (ETDEWEB)

    Bowen, A.J.; Mortensen, N.G.

    2004-12-01

    The influence of rugged terrain on the prediction accuracy of the Wind Atlas Analysis and Application Program (WAsP) is investigated using a case study of field measurements taken in rugged terrain. The parameters that could cause substantial errors in a prediction are identified and discussed. In particular, the effects from extreme orography are investigated. A suitable performance indicator is developed which predicts the sign and approximate magnitude of such errors due to orography. This procedure allows the user to assess the consequences of using WAsP outside its operating envelope and could provide a means of correction for rugged terrain effects. (au)

  5. Application of Ordinal Set Pair Analysis in Annual Rainfall Prediction of Liao River Basin

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    [Objective] The research aimed to study the application of ordinal set pair analysis in the annual precipitation prediction of Liao River basin.[Method] The ordinal theory was introduced into the set pair analysis modeling,and the prediction model of set pair analysis was improved.A kind of rainfall prediction model based on the ordinal set pair analysis (OSPA) was put forward.The time sequence of annual rainfall in the hydrological rainfall station of Liao River basin during 1956-2006 was the research obje...

  6. Performance Analysis and Implementationof Predictable Streaming Applications onMultiprocessor Systems-on-Chip

    OpenAIRE

    2010-01-01

    Driven by the increasing capacity of integrated circuits, multiprocessorsystems-on-chip (MPSoCs) are widely used in modern consumer electron-ics devices. In this thesis, the performance analysis and implementationmethodologies are explored to design predictable streaming applications onMPSoCs computing platforms. The application functionality and concur-rency are described in synchronous data flow (SDF) computational models,and two state-of-the-art architecture templates are adopted as multip...

  7. Prediction

    CERN Document Server

    Sornette, Didier

    2010-01-01

    This chapter first presents a rather personal view of some different aspects of predictability, going in crescendo from simple linear systems to high-dimensional nonlinear systems with stochastic forcing, which exhibit emergent properties such as phase transitions and regime shifts. Then, a detailed correspondence between the phenomenology of earthquakes, financial crashes and epileptic seizures is offered. The presented statistical evidence provides the substance of a general phase diagram for understanding the many facets of the spatio-temporal organization of these systems. A key insight is to organize the evidence and mechanisms in terms of two summarizing measures: (i) amplitude of disorder or heterogeneity in the system and (ii) level of coupling or interaction strength among the system's components. On the basis of the recently identified remarkable correspondence between earthquakes and seizures, we present detailed information on a class of stochastic point processes that has been found to be particu...

  8. Emory University: MEDICI (Mining Essentiality Data to Identify Critical Interactions) for Cancer Drug Target Discovery and Development | Office of Cancer Genomics

    Science.gov (United States)

    The CTD2 Center at Emory University has developed a computational methodology to combine high-throughput knockdown data with known protein network topologies to infer the importance of protein-protein interactions (PPIs) for the survival of cancer cells.  Applying these data to the Achilles shRNA results, the CCLE cell line characterizations, and known and newly identified PPIs provides novel insights for potential new drug targets for cancer therapies and identifies important PPI hubs.

  9. Cellular Signaling Pathways in Insulin Resistance-Systems Biology Analyses of Microarray Dataset Reveals New Drug Target Gene Signatures of Type 2 Diabetes Mellitus.

    Science.gov (United States)

    Muhammad, Syed Aun; Raza, Waseem; Nguyen, Thanh; Bai, Baogang; Wu, Xiaogang; Chen, Jake

    2017-01-01

    Purpose: Type 2 diabetes mellitus (T2DM) is a chronic and metabolic disorder affecting large set of population of the world. To widen the scope of understanding of genetic causes of this disease, we performed interactive and toxicogenomic based systems biology study to find potential T2DM related genes after cDNA differential analysis. Methods: From the list of 50-differential expressed genes (p new drug target molecules for different diseases and can speed up drug discovery outcomes.

  10. Clinicopathological significance and potential drug target of CDH1 in breast cancer: a meta-analysis and literature review

    Directory of Open Access Journals (Sweden)

    Huang R

    2015-09-01

    Full Text Available Ruixue Huang,* Ping Ding,* Fei Yang*Department of Occupational and Environmental Health, School of Public Health, Central South University, Changsha, Hunan, People’s Republic of China*All authors contributed equally to this workAbstract: CDH1, as a tumor suppressor gene, contributes sporadic breast cancer (BC progression. However, the association between CDH1 hypermethylation and BC, and its clinicopathological significance remains unclear. We conducted a meta-analysis to investigate the relationship between the CDH1 methylation profile and the major clinicopathological features. A detailed literature was searched through the electronic databases PubMed, Web of Science™, and EMBASE™ for related research publications. The data were extracted and assessed by two reviewers independently. Odds ratios (ORs with corresponding confidence intervals (CIs were calculated and summarized respectively. The frequency of CDH1 methylation was significantly higher in invasive ductal carcinoma than in normal breast tissues (OR =5.83, 95% CI 3.76–9.03, P<0.00001. CDH1 hypermethylation was significantly higher in estrogen receptor (ER-negative BC than in ER-positive BC (OR =0.62, 95% CI 0.43–0.87, P=0.007. In addition, we found that the CDH1 was significantly methylated in HER2-negative BC than in HER2-positive BC (OR =0.26, 95% CI 0.15–0.44, P<0.00001. However, CDH1 methylation frequency was not associated with progesterone receptor (PR status, or with grades, stages, or lymph node metastasis of BC patients. Our results indicate that CDH1 hypermethylation is a potential novel drug target for developing personalized therapy. CDH1 hypermethylation is strongly associated with ER-negative and HER2-negative BC, respectively, suggesting CDH1 methylation status could contribute to the development of novel therapeutic approaches for the treatment of ER-negative or HER2-negative BC with aggressive tumor biology.Keywords: methylation, estrogen receptor, HER2

  11. A novel blood-brain barrier co-culture system for drug targeting of Alzheimer's disease: establishment by using acitretin as a model drug.

    Directory of Open Access Journals (Sweden)

    Christian Freese

    Full Text Available In the pathogenesis of Alzheimer's disease (AD the homeostasis of amyloid precursor protein (APP processing in the brain is impaired. The expression of the competing proteases ADAM10 (a disintegrin and metalloproteinase 10 and BACE-1 (beta site APP cleaving enzyme 1 is shifted in favor of the A-beta generating enzyme BACE-1. Acitretin--a synthetic retinoid-e.g., has been shown to increase ADAM10 gene expression, resulting in a decreased level of A-beta peptides within the brain of AD model mice and thus is of possible value for AD therapy. A striking challenge in evaluating novel therapeutically applicable drugs is the analysis of their potential to overcome the blood-brain barrier (BBB for central nervous system targeting. In this study, we established a novel cell-based bio-assay model to test ADAM10-inducing drugs for their ability to cross the BBB. We therefore used primary porcine brain endothelial cells (PBECs and human neuroblastoma cells (SH-SY5Y transfected with an ADAM10-promoter luciferase reporter vector in an indirect co-culture system. Acitretin served as a model substance that crosses the BBB and induces ADAM10 expression. We ensured that ADAM10-dependent constitutive APP metabolism in the neuronal cells was unaffected under co-cultivation conditions. Barrier properties established by PBECs were augmented by co-cultivation with SH-SY5Y cells and they remained stable during the treatment with acitretin as demonstrated by electrical resistance measurement and permeability-coefficient determination. As a consequence of transcellular acitretin transport measured by HPLC, the activity of the ADAM10-promoter reporter gene was significantly increased in co-cultured neuronal cells as compared to vehicle-treated controls. In the present study, we provide a new bio-assay system relevant for the study of drug targeting of AD. This bio-assay can easily be adapted to analyze other Alzheimer- or CNS disease-relevant targets in neuronal cells

  12. A novel blood-brain barrier co-culture system for drug targeting of Alzheimer's disease: establishment by using acitretin as a model drug.

    Science.gov (United States)

    Freese, Christian; Reinhardt, Sven; Hefner, Gudrun; Unger, Ronald E; Kirkpatrick, C James; Endres, Kristina

    2014-01-01

    In the pathogenesis of Alzheimer's disease (AD) the homeostasis of amyloid precursor protein (APP) processing in the brain is impaired. The expression of the competing proteases ADAM10 (a disintegrin and metalloproteinase 10) and BACE-1 (beta site APP cleaving enzyme 1) is shifted in favor of the A-beta generating enzyme BACE-1. Acitretin--a synthetic retinoid-e.g., has been shown to increase ADAM10 gene expression, resulting in a decreased level of A-beta peptides within the brain of AD model mice and thus is of possible value for AD therapy. A striking challenge in evaluating novel therapeutically applicable drugs is the analysis of their potential to overcome the blood-brain barrier (BBB) for central nervous system targeting. In this study, we established a novel cell-based bio-assay model to test ADAM10-inducing drugs for their ability to cross the BBB. We therefore used primary porcine brain endothelial cells (PBECs) and human neuroblastoma cells (SH-SY5Y) transfected with an ADAM10-promoter luciferase reporter vector in an indirect co-culture system. Acitretin served as a model substance that crosses the BBB and induces ADAM10 expression. We ensured that ADAM10-dependent constitutive APP metabolism in the neuronal cells was unaffected under co-cultivation conditions. Barrier properties established by PBECs were augmented by co-cultivation with SH-SY5Y cells and they remained stable during the treatment with acitretin as demonstrated by electrical resistance measurement and permeability-coefficient determination. As a consequence of transcellular acitretin transport measured by HPLC, the activity of the ADAM10-promoter reporter gene was significantly increased in co-cultured neuronal cells as compared to vehicle-treated controls. In the present study, we provide a new bio-assay system relevant for the study of drug targeting of AD. This bio-assay can easily be adapted to analyze other Alzheimer- or CNS disease-relevant targets in neuronal cells, as their

  13. Predicting when precipitation-driven synthesis is feasible : application to biocatalysis

    NARCIS (Netherlands)

    Ulijn, R.V.; Janssen, A.E.M.; Moore, B.D.; Halling, P.J.

    2001-01-01

    Precipitation-driven synthesis offers the possibility of obtaining high reaction yields using very low volume reactors and is finding increasing applications in biocatalysis. Here, a model that allows straightforward prediction of when such a precipitation-driven reaction will be thermodynamically f

  14. The Research and Application of Prediction Control in Multi-variable Control Process

    Institute of Scientific and Technical Information of China (English)

    WEI Shuangying

    2006-01-01

    The modern industrial control objects become more and more complicated, and higher control quality is required, so a series of new control strategies appear, applied, modified and develop quickly. This paper researches a new control strategy-prediction control-and its application in Multi-Variable Control Process. The research result is worthy for automatic control in process industry.

  15. Application of ATOVS Microwave Radiance Assimilation to Rainfall Prediction in Summer 2004

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Experiments are performed in this paper to understand the influence of satellite radiance data on the initial field of a numerical prediction system and rainfall prediction. First, Advanced Microwave Sounder Unit A (AMSU-A) and Unit B (AMSU-B) radiance data are directly used by three-dimensional variational data assimilation to improve the background field of the numerical model. Then, the detailed effect of the radiance data on the background field is analyzed. Secondly, the background field, which is formed by application of Advanced Television and Infrared Observation Satellite Operational Vertical Sounder (ATOVS) microwave radiance assimilation, is employed to simulate some heavy rainfall cases.The experiment results show that the assimilation of AMSU-A (B) microwave radiance data has a certain impact on the geopotential height, temperature, relative humidity and flow fields. And the impacts on the background field are mostly similar in the different months in summer. The heavy rainfall experiments reveal that the application of AMSU-A (B) microwave radiance data can improve the rainfall prediction significantly. In particular, the AMSU-A radiance data can significantly enhance the prediction of rainfall above 10 mm within 48 h, and the AMSU-B radiance data can improve the prediction of rainfall above 50 mm within 24 h. The present study confirms that the direct assimilation of satellite radiance data is an effective way to improve the prediction of heavy rainfall in the summer in China.

  16. Probabilistic application of a fugacity model to predict triclosan fate during wastewater treatment.

    Science.gov (United States)

    Bock, Michael; Lyndall, Jennifer; Barber, Timothy; Fuchsman, Phyllis; Perruchon, Elyse; Capdevielle, Marie

    2010-07-01

    The fate and partitioning of the antimicrobial compound, triclosan, in wastewater treatment plants (WWTPs) is evaluated using a probabilistic fugacity model to predict the range of triclosan concentrations in effluent and secondary biosolids. The WWTP model predicts 84% to 92% triclosan removal, which is within the range of measured removal efficiencies (typically 70% to 98%). Triclosan is predominantly removed by sorption and subsequent settling of organic particulates during primary treatment and by aerobic biodegradation during secondary treatment. Median modeled removal efficiency due to sorption is 40% for all treatment phases and 31% in the primary treatment phase. Median modeled removal efficiency due to biodegradation is 48% for all treatment phases and 44% in the secondary treatment phase. Important factors contributing to variation in predicted triclosan concentrations in effluent and biosolids include influent concentrations, solids concentrations in settling tanks, and factors related to solids retention time. Measured triclosan concentrations in biosolids and non-United States (US) effluent are consistent with model predictions. However, median concentrations in US effluent are over-predicted with this model, suggesting that differences in some aspect of treatment practices not incorporated in the model (e.g., disinfection methods) may affect triclosan removal from effluent. Model applications include predicting changes in environmental loadings associated with new triclosan applications and supporting risk analyses for biosolids-amended land and effluent receiving waters.

  17. Erratum: Probabilistic application of a fugacity model to predict triclosan fate during wastewater treatment.

    Science.gov (United States)

    Bock, Michael; Lyndall, Jennifer; Barber, Timothy; Fuchsman, Phyllis; Perruchon, Elyse; Capdevielle, Marie

    2010-10-01

    The fate and partitioning of the antimicrobial compound, triclosan, in wastewater treatment plants (WWTPs) is evaluated using a probabilistic fugacity model to predict the range of triclosan concentrations in effluent and secondary biosolids. The WWTP model predicts 84% to 92% triclosan removal, which is within the range of measured removal efficiencies (typically 70% to 98%). Triclosan is predominantly removed by sorption and subsequent settling of organic particulates during primary treatment and by aerobic biodegradation during secondary treatment. Median modeled removal efficiency due to sorption is 40% for all treatment phases and 31% in the primary treatment phase. Median modeled removal efficiency due to biodegradation is 48% for all treatment phases and 44% in the secondary treatment phase. Important factors contributing to variation in predicted triclosan concentrations in effluent and biosolids include influent concentrations, solids concentrations in settling tanks, and factors related to solids retention time. Measured triclosan concentrations in biosolids and non-United States (US) effluent are consistent with model predictions. However, median concentrations in US effluent are over-predicted with this model, suggesting that differences in some aspect of treatment practices not incorporated in the model (e.g., disinfection methods) may affect triclosan removal from effluent. Model applications include predicting changes in environmental loadings associated with new triclosan applications and supporting risk analyses for biosolids-amended land and effluent receiving waters.

  18. Application of uncertainty reasoning based on cloud model in time series prediction

    Institute of Scientific and Technical Information of China (English)

    张锦春; 胡谷雨

    2003-01-01

    Time series prediction has been successfully used in several application areas, such as meteoro-logical forecasting, market prediction, network traffic forecasting, etc. , and a number of techniques have been developed for modeling and predicting time series. In the traditional exponential smoothing method, a fixed weight is assigned to data history, and the trend changes of time series are ignored. In this paper, an uncertainty reasoning method, based on cloud model, is employed in time series prediction, which uses cloud logic controller to adjust the smoothing coefficient of the simple exponential smoothing method dynamically to fit the current trend of the time series. The validity of this solution was proved by experiments on various data sets.

  19. Application of uncertainty reasoning based on cloud model in time series prediction

    Institute of Scientific and Technical Information of China (English)

    张锦春; 胡谷雨

    2003-01-01

    Time series prediction has been successfully used in several application areas, such as meteorological forecasting, market prediction, network traffic forecasting, etc., and a number of techniques have been developed for modeling and predicting time series. In the traditional exponential smoothing method, a fixed weight is assigned to data history, and the trend changes of time series are ignored. In this paper, an uncertainty reasoning method, based on cloud model, is employed in time series prediction, which uses cloud logic controller to adjust the smoothing coefficient of the simple exponential smoothing method dynamically to fit the current trend of the time series. The validity of this solution was proved by experiments on various data sets.

  20. Application of short-term water demand prediction model to Seoul.

    Science.gov (United States)

    Joo, C N; Koo, J Y; Yu, M J

    2002-01-01

    To predict daily water demand for Seoul, Korea, the artificial neural network (ANN) was used. For the cross correlation, the factors affecting water demand such as maximum temperature, humidity, and wind speed as natural factors, holidays as a social factor and daily demand 1 day before were used. From the results of learning using various hidden layers and units in order to establish the structure of optimal ANN, the case of 3 hidden layers and numbers of unit with the same number of input factors showed the best result and, therefore, it was applied to seasonal water demand prediction. The performance of ANN was compared with a multiple regression method. We discuss the representation ability of the model building process and the applicability of the ANN approach for the daily water demand prediction. ANN provided reasonable results for time series prediction.

  1. Application of computational intelligence platform in coal and gas outburst prediction

    Institute of Scientific and Technical Information of China (English)

    FU Hua; JING Xiao-liang; LIANG Ming-guang

    2012-01-01

    The present situation of lacking fast and effective coal and gas outburst prediction techniques will lead to long outburst prevention cycles and poor accurate prediction effects and slows down coal roadway drive speed seriously.Also,due to historical and economic reasons,some coal mines in China are equipped with poor safety equipment,and the staff professional capability is low.What's worse,artificial and mine geological conditions have great influences on the traditional technologies of coal and gas outburst prediction.Therefore,seeking a new fast and efficient coal and gas outburst prediction method is necessary.By using system engineering theory,combined with the current mine production conditions and based on the coal and gas outburst composite hypothesis,a coal and gas outburst spatiotemporal forecasting system was established.This system can guide forecasting work schedule,optimize prediction technologies,carry out step-by-step prediction and eliminate hazard hierarchically.From the point of view of application,the proposed system improves the prediction efficiency and accuracy.On this basis,computational intelligence methods to construct disaster information analysis platform were used.Feed-back results provide decision support to mine safety supervisors.

  2. Application of Wavelet Entropy to Predict Atrial Fibrillation Progression from the Surface ECG

    Directory of Open Access Journals (Sweden)

    Raúl Alcaraz

    2012-01-01

    Full Text Available Atrial fibrillation (AF is the most common supraventricular arrhythmia in clinical practice, thus, being the subject of intensive research both in medicine and engineering. Wavelet Entropy (WE is a measure of the disorder degree of a specific phenomena in both time and frequency domains, allowing to reveal underlying dynamical processes out of sight for other methods. The present work introduces two different WE applications to the electrocardiogram (ECG of patients in AF. The first application predicts the spontaneous termination of paroxysmal AF (PAF, whereas the second one deals with the electrical cardioversion (ECV outcome in persistent AF patients. In both applications, WE was used with the objective of assessing the atrial fibrillatory (f waves organization. Structural changes into the f waves reflect the atrial activity organization variation, and this fact can be used to predict AF progression. To this respect, results in the prediction of PAF termination regarding sensitivity, specificity, and accuracy were 95.38%, 91.67%, and 93.60%, respectively. On the other hand, for ECV outcome prediction, 85.24% sensitivity, 81.82% specificity, and 84.05% accuracy were obtained. These results turn WE as the highest single predictor of spontaneous PAF termination and ECV outcome, thus being a promising tool to characterize non-invasive AF signals.

  3. On quantitative prediction of mine structure and application of the method

    Institute of Scientific and Technical Information of China (English)

    夏玉成; 樊怀仁

    2002-01-01

    Coal mining activity is often restricted by geologic structural conditions, so it is very important to know the distribution situation of mine structures in advance of mining. For this reason, traditional qualitative procedure must give way to quantitative prediction method backed by mathematics theory and computer technology. This paper explores some relevant problems with the method, introducing a software, MSPS, used to predict automatically and quantitatively the relative complexities of geologic structures in different blocks of a coal mining area, with an application example employing the software to select the most suitable mining sites.

  4. Scenario-based, closed-loop model predictive control with application to emergency vehicle scheduling

    Science.gov (United States)

    Goodwin, Graham. C.; Medioli, Adrian. M.

    2013-08-01

    Model predictive control has been a major success story in process control. More recently, the methodology has been used in other contexts, including automotive engine control, power electronics and telecommunications. Most applications focus on set-point tracking and use single-sequence optimisation. Here we consider an alternative class of problems motivated by the scheduling of emergency vehicles. Here disturbances are the dominant feature. We develop a novel closed-loop model predictive control strategy aimed at this class of problems. We motivate, and illustrate, the ideas via the problem of fluid deployment of ambulance resources.

  5. Theory of multivariate compound extreme value distribution and its application to extreme sea state prediction

    Institute of Scientific and Technical Information of China (English)

    LIU Defu; WANG Liping; PANG Liang

    2006-01-01

    In this paper, a new type of distribution,multivariate compound extreme value distribution(MCEVD), is introduced by compounding a discrete distribution with a multivariate continuous distribution of extreme sea events. In its engineering application the number over certain threshold level per year is fitting to Poisson distribution and the corresponding extreme sea events are fitting to Nested Logistic distribution, then the Poisson-Nested logistic trivariate compound extreme value distribution (PNLTCED) is proposed to predict extreme wave heights, periods and wind speeds in Yellow Sea. The new model gives more stable and reasonable predicted results.

  6. Incremental multivariable predictive functional control and its application in a gas fractionation unit

    Institute of Scientific and Technical Information of China (English)

    施惠元; 苏成利; 曹江涛; 李平; 宋英莉; 李宁波

    2015-01-01

    The control of gas fractionation unit (GFU) in petroleum industry is very difficult due to multivariable characteristics and a large time delay. PID controllers are still applied in most industry processes. However, the traditional PID control has been proven not sufficient and capable for this particular petro-chemical process. In this work, an incremental multivariable predictive functional control (IMPFC) algorithm was proposed with less online computation, great precision and fast response. An incremental transfer function matrix model was set up through the step-response data, and predictive outputs were deduced with the theory of single-value optimization. The results show that the method can optimize the incremental control variable and reject the constraint of the incremental control variable with the positional predictive functional control algorithm, and thereby making the control variable smoother. The predictive output error and future set-point were approximated by a polynomial, which can overcome the problem under the model mismatch and make the predictive outputs track the reference trajectory. Then, the design of incremental multivariable predictive functional control was studied. Simulation and application results show that the proposed control strategy is effective and feasible to improve control performance and robustness of process.

  7. Disorder Prediction Methods, Their Applicability to Different Protein Targets and Their Usefulness for Guiding Experimental Studies

    Directory of Open Access Journals (Sweden)

    Jennifer D. Atkins

    2015-08-01

    Full Text Available The role and function of a given protein is dependent on its structure. In recent years, however, numerous studies have highlighted the importance of unstructured, or disordered regions in governing a protein’s function. Disordered proteins have been found to play important roles in pivotal cellular functions, such as DNA binding and signalling cascades. Studying proteins with extended disordered regions is often problematic as they can be challenging to express, purify and crystallise. This means that interpretable experimental data on protein disorder is hard to generate. As a result, predictive computational tools have been developed with the aim of predicting the level and location of disorder within a protein. Currently, over 60 prediction servers exist, utilizing different methods for classifying disorder and different training sets. Here we review several good performing, publicly available prediction methods, comparing their application and discussing how disorder prediction servers can be used to aid the experimental solution of protein structure. The use of disorder prediction methods allows us to adopt a more targeted approach to experimental studies by accurately identifying the boundaries of ordered protein domains so that they may be investigated separately, thereby increasing the likelihood of their successful experimental solution.

  8. The application study on the multi-scales integrated prediction method to fractured reservoir description

    Institute of Scientific and Technical Information of China (English)

    Chen Shuang-Quan; Zeng Lian-Bo; Huang Ping; Sun Shao-Han; Zhang Wan-Lu; Li Xiang-Yang

    2016-01-01

    In this paper, we implement three scales of fracture integrated prediction study by classifying it to macro- (> 1/4λ), meso- (> 1/100λ and < 1/4λ) and micro- (< 1/100λ) scales. Based on the multi-scales rock physics modelling technique, the seismic azimuthal anisotropy characteristic is analyzed for distinguishing the fractures of meso-scale. Furthermore, by integrating geological core fracture description, image well-logging fracture interpretation, seismic attributes macro-scale fracture prediction and core slice micro-scale fracture characterization, an comprehensive multi-scale fracture prediction methodology and technique workfl ow are proposed by using geology, well-logging and seismic multi-attributes. Firstly, utilizing the geology core slice observation (Fractures description) and image well-logging data interpretation results, the main governing factors of fracture development are obtained, and then the control factors of the development of regional macro-scale fractures are carried out via modelling of the tectonic stress field. For the meso-scale fracture description, the poststack geometric attributes are used to describe the macro-scale fracture as well, the prestack attenuation seismic attribute is used to predict the meso-scale fracture. Finally, by combining lithological statistic inversion with superposed results of faults, the relationship of the meso-scale fractures, lithology and faults can be reasonably interpreted and the cause of meso-scale fractures can be verified. The micro-scale fracture description is mainly implemented by using the electron microscope scanning of cores. Therefore, the development of fractures in reservoirs is assessed by valuating three classes of fracture prediction results. An integrated fracture prediction application to a realfi eld in Sichuan basin, where limestone reservoir fractures developed, is implemented. The application results in the study area indicates that the proposed multi-scales integrated

  9. A Foundation for the Accurate Prediction of the Soft Error Vulnerability of Scientific Applications

    Energy Technology Data Exchange (ETDEWEB)

    Bronevetsky, G; de Supinski, B; Schulz, M

    2009-02-13

    Understanding the soft error vulnerability of supercomputer applications is critical as these systems are using ever larger numbers of devices that have decreasing feature sizes and, thus, increasing frequency of soft errors. As many large scale parallel scientific applications use BLAS and LAPACK linear algebra routines, the soft error vulnerability of these methods constitutes a large fraction of the applications overall vulnerability. This paper analyzes the vulnerability of these routines to soft errors by characterizing how their outputs are affected by injected errors and by evaluating several techniques for predicting how errors propagate from the input to the output of each routine. The resulting error profiles can be used to understand the fault vulnerability of full applications that use these routines.

  10. A hybrid statistical-dynamical framework for meteorological drought prediction: Application to the southwestern United States

    Science.gov (United States)

    Madadgar, Shahrbanou; AghaKouchak, Amir; Shukla, Shraddhanand; Wood, Andrew W.; Cheng, Linyin; Hsu, Kou-Lin; Svoboda, Mark

    2016-07-01

    Improving water management in water stressed-regions requires reliable seasonal precipitation predication, which remains a grand challenge. Numerous statistical and dynamical model simulations have been developed for predicting precipitation. However, both types of models offer limited seasonal predictability. This study outlines a hybrid statistical-dynamical modeling framework for predicting seasonal precipitation. The dynamical component relies on the physically based North American Multi-Model Ensemble (NMME) model simulations (99 ensemble members). The statistical component relies on a multivariate Bayesian-based model that relates precipitation to atmosphere-ocean teleconnections (also known as an analog-year statistical model). Here the Pacific Decadal Oscillation (PDO), Multivariate ENSO Index (MEI), and Atlantic Multidecadal Oscillation (AMO) are used in the statistical component. The dynamical and statistical predictions are linked using the so-called Expert Advice algorithm, which offers an ensemble response (as an alternative to the ensemble mean). The latter part leads to the best precipitation prediction based on contributing statistical and dynamical ensembles. It combines the strength of physically based dynamical simulations and the capability of an analog-year model. An application of the framework in the southwestern United States, which has suffered from major droughts over the past decade, improves seasonal precipitation predictions (3-5 month lead time) by 5-60% relative to the NMME simulations. Overall, the hybrid framework performs better in predicting negative precipitation anomalies (10-60% improvement over NMME) than positive precipitation anomalies (5-25% improvement over NMME). The results indicate that the framework would likely improve our ability to predict droughts such as the 2012-2014 event in the western United States that resulted in significant socioeconomic impacts.

  11. Application and limitations on thermal and spectroscopic methods for shelf-life prediction

    Energy Technology Data Exchange (ETDEWEB)

    Woo, Lecon [Baxter Healthcare, Round Lake, IL 60073 (United States)]. E-mail: Wool@baxter.com; Ling, Michael T.K. [Baxter Healthcare, Round Lake, IL 60073 (United States); Eu, Bruce [Baxter Healthcare, Round Lake, IL 60073 (United States); Sandford, Craig [Baxter Healthcare, Round Lake, IL 60073 (United States)

    2006-03-15

    In medical products, shelf-life after thermoplastic processing and sterilization is important, and ionizing radiation has become a preferred sterilization mode for medical devices. We have employed successfully thermal analytical methods to predict shelf-life for many polyolefin materials. However, as the material of construction becoming more sophisticated: multiphase alloys and blends, multi-layer constructions, etc., issues existed that require clarification as to what extent these methodologies are applicable. We have employed thermal analytical methods in conjunction with other spectroscopic and morphological methods to study the applicability and limitation of these techniques. Results combined with real life and simulated aging experiments will be presented in this article.

  12. Prediction of Critical Power and W' in Hypoxia: Application to Work-Balance Modelling.

    Science.gov (United States)

    Townsend, Nathan E; Nichols, David S; Skiba, Philip F; Racinais, Sebastien; Périard, Julien D

    2017-01-01

    Purpose: Develop a prediction equation for critical power (CP) and work above CP (W') in hypoxia for use in the work-balance ([Formula: see text]) model. Methods: Nine trained male cyclists completed cycling time trials (TT; 12, 7, and 3 min) to determine CP and W' at five altitudes (250, 1,250, 2,250, 3,250, and 4,250 m). Least squares regression was used to predict CP and W' at altitude. A high-intensity intermittent test (HIIT) was performed at 250 and 2,250 m. Actual and predicted CP and W' were used to compute W' during HIIT using differential ([Formula: see text]) and integral ([Formula: see text]) forms of the [Formula: see text] model. Results: CP decreased at altitude (P study are suitable for use with the [Formula: see text] model in acute hypoxia. This enables the application of [Formula: see text] modelling to training prescription and competition analysis at altitude.

  13. Genetic Modeling of GIS-Based Cell Clusters and Its Application in Mineral Resources Prediction

    Institute of Scientific and Technical Information of China (English)

    2003-01-01

    This paper presents a synthetic analysis method for multi-sourced geological data from geographic information system (GIS). In the previous practices of mineral resources prediction, a usually adopted methodology has been statistical analysis of cells delimitated based on thoughts of random sampiing. That might lead to insufficient utilization of local spatial information, for a cell is treated as a point without internal structure. We now take "cell clusters", i. e. , spatial associations of cells, as basic units of statistics, thus the spatial configuration information of geological variables is easier to be detected and utilized, and the accuracy and reliability of prediction are improved. We build a linear multi-discriminating model for the clusters via genetic algorithm. Both the right-judgment rates and the in-class vs. between-class distance ratios are considered to form the evolutional adaptive values of the population. An application of the method in gold mineral resources prediction in east Xinjiang, China is presented.

  14. Application of rheology for assessment and prediction of the long-term physical stability of emulsions.

    Science.gov (United States)

    Tadros, Tharwat

    2004-05-20

    This review deals with the use of rheology for assessment and prediction of the long-term physical stability of emulsions. It starts with an introduction, highlighting the importance of having accelerated test to predict emulsion stability. This is followed by a section on the stability/instability of emulsion systems, giving a brief summary of the driving force of each instability process and its prevention. The classical techniques that can be applied for assessment of creaming or sedimentation, flocculation, Ostwald ripening, coalescence and phase inversion are briefly described. This is followed by several sections on the application of rheological techniques to assess and predict each of these instabilities. This involves the use of steady state shear stress-shear rate measurements, constant stress (creep) measurements and dynamic (oscillatory) techniques. The last section gives an example of model emulsions to illustrate the correlation between the various break-down processes with the rheological characteristics of the system.

  15. PREDICTION TECHNIQUES OF CHAOTIC TIME SERIES AND ITS APPLICATIONS AT LOW NOISE LEVEL

    Institute of Scientific and Technical Information of China (English)

    MA Jun-hai; WANG Zhi-qiang; CHEN Yu-shu

    2006-01-01

    The paper not only studies the noise reduction methods of chaotic time series with noise and its reconstruction techniques, but also discusses prediction techniques of chaotic time series and its applications based on chaotic data noise reduction. In the paper, we first decompose the phase space of chaotic time series to range space and null noise space. Secondly we restructure original chaotic time series in range space. Lastly on the basis of the above, we establish order of the nonlinear model and make use of the nonlinear model to predict some research. The result indicates that the nonlinear modelhas very strong ability of approximation function, and Chaos predict method has certain tutorial significance to the practical problems.

  16. Constrained Predictive Control and its application to a Coupled-tanks Apparatus

    DEFF Research Database (Denmark)

    Poulsen, Niels Kjølstad; Kouvaritakis, Basil; Cannon, Mark

    2001-01-01

    The focus of this paper is the development and application to experimental equipment of fast constrained predictive control algorithms. A review of QP based algorithm and an alternative using interpolation and LP is considered. Despite its undemanding computational nature, the latter algorithm is...... is found to perform well both in simulation and when applied to an actual coupled-tanks rig. The advantages of the algorithm are further illustrated by comparison with desaturated PID control....

  17. Comparisons and applications of numerical simulation methods for predicting aerodynamic heating around complex configurations

    Institute of Scientific and Technical Information of China (English)

    Jin-Ling Luo; Hong-Lin Kang; Jian Li; Wu-Ye Dai

    2011-01-01

    Numerical simulation methods of aerodynamic heating were compared by considering the influence of numerical schemes and turbulence models, and attempting to investigate the applicability of numerical simulation methods on predicting heat flux in engineering applications. For some typical cases provided with detailed experimental data, four spatial schemes and four turbulence models were adopted to calculate surface heat flux. By analyzing and comparing,some influencing regularities of numerical schemes and turbulence models on calculating heat flux had been acquired. It is clear that AUSM+-up scheme with rapid compressibilitymodified high Reynolds number k-ω model should be appropriate for calculating heat flux. The numerical methods selected as preference above were applied to calculate the heat flux of a 3-D complex geometry in high speed turbulent flows. The results indicated that numerical simulation can capture the complex flow phenomena and reveal the mechanism of aerodynamic heating. Especially, the numerical result of the heat flux at the stagnation point of the wedge was well in agreement with the prediction of Kemp-Riddel formula, and the surface heat flux distribution was consistent with experiment results, which implied that numerical simulation can be introduced to predict heat flux in engineering applications.

  18. Prediction of Protein-Peptide Interactions: Application of the XPairIT to Anthrax Lethal Factor and Substrates

    Science.gov (United States)

    2013-09-01

    Prediction of Protein-Peptide Interactions: Application of the XPairIt API to Anthrax Lethal Factor and Substrates by Margaret M. Hurley and...Peptide Interactions: Application of the XPairIt API to Anthrax Lethal Factor and Substrates Margaret M. Hurley and Michael S. Sellers Weapons and...Prediction of Protein-Peptide Interactions: Application of the XPairIt API to Anthrax Lethal Factor and Substrates 5a. CONTRACT NUMBER ORAUW911QX-04-C

  19. Life prediction of coated and uncoated metallic interconnect for solid oxide fuel cell applications

    Science.gov (United States)

    Liu, W. N.; Sun, X.; Stephens, E.; Khaleel, M. A.

    In this paper, we present an integrated experimental and modeling methodology in predicting the life of coated and uncoated metallic interconnect (IC) for solid oxide fuel cell (SOFC) applications. The ultimate goal is to provide cell designer and manufacture with a predictive methodology such that the life of the IC system can be managed and optimized through different coating thickness to meet the overall cell designed life. Crofer 22 APU is used as the example IC material system. The life of coated and uncoated Crofer 22 APU under isothermal cooling was predicted by comparing the predicted interfacial strength and the interfacial stresses induced by the cooling process from the operating temperature to room temperature, together with the measured oxide scale growth kinetics. It was found that the interfacial strength between the oxide scale and the Crofer 22 APU substrate decreases with the growth of the oxide scale, and that the interfacial strength for the oxide scale/spinel coating interface is much higher than that of the oxide scale/Crofer 22 APU substrate interface. As expected, the predicted life of the coated Crofer 22 APU is significantly longer than that of the uncoated Crofer 22 APU.

  20. Link prediction measures considering different neighbors’ effects and application in social networks

    Science.gov (United States)

    Luo, Peng; Wu, Chong; Li, Yongli

    Link prediction measures have been attracted particular attention in the field of mathematical physics. In this paper, we consider the different effects of neighbors in link prediction and focus on four different situations: only consider the individual’s own effects; consider the effects of individual, neighbors and neighbors’ neighbors; consider the effects of individual, neighbors, neighbors’ neighbors, neighbors’ neighbors’ neighbors and neighbors’ neighbors’ neighbors’ neighbors; consider the whole network participants’ effects. Then, according to the four situations, we present our link prediction models which also take the effects of social characteristics into consideration. An artificial network is adopted to illustrate the parameter estimation based on logistic regression. Furthermore, we compare our methods with the some other link prediction methods (LPMs) to examine the validity of our proposed model in online social networks. The results show the superior of our proposed link prediction methods compared with others. In the application part, our models are applied to study the social network evolution and used to recommend friends and cooperators in social networks.

  1. Introducing a Novel Applicant Ranking Tool to Predict Future Resident Performance: A Pilot Study.

    Science.gov (United States)

    Bowe, Sarah N; Weitzel, Erik K; Hannah, William N; Fitzgerald, Brian M; Kraus, Gregory P; Nagy, Christopher J; Harrison, Stephen A

    2017-01-01

    The purposes of this study are to (1) introduce our novel Applicant Ranking Tool that aligns with the Accreditation Council for Graduate Medical Education competencies and (2) share our preliminary results comparing applicant rank to current performance. After a thorough literature review and multiple roundtable discussions, an Applicant Ranking Tool was created. Feasibility, satisfaction, and critiques were discussed via open feedback session. Inter-rater reliability was assessed using weighted kappa statistic (κ) and Kendall coefficient of concordance (W). Fisher's exact tests evaluated the ability of the tool to stratify performance into the top or bottom half of their class. Internal medicine and anesthesiology residents served as the pilot cohorts. The tool was considered user-friendly for both data input and analysis. Inter-rater reliability was strongest with intradisciplinary evaluation (W = 0.8-0.975). Resident performance was successfully stratified into those functioning in the upper vs. lower half of their class within the Clinical Anesthesia-3 grouping (p = 0.008). This novel Applicant Ranking Tool lends support for the use of both cognitive and noncognitive traits in predicting resident performance. While the ability of this instrument to accurately predict future resident performance will take years to answer, this pilot study suggests the instrument is worthy of ongoing investigation.

  2. Comparison of FDA Approved Kinase Targets to Clinical Trial Ones: Insights from Their System Profiles and Drug-Target Interaction Networks

    Directory of Open Access Journals (Sweden)

    Jingyu Xu

    2016-01-01

    Full Text Available Kinase is one of the most productive classes of established targets, but the majority of approved drugs against kinase were developed only for cancer. Intensive efforts were therefore exerted for releasing its therapeutic potential by discovering new therapeutic area. Kinases in clinical trial could provide great opportunities for treating various diseases. However, no systematic comparison between system profiles of established targets and those of clinical trial ones was conducted. The reveal of probable difference or shift of trend would help to identify key factors defining druggability of established targets. In this study, a comparative analysis of system profiles of both types of targets was conducted. Consequently, the systems profiles of the majority of clinical trial kinases were identified to be very similar to those of established ones, but percentages of established targets obeying the system profiles appeared to be slightly but consistently higher than those of clinical trial targets. Moreover, a shift of trend in the system profiles from the clinical trial to the established targets was identified, and popular kinase targets were discovered. In sum, this comparative study may help to facilitate the identification of the druggability of established drug targets by their system profiles and drug-target interaction networks.

  3. COBRA: A Computational Brewing Application for Predicting the Molecular Composition of Organic Aerosols

    Energy Technology Data Exchange (ETDEWEB)

    Fooshee, David R.; Nguyen, Tran B.; Nizkorodov, Sergey A.; Laskin, Julia; Laskin, Alexander; Baldi, Pierre

    2012-05-08

    Atmospheric organic aerosols (OA) represent a significant fraction of airborne particulate matter and can impact climate, visibility, and human health. These mixtures are difficult to characterize experimentally due to the enormous complexity and dynamic nature of their chemical composition. We introduce a novel Computational Brewing Application (COBRA) and apply it to modeling oligomerization chemistry stemming from condensation and addition reactions of monomers pertinent to secondary organic aerosol (SOA) formed by photooxidation of isoprene. COBRA uses two lists as input: a list of chemical structures comprising the molecular starting pool, and a list of rules defining potential reactions between molecules. Reactions are performed iteratively, with products of all previous iterations serving as reactants for the next one. The simulation generated thousands of molecular structures in the mass range of 120-500 Da, and correctly predicted ~70% of the individual SOA constituents observed by high-resolution mass spectrometry (HR-MS). Selected predicted structures were confirmed with tandem mass spectrometry. Esterification and hemiacetal formation reactions were shown to play the most significant role in oligomer formation, whereas aldol condensation was shown to be insignificant. COBRA is not limited to atmospheric aerosol chemistry, but is broadly applicable to the prediction of reaction products in other complex mixtures for which reasonable reaction mechanisms and seed molecules can be supplied by experimental or theoretical methods.

  4. Application of infinite model predictive control methodology to other advanced controllers.

    Science.gov (United States)

    Abu-Ayyad, M; Dubay, R; Hernandez, J M

    2009-01-01

    This paper presents an application of most recent developed predictive control algorithm an infinite model predictive control (IMPC) to other advanced control schemes. The IMPC strategy was derived for systems with different degrees of nonlinearity on the process gain and time constant. Also, it was shown that IMPC structure uses nonlinear open-loop modeling which is conducted while closed-loop control is executed every sampling instant. The main objective of this work is to demonstrate that the methodology of IMPC can be applied to other advanced control strategies making the methodology generic. The IMPC strategy was implemented on several advanced controllers such as PI controller using Smith-Predictor, Dahlin controller, simplified predictive control (SPC), dynamic matrix control (DMC), and shifted dynamic matrix (m-DMC). Experimental work using these approaches combined with IMPC was conducted on both single-input-single-output (SISO) and multi-input-multi-output (MIMO) systems and compared with the original forms of these advanced controllers. Computer simulations were performed on nonlinear plants demonstrating that the IMPC strategy can be readily implemented on other advanced control schemes providing improved control performance. Practical work included real-time control applications on a DC motor, plastic injection molding machine and a MIMO three zone thermal system.

  5. Study on Application of Grey Prediction Model in Superalloy MAR-247 Machining

    Directory of Open Access Journals (Sweden)

    Chen Shao-Hsien

    2015-01-01

    Full Text Available Superalloy MAR-247 is mainly applied in the space industry and die industry. With its characteristics of mechanical property, fatigue resistance, and high temperature corrosion resistance, therefore, it is mainly applied in machine parts of high temperature and corrosion resistance, such as turbine blades and rotor of the aeroengine and turbine assembly in the nuclear power plant. However, considering that its properties of high strength, low thermal conductivity, being difficult to soften, and work hardening may reduce the life of cutting-tool and weaken the surface accuracy, the study provided minimizing experiment occurring during milling process for superalloy material. As a statistical approach used to analyse experiment data, this study used GM(1,1 in the grey prediction model to conduct simulation and then predict and analyze its characteristics based on the experimental data, focusing on the tool life and surface accuracy. Moreover, with the superalloy machining parameters of the current effective application improved grey prediction model, it can decrease the errors, extend the tool life, and improve the prediction precision of surface accuracy.

  6. Chemometrics applications in biotechnology processes: predicting column integrity and impurity clearance during reuse of chromatography resin.

    Science.gov (United States)

    Rathore, Anurag S; Mittal, Shachi; Lute, Scott; Brorson, Kurt

    2012-01-01

    Separation media, in particular chromatography media, is typically one of the major contributors to the cost of goods for production of a biotechnology therapeutic. To be cost-effective, it is industry practice that media be reused over several cycles before being discarded. The traditional approach for estimating the number of cycles a particular media can be reused for involves performing laboratory scale experiments that monitor column performance and carryover. This dataset is then used to predict the number of cycles the media can be used at manufacturing scale (concurrent validation). Although, well accepted and widely practiced, there are challenges associated with extrapolating the laboratory scale data to manufacturing scale due to differences that may exist across scales. Factors that may be different include: level of impurities in the feed material, lot to lot variability in feedstock impurities, design of the column housing unit with respect to cleanability, and homogeneity of the column packing. In view of these challenges, there is a need for approaches that may be able to predict column underperformance at the manufacturing scale over the product lifecycle. In case such an underperformance is predicted, the operators can unpack and repack the chromatography column beforehand and thus avoid batch loss. Chemometrics offers one such solution. In this article, we present an application of chemometrics toward the analysis of a set of chromatography profiles with the intention of predicting the various events of column underperformance including the backpressure buildup and inefficient deoxyribonucleic acid clearance.

  7. Grey Markov chain and its application in drift prediction model of FOGs

    Institute of Scientific and Technical Information of China (English)

    Fan Chunling; Jin Zhihua; Tian Weifeng; Qian Feng

    2005-01-01

    A novel grey Markov chain predictive model is discussed to reduce drift influence on the output of fiber optical gyroscopes (FOGs) and to improve FOGs' measurement precision. The proposed method possesses advantages of grey model and Markov chain. It makes good use of dynamic modeling idea of the grey model to predict general trend of original data. Then according to the trend, states are divided so that it can overcome the disadvantage of high computational cost of state transition probability matrix in Markov chain. Moreover, the presented approach expands the applied scope of the grey model and makes it be fit for prediction of random data with bigger fluctuation. The numerical results of real drift data from a certain type FOG verify the effectiveness of the proposed grey Markov chain model powerfully. The Markov chain is also investigated to provide a comparison with the grey Markov chain model. It is shown that the hybrid grey Markov chain prediction model has higher modeling precision than Markov chain itself, which prove this proposed method is very applicable and effective.

  8. MEDICI: Mining Essentiality Data to Identify Critical Interactions for Cancer Drug Target Discovery and Development | Office of Cancer Genomics

    Science.gov (United States)

    Protein-protein interactions (PPIs) mediate the transmission and regulation of oncogenic signals that are essential to cellular proliferation and survival, and thus represent potential targets for anti-cancer therapeutic discovery. Despite their significance, there is no method to experimentally disrupt and interrogate the essentiality of individual endogenous PPIs. The ability to computationally predict or infer PPI essentiality would help prioritize PPIs for drug discovery and help advance understanding of cancer biology.

  9. On the applicability of brain reading for predictive human-machine interfaces in robotics.

    Directory of Open Access Journals (Sweden)

    Elsa Andrea Kirchner

    Full Text Available The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR, a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

  10. Recent progress in nanomedicine: therapeutic, diagnostic and theranostic applications

    NARCIS (Netherlands)

    Rizzo, L.Y.; Theek, B.; Storm, G.; Kiessling, F.; Lammers, T.G.G.M.

    2013-01-01

    In recent years, the use of nanomedicine formulations for therapeutic and diagnostic applications has increased exponentially. Many different systems and strategies have been developed for drug targeting to pathological sites, as well as for visualizing and quantifying important (patho-) physiologic

  11. Applications of Kalman filters based on non-linear functions to numerical weather predictions

    Directory of Open Access Journals (Sweden)

    G. Galanis

    2006-10-01

    Full Text Available This paper investigates the use of non-linear functions in classical Kalman filter algorithms on the improvement of regional weather forecasts. The main aim is the implementation of non linear polynomial mappings in a usual linear Kalman filter in order to simulate better non linear problems in numerical weather prediction. In addition, the optimal order of the polynomials applied for such a filter is identified. This work is based on observations and corresponding numerical weather predictions of two meteorological parameters characterized by essential differences in their evolution in time, namely, air temperature and wind speed. It is shown that in both cases, a polynomial of low order is adequate for eliminating any systematic error, while higher order functions lead to instabilities in the filtered results having, at the same time, trivial contribution to the sensitivity of the filter. It is further demonstrated that the filter is independent of the time period and the geographic location of application.

  12. Generalized Pareto Distribution Model and Its Application to Hydrocarbon Resource Structure Prediction of the Huanghua Depression

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    The generalized Pareto distribution model is a kind of hydrocarbon pool size probability statistical method for resource assessment. By introducing the time variable, resource conversion rate and the geological variable, resource density, such model can describe not only different types of basins, but also any exploration samples at different phases of exploration, up to the parent population. It is a dynamic distribution model with profound geological significance and wide applicability. Its basic principle and the process of resource assessment are described in this paper. The petroleum accumulation system is an appropriate assessment unit for such method. The hydrocarbon resource structure of the Huanghua Depression in Bohai Bay Basin was predicted by using this model. The prediction results accord with the knowledge of exploration in the Huanghua Depression, and point out the remaining resources potential and structure of different petroleum accumulation systems, which are of great significance for guiding future exploration in the Huanghua Depression.

  13. Analysis of Linear Prediction for Soil Characterization in GPR Data for Countermine Applications

    Science.gov (United States)

    Ratto, Christopher R.; Morton, Kenneth D.; Collins, Leslie M.; Torrione, Peter A.

    2014-11-01

    Ground-penetrating radar (GPR) is a versatile technology for subsurface sensing, and has shown promise in countermine applications when a target detection algorithm is employed. Because the soil environment is naturally heterogeneous and nonstationary, many detection algorithms have taken the form of adaptive filters operating on the real-aperture radar data. In particular, linear prediction techniques have received much attention for their ability to screen for anomalous signals that differ from the background. In this work, we demonstrate that linear prediction may provide a low-dimensional feature set that is indicative of various soil properties. Experiments were performed with simulated and field-collected GPR data, and results provide greater understanding of how linear predictors might be useful in landmine detection over varying terrain.

  14. Use of Microarray Test Data for Toxicogenomic Prediction-Multi-Intelligent Systems for Toxicogenomic Applications (MISTA)

    Energy Technology Data Exchange (ETDEWEB)

    Wasson, J.S.; Lu, P.-Y.

    2005-09-12

    The YAHSGS LLC and Oak Ridge National Laboratory established a CRADA to develop a computational neural network and wavelets software to facilitate providing national needs for toxicity prediction and overcome the voracious drain of resources (money and time) being directed to the development of pharmaceutical agents. The research project was supported through a STTR Phase I task by NIEHS in 2004. The research deploys state-of-the-art computational neural networks and wavelets to make toxicity prediction on three independent bases: (1) quantitative structure-activity relationships, (2) microarray data, and (3) Massively Parallel Signature Sequencing technology. Upon completion of Phase I, a prototype software Multi-Intelligent System for Toxicogenomic and Applications (MISTA) was developed, the utility's feasibility was demonstrated, and a Phase II proposal was jointly prepared and submitted to NIEHS for funding evaluation. The goals and objectives of the program have been achieved.

  15. Analysis Of The Method Of Predictive Control Applicable To Active Magnetic Suspension Systems Of Aircraft Engines

    Directory of Open Access Journals (Sweden)

    Kurnyta-Mazurek Paulina

    2015-12-01

    Full Text Available Conventional controllers are usually synthesized on the basis of already known parameters associated with the model developed for the object to be controlled. However, sometimes it proves extremely difficult or even infeasible to find out these parameters, in particular when they subject to changes during the exploitation lifetime. If so, much more sophisticated control methods have to be applied, e.g. the method of predictive control. Thus, the paper deals with application of the predictive control approach to follow-up tracking of an active magnetic suspension where the mathematical and simulation models for such a control system are disclosed with preliminary results from simulation investigations of the control system in question.

  16. AN APPLICATION OF HYBRID CLUSTERING AND NEURAL BASED PREDICTION MODELLING FOR DELINEATION OF MANAGEMENT ZONES

    Directory of Open Access Journals (Sweden)

    Babankumar S. Bansod

    2011-02-01

    Full Text Available Starting from descriptive data on crop yield and various other properties, the aim of this study is to reveal the trends on soil behaviour, such as crop yield. This study has been carried out by developing web application that uses a well known technique- Cluster Analysis. The cluster analysis revealed linkages between soil classes for the same field as well as between different fields, which can be partly assigned to crops rotation and determination of variable soil input rates. A hybrid clustering algorithm has been developed taking into account the traits of two clustering technologies: i Hierarchical clustering, ii K-means clustering. This hybrid clustering algorithm is applied to sensor- gathered data about soil and analysed, resulting in the formation of well delineatedmanagement zones based on various properties of soil, such as, ECa , crop yield, etc. One of the purposes of the study was to identify the main factors affecting the crop yield and the results obtained were validated with existing techniques. To accomplish this purpose, geo-referenced soil information has been examined. Also, based on this data, statistical method has been used to classify and characterize the soil behaviour. This is done using a prediction model, developed to predict the unknown behaviour of clusters based on the known behaviour of other clusters. In predictive modeling, data has been collected for the relevant predictors, a statistical model has been formulated, predictions were made and the model can be validated (or revised as additional data becomes available. The model used in the web application has been formed taking into account neural network based minimum hamming distance criterion.

  17. Industrial application of model predictive control to a milk powder spray drying plant

    DEFF Research Database (Denmark)

    Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik;

    2016-01-01

    In this paper, we present our first results from an industrial application of model predictive control (MPC) with real-time steady-state target optimization (RTO) for control of an industrial spray dryer that produces enriched milk powder. The MPC algorithm is based on a continuous-time transfer...... provides significantly better control of the residual moisture content, increases the throughput and decreases the energy consumption compared to conventional PI-control. The MPC operates the spray dryer closer to the residual moisture constraint of the powder product. Thus, the same amount of feed...

  18. Application of the cracked pipe element to creep crack growth prediction

    Energy Technology Data Exchange (ETDEWEB)

    Brochard, J.; Charras, T. [C.E.A.-C.E.-Saclay DRN/DMT, Gif Sur Yvette (France); Ghoudi, M. [C.E.A.-C.E.-Saclay, Gif Sur Yvette (France)

    1997-04-01

    Modifications to a computer code for ductile fracture assessment of piping systems with postulated circumferential through-wall cracks under static or dynamic loading are very briefly described. The modifications extend the capabilities of the CASTEM2000 code to the determination of fracture parameters under creep conditions. The main advantage of the approach is that thermal loads can be evaluated as secondary stresses. The code is applicable to piping systems for which crack propagation predictions differ significantly depending on whether thermal stresses are considered as primary or secondary stresses.

  19. High applicability of two-dimensional phosphorous in Kagome lattice predicted from first-principles calculations

    OpenAIRE

    Peng-Jen Chen; Horng-Tay Jeng

    2016-01-01

    A new semiconducting phase of two-dimensional phosphorous in the Kagome lattice is proposed from first-principles calculations. The band gaps of the monolayer (ML) and bulk Kagome phosphorous (Kagome-P) are 2.00 and 1.11 eV, respectively. The magnitude of the band gap is tunable by applying the in-plane strain and/or changing the number of stacking layers. High optical absorption coefficients at the visible light region are predicted for multilayer Kagome-P, indicating potential applications ...

  20. Big Data and Predictive Analytics: Applications in the Care of Children.

    Science.gov (United States)

    Suresh, Srinivasan

    2016-04-01

    Emerging changes in the United States' healthcare delivery model have led to renewed interest in data-driven methods for managing quality of care. Analytics (Data plus Information) plays a key role in predictive risk assessment, clinical decision support, and various patient throughput measures. This article reviews the application of a pediatric risk score, which is integrated into our hospital's electronic medical record, and provides an early warning sign for clinical deterioration. Dashboards that are a part of disease management systems, are a vital tool in peer benchmarking, and can help in reducing unnecessary variations in care.

  1. Interactions of dendrimers with biological drug targets: reality or mystery - a gap in drug delivery and development research.

    Science.gov (United States)

    Ahmed, Shaimaa; Vepuri, Suresh B; Kalhapure, Rahul S; Govender, Thirumala

    2016-07-21

    Dendrimers have emerged as novel and efficient materials that can be used as therapeutic agents/drugs or as drug delivery carriers to enhance therapeutic outcomes. Molecular dendrimer interactions are central to their applications and realising their potential. The molecular interactions of dendrimers with drugs or other materials in drug delivery systems or drug conjugates have been extensively reported in the literature. However, despite the growing application of dendrimers as biologically active materials, research focusing on the mechanistic analysis of dendrimer interactions with therapeutic biological targets is currently lacking in the literature. This comprehensive review on dendrimers over the last 15 years therefore attempts to identify the reasons behind the apparent lack of dendrimer-receptor research and proposes approaches to address this issue. The structure, hierarchy and applications of dendrimers are briefly highlighted, followed by a review of their various applications, specifically as biologically active materials, with a focus on their interactions at the target site. It concludes with a technical guide to assist researchers on how to employ various molecular modelling and computational approaches for research on dendrimer interactions with biological targets at a molecular level. This review highlights the impact of a mechanistic analysis of dendrimer interactions on a molecular level, serves to guide and optimise their discovery as medicinal agents, and hopes to stimulate multidisciplinary research between scientific, experimental and molecular modelling research teams.

  2. Unexpected binding orientation of bulky-B-ring anti-androgens and implications for future drug targets.

    Science.gov (United States)

    Duke, Charles B; Jones, Amanda; Bohl, Casey E; Dalton, James T; Miller, Duane D

    2011-06-01

    Several new androgen receptor antagonists were synthesized and found to have varying activities across typically anti-androgen resistant mutants (Thr877 → Ala and Trp741 → Leu) and markedly improved potency over previously reported pan-antagonists. X-ray crystallography of a new anti-androgen in an androgen receptor mutant (Thr877 → Ala) shows that the receptor can accommodate the added bulk presented by phenyl to naphthyl substitution, casting doubt on previous reports of predicted binding orientation and the causes of antagonism in bulky-B-ring antagonists.

  3. Distributed Measuring System for Predictive Diagnosis of Uninterruptible Power Supplies in Safety-Critical Applications

    Directory of Open Access Journals (Sweden)

    Sergio Saponara

    2016-04-01

    Full Text Available This work proposes a scalable architecture of an Uninterruptible Power Supply (UPS system, with predictive diagnosis capabilities, for safety critical applications. A Failure Mode and Effect Analysis (FMEA has identified the faults occurring in the energy storage unit, based on Valve Regulated Lead-Acid batteries, and in the 3-phase high power transformers, used in switching converters and for power isolation, as the main bottlenecks for power system reliability. To address these issues, a distributed network of measuring nodes is proposed, where vibration-based mechanical stress diagnosis is implemented together with electrical (voltage, current, impedance and thermal degradation analysis. Power system degradation is tracked through multi-channel measuring nodes with integrated digital signal processing in the transformed frequency domain, from 0.1 Hz to 1 kHz. Experimental measurements on real power systems for safety-critical applications validate the diagnostic unit.

  4. Spatial models for probabilistic prediction of wind power with application to annual-average and high temporal resolution data

    DEFF Research Database (Denmark)

    Lenzi, Amanda; Pinson, Pierre; Clemmensen, Line Katrine Harder;

    2016-01-01

    Producing accurate spatial predictions for wind power generation together with a quantification of uncertainties is required to plan and design optimal networks of wind farms. Toward this aim, we propose spatial models for predicting wind power generation at two different time scales: for annual...... that our method makes it possible to obtain fast and accurate predictions from posterior marginals for wind power generation. The proposed method is applicable in scientific areas as diverse as climatology, environmental sciences, earth sciences and epidemiology....

  5. Local structure based method for prediction of the biochemical function of proteins: Applications to glycoside hydrolases.

    Science.gov (United States)

    Parasuram, Ramya; Mills, Caitlyn L; Wang, Zhouxi; Somasundaram, Saroja; Beuning, Penny J; Ondrechen, Mary Jo

    2016-01-15

    similarity at the predicted active site with the known members of the GH16 family, with the closest match to the endoglucanase subfamily. The method discussed herein can predict whether an SG protein is correctly or incorrectly annotated and can sometimes provide a reliable functional annotation. Examples of application of the method across folds, comparing active sites between two proteins of different structural folds, are also given.

  6. Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques

    Directory of Open Access Journals (Sweden)

    Suranjan Panigrahi

    2010-03-01

    of 83.50% to 96.04%. The PVI pool model provided better average testing prediction accuracy of 94% with respect to other vegetation models, for which it ranged from 89–93%. Similarly, the PVI pool model provided coefficient of determination (r2 value of 0.45 as compared to 0.31–0.37 for other index models. Log10 data transformation technique was used to enhance the prediction ability of the PVI models of years 1998, 1999, and 2001 as it was chosen as the preferred index. Another model (Transformed PVI (Pool was developed using the log10 transformed PVI image information to show its global application. The transformed PVI models provided average corn yield prediction accuracies of 90%, 97%, and 98% for years 1998, 1999, and 2001, respectively. The pool PVI transformed model provided as average testing accuracy of 93% along with r2 value of 0.72 and standard error of prediction of 0.05 t/ha.

  7. Advances and applications of binding affinity prediction methods in drug discovery.

    Science.gov (United States)

    Parenti, Marco Daniele; Rastelli, Giulio

    2012-01-01

    Nowadays, the improvement of R&D productivity is the primary commitment in pharmaceutical research, both in big pharma and smaller biotech companies. To reduce costs, to speed up the discovery process and to increase the chance of success, advanced methods of rational drug design are very helpful, as demonstrated by several successful applications. Among these, computational methods able to predict the binding affinity of small molecules to specific biological targets are of special interest because they can accelerate the discovery of new hit compounds. Here we provide an overview of the most widely used methods in the field of binding affinity prediction, as well as of our own work in developing BEAR, an innovative methodology specifically devised to overtake some limitations in existing approaches. The BEAR method was successfully validated against different biological targets, and proved its efficacy in retrieving active compounds from virtual screening campaigns. The results obtained so far indicate that BEAR may become a leading tool in the drug discovery pipeline. We primarily discuss advantages and drawbacks of each technique and show relevant examples and applications in drug discovery.

  8. New drugs targeting the cardiac ultra-rapid delayed-rectifier current (I Kur): rationale, pharmacology and evidence for potential therapeutic value.

    Science.gov (United States)

    Ford, John W; Milnes, James T

    2008-08-01

    There is a clear unmet medical need for new pharmacologic therapies for the treatment of atrial fibrillation (AF) with improved efficacy and safety. This article reviews the development of new and novel Kv1.5/ultra-rapid delayed-rectifier current (I Kur) inhibitors and presents evidence that Kv1.5 modulation provides an atrial-selective mechanism for treating AF. Academia and industry have invested heavily in Kv1.5 (>500 scientific publications and >50 patents published since 1993); however, to realize the full value of this therapeutic drug target, clinical efficacy and safety data are required for a selective Kv1.5 modulator. The reward for demonstrating clinical efficacy and safety in a pivotal Phase 3 trial, on regulatory approval, is "first in class" status.

  9. Design and characterization of a device to quantify the magnetic drug targeting efficiency of magnetic nanoparticles in a tube flow phantom by magnetic particle spectroscopy

    Science.gov (United States)

    Radon, Patricia; Löwa, Norbert; Gutkelch, Dirk; Wiekhorst, Frank

    2017-04-01

    The aim of magnetic drug targeting (MDT) is to transfer a therapeutic drug coupled to magnetic nanoparticles (MNP) to desired disease locations (e.g. tumor region) with the help of magnetic field gradients. To transfer the MDT approach into clinical practice a number of important issues remain to be solved. We developed and characterized an in-vitro flow phantom to provide a defined and reproducible MDT environment. The tube system of the flow phantom is directed through the detection coil of a magnetic particle spectroscopy (MPS) device to determine the targeting efficiency. MPS offers an excellent temporal resolution of seconds and an outstanding specific sensitivity of some nanograms of iron. In the flow phantom different MNP types, magnet geometries and tube materials can be employed to vary physical parameters like diameter, flow rate, magnetic targeting gradient, and MNP properties.

  10. Comparative genomic studies and in-silco strategies on Leishmania brazilensis, Leishmania infantum and Leishmania major: Conserved features, putative functions and potential drug target

    Directory of Open Access Journals (Sweden)

    Rakesh N. R.

    2013-06-01

    Full Text Available Leishmaniasis is a parasitic disease found largely in the tropics, which the World Health Organization has estimated infects 12 million people worldwide each year. More recently cases have been reported in Europe among intravenous drug users with HIV. At least 20 Leishmania species infect humans. New world parasite Leishmania. braziliensis is the causative agent of mucocutaneous Leishmaniasis. The old world species Leishmania. major and Leishmania. infantum, which are present in Africa, Europe and Asia, are parasites that cause cutaneous and visceral Leishmaniasis respectively. Aim of this Study is determination of major common genes and Protein identified Gene location on each of the chromosomes, and identification of a common protein drug target Promastigote surface antigen with available lead molecule acetylglucosamine (6-(acetylamino-6-deoxyhexopyranose and docking studies on those considered Leishmania species.

  11. Novel approach to meta-analysis of microarray datasets reveals muscle remodeling-related drug targets and biomarkers in Duchenne muscular dystrophy.

    Directory of Open Access Journals (Sweden)

    Ekaterina Kotelnikova

    2012-02-01

    Full Text Available Elucidation of new biomarkers and potential drug targets from high-throughput profiling data is a challenging task due to a limited number of available biological samples and questionable reproducibility of differential changes in cross-dataset comparisons. In this paper we propose a novel computational approach for drug and biomarkers discovery using comprehensive analysis of multiple expression profiling datasets.The new method relies on aggregation of individual profiling experiments combined with leave-one-dataset-out validation approach. Aggregated datasets were studied using Sub-Network Enrichment Analysis algorithm (SNEA to find consistent statistically significant key regulators within the global literature-extracted expression regulation network. These regulators were linked to the consistent differentially expressed genes.We have applied our approach to several publicly available human muscle gene expression profiling datasets related to Duchenne muscular dystrophy (DMD. In order to detect both enhanced and repressed processes we considered up- and down-regulated genes separately. Applying the proposed approach to the regulators search we discovered the disturbance in the activity of several muscle-related transcription factors (e.g. MYOG and MYOD1, regulators of inflammation, regeneration, and fibrosis. Almost all SNEA-derived regulators of down-regulated genes (e.g. AMPK, TORC2, PPARGC1A correspond to a single common pathway important for fast-to-slow twitch fiber type transition. We hypothesize that this process can affect the severity of DMD symptoms, making corresponding regulators and downstream genes valuable candidates for being potential drug targets and exploratory biomarkers.

  12. Cellular Signaling Pathways in Insulin Resistance-Systems Biology Analyses of Microarray Dataset Reveals New Drug Target Gene Signatures of Type 2 Diabetes Mellitus

    Science.gov (United States)

    Muhammad, Syed Aun; Raza, Waseem; Nguyen, Thanh; Bai, Baogang; Wu, Xiaogang; Chen, Jake

    2017-01-01

    Purpose: Type 2 diabetes mellitus (T2DM) is a chronic and metabolic disorder affecting large set of population of the world. To widen the scope of understanding of genetic causes of this disease, we performed interactive and toxicogenomic based systems biology study to find potential T2DM related genes after cDNA differential analysis. Methods: From the list of 50-differential expressed genes (p < 0.05), we found 9-T2DM related genes using extensive data mapping. In our constructed gene-network, T2DM-related differentially expressed seeder genes (9-genes) are found to interact with functionally related gene signatures (31-genes). The genetic interaction network of both T2DM-associated seeder as well as signature genes generally relates well with the disease condition based on toxicogenomic and data curation. Results: These networks showed significant enrichment of insulin signaling, insulin secretion and other T2DM-related pathways including JAK-STAT, MAPK, TGF, Toll-like receptor, p53 and mTOR, adipocytokine, FOXO, PPAR, P13-AKT, and triglyceride metabolic pathways. We found some enriched pathways that are common in different conditions. We recognized 11-signaling pathways as a connecting link between gene signatures in insulin resistance and T2DM. Notably, in the drug-gene network, the interacting genes showed significant overlap with 13-FDA approved and few non-approved drugs. This study demonstrates the value of systems genetics for identifying 18 potential genes associated with T2DM that are probable drug targets. Conclusions: This integrative and network based approaches for finding variants in genomic data expect to accelerate identification of new drug target molecules for different diseases and can speed up drug discovery outcomes. PMID:28179884

  13. Structure-Bioactivity Relationship for Benzimidazole Thiophene Inhibitors of Polo-Like Kinase 1 (PLK1, a Potential Drug Target in Schistosoma mansoni.

    Directory of Open Access Journals (Sweden)

    Thavy Long

    2016-01-01

    Full Text Available Schistosoma flatworm parasites cause schistosomiasis, a chronic and debilitating disease of poverty in developing countries. Praziquantel is employed for treatment and disease control. However, its efficacy spectrum is incomplete (less active or inactive against immature stages of the parasite and there is a concern of drug resistance. Thus, there is a need to identify new drugs and drug targets.We show that RNA interference (RNAi of the Schistosoma mansoni ortholog of human polo-like kinase (huPLK1 elicits a deleterious phenotypic alteration in post-infective larvae (schistosomula or somules. Phenotypic screening and analysis of schistosomula and adult S. mansoni with small molecule inhibitors of huPLK1 identified a number of potent anti-schistosomals. Among these was a GlaxoSmithKline (GSK benzimidazole thiophene inhibitor that has completed Phase I clinical trials for treatment of solid tumor malignancies. We then obtained GSKs Published Kinase Inhibitor Sets (PKIS 1 and 2, and phenotypically screened an expanded series of 38 benzimidazole thiophene PLK1 inhibitors. Computational analysis of controls and PLK1 inhibitor-treated populations of somules demonstrated a distinctive phenotype distribution. Using principal component analysis (PCA, the phenotypes exhibited by these populations were mapped, visualized and analyzed through projection to a low-dimensional space. The phenotype distribution was found to have a distinct shape and topology, which could be elicited using cluster analysis. A structure-activity relationship (SAR was identified for the benzimidazole thiophenes that held for both somules and adult parasites. The most potent inhibitors produced marked phenotypic alterations at 1-2 μM within 1 h. Among these were compounds previously characterized as potent inhibitors of huPLK1 in cell assays.The reverse genetic and chemical SAR data support a continued investigation of SmPLK1 as a possible drug target and/or the prosecution of

  14. Homology modeling of NAD+-dependent DNA ligase of the Wolbachia endosymbiont of Brugia malayi and its drug target potential using dispiro-cycloalkanones.

    Science.gov (United States)

    Shrivastava, Nidhi; Nag, Jeetendra K; Pandey, Jyoti; Tripathi, Rama Pati; Shah, Priyanka; Siddiqi, Mohammad Imran; Misra-Bhattacharya, Shailja

    2015-07-01

    Lymphatic filarial nematodes maintain a mutualistic relationship with the endosymbiont Wolbachia. Depletion of Wolbachia produces profound defects in nematode development, fertility, and viability and thus has great promise as a novel approach for treating filarial diseases. NAD(+)-dependent DNA ligase is an essential enzyme of DNA replication, repair, and recombination. Therefore, in the present study, the antifilarial drug target potential of the NAD(+)-dependent DNA ligase of the Wolbachia symbiont of Brugia malayi (wBm-LigA) was investigated using dispiro-cycloalkanone compounds. Dispiro-cycloalkanone specifically inhibited the nick-closing and cohesive-end ligation activities of the enzyme without inhibiting human or T4 DNA ligase. The mode of inhibition was competitive with the NAD(+) cofactor. Docking studies also revealed the interaction of these compounds with the active site of the target enzyme. The adverse effects of these inhibitors were observed on adult and microfilarial stages of B. malayi in vitro, and the most active compounds were further monitored in vivo in jirds and mastomys rodent models. Compounds 1, 2, and 5 had severe adverse effects in vitro on the motility of both adult worms and microfilariae at low concentrations. Compound 2 was the best inhibitor, with the lowest 50% inhibitory concentration (IC50) (1.02 μM), followed by compound 5 (IC50, 2.3 μM) and compound 1 (IC50, 2.9 μM). These compounds also exhibited the same adverse effect on adult worms and microfilariae in vivo (P < 0.05). These compounds also tremendously reduced the wolbachial load, as evident by quantitative real-time PCR (P < 0.05). wBm-LigA thus shows great promise as an antifilarial drug target, and dispiro-cycloalkanone compounds show great promise as antifilarial lead candidates.

  15. Molecular interaction of a kinase inhibitor midostaurin with anticancer drug targets, S100A8 and EGFR: transcriptional profiling and molecular docking study for kidney cancer therapeutics.

    Directory of Open Access Journals (Sweden)

    Zeenat Mirza

    Full Text Available The S100A8 and epidermal growth factor receptor (EGFR proteins are proto-oncogenes that are strongly expressed in a number of cancer types. EGFR promotes cellular proliferation, differentiation, migration and survival by activating molecular pathways. Involvement of proinflammatory S100A8 in tumor cell differentiation and progression is largely unclear and not studied in kidney cancer (KC. S100A8 and EGFR are potential therapeutic biomarkers and anticancer drug targets for KC. In this study, we explored molecular mechanisms of interaction profiles of both molecules with potential anticancer drugs. We undertook transcriptional profiling in Saudi KCs using Affymetrix HuGene 1.0 ST arrays. We identified 1478 significantly expressed genes, including S100A8 and EGFR overexpression, using cut-off p value <0.05 and fold change ≥2. Additionally, we compared and confirmed our findings with expression data available at NCBI's GEO database. A significant number of genes associated with cancer showed involvement in cell cycle progression, DNA repair, tumor morphology, tissue development, and cell survival. Atherosclerosis signaling, leukocyte extravasation signaling, notch signaling, and IL-12 signaling were the most significantly disrupted signaling pathways. The present study provides an initial transcriptional profiling of Saudi KC patients. Our analysis suggests distinct transcriptomic signatures and pathways underlying molecular mechanisms of KC progression. Molecular docking analysis revealed that the kinase inhibitor "midostaurin" has amongst the selected drug targets, the best ligand properties to S100A8 and EGFR, with the implication that its binding inhibits downstream signaling in KC. This is the first structure-based docking study for the selected protein targets and anticancer drug, and the results indicate S100A8 and EGFR as attractive anticancer targets and midostaurin with effective drug properties for therapeutic intervention in KC.

  16. Applications of Conditional Nonlinear Optimal Perturbation in Predictability Study and Sensitivity Analysis of Weather and Climate

    Institute of Scientific and Technical Information of China (English)

    2006-01-01

    Considering the limitation of the linear theory of singular vector (SV), the authors and their collaborators proposed conditional nonlinear optimal perturbation (CNOP) and then applied it in the predictability study and the sensitivity analysis of weather and climate system. To celebrate the 20th anniversary of Chinese National Committee for World Climate Research Programme (WCRP), this paper is devoted to reviewing the main results of these studies. First, CNOP represents the initial perturbation that has largest nonlinear evolution at prediction time, which is different from linear singular vector (LSV) for the large magnitude of initial perturbation or/and the long optimization time interval. Second, CNOP,rather than linear singular vector (LSV), represents the initial anomaly that evolves into ENSO events most probably. It is also the CNOP that induces the most prominent seasonal variation of error growth for ENSO predictability; furthermore, CNOP was applied to investigate the decadal variability of ENSO asymmetry. It is demonstrated that the changing nonlinearity causes the change of ENSO asymmetry.Third, in the studies of the sensitivity and stability of ocean's thermohaline circulation (THC), the non-linear asymmetric response of THC to finite amplitude of initial perturbations was revealed by CNOP.Through this approach the passive mechanism of decadal variation of THC was demonstrated; Also the authors studies the instability and sensitivity analysis of grassland ecosystem by using CNOP and show the mechanism of the transitions between the grassland and desert states. Finally, a detailed discussion on the results obtained by CNOP suggests the applicability of CNOP in predictability studies and sensitivity analysis.

  17. A Human Pluripotent Stem Cell Surface N-Glycoproteome Resource Reveals Markers, Extracellular Epitopes, and Drug Targets

    Directory of Open Access Journals (Sweden)

    Kenneth R. Boheler

    2014-07-01

    Full Text Available Detailed knowledge of cell-surface proteins for isolating well-defined populations of human pluripotent stem cells (hPSCs would significantly enhance their characterization and translational potential. Through a chemoproteomic approach, we developed a cell-surface proteome inventory containing 496 N-linked glycoproteins on human embryonic (hESCs and induced PSCs (hiPSCs. Against a backdrop of human fibroblasts and 50 other cell types, >100 surface proteins of interest for hPSCs were revealed. The >30 positive and negative markers verified here by orthogonal approaches provide experimental justification for the rational selection of pluripotency and lineage markers, epitopes for cell isolation, and reagents for the characterization of putative hiPSC lines. Comparative differences between the chemoproteomic-defined surfaceome and the transcriptome-predicted surfaceome directly led to the discovery that STF-31, a reported GLUT-1 inhibitor, is toxic to hPSCs and efficient for selective elimination of hPSCs from mixed cultures.

  18. In silico design of fragment-based drug targeting host processing α-glucosidase i for dengue fever

    Science.gov (United States)

    Toepak, E. P.; Tambunan, U. S. F.

    2017-02-01

    Dengue is a major health problem in the tropical and sub-tropical regions. The development of antiviral that targeting dengue’s host enzyme can be more effective and efficient treatment than the viral enzyme. Host enzyme processing α-glucosidase I has an important role in the maturation process of dengue virus envelope glycoprotein. The inhibition of processing α-glucosidase I can become a promising target for dengue fever treatment. The antiviral approach using in silico fragment-based drug design can generate drug candidates with high binding affinity. In this research, 198.621 compounds were obtained from ZINC15 Biogenic Database. These compounds were screened to find the favorable fragments according to Rules of Three and pharmacological properties. The screening fragments were docked into the active site of processing α-glucosidase I. The potential fragment candidates from the molecular docking simulation were linked with castanospermine (CAST) to generate ligands with a better binding affinity. The Analysis of ligand - enzyme interaction showed ligands with code LRS 22, 28, and 47 have the better binding free energy than the standard ligand. Ligand LRS 28 (N-2-4-methyl-5-((1S,3S,6S,7R,8R,8aR)-1,6,7,8-tetrahydroxyoctahydroindolizin-3-yl) pentyl) indolin-1-yl) propionamide) itself among the other ligands has the lowest binding free energy. Pharmacological properties prediction also showed the ligands LRS 22, 28, and 47 can be promising as the dengue fever drug candidates.

  19. Rasch-based high-dimensionality data reduction and class prediction with applications to microarray gene expression data

    CERN Document Server

    Kastrin, Andrej

    2010-01-01

    Class prediction is an important application of microarray gene expression data analysis. The high-dimensionality of microarray data, where number of genes (variables) is very large compared to the number of samples (obser- vations), makes the application of many prediction techniques (e.g., logistic regression, discriminant analysis) difficult. An efficient way to solve this prob- lem is by using dimension reduction statistical techniques. Increasingly used in psychology-related applications, Rasch model (RM) provides an appealing framework for handling high-dimensional microarray data. In this paper, we study the potential of RM-based modeling in dimensionality reduction with binarized microarray gene expression data and investigate its prediction ac- curacy in the context of class prediction using linear discriminant analysis. Two different publicly available microarray data sets are used to illustrate a general framework of the approach. Performance of the proposed method is assessed by re-randomization s...

  20. Comparative Proteomic Analysis of Aminoglycosides Resistant and Susceptible Mycobacterium tuberculosis Clinical Isolates for Exploring Potential Drug Targets.

    Science.gov (United States)

    Sharma, Divakar; Kumar, Bhavnesh; Lata, Manju; Joshi, Beenu; Venkatesan, Krishnamurthy; Shukla, Sangeeta; Bisht, Deepa

    2015-01-01

    Aminoglycosides, amikacin (AK) and kanamycin (KM) are second line anti-tuberculosis drugs used to treat tuberculosis (TB) and resistance to them affects the treatment. Membrane and membrane associated proteins have an anticipated role in biological processes and pathogenesis and are potential targets for the development of new diagnostics/vaccine/therapeutics. In this study we compared membrane and membrane associated proteins of AK and KM resistant and susceptible Mycobacterium tuberculosis isolates by 2DE coupled with MALDI-TOF/TOF-MS and bioinformatic tools. Twelve proteins were found to have increased intensities (PDQuest Advanced Software) in resistant isolates and were identified as ATP synthase subunit alpha (Rv1308), Trigger factor (Rv2462c), Dihydrolipoyl dehydrogenase (Rv0462), Elongation factor Tu (Rv0685), Transcriptional regulator MoxR1(Rv1479), Universal stress protein (Rv2005c), 35kDa hypothetical protein (Rv2744c), Proteasome subunit alpha (Rv2109c), Putative short-chain type dehydrogenase/reductase (Rv0148), Bacterioferritin (Rv1876), Ferritin (Rv3841) and Alpha-crystallin/HspX (Rv2031c). Among these Rv2005c, Rv2744c and Rv0148 are proteins with unknown functions. Docking showed that both drugs bind to the conserved domain (Usp, PspA and SDR domain) of these hypothetical proteins and GPS-PUP predicted potential pupylation sites within them. Increased intensities of these proteins and proteasome subunit alpha might not only be neutralized/modulated the drug molecules but also involved in protein turnover to overcome the AK and KM resistance. Besides that Rv1876, Rv3841 and Rv0685 were found to be associated with iron regulation signifying the role of iron in resistance. Further research is needed to explore how these potential protein targets contribute to resistance of AK and KM.

  1. Neural networks for learning and prediction with applications to remote sensing and speech perception

    Science.gov (United States)

    Gjaja, Marin N.

    1997-11-01

    Neural networks for supervised and unsupervised learning are developed and applied to problems in remote sensing, continuous map learning, and speech perception. Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART networks synthesize fuzzy logic and neural networks, and supervised ARTMAP networks incorporate ART modules for prediction and classification. New ART and ARTMAP methods resulting from analyses of data structure, parameter specification, and category selection are developed. Architectural modifications providing flexibility for a variety of applications are also introduced and explored. A new methodology for automatic mapping from Landsat Thematic Mapper (TM) and terrain data, based on fuzzy ARTMAP, is developed. System capabilities are tested on a challenging remote sensing problem, prediction of vegetation classes in the Cleveland National Forest from spectral and terrain features. After training at the pixel level, performance is tested at the stand level, using sites not seen during training. Results are compared to those of maximum likelihood classifiers, back propagation neural networks, and K-nearest neighbor algorithms. Best performance is obtained using a hybrid system based on a convex combination of fuzzy ARTMAP and maximum likelihood predictions. This work forms the foundation for additional studies exploring fuzzy ARTMAP's capability to estimate class mixture composition for non-homogeneous sites. Exploratory simulations apply ARTMAP to the problem of learning continuous multidimensional mappings. A novel system architecture retains basic ARTMAP properties of incremental and fast learning in an on-line setting while adding components to solve this class of problems. The perceptual magnet effect is a language-specific phenomenon arising early in infant speech development that is characterized by a warping of speech sound perception. An

  2. Modeling, molecular dynamics, and docking assessment of transcription factor rho: a potential drug target in Brucella melitensis 16M

    Directory of Open Access Journals (Sweden)

    Pradeepkiran JA

    2015-03-01

    Full Text Available Jangampalli Adi Pradeepkiran,1 Konidala Kranthi Kumar,1 Yellapu Nanda Kumar,2 Matcha Bhaskar11Division of Animal Biotechnology, Department of Zoology, Sri Venkateswara University, Tirupati, 2Biomedical Informatics Centre, Vector Control Research Centre, Indian Council of Medical Research, Pondicherry, India Abstract: The zoonotic disease brucellosis, a chronic condition in humans affecting renal and cardiac systems and causing osteoarthritis, is caused by Brucella, a genus of Gram-negative, facultative, intracellular pathogens. The mode of transmission and the virulence of the pathogens are still enigmatic. Transcription regulatory elements, such as rho proteins, play an important role in the termination of transcription and/or the selection of genes in Brucella. Adverse effects of the transcription inhibitors play a key role in the non-successive transcription challenges faced by the pathogens. In the investigation presented here, we computationally predicted the transcription termination factor rho (TtFRho inhibitors against Brucella melitensis 16M via a structure-based method. In view the unknown nature of its crystal structure, we constructed a robust three-dimensional homology model of TtFRho’s structure by comparative modeling with the crystal structure of the Escherichia coli TtFRho (Protein Data Bank ID: 1PVO as a template in MODELLER (v 9.10. The modeled structure was optimized by applying a molecular dynamics simulation for 2 ns with the CHARMM (Chemistry at HARvard Macromolecular Mechanics 27 force field in NAMD (NAnoscale Molecular Dynamics program; v 2.9 and then evaluated by calculating the stereochemical quality of the protein. The flexible docking for the interaction phenomenon of the template consists of ligand-related inhibitor molecules from the ZINC (ZINC Is Not Commercial database using a structure-based virtual screening strategy against minimized TtFRho. Docking simulations revealed two inhibitors compounds – ZINC

  3. Application of artificial neural network to predict Vickers microhardness of AA6061 friction stir welded sheets

    Institute of Scientific and Technical Information of China (English)

    Vahid Moosabeiki Dehabadi; Saeede Ghorbanpour; Ghasem Azimi

    2016-01-01

    The application of friction stir welding (FSW) is growing owing to the omission of difficulties in traditional welding processes. In the current investigation, artificial neural network (ANN) technique was employed to predict the microhardness of AA6061 friction stir welded plates. Specimens were welded employing triangular and tapered cylindrical pins. The effects of thread and conical shoulder of each pin profile on the microhardness of welded zone were studied using tow ANNs through the different distances from weld centerline. It is observed that using conical shoulder tools enhances the quality of welded area. Besides, in both pin profiles threaded pins and conical shoulders increase yield strength and ultimate tensile strength. Mean absolute percentage error (MAPE) for train and test data sets did not exceed 5.4% and 7.48%, respectively. Considering the accurate results and acceptable errors in the models’ responses, the ANN method can be used to economize material and time.

  4. A nonlinear modeling approach using weighted piecewise series and its applications to predict unsteady flows

    Science.gov (United States)

    Yao, Weigang; Liou, Meng-Sing

    2016-08-01

    To preserve nonlinearity of a full-order system over a range of parameters of interest, we propose an accurate and robust nonlinear modeling approach by assembling a set of piecewise linear local solutions expanded about some sampling states. The work by Rewienski and White [1] on micromachined devices inspired our use of piecewise linear local solutions to study nonlinear unsteady aerodynamics. These local approximations are assembled via nonlinear weights of radial basis functions. The efficacy of the proposed procedure is validated for a two-dimensional airfoil moving with different pitching motions, specifically AGARD's CT2 and CT5 problems [27], in which the flows exhibit different nonlinear behaviors. Furthermore, application of the developed aerodynamic model to a two-dimensional aero-elastic system proves the approach is capable of predicting limit cycle oscillations (LCOs) by using AGARD's CT6 [28] as a benchmark test. All results, based on inviscid solutions, confirm that our nonlinear model is stable and accurate, against the full model solutions and measurements, and for predicting not only aerodynamic forces but also detailed flowfields. Moreover, the model is robust for inputs that considerably depart from the base trajectory in form and magnitude. This modeling provides a very efficient way for predicting unsteady flowfields with varying parameters because it needs only a tiny fraction of the cost of a full-order modeling for each new condition-the more cases studied, the more savings rendered. Hence, the present approach is especially useful for parametric studies, such as in the case of design optimization and exploration of flow phenomena.

  5. Mathematical Model to Predict Skin Concentration after Topical Application of Drugs

    Directory of Open Access Journals (Sweden)

    Hiroaki Todo

    2013-12-01

    Full Text Available Skin permeation experiments have been broadly done since 1970s to 1980s as an evaluation method for transdermal drug delivery systems. In topically applied drug and cosmetic formulations, skin concentration of chemical compounds is more important than their skin permeations, because primary target site of the chemical compounds is skin surface or skin tissues. Furthermore, the direct pharmacological reaction of a metabolically stable drug that binds with specific receptors of known expression levels in an organ can be determined by Hill’s equation. Nevertheless, little investigation was carried out on the test method of skin concentration after topically application of chemical compounds. Recently we investigated an estimating method of skin concentration of the chemical compounds from their skin permeation profiles. In the study, we took care of “3Rs” issues for animal experiments. We have proposed an equation which was capable to estimate animal skin concentration from permeation profile through the artificial membrane (silicone membrane and animal skin. This new approach may allow the skin concentration of a drug to be predicted using Fick’s second law of diffusion. The silicone membrane was found to be useful as an alternative membrane to animal skin for predicting skin concentration of chemical compounds, because an extremely excellent extrapolation to animal skin concentration was attained by calculation using the silicone membrane permeation data. In this chapter, we aimed to establish an accurate and convenient method for predicting the concentration profiles of drugs in the skin based on the skin permeation parameters of topically active drugs derived from steady-state skin permeation experiments.

  6. Application of Petri nets to reliability prediction of occupant safety systems with partial detection and repair

    Energy Technology Data Exchange (ETDEWEB)

    Kleyner, Andre, E-mail: andre.v.kleyner@delphi.co [Delphi Corporation, Electronics and Safety Division, P.O. Box 9005, M.S. CTC 2E, Kokomo, IN 46904 (United States); Volovoi, Vitali, E-mail: vitali.volovoi@ae.gatech.ed [School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332 (United States)

    2010-06-15

    This paper presents an application of stochastic Petri nets (SPN) to calculate the availability of safety critical on-demand systems. Traditional methods of estimating system reliability include standards-based or field return-based reliability prediction methods. These methods do not take into account the effect of fault-detection capability and penalize the addition of detection circuitry due to the higher parts count. Therefore, calculating system availability, which can be linked to the system's probability of failure on demand (P{sub fd}), can be a better alternative to reliability prediction. The process of estimating the P{sub fd} of a safety system can be further complicated by the presence of system imperfections such as partial-fault detection by users and untimely or uncompleted repairs. Additionally, most system failures cannot be represented by Poisson process Markov chain methods, which are commonly utilized for the purposes of estimating P{sub fd}, as these methods are not well-suited for the analysis of non-Poisson failures. This paper suggests a methodology and presents a case study of SPN modeling adequately handling most of the above problems. The model will be illustrated with a case study of an automotive electronics airbag controller as an example of a safety critical on-demand system.

  7. Design and Application of Offset-Free Model Predictive Control Disturbance Observation Method

    Directory of Open Access Journals (Sweden)

    Xue Wang

    2016-01-01

    Full Text Available Model predictive control (MPC with its lower request to the mathematical model, excellent control performance, and convenience online calculation has developed into a very important subdiscipline with rich theory foundation and practical application. However, unmeasurable disturbance is widespread in industrial processes, which is difficult to deal with directly at present. In most of the implemented MPC strategies, the method of incorporating a constant output disturbance into the process model is introduced to solve this problem, but it fails to achieve offset-free control once the unmeasured disturbances access the process. Based on the Kalman filter theory, the problem is solved by using a more general disturbance model which is superior to the constant output disturbance model. This paper presents the necessary conditions for offset-free model predictive control based on the model. By applying disturbance model, the unmeasurable disturbance vectors are augmented as the states of control system, and the Kalman filer is used to estimate unmeasurable disturbance and its effect on the output. Then, the dynamic matrix control (DMC algorithm is improved by utilizing the feed-forward compensation control strategy with the disturbance estimated.

  8. Application of remote sensing for prediction and detection of thermal pollution, phase 2

    Science.gov (United States)

    Veziroglu, T. N.; Lee, S. S.

    1975-01-01

    The development of a predictive mathematical model for thermal pollution in connection with remote sensing measurements was continued. A rigid-lid model has been developed and its application to far-field study has been completed. The velocity and temperature fields have been computed for different atmospheric conditions and for different boundary currents produced by tidal effects. In connection with the theoretical work, six experimental studies of the two sites in question (Biscayne Bay site and Hutchinson Island site) have been carried out. The temperature fields obtained during the tests at the Biscayne Bay site have been compared with the predictions of the rigid-lid model and these results are encouraging. The rigid-lid model is also being applied to near-field study. Preliminary results for a simple case have been obtained and execution of more realistic cases has been initiated. The development of a free-surface model also been initiated. The governing equations have been formulated and the computer programs have been written.

  9. Predicting Global Minimum in Complex Beryllium Borate System for Deep-ultraviolet Functional Optical Applications

    Science.gov (United States)

    Bian, Qiang; Yang, Zhihua; Wang, Ying; Cao, Chao; Pan, Shilie

    2016-10-01

    Searching for high performance materials for optical communication and laser industry in deep-ultraviolet (DUV) region has been the subject of considerable interest. Such materials by design from scratching on multi-component complex crystal systems are challenging. Here, we predict, through density function calculations and unbiased structure searching techniques, the formation of quaternary NaBeBO3 compounds at ambient pressure. Among the four low-energy phases, the P63/m structure exhibits a DUV cutoff edge of 20 nm shorter than α-BaB2O4 (189 nm) – the best-known DUV birefringent material. While the P-6 structure exhibits one time second-harmonic generation efficiency of KH2PO4 and possesses excellent crystal growth habit without showing any layer habit as observed in the only available DUV nonlinear optical material KBe2BO3F2, whose layer habit limits its wide industrial applications. These NaBeBO3 structures are promising candidates for the next generation of DUV optical materials, and the structure prediction technique will shed light on future optical materials design.

  10. Application of the PredictAD Software Tool to Predict Progression in Patients with Mild Cognitive Impairment

    DEFF Research Database (Denmark)

    Simonsen, Anja H; Mattila, Jussi; Hejl, Anne-Mette

    2012-01-01

    of incremental data presentation using the software tool. A 5th phase was done with all available patient data presented on paper charts. Classifications by the clinical raters were compared to the clinical diagnoses made by the Alzheimer's Disease Neuroimaging Initiative investigators. Results: A statistical......Background: The PredictAD tool integrates heterogeneous data such as imaging, cerebrospinal fluid biomarkers and results from neuropsychological tests for compact visualization in an interactive user interface. This study investigated whether the software tool could assist physicians in the early...

  11. Application of the Condensed Fukui Function to Predict Reactivity in Core–Shell Transition Metal Nanoparticles

    Energy Technology Data Exchange (ETDEWEB)

    Allison, Thomas C.; Tong, Yu ye J.

    2013-07-01

    Chemical reactivity descriptors are a powerful means for understanding reactivity in a wide variety of chemical compounds. These descriptors, rooted in density functional theory, have found broad application in organic chemical reactions, but have not been as widely applied for other classes of chemical species such as nanoparticles, which are the subject of this article. Specifically, we explore application of the Fukui function, the global hardness and softness, the local softness, and the dual descriptor to pure metallic and core–shell nanoparticles, with and without a CO molecule bound to the surface. We find that the Fukui function is useful in predicting and interpreting chemical reactivity, and that it correlates well with the results of the popular d-band center method. Differences in the Fukui function before and after bonding of a CO molecule to the surface of a nanoparticle reveal interesting information about the reactivity of the nanoparticle surface. The change in the Fukui function when an electric field is applied to the molecule is also considered. Though the results are generally good, some of the limitations of this approach become clear.

  12. High applicability of two-dimensional phosphorous in Kagome lattice predicted from first-principles calculations.

    Science.gov (United States)

    Chen, Peng-Jen; Jeng, Horng-Tay

    2016-03-16

    A new semiconducting phase of two-dimensional phosphorous in the Kagome lattice is proposed from first-principles calculations. The band gaps of the monolayer (ML) and bulk Kagome phosphorous (Kagome-P) are 2.00 and 1.11 eV, respectively. The magnitude of the band gap is tunable by applying the in-plane strain and/or changing the number of stacking layers. High optical absorption coefficients at the visible light region are predicted for multilayer Kagome-P, indicating potential applications for solar cell devices. The nearly dispersionless top valence band of the ML Kagome-P with high density of states at the Fermi level leads to superconductivity with Tc of ~9 K under the optimal hole doping concentration. We also propose that the Kagome-P can be fabricated through the manipulation of the substrate-induced strain during the process of the sample growth. Our work demonstrates the high applicability of the Kagome-P in the fields of electronics, photovoltaics, and superconductivity.

  13. Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks

    Directory of Open Access Journals (Sweden)

    K.Srinivas

    2010-03-01

    Full Text Available The healthcare environment is generally perceived as being ‘information rich’ yet ‘knowledge poor’. There is a wealth of data available within the healthcare systems. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. Knowledge discovery and data mining have found numerous applications in business and scientific domain. Valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, we briefly examine the potential use of classification based data mining techniques such as Rule based, Decision tree, Naïve Bayes and Artificial Neural Network to massive volume of healthcare data. The healthcare industrycollects huge amounts of healthcare data which, unfortunately, are not “mined” to discover hidden information. For data preprocessing and effective decision making One Dependency Augmented Naïve Bayes classifier (ODANB and naive credal classifier 2 (NCC2 are used. This is an extension of naive Bayes to imprecise probabilities that aims at delivering robust classifications also when dealing with small or incomplete data sets. Discovery of hidden patterns and relationships often goes unexploited. Using medical profiles such as age, sex, blood pressure and blood sugar it can predict the likelihood of patients getting a heart disease. It enables significant knowledge, e.g. patterns, relationships between medical factors related to heart disease, to be established.

  14. A POTENTIAL DRUG TARGET FOR PARASITES—LACTATE DEHYDROGENASE%寄生虫潜在药物靶标—乳酸脱氢酶

    Institute of Scientific and Technical Information of China (English)

    李莎; 董辉; 黄兵

    2015-01-01

    Parasites include protozoon, trematodes, cestodes, nematode, acanthocephalan and so on, which cause diseases in human beings and animals. The misuse of antihelmintics has led to wide and serious drug-resistance. Therefore, there is an extremely urgent need for development of new drugs. Lactate dehydrogenase (LDH) acts as the terminal enzyme on glycolytic pathway and catalyzes the reversible reaction of pyruvate to lactate. In the process of this metabolic pathway, NADH and NAD+ serve as coenzymes thus the energy resources are generated for parasites. This article summarizes the methods used in studying antiparasitic drug targets and progress in research on LDH as a potential drug target for parasites, representing important theoretical signifi cance in the research on the molecular mechanisms of drugs and development of new drugs.%寄生虫是包括原虫、吸虫、绦虫、线虫、棘头虫等在内的一大类生物的总称,可引起重要的人畜寄生虫病.抗寄生虫药物的长期使用甚至是滥用使得寄生虫对现有药物产生了明显的抗药性,急需开发新型药物以有效控制寄生虫感染.乳酸脱氢酶(lactate dehydrogenase, LDH)是糖酵解途径的末端酶,在还原型辅酶Ⅰ(NADH)和氧化型辅酶Ⅰ(NAD+)的辅助下,催化丙酮酸与乳酸之间的可逆反应,释放能量供寄生虫所需.本文针对抗寄生虫药物靶标的鉴定方法以及寄生虫LDH作为潜在药物靶标的研究进展进行了综述,对阐明药物的分子作用机理及研发新药具有重要的理论意义.

  15. Prediction of circulation control performance characteristics for Super STOL and STOL applications

    Science.gov (United States)

    Naqvi, Messam Abbas

    due to the lack of a simple prediction capability. This research effort was focused on the creation of a rapid prediction capability of Circulation Control Aerodynamic Characteristics which could help designers with rapid performance estimates for design space exploration. A morphological matrix was created with the available set of options which could be chosen to create this prediction capability starting with purely analytical physics based modeling to high fidelity CFD codes. Based on the available constraints, and desired accuracy meta-models have been created around the two dimensional circulation control performance results computed using Navier Stokes Equations (Computational Fluid Dynamics). DSS2, a two dimensional RANS code written by Professor Lakshmi Sankar was utilized for circulation control airfoil characteristics. The CFD code was first applied to the NCCR 1510-7607N airfoil to validate the model with available experimental results. It was then applied to compute the results of a fractional factorial design of experiments array. Metamodels were formulated using the neural networks to the results obtained from the Design of Experiments. Additional validation runs were performed to validate the model predictions. Metamodels are not only capable of rapid performance prediction, but also help generate the relation trends of response matrices with control variables and capture the complex interactions between control variables. Quantitative as well as qualitative assessments of results were performed by computation of aerodynamic forces & moments and flow field visualizations. Wing characteristics in three dimensions were obtained by integration over the whole wing using Prandtl's Wing Theory. The baseline Super STOL configuration [3] was then analyzed with the application of circulation control technology. The desired values of lift and drag to achieve the target values of Takeoff & Landing performance were compared with the optimal configurations obtained

  16. Prediction and theoretical characterization of p-type organic semiconductor crystals for field-effect transistor applications.

    Science.gov (United States)

    Atahan-Evrenk, Sule; Aspuru-Guzik, Alán

    2014-01-01

    The theoretical prediction and characterization of the solid-state structure of organic semiconductors has tremendous potential for the discovery of new high performance materials. To date, the theoretical analysis mostly relied on the availability of crystal structures obtained through X-ray diffraction. However, the theoretical prediction of the crystal structures of organic semiconductor molecules remains a challenge. This review highlights some of the recent advances in the determination of structure-property relationships of the known organic semiconductor single-crystals and summarizes a few available studies on the prediction of the crystal structures of p-type organic semiconductors for transistor applications.

  17. Performances and reliability predictions of optical data transmission links using a system simulator for aerospace applications

    Science.gov (United States)

    Bechou, L.; Deshayes, Y.; Aupetit-Berthelemot, C.; Guerin, A.; Tronche, C.

    Space missions for Earth Observation are called upon to carry a growing number of instruments in their payload, whose performances are increasing. Future space systems are therefore intended to generate huge amounts of data and a key challenge in coming years will therefore lie in the ability to transmit that significant quantity of data to ground. Thus very high data rate Payload Telemetry (PLTM) systems will be required to face the demand of the future Earth Exploration Satellite Systems and reliability is one of the major concern of such systems. An attractive approach associated with the concept of predictive modeling consists in analyzing the impact of components malfunctioning on the optical link performances taking into account the network requirements and experimental degradation laws. Reliability estimation is traditionally based on life-testing and a basic approach is to use Telcordia requirements (468GR) for optical telecommunication applications. However, due to the various interactions between components, operating lifetime of a system cannot be taken as the lifetime of the less reliable component. In this paper, an original methodology is proposed to estimate reliability of an optical communication system by using a dedicated system simulator for predictive modeling and design for reliability. At first, we present frameworks of point-to-point optical communication systems for space applications where high data rate (or frequency bandwidth), lower cost or mass saving are needed. Optoelectronics devices used in these systems can be similar to those found in terrestrial optical network. Particularly we report simulation results of transmission performances after introduction of DFB Laser diode parameters variations versus time extrapolated from accelerated tests based on terrestrial or submarine telecommunications qualification standards. Simulations are performed to investigate and predict the consequence of degradations of the Laser diode (acting as a

  18. Applications of the predictability of the Coherent Noise Model to aftershock sequences

    Science.gov (United States)

    Christopoulos, Stavros-Richard; Sarlis, Nicholas

    2014-05-01

    A study [1] of the coherent noise model [2-4] in natural time [5-7] has shown that it exhibits predictability. Interestingly, one of the predictors suggested [1] for the coherent noise model can be generalized and applied to the case of (real) aftershock sequences. The results obtained [8] so far are beyond chance. Here, we apply this approach to several aftershock sequences of strong earthquakes with magnitudes Mw ≥6.9 in Indonesia, California and Greece, including the Mw9.2 earthquake that occurred on 26 December 2004 in Sumatra. References. [1] N. V. Sarlis and S.-R. G. Christopoulos, Predictability of the coherent-noise model and its applications, Physical Review E, 85, 051136, 2012. [2] M.E.J. Newman, Self-organized criticality, evolution and the fossil extinction record, Proc. R. Soc. London B, 263, 1605-1610, 1996. [3] M. E. J. Newman and K. Sneppen, Avalanches, scaling, and coherent noise, Phys. Rev. E, 54, 6226-6231, 1996. [4] K. Sneppen and M. Newman, Coherent noise, scale invariance and intermittency in large systems, Physica D, 110, 209 - 222. [5] P. Varotsos, N. Sarlis, and E. Skordas, Spatiotemporal complexity aspects on the interrelation between Seismic Electric Signals and seismicity, Practica of Athens Academy, 76, 294-321, 2001. [6] P.A. Varotsos, N.V. Sarlis, and E.S. Skordas, Long-range correlations in the electric signals that precede rupture, Phys. Rev. E, 66, 011902, 2002. [7] Varotsos P. A., Sarlis N. V. and Skordas E. S., Natural Time Analysis: The new view of time. Precursory Seismic Electric Signals, Earthquakes and other Complex Time-Series (Springer-Verlag, Berlin Heidelberg) 2011. [8] N. V. Sarlis and S.-R. G. Christopoulos, "Visualization of the significance of Receiver Operating Characteristics based on confidence ellipses", Computer Physics Communications, http://dx.doi.org/10.1016/j.cpc.2013.12.009

  19. Simulation of magnetic drug targeting through tracheobronchial airways in the presence of an external non-uniform magnetic field using Lagrangian magnetic particle tracking

    Energy Technology Data Exchange (ETDEWEB)

    Pourmehran, O., E-mail: oveis87@yahoo.com; Rahimi-Gorji, M.; Gorji-Bandpy, M., E-mail: gorji@nit.ac.ir; Gorji, T.B.

    2015-11-01

    Drug delivery technologies are an important area within biomedicine. Targeted drug delivery aims to reduce the undesired side effects of drug usage by directing or capturing the active agents near a desired site within the body. Herein, a numerical investigation of magnetic drug targeting (MDT) using aerosol drugs named polystyrene particle (PMS40) in human lung is presented considering one-way coupling on the transport and capture of the magnetic particle. A realistic 3D geometry based on CT scan images is provided for CFD simulation. An external non-uniform magnetic field is applied. Parametric investigation is conducted and the influence of particle diameter, magnetic source position, and magnetic number (Mn) on the deposition efficiency and particle behavior is reported. According to the results, the magnetic field increased deposition efficiency of particles in a target region, the efficiency of deposition and MDT technique has a direct relation with increasing the particle diameter for magnetic number of 1 Tesla (T) and lower (Mn≤1(T)). Also it can be seen that there is an inverse relation between the particle diameter and deposition efficiency when Mn is more than 1 (T). - Highlights: • A realistic 3D geometry of human tracheobronchial airway based on CT scan image. • External non-uniform magnetic field applied to target the magnetic drug career. • Lagrangian particle tracking using discrete phase model applied. • The efficiency of deposition is dependent of magnetic number and particle diameter.

  20. HIV-1 Reverse Transcriptase Still Remains a New Drug Target: Structure, Function, Classical Inhibitors, and New Inhibitors with Innovative Mechanisms of Actions

    Directory of Open Access Journals (Sweden)

    Francesca Esposito

    2012-01-01

    Full Text Available During the retrotranscription process, characteristic of all retroviruses, the viral ssRNA genome is converted into integration-competent dsDNA. This process is accomplished by the virus-coded reverse transcriptase (RT protein, which is a primary target in the current treatments for HIV-1 infection. In particular, in the approved therapeutic regimens two classes of drugs target RT, namely, nucleoside RT inhibitors (NRTIs and nonnucleoside RT inhibitors (NNRTIs. Both classes inhibit the RT-associated polymerase activity: the NRTIs compete with the natural dNTP substrate and act as chain terminators, while the NNRTIs bind to an allosteric pocket and inhibit polymerization noncompetitively. In addition to these two classes, other RT inhibitors (RTIs that target RT by distinct mechanisms have been identified and are currently under development. These include translocation-defective RTIs, delayed chain terminators RTIs, lethal mutagenesis RTIs, dinucleotide tetraphosphates, nucleotide-competing RTIs, pyrophosphate analogs, RT-associated RNase H function inhibitors, and dual activities inhibitors. This paper describes the HIV-1 RT function and molecular structure, illustrates the currently approved RTIs, and focuses on the mechanisms of action of the newer classes of RTIs.

  1. Gene Network Analysis of Metallo Beta Lactamase Family Proteins Indicates the Role of Gene Partners in Antibiotic Resistance and Reveals Important Drug Targets.

    Science.gov (United States)

    Parimelzaghan, Anitha; Anbarasu, Anand; Ramaiah, Sudha

    2016-06-01

    Metallo Beta (β) Lactamases (MBL) are metal dependent bacterial enzymes that hydrolyze the β-lactam antibiotics. In recent years, MBL have received considerable attention because it inactivates most of the β-lactam antibiotics. Increase in dissemination of MBL encoding antibiotic resistance genes in pathogenic bacteria often results in unsuccessful treatments. Gene interaction network of MBL provides a complete understanding on the molecular basis of MBL mediated antibiotic resistance. In our present study, we have constructed the MBL network of 37 proteins with 751 functional partners from pathogenic bacterial spp. We found 12 highly interconnecting clusters. Among the 37 MBL proteins considered in the present study, 22 MBL proteins are from B3 subclass, 14 are from B1 subclass and only one is from B2 subclass. Global topological parameters are used to calculate and compare the probability of interactions in MBL proteins. Our results indicate that the proteins associated within the network have a strong influence in antibiotic resistance mechanism. Interestingly, several drug targets are identified from the constructed network. We believe that our results would be helpful for researchers exploring MBL-mediated antibiotic resistant mechanisms.

  2. Efficient drug targeting to rat alveolar macrophages by pulmonary administration of ciprofloxacin incorporated into mannosylated liposomes for treatment of respiratory intracellular parasitic infections.

    Science.gov (United States)

    Chono, Sumio; Tanino, Tomoharu; Seki, Toshinobu; Morimoto, Kazuhiro

    2008-04-01

    The efficacy of pulmonary administration of ciprofloxacin (CPFX) incorporated into mannosylated liposomes (mannosylated CPFX-liposomes) for the treatment of respiratory intracellular parasitic infections was evaluated. In brief, mannosylated CPFX-liposomes with 4-aminophenyl-a-d-mannopyranoside (particle size: 1000 nm) were prepared, and the drug targeting to alveolar macrophages (AMs) following pulmonary administration was examined in rats. Furthermore, the antibacterial and mutant prevention effects of mannosylated CPFX-liposomes in AMs were evaluated by pharmacokinetic/pharmacodynamic (PK/PD) analysis. The targeting efficiency of CPFX to rat AMs following pulmonary administration of mannosylated CPFX-liposomes was significantly greater than that of CPFX incorporated into unmodified liposomes (unmodified CPFX-liposomes; particle size: 1000 nm). According to PK/PD analysis, the mannosylated CPFX-liposomes exhibited potent antibacterial effects against many bacteria although unmodified CPFX-liposomes were ineffective against several types of bacteria, and the probability of microbial mutation by mannosylated CPFX-liposomes was extremely low. The present study indicates that mannosylated CPFX-liposomes as pulmonary administration system could be useful for the treatment of respiratory intracellular parasitic infections.

  3. Profound activity of the anti-cancer drug bortezomib against Echinococcus multilocularis metacestodes identifies the proteasome as a novel drug target for cestodes.

    Directory of Open Access Journals (Sweden)

    Britta Stadelmann

    2014-12-01

    Full Text Available A library of 426 FDA-approved drugs was screened for in vitro activity against E. multilocularis metacestodes employing the phosphoglucose isomerase (PGI assay. Initial screening at 20 µM revealed that 7 drugs induced considerable metacestode damage, and further dose-response studies revealed that bortezomib (BTZ, a proteasome inhibitor developed for the chemotherapy of myeloma, displayed high anti-metacestodal activity with an EC50 of 0.6 µM. BTZ treatment of E. multilocularis metacestodes led to an accumulation of ubiquinated proteins and unequivocally parasite death. In-gel zymography assays using E. multilocularis extracts demonstrated BTZ-mediated inhibition of protease activity in a band of approximately 23 kDa, the same size at which the proteasome subunit beta 5 of E. multilocularis could be detected by Western blot. Balb/c mice experimentally infected with E. multilocularis metacestodes were used to assess BTZ treatment, starting at 6 weeks post-infection by intraperitoneal injection of BTZ. This treatment led to reduced parasite weight, but to a degree that was not statistically significant, and it induced adverse effects such as diarrhea and neurological symptoms. In conclusion, the proteasome was identified as a drug target in E. multilocularis metacestodes that can be efficiently inhibited by BTZ in vitro. However, translation of these findings into in vivo efficacy requires further adjustments of treatment regimens using BTZ, or possibly other proteasome inhibitors.

  4. Profound activity of the anti-cancer drug bortezomib against Echinococcus multilocularis metacestodes identifies the proteasome as a novel drug target for cestodes.

    Science.gov (United States)

    Stadelmann, Britta; Aeschbacher, Denise; Huber, Cristina; Spiliotis, Markus; Müller, Joachim; Hemphill, Andrew

    2014-12-01

    A library of 426 FDA-approved drugs was screened for in vitro activity against E. multilocularis metacestodes employing the phosphoglucose isomerase (PGI) assay. Initial screening at 20 µM revealed that 7 drugs induced considerable metacestode damage, and further dose-response studies revealed that bortezomib (BTZ), a proteasome inhibitor developed for the chemotherapy of myeloma, displayed high anti-metacestodal activity with an EC50 of 0.6 µM. BTZ treatment of E. multilocularis metacestodes led to an accumulation of ubiquinated proteins and unequivocally parasite death. In-gel zymography assays using E. multilocularis extracts demonstrated BTZ-mediated inhibition of protease activity in a band of approximately 23 kDa, the same size at which the proteasome subunit beta 5 of E. multilocularis could be detected by Western blot. Balb/c mice experimentally infected with E. multilocularis metacestodes were used to assess BTZ treatment, starting at 6 weeks post-infection by intraperitoneal injection of BTZ. This treatment led to reduced parasite weight, but to a degree that was not statistically significant, and it induced adverse effects such as diarrhea and neurological symptoms. In conclusion, the proteasome was identified as a drug target in E. multilocularis metacestodes that can be efficiently inhibited by BTZ in vitro. However, translation of these findings into in vivo efficacy requires further adjustments of treatment regimens using BTZ, or possibly other proteasome inhibitors.

  5. Atypical GTPases as drug targets.

    Science.gov (United States)

    Soundararajan, Meera; Eswaran, Jeyanthy

    2012-01-01

    The Ras GTPases are the founding members of large Ras superfamily, which constitutes more than 150 of these important class of enzymes. These GTPases function as GDP-GTP-regulated binary switches that control many fundamental cellular processes. There are a number of GTPases that have been identified recently, which do not confine to this prototype termed as "atypical GTPases" but have proved to play a remarkable role in vital cellular functions. In this review, we provide an overview of the crucial physiological functions mediated by RGK and Centaurin class of multi domain atypical GTPases. Moreover, the recently available atypical GTPase structures of the two families, regulation, physiological functions and their critical roles in various diseases will be discussed. In summary, this review will highlight the emerging atypical GTPase family which allows us to understand novel regulatory mechanisms and thus providing new avenues for drug discovery programs.

  6. Drug targeting through pilosebaceous route.

    Science.gov (United States)

    Chourasia, Rashmi; Jain, Sanjay K

    2009-10-01

    Local skin targeting is of interest for the pharmaceutical and the cosmetic industry. A topically applied substance has basically three possibilities to penetrate into the skin: transcellular, intercellular, and follicular. The transfollicular path has been largely ignored because hair follicles constitute only 0.1% of the total skin. The hair follicle is a skin appendage with a complex structure containing many cell types that produce highly specialised proteins. The hair follicle is in a continuous cycle: anagen is the hair growth phase, catagen the involution phase and telogen is the resting phase. Nonetheless, the hair follicle has great potential for skin treatment, owing to its deep extension into the dermis and thus provides much deeper penetration and absorption of compounds beneath the skin than seen with the transdermal route. In the case of skin diseases and of cosmetic products, delivery to sweat glands or to the pilosebaceous unit is essential for the effectiveness of the drug. Increased accumulation in the pilosebaceous unit could treat alopecia, acne and skin cancer more efficiently and improve the effect of cosmetic substances and nutrients. Therefore, we review herein various drug delivery systems, including liposomes, niosomes, microspheres, nanoparticles, nanoemulsions, lipid nanocarriers, gene therapy and discuss the results of recent researches. We also review the drugs which have been investigated for pilosebaceous delivery.

  7. Analysis of substructural variation in families of enzymatic proteins with applications to protein function prediction

    Directory of Open Access Journals (Sweden)

    Fofanov Viacheslav Y

    2010-05-01

    Full Text Available Abstract Background Structural variations caused by a wide range of physico-chemical and biological sources directly influence the function of a protein. For enzymatic proteins, the structure and chemistry of the catalytic binding site residues can be loosely defined as a substructure of the protein. Comparative analysis of drug-receptor substructures across and within species has been used for lead evaluation. Substructure-level similarity between the binding sites of functionally similar proteins has also been used to identify instances of convergent evolution among proteins. In functionally homologous protein families, shared chemistry and geometry at catalytic sites provide a common, local point of comparison among proteins that may differ significantly at the sequence, fold, or domain topology levels. Results This paper describes two key results that can be used separately or in combination for protein function analysis. The Family-wise Analysis of SubStructural Templates (FASST method uses all-against-all substructure comparison to determine Substructural Clusters (SCs. SCs characterize the binding site substructural variation within a protein family. In this paper we focus on examples of automatically determined SCs that can be linked to phylogenetic distance between family members, segregation by conformation, and organization by homology among convergent protein lineages. The Motif Ensemble Statistical Hypothesis (MESH framework constructs a representative motif for each protein cluster among the SCs determined by FASST to build motif ensembles that are shown through a series of function prediction experiments to improve the function prediction power of existing motifs. Conclusions FASST contributes a critical feedback and assessment step to existing binding site substructure identification methods and can be used for the thorough investigation of structure-function relationships. The application of MESH allows for an automated

  8. Prediction of First-Order Vessel Responses with Applications to Decision Support Systems

    DEFF Research Database (Denmark)

    Nielsen, Ulrik D.; Iseki, Toshio

    2015-01-01

    The paper presents a practical and simple approach for making vessel response predictions. Features of the procedure include a) predictions which are scaled so to better agree with corresponding true, future values to be measured at the time the predictions apply at; and b) predictions that are a......The paper presents a practical and simple approach for making vessel response predictions. Features of the procedure include a) predictions which are scaled so to better agree with corresponding true, future values to be measured at the time the predictions apply at; and b) predictions...... that are assigned an uncertainty measure to reflect a level of confidence. The approach is tested with full-scale data and the obtained results/predictions agree well with measured values. Potentially, the procedure is therefore very useful in future developments of general decision support systems....

  9. Prediction of Biomass Production and Nutrient Uptake in Land Application Using Partial Least Squares Regression Analysis

    Directory of Open Access Journals (Sweden)

    Vasileios A. Tzanakakis

    2014-12-01

    Full Text Available Partial Least Squares Regression (PLSR can integrate a great number of variables and overcome collinearity problems, a fact that makes it suitable for intensive agronomical practices such as land application. In the present study a PLSR model was developed to predict important management goals, including biomass production and nutrient recovery (i.e., nitrogen and phosphorus, associated with treatment potential, environmental impacts, and economic benefits. Effluent loading and a considerable number of soil parameters commonly monitored in effluent irrigated lands were considered as potential predictor variables during the model development. All data were derived from a three year field trial including plantations of four different plant species (Acacia cyanophylla, Eucalyptus camaldulensis, Populus nigra, and Arundo donax, irrigated with pre-treated domestic effluent. PLSR method was very effective despite the small sample size and the wide nature of data set (with many highly correlated inputs and several highly correlated responses. Through PLSR method the number of initial predictor variables was reduced and only several variables were remained and included in the final PLSR model. The important input variables maintained were: Effluent loading, electrical conductivity (EC, available phosphorus (Olsen-P, Na+, Ca2+, Mg2+, K2+, SAR, and NO3−-N. Among these variables, effluent loading, EC, and nitrates had the greater contribution to the final PLSR model. PLSR is highly compatible with intensive agronomical practices such as land application, in which a large number of highly collinear and noisy input variables is monitored to assess plant species performance and to detect impacts on the environment.

  10. On Spatiotemporal Series Analysis and Its Application to Predict the Regional Short Term Climate Process

    Institute of Scientific and Technical Information of China (English)

    王革丽; 杨培才; 吕达仁

    2004-01-01

    Based on the theory of reconstructing state space, a technique for spatiotemporal series prediction is presented. By means of this technique and NCEP/NCAR data of the monthly mean geopotential height anomaly of the 500-hPa isobaric surface in the Northern Hemisphere, a regional prediction experiment is also carried out. If using the correlation coefficient R between the observed field and the prediction field to measure the prediction accuracy, the averaged R given by 48 prediction samples reaches 21%, which corresponds to the current prediction level for the short range climate process.

  11. Fast Prediction with Sparse Multikernel LS-SVR Using Multiple Relevant Time Series and Its Application in Avionics System

    Directory of Open Access Journals (Sweden)

    Yang M. Guo

    2015-01-01

    Full Text Available Health trend prediction is critical to ensure the safe operation of highly reliable systems. However, complex systems often present complex dynamic behaviors and uncertainty, which makes it difficult to develop a precise physical prediction model. Therefore, time series is often used for prediction in this case. In this paper, in order to obtain better prediction accuracy in shorter computation time, we propose a new scheme which utilizes multiple relevant time series to enhance the completeness of the information and adopts a prediction model based on least squares support vector regression (LS-SVR to perform prediction. In the scheme, we apply two innovative ways to overcome the drawbacks of the reported approaches. One is to remove certain support vectors by measuring the linear correlation to increase sparseness of LS-SVR; the other one is to determine the linear combination weights of multiple kernels by calculating the root mean squared error of each basis kernel. The results of prediction experiments indicate preliminarily that the proposed method is an effective approach for its good prediction accuracy and low computation time, and it is a valuable method in applications.

  12. Predicting a quaternary tungsten oxide for sustainable photovoltaic application by density functional theory

    Energy Technology Data Exchange (ETDEWEB)

    Sarker, Pranab; Huda, Muhammad N., E-mail: huda@uta.edu [Department of Physics, University of Texas at Arlington, Arlington, Texas 76019 (United States); Al-Jassim, Mowafak M. [National Renewable Energy Laboratory, Golden, Colorado 80401 (United States)

    2015-12-07

    A quaternary oxide, CuSnW{sub 2}O{sub 8} (CTTO), has been predicted by density functional theory (DFT) to be a suitable material for sustainable photovoltaic applications. CTTO possesses band gaps of 1.25 eV (indirect) and 1.37 eV (direct), which were evaluated using the hybrid functional (HSE06) as a post-DFT method. The hole mobility of CTTO was higher than that of silicon. Further, optical absorption calculations demonstrate that CTTO is a better absorber of sunlight than Cu{sub 2}ZnSnS{sub 4} and CuIn{sub x}Ga{sub 1−x}Se{sub 2} (x = 0.5). In addition, CTTO exhibits rigorous thermodynamic stability comparable to WO{sub 3}, as investigated by different thermodynamic approaches such as bonding cohesion, fragmentation tendency, and chemical potential analysis. Chemical potential analysis further revealed that CTTO can be synthesized at flexible experimental growth conditions, although the co-existence of at least one secondary phase is likely. Finally, like other Cu-based compounds, the formation of Cu vacancies is highly probable, even at Cu-rich growth condition, which could introduce p-type activity in CTTO.

  13. Prediction of Bending Stiffness for Laminated CFRP and Its Application to Manufacturing of Roof Reinforcement

    Directory of Open Access Journals (Sweden)

    Jeong-Min Lee

    2014-05-01

    Full Text Available Recently, carbon fiber reinforced plastic (CFRP with high strength, stiffness, and lightweight is used widely in number of composite applications such as commercial aircraft, transportation, machinery, and sports equipment. Especially, it is necessary to apply lightweight materials to car components for reducing energy consumption and CO2 emissions. In case of car roof reinforcement manufactured using CFRP, superior strength and bending stiffness are required for the safety of drivers in the rollover accident. Mechanical properties of CFRP laminates are generally dependent on the stacking sequence. Therefore, research of stacking sequence using CFRP prepreg is required for superior bending stiffness. In this study, the 3-point bending FE-analysis for predicting the bending stiffness of CFRP roof reinforcement was carried out on three cases [0PW∘]5, [0PW°/0UD°/0-PW°]s, and [0UD∘]5. Material properties that the six independent elastic constants are E11, E22, G12, G23, G13, and ν12 used in FE-analysis were evaluated by the tensile test in 0°, 45°, and 90° directions. Through structural strength analysis of the automobile roof reinforcement fabricated using CFRP, the effect of the stacking sequence on the bending stiffness was evaluated and validated through experiments under the same conditions as the analysis.

  14. An application of characteristic function in order to predict reliability and lifetime of aeronautical hardware

    Science.gov (United States)

    Żurek, Józef; Kaleta, Ryszard; Zieja, Mariusz

    2016-06-01

    The forecasting of reliability and life of aeronautical hardware requires recognition of many and various destructive processes that deteriorate the health/maintenance status thereof. The aging of technical components of aircraft as an armament system proves of outstanding significance to reliability and safety of the whole system. The aging process is usually induced by many and various factors, just to mention mechanical, biological, climatic, or chemical ones. The aging is an irreversible process and considerably affects (i.e. reduces) reliability and lifetime of aeronautical equipment. Application of the characteristic function of the aging process is suggested to predict reliability and lifetime of aeronautical hardware. An increment in values of diagnostic parameters is introduced to formulate then, using the characteristic function and after some rearrangements, the partial differential equation. An analytical dependence for the characteristic function of the aging process is a solution to this equation. With the inverse transformation applied, the density function of the aging of aeronautical hardware is found. Having found the density function, one can determine the aeronautical equipment's reliability and lifetime. The in-service collected or the life tests delivered data are used to attain this goal. Coefficients in this relationship are found using the likelihood function.

  15. Applications of population genetics to animal breeding, from wright, fisher and lush to genomic prediction.

    Science.gov (United States)

    Hill, William G

    2014-01-01

    Although animal breeding was practiced long before the science of genetics and the relevant disciplines of population and quantitative genetics were known, breeding programs have mainly relied on simply selecting and mating the best individuals on their own or relatives' performance. This is based on sound quantitative genetic principles, developed and expounded by Lush, who attributed much of his understanding to Wright, and formalized in Fisher's infinitesimal model. Analysis at the level of individual loci and gene frequency distributions has had relatively little impact. Now with access to genomic data, a revolution in which molecular information is being used to enhance response with "genomic selection" is occurring. The predictions of breeding value still utilize multiple loci throughout the genome and, indeed, are largely compatible with additive and specifically infinitesimal model assumptions. I discuss some of the history and genetic issues as applied to the science of livestock improvement, which has had and continues to have major spin-offs into ideas and applications in other areas.

  16. Application of viscoelastic continuum damage approach to predict fatigue performance of Binzhou perpetual pavements

    Directory of Open Access Journals (Sweden)

    Wei Cao

    2016-04-01

    Full Text Available For this study, the Binzhou perpetual pavement test sections constructed in Shandong Province, China, were simulated for long-term fatigue performance using the layered viscoelastic pavement analysis for critical distresses (LVECD finite element software package. In this framework, asphalt concrete was treated in the context of linear viscoelastic continuum damage theory. A recently developed unified fatigue failure criterion that defined the boundaries of the applicable region of the theory was also incorporated. The mechanistic modeling of the fatigue mechanisms was able to accommodate the complex temperature variations and loading conditions of the field pavements in a rigorous manner. All of the material models were conveniently characterized by dynamic modulus tests and direct tension cyclic fatigue tests in the laboratory using cylindrical specimens. By comparing the obtained damage characteristic curves and failure criteria, it is found that mixtures with small aggregate particle sizes, a dense gradation, and modified asphalt binder tended to exhibit the best fatigue resistance at the material level. The 15-year finite element structural simulation results for all the test sections indicate that fatigue performance has a strong dependence on the thickness of the asphalt pavements. Based on the predicted location and severity of the fatigue damage, it is recommended that Sections 1 and 3 of the Binzhou test sections be employed for perpetual pavement design.

  17. Assessment of the prediction error in a large-scale application of a dynamic soil acidification model

    NARCIS (Netherlands)

    Kros, J.; Mol-Dijkstra, J.P.; Pebesma, E.J.

    2002-01-01

    The prediction error of a relatively simple soil acidification model (SMART2) was assessed before and after calibration, focussing on the aluminium and nitrate concentrations on a block scale. Although SMART2 is especially developed for application ona national to European scale, it still runs at a

  18. New scheme of anticipating synchronization for arbitrary anticipation time and its application to long-term prediction of chaotic states

    Institute of Scientific and Technical Information of China (English)

    Sun Zhong-Kui; Xu Wei; Yang Xiao-Li

    2007-01-01

    How to predict the dynamics of nonlinear chaotic systems is still a challenging subject with important real-life applications. The present paper deals with this important yet difficult problem via a new scheme of anticipating synchronization. A global, robust, analytical and delay-independent sufficient condition is obtained to guarantee the existence of anticipating synchronization manifold theoretically in the framework of the Krasovskii-Lyapunov theory.that the receiver system can synchronize with the future state of a transmitter system for an arbitrarily long anticipation time, which allows one to predict the dynamics of chaotic transmitter at any point of time if necessary. Also it is simple to implement in practice. A classical chaotic system is employed to demonstrate the application of the proposed scheme to the long-term prediction of chaotic states.

  19. Application of artificial neural networks and DFT-based parameters for prediction of reaction kinetics of ethylbenzene dehydrogenase

    Science.gov (United States)

    Szaleniec, Maciej; Witko, Małgorzata; Tadeusiewicz, Ryszard; Goclon, Jakub

    2006-03-01

    Artificial neural networks (ANNs) are used for classification and prediction of enzymatic activity of ethylbenzene dehydrogenase from EbN1 Azoarcus sp. bacterium. Ethylbenzene dehydrogenase (EBDH) catalyzes stereo-specific oxidation of ethylbenzene and its derivates to alcohols, which find its application as building blocks in pharmaceutical industry. ANN systems are trained based on theoretical variables derived from Density Functional Theory (DFT) modeling, topological descriptors, and kinetic parameters measured with developed spectrophotometric assay. Obtained models exhibit high degree of accuracy (100% of correct classifications, correlation between predicted and experimental values of reaction rates on the 0.97 level). The applicability of ANNs is demonstrated as useful tool for the prediction of biochemical enzyme activity of new substrates basing only on quantum chemical calculations and simple structural characteristics. Multi Linear Regression and Molecular Field Analysis (MFA) are used in order to compare robustness of ANN and both classical and 3D-quantitative structure-activity relationship (QSAR) approaches.

  20. Improvement of Markov Chain Model for Occurrence Degree Prediction of Myzus persicae (Sulzer) and Its Application

    Institute of Scientific and Technical Information of China (English)

    REN Guangwei; WANG Xiufang; WANG Xinwei; ZHOU Xiansheng; DONG Xiaowei

    2008-01-01

    For long-term prediction of occurrence degree of tobacco aphid Myzus persicae (Sulzer), Markov chain method was used to establish prediction model for occurrence degree of tobacco aphid. With 4 levels of occurrence degree, Markov chain model was established based on the data in 1987-2004. The results indicated that the accuracy for total prediction in 2005-2007 and the back prediction in 1987-2004 reached 88.89% and 85.12%, respectively. The method is simple and feasible for long-term prediction of occurrence degree of tobacco aphid.

  1. Study and Application of Fault Prediction Methods with Improved Reservoir Neural Networks

    Institute of Scientific and Technical Information of China (English)

    Qunxiong Zhu; Yiwen Jia; Di Peng; Yuan Xu

    2014-01-01

    Time-series prediction is one of the major methodologies used for fault prediction. The methods based on recur-rent neural networks have been widely used in time-series prediction for their remarkable non-liner mapping ability. As a new recurrent neural network, reservoir neural network can effectively process the time-series pre-diction. However, the il-posedness problem of reservoir neural networks has seriously restricted the generaliza-tion performance. In this paper, a fault prediction algorithm based on time-series is proposed using improved reservoir neural networks. The basic idea is taking structure risk into consideration, that is, the cost function in-volves not only the experience risk factor but also the structure risk factor. Thus a regulation coefficient is intro-duced to calculate the output weight of the reservoir neural network. As a result, the amplitude of output weight is effectively controlled and the il-posedness problem is solved. Because the training speed of ordinary reservoir networks is naturally fast, the improved reservoir networks for time-series prediction are good in speed and gen-eralization ability. Experiments on Mackey-Glass and sunspot time series prediction prove the effectiveness of the algorithm. The proposed algorithm is applied to TE process fault prediction. We first forecast some time-series obtained from TE and then predict the fault type adopting the static reservoirs with the predicted data. The final prediction correct rate reaches 81%.

  2. A review of the applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles.

    Science.gov (United States)

    Jones, David E; Ghandehari, Hamidreza; Facelli, Julio C

    2016-08-01

    This article presents a comprehensive review of applications of data mining and machine learning for the prediction of biomedical properties of nanoparticles of medical interest. The papers reviewed here present the results of research using these techniques to predict the biological fate and properties of a variety of nanoparticles relevant to their biomedical applications. These include the influence of particle physicochemical properties on cellular uptake, cytotoxicity, molecular loading, and molecular release in addition to manufacturing properties like nanoparticle size, and polydispersity. Overall, the results are encouraging and suggest that as more systematic data from nanoparticles becomes available, machine learning and data mining would become a powerful aid in the design of nanoparticles for biomedical applications. There is however the challenge of great heterogeneity in nanoparticles, which will make these discoveries more challenging than for traditional small molecule drug design.

  3. Application of Gray Markov SCGM(1,1) c Model to Prediction of Accidents Deaths in Coal Mining.

    Science.gov (United States)

    Lan, Jian-Yi; Zhou, Ying

    2014-01-01

    The prediction of mine accident is the basis of aviation safety assessment and decision making. Gray prediction is suitable for such kinds of system objects with few data, short time, and little fluctuation, and Markov chain theory is just suitable for forecasting stochastic fluctuating dynamic process. Analyzing the coal mine accident human error cause, combining the advantages of both Gray prediction and Markov theory, an amended Gray Markov SCGM(1,1) c model is proposed. The gray SCGM(1,1) c model is applied to imitate the development tendency of the mine safety accident, and adopt the amended model to improve prediction accuracy, while Markov prediction is used to predict the fluctuation along the tendency. Finally, the new model is applied to forecast the mine safety accident deaths from 1990 to 2010 in China, and, 2011-2014 coal accidents deaths were predicted. The results show that the new model not only discovers the trend of the mine human error accident death toll but also overcomes the random fluctuation of data affecting precision. It possesses stronger engineering application.

  4. A Quantitative Structure Activity Relationship for acute oral toxicity of pesticides on rats: Validation, domain of application and prediction.

    Science.gov (United States)

    Hamadache, Mabrouk; Benkortbi, Othmane; Hanini, Salah; Amrane, Abdeltif; Khaouane, Latifa; Si Moussa, Cherif

    2016-02-13

    Quantitative Structure Activity Relationship (QSAR) models are expected to play an important role in the risk assessment of chemicals on humans and the environment. In this study, we developed a validated QSAR model to predict acute oral toxicity of 329 pesticides to rats because a few QSAR models have been devoted to predict the Lethal Dose 50 (LD50) of pesticides on rats. This QSAR model is based on 17 molecular descriptors, and is robust, externally predictive and characterized by a good applicability domain. The best results were obtained with a 17/9/1 Artificial Neural Network model trained with the Quasi Newton back propagation (BFGS) algorithm. The prediction accuracy for the external validation set was estimated by the Q(2)ext and the root mean square error (RMS) which are equal to 0.948 and 0.201, respectively. 98.6% of external validation set is correctly predicted and the present model proved to be superior to models previously published. Accordingly, the model developed in this study provides excellent predictions and can be used to predict the acute oral toxicity of pesticides, particularly for those that have not been tested as well as new pesticides.

  5. Research on prediction for coal and gas outburst based on Matlab neural network toolbox and its application

    Institute of Scientific and Technical Information of China (English)

    XIAO Hong-fei; XU Zhi-sheng; TIAN Yun-li

    2007-01-01

    In order to predict the danger of coal and gas outburst in mine coal layer correctly,on the basis of the VLBP and LMBP algorithm in Matlab neural network toolbox.one kind of modified BP neural network was put forth to speed up the network convergence speed in this paper.Firstly,according to the characteristics of coal and gas outburst.five key influencing factors such as excavation depth,pressure of gas,and geologic destroy degree were selected as the judging indexes of coal and gas outburst.Secondly,the prediction model for coal and gas outburst was built.Finally,it was verified by practical examples.Practical application demonstrates that,on the one hand,the modified BP prediction model based on the Matlab neural network toolbox can overcome the disadvantages of constringency and,on the other hand,it has fast convergence speed and good prediction accuracy.The analysis and computing results show that the computing speed by LMBP algorithm is faster than by VLBP algonthm but needs more memory.And the resuits show that the prediction results are identical with actual results and this model is a very efficient prediction method for mine coal and gas outburst,and has an important practical meaning for the mine production safety.So we conclude that it can be used to predict coal and gas outburst precisely in actual engineering.

  6. 抗艾滋病药物靶向递送系统的研究进展%Anti-AIDS drugs targeting drug delivery systems: research advances

    Institute of Scientific and Technical Information of China (English)

    谢向阳; 韩亮; 陈晨; 廖祥茹

    2011-01-01

    The discovery and utilization of anti-AIDS drugs therapy have not only increased lifespan, but also enhanced the quality of life of HIV infected people.However,limitations of currently available drug regimens and dosage forms often fail to effectively reduce the HIV viral load in the viral reservoirs in vivo.To overcome the drawbacks of present anti-AIDS drugs' dosage forms,engineered nanocarriers including polymeric nanoparticles,liposomes,solid lipid nanoparticles and dendrimers are developed to facilitate these drugs targeting to the HIV viral reservoirs. This article reviews recent advances in the field of targeting drug delivery systems fir the treatment of AIDS.%抗艾滋病药物的发现及使用有效地延长了患者的生命,提高了患者的生活质量,但其治疗针对性不强,不能有效清除体内特定部位的HIV病毒.为此,人们采用多种技术手段.制备各种形式的抗艾滋病药物递送载体.如纳米粒、脂质体、树状大分子等,希望针对不同细胞和解剖学的病原体库进行靶向药物递送.本文对近年来有关抗艾滋病药物靶向制剂研究的进展做一综述.

  7. CMC Property Variability and Life Prediction Methods for Turbine Engine Component Application

    Science.gov (United States)

    Cheplak, Matthew L.

    2004-01-01

    The ever increasing need for lower density and higher temperature-capable materials for aircraft engines has led to the development of Ceramic Matrix Composites (CMCs). Today's aircraft engines operate with >3000"F gas temperatures at the entrance to the turbine section, but unless heavily cooled, metallic components cannot operate above approx.2000 F. CMCs attempt to push component capability to nearly 2700 F with much less cooling, which can help improve engine efficiency and performance in terms of better fuel efficiency, higher thrust, and reduced emissions. The NASA Glenn Research Center has been researching the benefits of the SiC/SiC CMC for engine applications. A CMC is made up of a matrix material, fibers, and an interphase, which is a protective coating over the fibers. There are several methods or architectures in which the orientation of the fibers can be manipulated to achieve a particular material property objective as well as a particular component geometric shape and size. The required shape manipulation can be a limiting factor in the design and performance of the component if there is a lack of bending capability of the fiber as making the fiber more flexible typically sacrifices strength and other fiber properties. Various analysis codes are available (pcGINA, CEMCAN) that can predict the effective Young's Moduli, thermal conductivities, coefficients of thermal expansion (CTE), and various other properties of a CMC. There are also various analysis codes (NASAlife) that can be used to predict the life of CMCs under expected engine service conditions. The objective of this summer study is to utilize and optimize these codes for examining the tradeoffs between CMC properties and the complex fiber architectures that will be needed for several different component designs. For example, for the pcGINA code, there are six variations of architecture available. Depending on which architecture is analyzed, the user is able to specify the fiber tow size, tow

  8. The prediction of borate mineral equilibria in natural waters: Application to Searles Lake, California

    Science.gov (United States)

    Felmy, Andrew R.; Weare, John H.

    1986-12-01

    The chemical equilibrium model of HARVIEet al. (1984) has been extended to include borate species. The model is based upon the semi-empirical equations of PITZER (1973) and coworkers and is valid to high ionic strength (≈14 m) and high borate concentration. Excellent agreement with the existing emf, isopiestic and solubility data in the system (Na-K-Ca-Mg-H-Cl-SO4-CO2-B(OH)4-H2O) is obtained. Calculated mineral solubilities are in general within 10% of their experimental values, even at high ionic strengths. The model was applied to the multicomponent, high ionic strength (I ~ 10) and high borate concentration (BT ~ 0.5 m) Searles Lake evaporite deposit. Utilizing the chemical composition of the interstitial brine, the model predicts equilibrium between the brine and only those minerals which are known to be in contact with the brine. These calculations clearly demonstrate the applicability of the model to high ionic strength, high borate concentration natural waters. The model was also utilized to calculate the mineral sequences which should result from evaporation of the major source of water for Searles Lake, the Owens River. The geochemical conditions necessary for the formation of the most recent mud and saline units are examined. The final results indicate that the mineral sequences found in the most recent saline unit in Searles Lake can be produced by evaporation of a water close in composition to present Owens River water, provided primary dolomite formation is delayed and back reaction between the Parting Mud and the Upper Salt is inhibited.

  9. Application of Chaos Theory in the Prediction of Motorised Traffic Flows on Urban Networks

    Directory of Open Access Journals (Sweden)

    Aderemi Adewumi

    2016-01-01

    Full Text Available In recent times, urban road networks are faced with severe congestion problems as a result of the accelerating demand for mobility. One of the ways to mitigate the congestion problems on urban traffic road network is by predicting the traffic flow pattern. Accurate prediction of the dynamics of a highly complex system such as traffic flow requires a robust methodology. An approach for predicting Motorised Traffic Flow on Urban Road Networks based on Chaos Theory is presented in this paper. Nonlinear time series modeling techniques were used for the analysis of the traffic flow prediction with emphasis on the technique of computation of the Largest Lyapunov Exponent to aid in the prediction of traffic flow. The study concludes that algorithms based on the computation of the Lyapunov time seem promising as regards facilitating the control of congestion because of the technique’s effectiveness in predicting the dynamics of complex systems especially traffic flow.

  10. Joint multivariate statistical model and its applications to the synthetic earthquake prediction

    Institute of Scientific and Technical Information of China (English)

    韩天锡; 蒋淳; 魏雪丽; 韩梅; 冯德益

    2004-01-01

    Considering the problems that should be solved in the synthetic earthquake prediction at present, a new model is proposed in the paper. It is called joint multivariate statistical model combined by principal component analysis with discriminatory analysis. Principal component analysis and discriminatory analysis are very important theories in multivariate statistical analysis that has developed quickly in the late thirty years. By means of maximization information method, we choose several earthquake prediction factors whose cumulative proportions of total sample variances are beyond 90% from numerous earthquake prediction factors. The paper applies regression analysis and Mahalanobis discrimination to extrapolating synthetic prediction. Furthermore, we use this model to characterize and predict earthquakes in North China (30°~42°N, 108°~125°E) and better prediction results are obtained.

  11. Development and application of a statistical methodology to evaluate the predictive accuracy of building energy baseline models

    Energy Technology Data Exchange (ETDEWEB)

    Granderson, Jessica [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Energy Technologies Area Div.; Price, Phillip N. [Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). Energy Technologies Area Div.

    2014-03-01

    This paper documents the development and application of a general statistical methodology to assess the accuracy of baseline energy models, focusing on its application to Measurement and Verification (M&V) of whole-­building energy savings. The methodology complements the principles addressed in resources such as ASHRAE Guideline 14 and the International Performance Measurement and Verification Protocol. It requires fitting a baseline model to data from a ``training period’’ and using the model to predict total electricity consumption during a subsequent ``prediction period.’’ We illustrate the methodology by evaluating five baseline models using data from 29 buildings. The training period and prediction period were varied, and model predictions of daily, weekly, and monthly energy consumption were compared to meter data to determine model accuracy. Several metrics were used to characterize the accuracy of the predictions, and in some cases the best-­performing model as judged by one metric was not the best performer when judged by another metric.

  12. Applications of Displacement Transfer Functions to Deformed Shape Predictions of the GIII Swept-Wing Structure

    Science.gov (United States)

    Lung, Shun-Fat; Ko, William L.

    2016-01-01

    The displacement transfer functions (DTFs) were applied to the GIII swept wing for the deformed shape prediction. The calculated deformed shapes are very close to the correlated finite element results as well as the measured data. The convergence study showed that using 17 strain stations, the wing-tip displacement prediction error was 1.6 percent, and that there is no need to use a large number of strain stations for G-III wing shape predictions.

  13. Synergetic-bifurcated prediction model of slope occurrence and its application

    Institute of Scientific and Technical Information of China (English)

    HUANG Zhiquan; WANG Sijing

    2003-01-01

    Landslide prediction is one of the most important aspects of prevention and control for geological hazards and the environmental protection. In order to study the nonlinear methods for landslide prediction, the synergetic-bifurcated model of predicting the timing of slope failure is established by combining Synergetics with Bifurcation Theory based on single-state variable friction law in this paper. The synergetic effects and bifurcated process of the factors in the slope evolution can be characterized in the model. Taking the Xintan Landslide as an example, the prediction of landslide is carried out based on the model suggested.

  14. Trend modelling of wave parameters and application in onboard prediction of ship responses

    DEFF Research Database (Denmark)

    Montazeri, Najmeh; Nielsen, Ulrik Dam; Jensen, J. Juncher

    2015-01-01

    This paper presents a trend analysis for prediction of sea state parameters onboard shipsduring voyages. Given those parameters, a JONSWAP model and also the transfer functions, prediction of wave induced ship responses are thus made. The procedure is tested with full-scale data of an in-service ......This paper presents a trend analysis for prediction of sea state parameters onboard shipsduring voyages. Given those parameters, a JONSWAP model and also the transfer functions, prediction of wave induced ship responses are thus made. The procedure is tested with full-scale data of an in...

  15. Combining Predictive Densities using Nonlinear Filtering with Applications to US Economics Data

    NARCIS (Netherlands)

    M. Billio (Monica); R. Casarin (Roberto); F. Ravazzolo (Francesco); H.K. van Dijk (Herman)

    2011-01-01

    textabstractWe propose a multivariate combination approach to prediction based on a distributional state space representation of the weights belonging to a set of Bayesian predictive densities which have been obtained from alternative models. Several specifications of multivariate time-varying weigh

  16. Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction

    Science.gov (United States)

    Kuhn, David; Parida, Laxmi

    2016-01-01

    Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait prediction is usually represented as linear regression models. In many cases, for the same set of samples and markers, multiple traits are observed. Some of these traits might be correlated with each other. Therefore, modeling all the multiple traits together may improve the prediction accuracy. In this work, we view the multitrait prediction problem from a machine learning angle: as either a multitask learning problem or a multiple output regression problem, depending on whether different traits share the same genotype matrix or not. We then adapted multitask learning algorithms and multiple output regression algorithms to solve the multitrait prediction problem. We proposed a few strategies to improve the least square error of the prediction from these algorithms. Our experiments show that modeling multiple traits together could improve the prediction accuracy for correlated traits. Availability and implementation: The programs we used are either public or directly from the referred authors, such as MALSAR (http://www.public.asu.edu/~jye02/Software/MALSAR/) package. The Avocado data set has not been published yet and is available upon request. Contact: dhe@us.ibm.com PMID:27307640

  17. Predictive functional control(PFC) and its application in Chlorinated polyethylene process

    Institute of Scientific and Technical Information of China (English)

    李鸿亮; 苏宏业; 刘军; 褚健

    2003-01-01

    The main principle and the characteristic of Predictive Functional Control (PFC) strategy are presented in this paper and the corresponding control system aid design software APC-PFC is also introduced. For a chlorinated polyethylene (CPE) process, a design scheme of cascade predictive functional control system is described and the control performance is improved obviously.

  18. Novel Applications of Multi-task Learning and Multiple Output Regression to Multiple Genetic Trait Prediction

    Science.gov (United States)

    Given a set of biallelic molecular markers, such as SNPs, with genotype values encoded numerically on a collection of plant, animal or human samples, the goal of genetic trait prediction is to predict the quantitative trait values by simultaneously modeling all marker effects. Genetic trait predicti...

  19. Application of Artificial Neural Network to Predicting Hardenability of Gear Steel

    Institute of Scientific and Technical Information of China (English)

    GAO Xiu-hua; QI Ke-min; DENG Tian-yong; QIU Chun-lin; ZHOU Ping; DU Xian-bin

    2006-01-01

    The prediction of the hardenability and chemical composition of gear steel was studied using artificial neural networks. A software was used to quantitatively forecast the hardenability by its chemical composition or the chemical composition by its hardenability. The prediction result is more precise than that obtained from the traditional method based on the simple mathematical regression model.

  20. Application of support vector machine in the prediction of mechanical property of steel materials

    Institute of Scientific and Technical Information of China (English)

    Ling Wang; Zhichun Mu; Hui Guo

    2006-01-01

    The investigation of the influences of important parameters including steel chemical composition and hot rolling parameters on the mechanical properties of steel is a key for the systems that are used to predict mechanical properties. To improve the prediction accuracy, support vector machine was used to predict the mechanical properties of hot-rolled plain carbon steel Q235B. Support vector machine is a novel machine learning method, which is a powerful tool used to solve the problem characterized by small sample, nonlinearity, and high dimension with a good generalization performance. On the basis of the data collected from the supervisor of hotrolling process, the support vector regression algorithm was used to build prediction models, and the off-line simulation indicates that predicted and measured results are in good agreement.

  1. Application of Image Processing to Predict Compressive Behavior of Aluminum Foam

    Directory of Open Access Journals (Sweden)

    Kim Sanghoon

    2016-06-01

    Full Text Available An image processing technique was used to model the internal structure of aluminum foam in finite element analysis in order to predict the compressive behavior of the material. Finite element analysis and experimental tests were performed on aluminum foam with densities of 0.2, 0.25, and 0.3 g/cm3. It was found that although the compressive strength predicted from the finite element analysis was higher than that determined experimentally, the predicted compressive stress-strain curves exhibited a tendency similar to those determined from experiments for both densities. However, the behavior of the predicted compressive stress-strain curves was different from the experimental one as the applied strain increased. The difference between predicted and experimental stress-strain curves in a high strain range was due to contact between broken aluminum foam walls by the large deformation.

  2. Intrinsic Terminator Prediction and Its Application in Synechococcus sp. WH8102

    Institute of Scientific and Technical Information of China (English)

    Xiu-Feng Wan; Dong Xu

    2005-01-01

    A new method for intrinsic terminator prediction based on Rnall, an RNA local secondary structure prediction algorithm developed recently, and two U-tail score schemas are developed. By optimizing three parameters (thermodynamic energy of RNA hairpin structure, U-tail T weight, and U-tail hybridization energy), the method can recognize 92.25% of known terminators while rejecting 98.48% of predicted RNA local secondary structures in coding regions (negative control) as false intrinsic terminators in E. coli. This method was applied to scan the genome of Synechococcus sp. WH8102, and we predicted 266 intrinsic terminators, which included 232 protein-coding genes, 12 tRNA genes, and 3 rRNA genes. About 17% of these terminators are located at the end of operons. It is also identified 8 pairs of biodirectional terminators. The method for intrinsic terminator prediction has been incorporated into Rnall, which is available at http://digbio.missouri.edu/~wanx/Rnall/.

  3. Application of RVM for prediction of bead shape in underwater rotating arc welding

    Institute of Scientific and Technical Information of China (English)

    Du Jianhui; Shi Yonghua; Wang Guorong; Huang Guoxing

    2010-01-01

    Bead shape in underwater rotating arc welding was affected by several welding parameters.RVM(relevance vector machine)was used to build a model to predict weld bead shape.The training data set of RVM consists of the welding parameters which are rotational frequency,rotational radius,height of torch and welding current and the features of the bead shape.The maximum error and mean error for prediction of width are 0.10 mm and 0.09 mm,respectively,and the maximum error and mean error for prediction of penetration are 0.31 nun and 0.12 mm,respectively,which are showed that the prediction model can achieve higher prediction precision at reasonably small size of training data set.

  4. PREDICTION OF MECHANICAL PROPERTY OF WHISKER REINFORCED METAL MATRIX COMPOSITE:PART-Ⅱ. VERIFICATION & APPLICATION

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    The present paper continues the discussion in Part-I. Model and Formulation. Based on the theory proposed in Part-I, the formulae predicting stiffness moduli of the composites in some typical cases of whisker orientations and loading conditions are derived and compared with theoretical representatives in literatures, experimental measurement and commonly-used empirical formulae. It seems that (1) with whisker reinforcing and matrix-hardening considered, the present prediction is in well agreement with the experimental measurement; (2) the present theory can predict accurate moduli with the proper pre-calculated parameters; (3) the upper-bound and lower-bound of the present theory are just the predictions of equal strain theory and equal stress theory; (4) the present theory provides a physical explanation and theoretical base for the present commonly-used empirical formulae. Compared with the microscopic mechanical theories, the present theory is competent for modulus prediction of practical engineering composite in accuracy and simplicity.

  5. Development and Application of Intelligent Prediction Software for Broken Rock Zone Thickness of Drifts

    Institute of Scientific and Technical Information of China (English)

    XU Guo-an; JING Hong-wen; LI Kai-ge; CHEN Kun-fu

    2005-01-01

    In order to seek the economical, practical and effective method of obtaining the thickness of broken rock zone, an emerging intelligent prediction method with adaptive neuro-fuzzy inference system (ANFIS) was introduced into the thickness prediction. And the software with functions of creating and applying prediction systems was developed on the platform of MATLAB6.5. The software was used to predict the broken rock zone thickness of drifts at Liangbei coal mine, Xinlong Company of Coal Industry in Xuchang city of Henan province. The results show that the predicted values accord well with the in situ measured ones. Thereby the validity of the software is validated and it provides a new approach to obtaining the broken zone thickness.

  6. An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings

    Directory of Open Access Journals (Sweden)

    Luis Gonzaga Baca Ruiz

    2016-08-01

    Full Text Available This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data provided by an energy consumption monitoring system in a compound of faculties and research centers at the University of Granada, and provide a methodology to predict future energy consumption using nonlinear autoregressive (NAR and the nonlinear autoregressive neural network with exogenous inputs (NARX, respectively. Results reveal that NAR and NARX neural networks are both suitable for performing energy consumption prediction, but also that exogenous data may help to improve the accuracy of predictions.

  7. [Application of near infrared reflectance spectroscopy to predict meat chemical compositions: a review].

    Science.gov (United States)

    Tao, Lin-Li; Yang, Xiu-Juan; Deng, Jun-Ming; Zhang, Xi

    2013-11-01

    In contrast to conventional methods for the determination of meat chemical composition, near infrared reflectance spectroscopy enables rapid, simple, secure and simultaneous assessment of numerous meat properties. The present review focuses on the use of near infrared reflectance spectroscopy to predict meat chemical compositions. The potential of near infrared reflectance spectroscopy to predict crude protein, intramuscular fat, fatty acid, moisture, ash, myoglobin and collagen of beef, pork, chicken and lamb is reviewed. This paper discusses existing questions and reasons in the current research. According to the published results, although published results vary considerably, they suggest that near-infrared reflectance spectroscopy shows a great potential to replace the expensive and time-consuming chemical analysis of meat composition. In particular, under commercial conditions where simultaneous measurements of different chemical components are required, near infrared reflectance spectroscopy is expected to be the method of choice. The majority of studies selected feature-related wavelengths using principal components regression, developed the calibration model using partial least squares and modified partial least squares, and estimated the prediction accuracy by means of cross-validation using the same sample set previously used for the calibration. Meat fatty acid composition predicted by near-infrared spectroscopy and non-destructive prediction and visualization of chemical composition in meat using near-infrared hyperspectral imaging and multivariate regression are the hot studying field now. On the other hand, near infrared reflectance spectroscopy shows great difference for predicting different attributes of meat quality which are closely related to the selection of calibration sample set, preprocessing of near-infrared spectroscopy and modeling approach. Sample preparation also has an important effect on the reliability of NIR prediction; in particular

  8. Research progress of drugs targeting amyloid β in the treatment of Alzheimer's disease%β淀粉样蛋白靶向药物治疗阿尔茨海默病的研究进展

    Institute of Scientific and Technical Information of China (English)

    赵绪韬; 傅毅; 董文心

    2013-01-01

    Neurotransmitter regulation can relieve Alzheimer's disease (AD) symptoms, but it is difficult to reverse the disease progression. In recent years, AD drugs targeting amyloid beta (Aβ) has been widely developed and mainly focused on delaying the progression and prevention. This article reviews the development of AD drug targeting Aβ%以调节神经递质为主的阿尔茨海默病(AD)治疗药物,虽能缓解症状,但难以逆转疾病进展.近期药物的研发重点主要聚焦于能有效延缓疾病进程及预防AD发生的药物,其中以β淀粉样蛋白(Aβ)为靶点的药物开发得到了较为广泛的研究.本文综述近年针对Aβ的AD药物研究进展.

  9. Applicability of the TCDD-TEQ approach to predict sublethal embryotoxicity in Fundulus heteroclitus.

    Science.gov (United States)

    Rigaud, Cyril; Couillard, Catherine M; Pellerin, Jocelyne; Légaré, Benoît; Hodson, Peter V

    2014-04-01

    The 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) toxic equivalent quantity (TCDD-TEQ) approach was used successfully to predict lethal embryotoxicity in salmonids, but its applicability to sublethal effects of mixtures of organohalogenated compounds in other fish species is poorly known. The sublethal toxicity of two dioxin-like compounds (DLCs), 3,3',4,4'-tetrachlorobiphenyl (PCB77) and 2,3,4,7,8-pentachlorodibenzofuran (2,3,4,7,8-PnCDF), two non-dioxin-like (NDL) polychlorinated biphenyls (PCBs), 2,2',5,5'-tetrachlorobiphenyl (PCB52) and 2,3,3',4',6-pentachlorobiphenyl (PCB110), and of Aroclor 1254, a complex commercial mixture of PCBs, was assessed in Fundulus heteroclitus embryos exposed by intravitelline injection. At 16 days post-fertilization, the two DLCs and Aroclor 1254 altered prey capture ability in addition to inducing classical aryl hydrocarbon receptor-mediated responses: ethoxyresorufin-O-deethylase (EROD) induction, craniofacial deformities and reduction in body length. None of these responses was induced by the two NDL PCBs, at doses up to 5400 ng g(-1)wet weight. Dose-response curves for prey capture ability for the 2 DLCs tested were not parallel to that of TCDD, violating a fundamental assumption for relative potency (ReP) estimation. Dose-response curves for EROD induction were parallel for 2,3,4,7,8-PnCDF and TCDD, but the ReP of 2,3,4,7,8-PnCDF for F. heteroclitus was 5-fold higher than the World Health Organization (WHO) fish toxic equivalent factor (TEF) based on embryolethality in salmonids. The chemically derived TCDD-TEQs of Aroclor 1254, calculated using 3,3',4,4',5-pentachlorobiphenyl (PCB126) concentrations and it ReP for F. heteroclitus, overestimated its potency to induce EROD activity possibly due to antagonistic interactions among PCBs. This study highlights the limitations of using TEFs based on salmonid toxicity data alone for risk assessment to other fish species. There is a need to assess the variability of RePs of DLCs in

  10. Earthquake ground motion prediction for real sedimentary basins: which numerical schemes are applicable?

    Science.gov (United States)

    Moczo, P.; Kristek, J.; Galis, M.; Pazak, P.

    2009-12-01

    Numerical prediction of earthquake ground motion in sedimentary basins and valleys often has to account for P-wave to S-wave speed ratios (Vp/Vs) as large as 5 and even larger, mainly in sediments below groundwater level. The ratio can attain values larger than 10 in unconsolidated sediments (e.g. in Ciudad de México). In a process of developing 3D optimally-accurate finite-difference schemes we encountered a serious problem with accuracy in media with large Vp/Vs ratio. This led us to investigate the very fundamental reasons for the inaccuracy. In order to identify the very basic inherent aspects of the numerical schemes responsible for their behavior with varying Vp/Vs ratio, we restricted to the most basic 2nd-order 2D numerical schemes on a uniform grid in a homogeneous medium. Although basic in the specified sense, the schemes comprise the decisive features for accuracy of wide class of numerical schemes. We investigated 6 numerical schemes: finite-difference_displacement_conventional grid (FD_D_CG) finite-element_Lobatto integration (FE_L) finite-element_Gauss integration (FE_G) finite-difference_displacement-stress_partly-staggered grid (FD_DS_PSG) finite-difference_displacement-stress_staggered grid (FD_DS_SG) finite-difference_velocity-stress_staggered grid (FD_VS_SG) We defined and calculated local errors of the schemes in amplitude and polarization. Because different schemes use different time steps, they need different numbers of time levels to calculate solution for a desired time window. Therefore, we normalized errors for a unit time. The normalization allowed for a direct comparison of errors of different schemes. Extensive numerical calculations for wide ranges of values of the Vp/Vs ratio, spatial sampling ratio, stability ratio, and entire range of directions of propagation with respect to the spatial grid led to interesting and surprising findings. Accuracy of FD_D_CG, FE_L and FE_G strongly depends on Vp/Vs ratio. The schemes are not

  11. Application of cellular neural network (CNN) to the prediction of missing air pollutant data

    Science.gov (United States)

    Şahin, Ülkü Alver; Bayat, Cuma; Uçan, Osman N.

    2011-07-01

    For air-quality assessments in most major urban centers, air pollutants are monitored using continuous samplers. Sometimes data are not collected due to equipment failure or during equipment calibration. In this paper, we predict daily air pollutant concentrations (PM 10 and SO 2) from the Yenibosna and Umraniye air pollution measurement stations in Istanbul for times at which pollution data was not recorded. We predicted these pollutant concentrations using the CNN model with meteorological parameters, estimating missing daily pollutant concentrations for two data sets from 2002 to 2003. These data sets had 50 and 20% of data missing. The results of the CNN model predictions are compared with the results of a multivariate linear regression (LR). Results show that the correlation between predicted and observed data was higher for all pollutants using the CNN model (0.54-0.87). The CNN model predicted SO 2 concentrations better than PM 10 concentrations. Another interesting result is that winter concentrations of all pollutants were predicted better than summer concentrations. Experiments showed that accurate predictions of missing air pollutant concentrations are possible using the new approach contained in the CNN model. We therefore proposed a new approach to model air-pollution monitoring problem using CNN.

  12. Application of Direct Assimilation of ATOVS Microwave Radiances to Typhoon Track Prediction

    Institute of Scientific and Technical Information of China (English)

    张华; 薛纪善; 朱国富; 庄世宇; 吴雪宝; 张风英

    2004-01-01

    In order to solve the difficult problem of typhoon track prediction due to the sparsity of conventional data over the tropical ocean, in this paper, the No. 0205 typhoon Rammasun of 4-6 July 2002 is studied and an experiment of the typhoon track prediction is made with the direct use of the Advanced TIROS-N Operational Vertical Sounder (ATOVS) microwave radiance data in three-dimensional variational data assimilation. The prediction result shows that the experiment with the ATOVS microwave radiance data can not only successfully predict the observed fact that typhoon Rammasun moves northward and turns right, but can also simulate the action of the fast movement of the typhoon, which cannot be simulated with only conventional radiosonde data. The skill of the typhoon track prediction with the ATOVS microwave radiance data is much better than that without the ATOVS data. The typhoon track prediction of the former scheme is consistent in time and in location with the observation. The direct assimilation of ATOVS microwave radiance data is an available way to solve the problem of the sparse observation data over the tropical ocean, and has great potential in being applied to typhoon track prediction.

  13. Prediction of Period-Doubling Bifurcation Based on Dynamic Recognition and Its Application to Power Systems

    Science.gov (United States)

    Chen, Danfeng; Wang, Cong

    In this paper, a bifurcation prediction approach is proposed based on dynamic recognition and further applied to predict the period-doubling bifurcation (PDB) of power systems. Firstly, modeling of the internal dynamics of nonlinear systems is obtained through deterministic learning (DL), and the modeling results are applied for constructing the dynamic training pattern database. Specifically, training patterns are chosen according to the hierarchical structured knowledge representation based on the qualitative property of dynamical systems, which is capable of arranging the dynamical models into a specific order in the pattern database. Then, a dynamic recognition-based bifurcation prediction approach is suggested. As a result, perturbations implying PDB on the testing patterns can be predicted through the minimum dynamic error between the training patterns and testing patterns by recalling the knowledge restored in the pattern database. Finally, the second-order single-machine to infinite bus power system model is introduced to check the effectiveness of this prediction approach, which implies PDB under small periodic parameter perturbations. The key point that determines the prediction effect mainly lies in two methods: (1) accurate approximation of the unknown system dynamics through DL guarantees the feasibility of the prediction process; (2) the qualitative property of PDB and the generalization ability of DL algorithm ensure the validity of the selected training patterns. Simulations are included to illustrate the effectiveness of the proposed approach.

  14. Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) using Complex Quantum Neuron (CQN): Applications to time series prediction.

    Science.gov (United States)

    Cui, Yiqian; Shi, Junyou; Wang, Zili

    2015-11-01

    Quantum Neural Networks (QNN) models have attracted great attention since it innovates a new neural computing manner based on quantum entanglement. However, the existing QNN models are mainly based on the real quantum operations, and the potential of quantum entanglement is not fully exploited. In this paper, we proposes a novel quantum neuron model called Complex Quantum Neuron (CQN) that realizes a deep quantum entanglement. Also, a novel hybrid networks model Complex Rotation Quantum Dynamic Neural Networks (CRQDNN) is proposed based on Complex Quantum Neuron (CQN). CRQDNN is a three layer model with both CQN and classical neurons. An infinite impulse response (IIR) filter is embedded in the Networks model to enable the memory function to process time series inputs. The Levenberg-Marquardt (LM) algorithm is used for fast parameter learning. The networks model is developed to conduct time series predictions. Two application studies are done in this paper, including the chaotic time series prediction and electronic remaining useful life (RUL) prediction.

  15. Multi-Objective Optimisation Method for Posture Prediction and Analysis with Consideration of Fatigue Effect and its Application Case

    CERN Document Server

    Ma, Liang; Chablat, Damien; Bennis, Fouad; Guillaume, François; 10.1016/j.cie.2009.06.003

    2009-01-01

    Automation technique has been widely used in manufacturing industry, but there are still manual handling operations required in assembly and maintenance work in industry. Inappropriate posture and physical fatigue might result in musculoskeletal disorders (MSDs) in such physical jobs. In ergonomics and occupational biomechanics, virtual human modelling techniques have been employed to design and optimize the manual operations in design stage so as to avoid or decrease potential MSD risks. In these methods, physical fatigue is only considered as minimizing the muscle or joint stress, and the fatigue effect along time for the posture is not considered enough. In this study, based on the existing methods and multiple objective optimisation method (MOO), a new posture prediction and analysis method is proposed for predicting the optimal posture and evaluating the physical fatigue in the manual handling operation. The posture prediction and analysis problem is mathematically described and a special application cas...

  16. Predicted reliability of aerospace electronics: Application of two advanced probabilistic concepts

    Science.gov (United States)

    Suhir, E.

    Two advanced probabilistic design-for-reliability (PDfR) concepts are addressed and discussed in application to the prediction, quantification and assurance of the aerospace electronics reliability: 1) Boltzmann-Arrhenius-Zhurkov (BAZ) model, which is an extension of the currently widely used Arrhenius model and, in combination with the exponential law of reliability, enables one to obtain a simple, easy-to-use and physically meaningful formula for the evaluation of the probability of failure (PoF) of a material or a device after the given time in operation at the given temperature and under the given stress (not necessarily mechanical), and 2) Extreme Value Distribution (EVD) technique that can be used to assess the number of repetitive loadings that result in the material/device degradation and eventually lead to its failure by closing, in a step-wise fashion, the gap between the bearing capacity (stress-free activation energy) of the material or the device and the demand (loading). It is shown that the material degradation (aging, damage accumulation, flaw propagation, etc.) can be viewed, when BAZ model is considered, as a Markovian process, and that the BAZ model can be obtained as the ultimate steady-state solution to the well-known Fokker-Planck equation in the theory of Markovian processes. It is shown also that the BAZ model addresses the worst, but a reasonably conservative, situation. It is suggested therefore that the transient period preceding the condition addressed by the steady-state BAZ model need not be accounted for in engineering evaluations. However, when there is an interest in understanding the transient degradation process, the obtained solution to the Fokker-Planck equation can be used for this purpose. As to the EVD concept, it attributes the degradation process to the accumulation of damages caused by a train of repetitive high-level loadings, while loadings of levels that are considerably lower than their extreme values do not contribute

  17. The malarial drug target Plasmodium falciparum 1-deoxy-D-xylulose-5-phosphate reductoisomerase (PfDXR): development of a 3-D model for identification of novel, structural and functional features and for inhibitor screening.

    Science.gov (United States)

    Goble, Jessica L; Adendorff, Matthew R; de Beer, Tjaart A P; Stephens, Linda L; Blatch, Gregory L

    2010-01-01

    A three-dimensional model of the malarial drug target protein PfDXR was generated, and validated using structure-checking programs and protein docking studies. Structural and functional features unique to PfDXR were identified using the model and comparative sequence analyses with apicomplexan and non-apicomplexan DXR proteins. Furthermore, we have used the model to develop an efficient approach to screen for potential tool compounds for use in the rational design of novel DXR inhibitors.

  18. An overview to understand the role of PE_PGRS family proteins in Mycobacterium tuberculosis H37 Rv and their potential as new drug targets.

    Science.gov (United States)

    Meena, Laxman S

    2015-01-01

    new drug targets.

  19. Differential expression and function of PDE8 and PDE4 in effector T cells: Implications for PDE8 as a drug target in inflammation.

    Directory of Open Access Journals (Sweden)

    Amanda G. Vang

    2016-08-01

    Full Text Available Abolishing the inhibitory signal of intracellular cAMP is a prerequisite for effector T (Teff cell function. The regulation of cAMP within leukocytes critically depends on its degradation by cyclic nucleotide phosphodiesterases (PDEs. We have previously shown that PDE8A, a PDE isoform with 40-100-fold greater affinity for cAMP than PDE4, is selectively expressed in Teff versus regulatory T (Treg cells and controls CD4+ Teff cell adhesion and chemotaxis. Here, we determined PDE8A expression and function in CD4+ Teff cell populations in vivo. Using magnetic bead separation to purify leukocyte populations from the lung draining hilar lymph node (HLN in a mouse model of ovalbumin-induced allergic airway disease (AAD, we found by Western immunoblot and quantitative (qRT-PCR that PDE8A protein and gene expression are enhanced in the CD4+ T cell fraction over the course of the acute inflammatory disease and recede at the late tolerant non-inflammatory stage. To evaluate PDE8A as a potential drug target, we compared the selective and combined effects of the recently characterized highly potent PDE8-selective inhibitor PF-04957325 with the PDE4-selective inhibitor piclamilast (PICL. As previously shown, PF-04957325 suppresses T cell adhesion to endothelial cells. In contrast, we found that PICL alone increased firm T cell adhesion to endothelial cells by approximately 20% and significantly abrogated the inhibitory effect of PF-04957325 on T cell adhesion by over 50% when cells were co-exposed to PICL and PF-04957325. Despite its robust effect on T cell adhesion, PF-04957325 was over two orders of magnitude less efficient than PICL in suppressing polyclonal Teff cell proliferation, and showed no effect on cytokine gene expression in these cells. More importantly, PDE8 inhibition did not suppress proliferation and cytokine production of myelin-antigen reactive proinflammatory Teff cells in vivo and in vitro. Thus, targeting PDE8 through PF-04957325

  20. Prediction of quantiles by statistical learning and application to GDP forecasting

    CERN Document Server

    Alquier, Pierre

    2012-01-01

    In this paper, we tackle the problem of prediction and confidence intervals for time series using a statistical learning approach and quantile loss functions. In a first time, we show that the Gibbs estimator (also known as Exponentially Weighted aggregate) is able to predict as well as the best predictor in a given family for a wide set of loss functions. In particular, using the quantile loss function of Koenker and Bassett (1978), this allows to build confidence intervals. We apply these results to the problem of prediction and confidence regions for the French Gross Domestic Product (GDP) growth, with promising results.

  1. Genetic algorithm based adaptive neural network ensemble and its application in predicting carbon flux

    Science.gov (United States)

    Xue, Y.; Liu, S.; Hu, Y.; Yang, J.; Chen, Q.

    2007-01-01

    To improve the accuracy in prediction, Genetic Algorithm based Adaptive Neural Network Ensemble (GA-ANNE) is presented. Intersections are allowed between different training sets based on the fuzzy clustering analysis, which ensures the diversity as well as the accuracy of individual Neural Networks (NNs). Moreover, to improve the accuracy of the adaptive weights of individual NNs, GA is used to optimize the cluster centers. Empirical results in predicting carbon flux of Duke Forest reveal that GA-ANNE can predict the carbon flux more accurately than Radial Basis Function Neural Network (RBFNN), Bagging NN ensemble, and ANNE. ?? 2007 IEEE.

  2. Research on Application of Regression Least Squares Support Vector Machine on Performance Prediction of Hydraulic Excavator

    Directory of Open Access Journals (Sweden)

    Zhan-bo Chen

    2014-01-01

    Full Text Available In order to improve the performance prediction accuracy of hydraulic excavator, the regression least squares support vector machine is applied. First, the mathematical model of the regression least squares support vector machine is studied, and then the algorithm of the regression least squares support vector machine is designed. Finally, the performance prediction simulation of hydraulic excavator based on regression least squares support vector machine is carried out, and simulation results show that this method can predict the performance changing rules of hydraulic excavator correctly.

  3. Application of retrospective time integration scheme to the prediction of torrential rain

    Institute of Scientific and Technical Information of China (English)

    Feng Guo-Lin; Dong Wen-Jie; Jia Xiao-Jing

    2004-01-01

    The retrospective time integration scheme presented on the principle of the self-memory of the atmosphere is applied to the mesoscale grid model MM5, constructing a mesoscale self-memorial model SMM5, and then the shortrange prediction experiments of torrential rain are performed in this paper. Results show that in comparison with MM5 the prediction accuracy of SMM5 is obviously improved due to its utilization of multiple time level past observations,and the precipitation area and intensity predicted by SMM5 are closer to observational fields than those by MM5.

  4. A Teleoperation System Based on Predictive Simulation and Its Application to Spacecraft Maintenance

    Institute of Scientific and Technical Information of China (English)

    LI Ming-fu; LI Shi-qi; ZHAO Di; ZHU Wen-ge

    2008-01-01

    A teleoperation system based on predictive simulation is proposed for the sake of compensating the large time delay in the process of teleoperation to a degree and providing a friendly operating interface. The framework and function architecture of the system is discussed firstly. Then, the operator interface and a graphics simulation system is described in detail. Furthermore, a predictive algorithm aiming at position control based teleoperation is studied in our research, and the relative framework of predictive simulation is discussed. Finally, the system is applied to spacecraft breakdown maintenance; multi-agent reinforcement learning based semi-autonomous teleoperation is discussed at the same time for safe operation.

  5. Application of artificial neural network for prediction of marine diesel engine performance

    Science.gov (United States)

    Mohd Noor, C. W.; Mamat, R.; Najafi, G.; Nik, W. B. Wan; Fadhil, M.

    2015-12-01

    This study deals with an artificial neural network (ANN) modelling of a marine diesel engine to predict the brake power, output torque, brake specific fuel consumption, brake thermal efficiency and volumetric efficiency. The input data for network training was gathered from engine laboratory testing running at various engine speed. The prediction model was developed based on standard back-propagation Levenberg-Marquardt training algorithm. The performance of the model was validated by comparing the prediction data sets with the measured experiment data. Results showed that the ANN model provided good agreement with the experimental data with high accuracy.

  6. Application of decision trees to the analysis of soil radon data for earthquake prediction.

    Science.gov (United States)

    Zmazek, B; Todorovski, L; Dzeroski, S; Vaupotic, J; Kobal, I

    2003-06-01

    Different regression methods have been used to predict radon concentration in soil gas on the basis of environmental data, i.e. barometric pressure, soil temperature, air temperature and rainfall. Analyses of the radon data from three stations in the Krsko basin, Slovenia, have shown that model trees outperform other regression methods. A model has been built which predicts radon concentration with a correlation of 0.8, provided it is influenced only by the environmental parameters. In periods with seismic activity this correlation is much lower. This decrease in predictive accuracy appears 1-7 days before earthquakes with local magnitude 0.8-3.3.

  7. Application of decision trees to the analysis of soil radon data for earthquake prediction

    Energy Technology Data Exchange (ETDEWEB)

    Zmazek, B. E-mail: boris.zmazek@ijs.si; Todorovski, L.; Dzeroski, S.; Vaupotic, J.; Kobal, I

    2003-06-01

    Different regression methods have been used to predict radon concentration in soil gas on the basis of environmental data, i.e. barometric pressure, soil temperature, air temperature and rainfall. Analyses of the radon data from three stations in the Krsko basin, Slovenia, have shown that model trees outperform other regression methods. A model has been built which predicts radon concentration with a correlation of 0.8, provided it is influenced only by the environmental parameters. In periods with seismic activity this correlation is much lower. This decrease in predictive accuracy appears 1-7 days before earthquakes with local magnitude 0.8-3.3.

  8. Advances in the research of the drugs targeting glucagon-like peptide-1 receptor%胰高血糖素样肽-1受体靶向药物研究进展

    Institute of Scientific and Technical Information of China (English)

    李彩娜; 申竹芳

    2009-01-01

    Glucagon-like peptide-1 (GLP-1) ,a 30 amino-acid peptide,is an incretin secreted from the in-testinal L cells. It can bind to the GLP-1 receptor to exert a variety of anti-diabetic activity. However,its half-life is only about 2 min in vivo,which may limit its application in clinic. Even though,GLP-1 receptor remains to be a new target for the research and development of anti-diabetic drugs. The aim is to discover long-term peptidic or non-pep-tidic glucagon-like peptide-1 receptor agonists,which could also resist the degradation by dipeptidyl peptidase IV (DPP IV).Up to date,besides of exenatide that has been approved by US Food Drug Administration ( FDA ) ,there are some peptides,such as liraglutide,in clinic study state and many other peptidic or non-peptidic compounds in pre-clinic study state. In this article,we reviewed the recent achievements regarding drugs targeting GLP-1 receptor.%胰高血糖素样肽-1(Ghcagon-like peptide-1,GLP-1)是一种肠道L细胞分泌的肠降糖素,由30个氨基酸组成.GLP-1与受体结合后具有一定的抗糖尿病作用,但其体内半衰期短(约2 min),临床应用受限.GLP-1受体是目前开发抗糖尿病药物的新靶点之一,旨在寻找可以同时激动GLP-1受体和耐受二肽基肽酶IV降解的长效肽类或非肽类化合物.目前该类多肽中exenatide已被美国食品药品管理局批准上市,liraglutide等处于临床研究阶段,尚有很多肽类或非肽类化合物处于临床前研究阶段.文中将近几年来靶向GLP-1受体的研究成果综述如下.

  9. Application of Excitation Function to the Prediction of RI Level Caused by Corona Discharge

    Institute of Scientific and Technical Information of China (English)

    ZHU Lingyu; JI Shengchang; HUI Sisi; GUO Jun; LI Yansong; FU Chenzhao

    2012-01-01

    Radio interference (RI), as an aftereffect of corona discharge, is an important research topic in the field of electromagnetic compatibility, where excitation function is applied broadly to the prediction of RI level. This paper presents the theory of excitation function method used in the RI level prediction. Then, some practical problems related to this method are discussed. The propagation procedure of corona current is solved by the phase-modal transformation, and the impedance matrix of multi transmission lines is calculated by a double logarithmic approximate model of Carson's Ground-Return impedance. At the same time, in order to calculate the RI level when total line corona is assumed, an analytical formula is deduced for integral operation. Based on the above solutions, an algorithm is presented and applied to the prediction of RI level of a practical overhead transmission line. Comparison of prediction and measurement results indicates that the algorithm proposed in this paper is effective and feasible.

  10. Application of coupled analysis methods for prediction of blast-induced dominant vibration frequency

    Science.gov (United States)

    Li, Haibo; Li, Xiaofeng; Li, Jianchun; Xia, Xiang; Wang, Xiaowei

    2016-03-01

    Blast-induced dominant vibration frequency (DVF) involves a complex, nonlinear and small sample system considering rock properties, blasting parameters and topography. In this study, a combination of grey relational analysis and dimensional analysis procedures for prediction of dominant vibration frequency are presented. Six factors are selected from extensive effect factor sequences based on grey relational analysis, and then a novel blast-induced dominant vibration frequency prediction is obtained by dimensional analysis. In addition, the prediction is simplified by sensitivity analysis with 195 experimental blast records. Validation is carried out for the proposed formula based on the site test database of the firstperiod blasting excavation in the Guangdong Lufeng Nuclear Power Plant (GLNPP). The results show the proposed approach has a higher fitting degree and smaller mean error when compared with traditional predictions.

  11. Application of GLBP Algorithm in the Prediction of Building Energy Consumption

    Directory of Open Access Journals (Sweden)

    Dinghao Lv

    2015-06-01

    Full Text Available Using BP neural network in past to predict the energy consumption of the building resulted in some shortcomings. Aiming at these shortages, a new algorithm which combined genetic algorithm with Levenberg-Marquardt algorithm (LM algorithm was proposed. The proposed algorithm was used to improve the neural network and predict the energy consumption of buildings. First, genetic algorithm was used to optimize the weight and threshold of Artificial Neural Network (ANN. Levenberg-Marquardt algorithm was adopted to optimize the neural network training. Then the predicting model was set up in terms of the main effecting factors of the energy consumption. Furthermore, a public building power consumption data for one month is collected by establishing a monitoring platform to train and test the model. Eventually, the simulation result proved that the proposed model was qualified to predict short-term energy consumption accurately and efficiently.

  12. Geary autocorrelation and DCCA coefficient: Application to predict apoptosis protein subcellular localization via PSSM

    Science.gov (United States)

    Liang, Yunyun; Liu, Sanyang; Zhang, Shengli

    2017-02-01

    Apoptosis is a fundamental process controlling normal tissue homeostasis by regulating a balance between cell proliferation and death. Predicting subcellular location of apoptosis proteins is very helpful for understanding its mechanism of programmed cell death. Prediction of apoptosis protein subcellular location is still a challenging and complicated task, and existing methods mainly based on protein primary sequences. In this paper, we propose a new position-specific scoring matrix (PSSM)-based model by using Geary autocorrelation function and detrended cross-correlation coefficient (DCCA coefficient). Then a 270-dimensional (270D) feature vector is constructed on three widely used datasets: ZD98, ZW225 and CL317, and support vector machine is adopted as classifier. The overall prediction accuracies are significantly improved by rigorous jackknife test. The results show that our model offers a reliable and effective PSSM-based tool for prediction of apoptosis protein subcellular localization.

  13. An application of generalized predictive control to rotorcraft terrain-following flight

    Science.gov (United States)

    Hess, Ronald A.; Jung, Yoon C.

    1989-01-01

    Generalized predictive control (GPC) describes an algorithm for the control of dynamic systems in which a control input is generated which minimizes a quadratic cost function consisting of a weighted sum of errors between desired and predicted future system output and future predicted control increments. The output predictions are obtained from an internal model of the plant dynamics. The GPC algorithm is first applied to a simplified rotorcraft terrain-following problem, and GPC performance is compared to that of a conventional compensatory automatic system in terms of flight-path following, control activity, and control law implementation. Next, more realistic vehicle dynamics are utilized, and the GPC algorithm is applied to simultaneous terrain following and velocity control in the presence of atmospheric disturbances and errors in the internal model of the vehicle. The online computational and sensing requirements for implementing the GPC algorithm are minimal. Its use for manual control models appears promising.

  14. Improved Accuracy of PSO and DE using Normalization: an Application to Stock Price Prediction

    Directory of Open Access Journals (Sweden)

    Savinderjit Kaur

    2012-09-01

    Full Text Available Data Mining is being actively applied to stock market since 1980s. It has been used to predict stock prices, stock indexes, for portfolio management, trend detection and for developing recommender systems. The various algorithms which have been used for the same include ANN, SVM, ARIMA, GARCH etc. Different hybrid models have been developed by combining these algorithms with other algorithms like roughest, fuzzy logic, GA, PSO, DE, ACO etc. to improve the efficiency. This paper proposes DE-SVM model (Differential Evolution- Support vector Machine for stock price prediction. DE has been used to select best free parameters combination for SVM to improve results. The paper also compares the results of prediction with the outputs of SVM alone and PSO-SVM model (Particle Swarm Optimization. The effect of normalization of data on the accuracy of prediction has also been studied.

  15. Hybrid video quality prediction: reviewing video quality measurement for widening application scope

    OpenAIRE

    Barkowsky, Marcus; Sedano, Inigo; Brunnstrom, Kjell; Leszczuk, Mikolaj; Staelens, Nicolas

    2015-01-01

    A tremendous number of objective video quality measurement algorithms have been developed during the last two decades. Most of them either measure a very limited aspect of the perceived video quality or they measure broad ranges of quality with limited prediction accuracy. This paper lists several perceptual artifacts that may be computationally measured in an isolated algorithm and some of the modeling approaches that have been proposed to predict the resulting quality from those algorithms....

  16. Prediction Methodology for Proton Single Event Burnout: Application to a STRIPFET Device

    CERN Document Server

    Siconolfi, Sara; Oser, Pascal; Spiezia, Giovanni; Hubert, Guillaume; David, Jean-Pierre

    2015-01-01

    This paper presents a single event burnout (SEB) sensitivity characterization for power MOSFETs, independent from tests, through a prediction model issued from TCAD analysis and the knowledge of device topology. The methodology is applied to a STRIPFET device and compared to proton data obtained at PSI, showing a good agreement in the order of magnitude of proton SEB cross section, and thus validating the prediction model as an alternative device characterization with respect to SEB.

  17. Application of Artificial Neural Network in Predicting the Survival Rate of Gastric Cancer Patients

    OpenAIRE

    Biglarian, A; E. Hajizadeh; Kazemnejad, A; Zali, MR

    2011-01-01

    "nBackground: The aim of this study was to predict the survival rate of Iranian gastric cancer patients using the Cox proportional hazard and artificial neural network models as well as comparing the ability of these approaches in predicting the survival of these patients."nMethods: In this historical cohort study, the data gathered from 436 registered gastric cancer patients who have had surgery between 2002 and 2007 at the Taleghani Hospital (a referral center for gastrointestinal...

  18. Prediction Model of Interval Grey Numbers with a Real Parameter and Its Application

    Directory of Open Access Journals (Sweden)

    Bo Zeng

    2014-01-01

    Full Text Available Grey prediction models have become common methods which are widely employed to solve the problems with “small examples and poor information.” However, modeling objects of existing grey prediction models are limited to the homogenous data sequences which only contain the same data type. This paper studies the methodology of building prediction models of interval grey numbers that are grey heterogeneous data sequence, with a real parameter. Firstly, the position of the real parameter in an interval grey number sequence is discussed, and the real number is expanded into an interval grey number by adopting the method of grey generation. On this basis, a prediction model of interval grey number with a real parameter is deduced and built. Finally, this novel model is successfully applied to forecast the concentration of organic pollutant DDT in the atmosphere. The analysis and research results in this paper extend the object of grey prediction from homogenous data sequence to grey heterogeneous data sequence. Those research findings are of positive significance in terms of enriching and improving the theory system of grey prediction models.

  19. Application of backpropagation artificial neural network prediction model for the PAH bioremediation of polluted soil.

    Science.gov (United States)

    Olawoyin, Richard

    2016-10-01

    The backpropagation (BP) artificial neural network (ANN) is a renowned and extensively functional mathematical tool used for time-series predictions and approximations; which also define results for non-linear functions. ANNs are vital tools in the predictions of toxicant levels, such as polycyclic aromatic hydrocarbons (PAH) potentially derived from anthropogenic activities in the microenvironment. In the present work, BP ANN was used as a prediction tool to study the potential toxicity of PAH carcinogens (PAHcarc) in soils. Soil samples (16 × 4 = 64) were collected from locations in South-southern Nigeria. The concentration of PAHcarc in laboratory cultivated white melilot, Melilotus alba roots grown on treated soils was predicted using ANN model training. Results indicated the Levenberg-Marquardt back-propagation training algorithm converged in 2.5E+04 epochs at an average RMSE value of 1.06E-06. The averagedR(2) comparison between the measured and predicted outputs was 0.9994. It may be deduced from this study that, analytical processes involving environmental risk assessment as used in this study can successfully provide prompt prediction and source identification of major soil toxicants.

  20. Application of Hansen Solubility Parameters to predict drug-nail interactions, which can assist the design of nail medicines.

    Science.gov (United States)

    Hossin, B; Rizi, K; Murdan, S

    2016-05-01

    We hypothesised that Hansen Solubility Parameters (HSPs) can be used to predict drug-nail affinities. Our aims were to: (i) determine the HSPs (δD, δP, δH) of the nail plate, the hoof membrane (a model for the nail plate), and of the drugs terbinafine HCl, amorolfine HCl, ciclopirox olamine and efinaconazole, by measuring their swelling/solubility in organic liquids, (ii) predict nail-drug interactions by comparing drug and nail HSPs, and (iii) evaluate the accuracy of these predictions using literature reports of experimentally-determined affinities of these drugs for keratin, the main constituent of the nail plate and hoof. Many solvents caused no change in the mass of nail plates, a few solvents deswelled the nail, while others swelled the nail to varying extents. Fingernail and toenail HSPs were almost the same, while hoof HSPs were similar, except for a slightly lower δP. High nail-terbinafine HCl, nail-amorolfine HCl and nail-ciclopirox olamine affinities, and low nail-efinaconazole affinities were then predicted, and found to accurately match experimental reports of these drugs' affinities to keratin. We therefore propose that drug and nail Hansen Solubility Parameters may be used to predict drug-nail interactions, and that these results can assist in the design of drugs for the treatment of nail diseases, such as onychomycosis and psoriasis. To our knowledge, this is the first report of the application of HSPs in ungual research.

  1. Development of a generally applicable morphokinetic algorithm capable of predicting the implantation potential of embryos transferred on Day 3

    Science.gov (United States)

    Petersen, Bjørn Molt; Boel, Mikkel; Montag, Markus; Gardner, David K.

    2016-01-01

    STUDY QUESTION Can a generally applicable morphokinetic algorithm suitable for Day 3 transfers of time-lapse monitored embryos originating from different culture conditions and fertilization methods be developed for the purpose of supporting the embryologist's decision on which embryo to transfer back to the patient in assisted reproduction? SUMMARY ANSWER The algorithm presented here can be used independently of culture conditions and fertilization method and provides predictive power not surpassed by other published algorithms for ranking embryos according to their blastocyst formation potential. WHAT IS KNOWN ALREADY Generally applicable algorithms have so far been developed only for predicting blastocyst formation. A number of clinics have reported validated implantation prediction algorithms, which have been developed based on clinic-specific culture conditions and clinical environment. However, a generally applicable embryo evaluation algorithm based on actual implantation outcome has not yet been reported. STUDY DESIGN, SIZE, DURATION Retrospective evaluation of data extracted from a database of known implantation data (KID) originating from 3275 embryos transferred on Day 3 conducted in 24 clinics between 2009 and 2014. The data represented different culture conditions (reduced and ambient oxygen with various culture medium strategies) and fertilization methods (IVF, ICSI). The capability to predict blastocyst formation was evaluated on an independent set of morphokinetic data from 11 218 embryos which had been cultured to Day 5. PARTICIPANTS/MATERIALS, SETTING, METHODS The algorithm was developed by applying automated recursive partitioning to a large number of annotation types and derived equations, progressing to a five-fold cross-validation test of the complete data set and a validation test of different incubation conditions and fertilization methods. The results were expressed as receiver operating characteristics curves using the area under the

  2. Applications of the gambling score in evaluating earthquake predictions and forecasts

    Science.gov (United States)

    Zhuang, Jiancang; Zechar, Jeremy D.; Jiang, Changsheng; Console, Rodolfo; Murru, Maura; Falcone, Giuseppe

    2010-05-01

    This study presents a new method, namely the gambling score, for scoring the performance earthquake forecasts or predictions. Unlike most other scoring procedures that require a regular scheme of forecast and treat each earthquake equally, regardless their magnitude, this new scoring method compensates the risk that the forecaster has taken. Starting with a certain number of reputation points, once a forecaster makes a prediction or forecast, he is assumed to have betted some points of his reputation. The reference model, which plays the role of the house, determines how many reputation points the forecaster can gain if he succeeds, according to a fair rule, and also takes away the reputation points bet by the forecaster if he loses. This method is also extended to the continuous case of point process models, where the reputation points betted by the forecaster become a continuous mass on the space-time-magnitude range of interest. For discrete predictions, we apply this method to evaluate performance of Shebalin's predictions made by using the Reverse Tracing of Precursors (RTP) algorithm and of the outputs of the predictions from the Annual Consultation Meeting on Earthquake Tendency held by China Earthquake Administration. For the continuous case, we use it to compare the probability forecasts of seismicity in the Abruzzo region before and after the L'aquila earthquake based on the ETAS model and the PPE model.

  3. Adaptive Wavelets Based on Second Generation Wavelet Transform and Their Applications to Trend Analysis and Prediction

    Institute of Scientific and Technical Information of China (English)

    DUAN Chen-dong; JIANG Hong-kai; HE Zheng-jia

    2004-01-01

    In order to make trend analysis and prediction to acquisition data in a mechanical equipment condition monitoring system, a new method of trend feature extraction and prediction of acquisition data is proposed which constructs an adaptive wavelet on the acquisition data by means of second generation wavelet transform (SGWT). Firstly, taking the vanishing moment number of the predictor as a constraint, the linear predictor and updater are designed according to the acquisition data by using symmetrical interpolating scheme. Then the trend of the data is obtained through doing SGWT decomposition, threshold processing and SGWT reconstruction. Secondly, under the constraint of the vanishing moment number of the predictor, another predictor based on the acquisition data is devised to predict the future trend of the data using a non-symmetrical interpolating scheme. A one-step prediction algorithm is presented to predict the future evolution trend with historical data. The proposed method obtained a desirable effect in peak-to-peak value trend analysis for a machine set in an oil refinery.

  4. Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils.

    Science.gov (United States)

    Daynac, Mathieu; Cortes-Cabrera, Alvaro; Prieto, Jose M

    2015-01-01

    Essential oils (EOs) are vastly used as natural antibiotics in Complementary and Alternative Medicine (CAM). Their intrinsic chemical variability and synergisms/antagonisms between its components make difficult to ensure consistent effects through different batches. Our aim is to evaluate the use of artificial neural networks (ANNs) for the prediction of their antimicrobial activity. Methods. The chemical composition and antimicrobial activity of 49 EOs, extracts, and/or fractions was extracted from NCCLS compliant works. The fast artificial neural networks (FANN) software was used and the output data reflected the antimicrobial activity of these EOs against four common pathogens: Staphylococcus aureus, Escherichia coli, Candida albicans, and Clostridium perfringens as measured by standardised disk diffusion assays. Results. ANNs were able to predict >70% of the antimicrobial activities within a 10 mm maximum error range. Similarly, ANNs were able to predict 2 or 3 different bioactivities at the same time. The accuracy of the prediction was only limited by the inherent errors of the popular antimicrobial disk susceptibility test and the nature of the pathogens. Conclusions. ANNs can be reliable, fast, and cheap tools for the prediction of the antimicrobial activity of EOs thus improving their use in CAM.

  5. Application of dynamic set-pair analysis in coal and gas outburst prediction

    Institute of Scientific and Technical Information of China (English)

    CAO Qing-kui; LI Li-jie; YU Bing

    2008-01-01

    Analyzed the factors which affected the coal and gas outburst, then established the corresponding indicator system. Built a dynamic set-pair analysis prediction model which combined of Markov model and set-pair analysis model, and then it applied to coal and gas outburst prediction. Finally, compared the prediction results with the actual results.As provided a reference to the coalmine in safety decision-making. The research results indicate that there are four districts in high dangerous level, two districts in middle level and one district in low level, which consistent with the actual situation; the dynamic set-pair analysis model has a good effect in predicting coal and gas outburst. Especially in the continuous time intervals, according to the data of mined exploration and the connec-tion degree analysis, we can deduce the dangerous levels of unexplored districts from the historical data. In different districts, the relevant indicators can be adjusted accordingly, so as to enhance the accuracy of the prediction.

  6. An application of a multi model approach for solar energy prediction in Southern Italy

    Science.gov (United States)

    Avolio, Elenio; Lo Feudo, Teresa; Calidonna, Claudia Roberta; Contini, Daniele; Torcasio, Rosa Claudia; Tiriolo, Luca; Montesanti, Stefania; Transerici, Claudio; Federico, Stefano

    2015-04-01

    The accuracy of the short and medium range forecast of solar irradiance is very important for solar energy integration into the grid. This issue is particularly important for Southern Italy where a significant availability of solar energy is associated with a poor development of the grid. In this work we analyse the performance of two deterministic models for the prediction of surface temperature and short-wavelength radiance for two sites in southern Italy. Both parameters are needed to forecast the power production from solar power plants, so the performance of the forecast for these meteorological parameters is of paramount importance. The models considered in this work are the RAMS (Regional Atmospheric Modeling System) and the WRF (Weather Research and Forecasting Model) and they were run for the summer 2013 at 4 km horizontal resolution over Italy. The forecast lasts three days. Initial and dynamic boundary conditions are given by the 12 UTC deterministic forecast of the ECMWF-IFS (European Centre for Medium Weather Range Forecast - Integrated Forecasting System) model, and were available every 6 hours. Verification is given against two surface stations located in Southern Italy, Lamezia Terme and Lecce, and are based on hourly output of models forecast. Results for the whole period for temperature show a positive bias for the RAMS model and a negative bias for the WRF model. RMSE is between 1 and 2 °C for both models. Results for the whole period for the short-wavelength radiance show a positive bias for both models (about 30 W/m2 for both models) and a RMSE of 100 W/m2. To reduce the model errors, a statistical post-processing technique, i.e the multi-model, is adopted. In this approach the two model's outputs are weighted with an adequate set of weights computed for a training period. In general, the performance is improved by the application of the technique, and the RMSE is reduced by a sizeable fraction (i.e. larger than 10% of the initial RMSE

  7. An application of earthquake prediction algorithm M8 in eastern Anatolia at the approach of the 2011 Van earthquake

    Indian Academy of Sciences (India)

    Masoud Mojarab; Vladimir Kossobokov; Hossein Memarian; Mehdi Zare

    2015-07-01

    On 23rd October 2011, an M7.3 earthquake near the Turkish city of Van, killed more than 600 people, injured over 4000, and left about 60,000 homeless. It demolished hundreds of buildings and caused great damages to thousand others in Van, Ercis, Muradiye, and Çaldıran. The earthquake’s epicenter is located about 70 km from a preceding M7.3 earthquake that occurred in November 1976 and destroyed several villages near the Turkey–Iran border and killed thousands of people. This study, by means of retrospective application of the M8 algorithm, checks to see if the 2011 Van earthquake could have been predicted. The algorithm is based on pattern recognition of Times of Increased Probability (TIP) of a target earthquake from the transient seismic sequence at lower magnitude ranges in a Circle of Investigation (CI). Specifically, we applied a modified M8 algorithm adjusted to a rather low level of earthquake detection in the region following three different approaches to determine seismic transients. In the first approach, CI centers are distributed on intersections of morphostructural lineaments recognized as prone to magnitude 7+ earthquakes. In the second approach, centers of CIs are distributed on local extremes of the seismic density distribution, and in the third approach, CI centers were distributed uniformly on the nodes of a 1°×1° grid. According to the results of the M8 algorithm application, the 2011 Van earthquake could have been predicted in any of the three approaches. We noted that it is possible to consider the intersection of TIPs instead of their union to improve the certainty of the prediction results. Our study confirms the applicability of a modified version of the M8 algorithm for predicting earthquakes at the Iranian–Turkish plateau, as well as for mitigation of damages in seismic events in which pattern recognition algorithms may play an important role.

  8. Application of extended Kalman filtering on aircraft pose prediction of image sequences

    Institute of Scientific and Technical Information of China (English)

    YANG Li-mei; GUO Li-hong

    2007-01-01

    In allusion to the character of monocular image sequences,a method based on extended Kalman filtering to predict the aircraft pose of image sequences is proposed. With α-β-γstable state filtering technique,a mathematics model is built to realize the prediction of aircraft pose of image sequences. In the model,not only the influence of noise during the image process is considered,but also the shortcoming of low precision in the constant velocity model is overcomed. The derivation of acceleration is considered as white noise. The predictive curve plotted with Matlab proves that the maximum of error of using this method is about 3. So its precision is higher and error standard deviation is lower than those of the constant velocity model.

  9. Prediction of Quantitative Traits Using Common Genetic Variants: Application to Body Mass Index

    Science.gov (United States)

    Bae, Sunghwan; Choi, Sungkyoung; Kim, Sung Min

    2016-01-01

    With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

  10. An application of Auto-regressive (AR model in predicting Aeroelastic Effectsof Lekki Cable Stayed Bridge

    Directory of Open Access Journals (Sweden)

    Hassan Abba Musa

    2016-06-01

    Full Text Available In current practice, the predictive analysis of stochastic problems encompasses a variety of statistical techniques from modeling, machine, and data mining that analyse current and historical facts to make predictions about future. Therefore, this research uses an AR Model whose codes are incorporated in the MATLAB software to predict possible aero-elastic effects of Lekki Bridge based on its existing parametric data and the conditions around the bridge. It was seen that, the fluctuating components of the wind velocity as displayed by the fluctuant curve will result in the vibration of the structure, even strengthening the resonance effect of the structure. Therefore, it suggested that, the natural frequency of the bridge should be set aside far from system frequency considering direct parametric excitation of pedestrian or vehicular traffic speed.

  11. Solute transport predicts scaling of surface reaction rates in porous media: Applications to silicate weathering

    CERN Document Server

    Hunt, Allen G; Ghanbarian, Behzad

    2013-01-01

    We apply our theory of conservative solute transport, based on concepts from percolation theory, directly and without modification to reactive solute transport. This theory has previously been shown to predict the observed range of dispersivity values for conservative solute transport over ten orders of magnitude of length scale. We now show that the temporal dependence derived for the solute velocity accurately predicts the time-dependence for the weathering of silicate minerals over nine orders of magnitude of time scale, while its predicted length dependence agrees with data obtained for reaction rates over five orders of magnitude of length scale. In both cases, it is possible to unify lab and field results. Thus, net reaction rates appear to be limited by solute transport velocities. We suggest the possible relevance of our results to landscape evolution of the earth's terrestrial surface.

  12. Reliable Lifetime Prediction for Passivated Fiber Bragg Gratings for Telecommunication Applications

    Directory of Open Access Journals (Sweden)

    Matthieu Lancry

    2014-03-01

    Full Text Available This paper is dedicated to the lifetime prediction of Type I Fiber Bragg gratings (FBG and to problems that happen when stabilization (also called passivation conditions or the industrial conditioning procedure depart from ageing ones (e.g., presence of hydrogen during the passivation process. For the first time, a reliable procedure to certify the predicted lifetime based on a “restricted” master curve built on real components (i.e., passivated FBG is presented. It is worth noting that both procedures (master curve built on non-passivated or on passivated components are based on the same model (demarcation energy approximation and the existence of a master curve fed with ageing data (reflectivity decay vs. time and temperature. If the Master Curve (MC build on passivated components can be derived from the original one, we can certify the lifetime prediction in a reliable manner.

  13. A Predictive Model of Enhanced Oil Recovery by Infill Drilling and Its Application

    Institute of Scientific and Technical Information of China (English)

    Xu Jianhong; Cheng Linsong; Ma Lili

    2007-01-01

    Infill drilling is now recognized as a viable improved recovery process. However, the reliable prediction of incremental recovery by infill drilling cannot be readily and accurately determined by present techniques. This paper proposes a hybrid predictive model of stream tube simulation and numerical simulation by using the contemporary theory of fluid flow in porous media. The model calculates the geometries of stream tubes, remaining oil distribution and water cut at different development stages in the near future, and uses a three-dimensional simulation to track fluid movement in each stream tube slice. This will help reservoir engineers to determine the feasibility of infill drilling. This predictive model is used to forecast the degree of control of well pattern, the ultimate incremental recovery of infill wells within an inverted 5-spot case in an oilfield and the economic benefit is also analyzed.

  14. Study of LZ-Based Location Prediction and Its Application to Transportation Recommender Systems

    Directory of Open Access Journals (Sweden)

    Patricia Noriega-Vivas

    2012-06-01

    Full Text Available Predicting users’ next location allows to anticipate their future context, thus providing additional time to be ready for that context and react consequently. This work is focused on a set of LZ-based algorithms (LZ, LeZi Update and Active LeZi capable of learning mobility patterns and estimating the next location with low resource needs, which makes it possible to execute them on mobile devices. The original algorithms have been divided into two phases, thus being possible to mix them and check which combination is the best one to obtain better prediction accuracy or lower resource consumption. To make such comparisons, a set of GSM-based mobility traces of 95 different users is considered. Finally, a prototype for mobile devices that integrates the predictors in a public transportation recommender system is described in order to show an example of how to take advantage of location prediction in an ubiquitous computing environment.

  15. The statistical prediction of offshore winds from land-based data for wind-energy applications

    DEFF Research Database (Denmark)

    Walmsley, J.L.; Barthelmie, R.J.; Burrows, W.R.

    2001-01-01

    Land-based meteorological measurements at two locations on the Danish coast are used to predict offshore wind speeds. Offshore wind-speed data are used only for developing the statistical prediction algorithms and for verification. As a first step, the two datasets were separated into nine...... percentile-based bins, with a minimum of 30 data records in each bin. Next, the records were randomly selected with approximately 70% of the data in each bin being used as a training set for development of the prediction algorithms, and the remaining 30% being reserved as a test set for evaluation purposes....... The binning procedure ensured that both training and test sets fairly represented the overall data distribution. To base the conclusions on firmer ground, five permutations of these training and test sets were created. Thus, all calculations were based on five cases, each one representing a different random...

  16. [A novel method of the genome-wide prediction for the target genes and its application].

    Science.gov (United States)

    Zhang, Jing-Jing; Feng, Jing; Zhu, Ying-Guo; Li, Yang-Sheng

    2006-10-01

    Based on the protein databases of several model species, this study developed a new method of the Genome-wide prediction for the target genes, using Hidden Markov model by Perl programming. The advantages of this method are high throughput, high quality and easy prediction, especially in the case of multi-domains proteins families. By this method, we predicted the PPR and TPR proteins families in whole genome of several model species. There were 536 PPR proteins and 199 TPR proteins in Oryza sativa ssp. japonica, 519 PPR proteins and 177 TPR proteins in Oryza sativa L. ssp. indica, 735 PPR proteins and 292 TPR proteins in Arabidopsis thaliana, 6 PPR proteins and 32 TPR proteins in Cyanidioschyzon merolae. Synechococcus and Thermophilic archaebacterium did not have PPR proteins. By contrast, 10 TPR proteins were found in Synechococcus and 4 TPR proteins were found in Thermophilic archaebacterium. Moreover, of these results, some further bioinformatics analyses were conducted.

  17. Application of multi regressive linear model and neural network for wear prediction of grinding mill liners

    Directory of Open Access Journals (Sweden)

    Farzaneh Ahmadzadeh

    2013-06-01

    Full Text Available The liner of an ore grinding mill is a critical component in the grinding process, necessary for both high metal recovery and shell protection. From an economic point of view, it is important to keep mill liners in operation as long as possible, minimising the downtime for maintenance or repair. Therefore, predicting their wear is crucial. This paper tests different methods of predicting wear in the context of remaining height and remaining life of the liners. The key concern is to make decisions on replacement and maintenance without stopping the mill for extra inspection as this leads to financial savings. The paper applies linear multiple regression and artificial neural networks (ANN techniques to determine the most suitable methodology for predicting wear. The advantages of the ANN model over the traditional approach of multiple regression analysis include its high accuracy.

  18. Prediction of Apparent Equivalent Thickness Using the Spontaneous Potential Method and Its Application to Oilfield Development

    Institute of Scientific and Technical Information of China (English)

    Wang Junheng; Pan Zhuping; Sun Shuwen; Guo Lei

    2007-01-01

    The upper spontaneous potential produced by oil and gas accumulation is of a stable potential field, and its intensity is directly proportional to the content of the source and inversely proportional to the radius apart from the source. Theoretical research and practical results show that anomalies of spontaneous potential can indicate oil-bearing sandstone bodies and locate the areas of oil and gas accumulation. In oil areas which have fewer reservoir beds, the petroleum reservoir thickness can be predicted by determining the linear relationship between potential intensity and apparent apparent equivalent thickness can be predicted by the linear equation h= -0.19x+0.74. On the basis of geological research, we use the spontaneous potential method to predict the equivalent thickness, helping in the selection of the most appropriate drill sites to enhance the probability of successful well boring so as to serve the next round development of the oilfield.

  19. Evaluation and Applications of the Prediction of Intensity Model Error (PRIME) Model

    Science.gov (United States)

    Bhatia, K. T.; Nolan, D. S.; Demaria, M.; Schumacher, A.

    2015-12-01

    Forecasters and end users of tropical cyclone (TC) intensity forecasts would greatly benefit from a reliable expectation of model error to counteract the lack of consistency in TC intensity forecast performance. As a first step towards producing error predictions to accompany each TC intensity forecast, Bhatia and Nolan (2013) studied the relationship between synoptic parameters, TC attributes, and forecast errors. In this study, we build on previous results of Bhatia and Nolan (2013) by testing the ability of the Prediction of Intensity Model Error (PRIME) model to forecast the absolute error and bias of four leading intensity models available for guidance in the Atlantic basin. PRIME forecasts are independently evaluated at each 12-hour interval from 12 to 120 hours during the 2007-2014 Atlantic hurricane seasons. The absolute error and bias predictions of PRIME are compared to their respective climatologies to determine their skill. In addition to these results, we will present the performance of the operational version of PRIME run during the 2015 hurricane season. PRIME verification results show that it can reliably anticipate situations where particular models excel, and therefore could lead to a more informed protocol for hurricane evacuations and storm preparations. These positive conclusions suggest that PRIME forecasts also have the potential to lower the error in the original intensity forecasts of each model. As a result, two techniques are proposed to develop a post-processing procedure for a multimodel ensemble based on PRIME. The first approach is to inverse-weight models using PRIME absolute error predictions (higher predicted absolute error corresponds to lower weights). The second multimodel ensemble applies PRIME bias predictions to each model's intensity forecast and the mean of the corrected models is evaluated. The forecasts of both of these experimental ensembles are compared to those of the equal-weight ICON ensemble, which currently

  20. Predicting diagnostic error in Radiology via eye-tracking and image analytics: Application in mammography

    Energy Technology Data Exchange (ETDEWEB)

    Voisin, Sophie [ORNL; Pinto, Frank M [ORNL; Morin-Ducote, Garnetta [University of Tennessee, Knoxville (UTK); Hudson, Kathy [University of Tennessee, Knoxville (UTK); Tourassi, Georgia [ORNL

    2013-01-01

    Purpose: The primary aim of the present study was to test the feasibility of predicting diagnostic errors in mammography by merging radiologists gaze behavior and image characteristics. A secondary aim was to investigate group-based and personalized predictive models for radiologists of variable experience levels. Methods: The study was performed for the clinical task of assessing the likelihood of malignancy of mammographic masses. Eye-tracking data and diagnostic decisions for 40 cases were acquired from 4 Radiology residents and 2 breast imaging experts as part of an IRB-approved pilot study. Gaze behavior features were extracted from the eye-tracking data. Computer-generated and BIRADs images features were extracted from the images. Finally, machine learning algorithms were used to merge gaze and image features for predicting human error. Feature selection was thoroughly explored to determine the relative contribution of the various features. Group-based and personalized user modeling was also investigated. Results: Diagnostic error can be predicted reliably by merging gaze behavior characteristics from the radiologist and textural characteristics from the image under review. Leveraging data collected from multiple readers produced a reasonable group model (AUC=0.79). Personalized user modeling was far more accurate for the more experienced readers (average AUC of 0.837 0.029) than for the less experienced ones (average AUC of 0.667 0.099). The best performing group-based and personalized predictive models involved combinations of both gaze and image features. Conclusions: Diagnostic errors in mammography can be predicted reliably by leveraging the radiologists gaze behavior and image content.

  1. On the Application of a Genetic Algorithm to the Predictability Problems Involving "On-Off" Switches

    Institute of Scientific and Technical Information of China (English)

    ZHENG Qin; DAI Yi; ZHANG Lu; SHA Jianxin; LU Xiaoqing

    2012-01-01

    The lower bound of maximum predictable time can be formulated into a constrained nonlinear optimization problem,and the traditional solutions to this problem are the filtering method and the conditional nonlinear optimal perturbation (CNOP) method.Usually,the CNOP method is implemented with the help of a gradient descent algorithm based on the adjoint method,which is named the ADJ-CNOP.However,with the increasing improvement of actual prediction models,more and more physical processes are taken into consideration in models in the form of parameterization,thus giving rise to the on-off switch problem,which tremendously affects the effectiveness of the conventional gradient descent algorithm based on the adjoint method.In this study,we attempted to apply a genetic algorithm (GA) to the CNOP method,named GA-CNOP,to solve the predictability problems involving on-off switches.As the precision of the filtering method depends uniquely on the division of the constraint region,its results were taken as benchmarks,and a series of comparisons between the ADJ-CNOP and the GA-CNOP were performed for the modified Lorenz equation.Results show that the GA-CNOP can always determine the accurate lower bound of maximum predictable time,even in non-smooth cases,while the ADJ-CNOP,owing to the effect of on-off switches,often yields the incorrect lower bound of maximum predictable time.Therefore,in non-smooth cases,using GAs to solve predictability problems is more effective than using the conventional optimization algorithm based on gradients,as long as genetic operators in GAs are properly configured.

  2. Critique of the two-fold measure of prediction success for ratios: application for the assessment of drug-drug interactions.

    Science.gov (United States)

    Guest, Eleanor J; Aarons, Leon; Houston, J Brian; Rostami-Hodjegan, Amin; Galetin, Aleksandra

    2011-02-01

    Current assessment of drug-drug interaction (DDI) prediction success is based on whether predictions fall within a two-fold range of the observed data. This strategy results in a potential bias toward successful prediction at lower interaction levels [ratio of the area under the concentration-time profile (AUC) in the presence of inhibitor/inducer compared with control is assessment of different DDI prediction algorithms if databases contain large proportion of interactions in this lower range. Therefore, the current study proposes an alternative method to assess prediction success with a variable prediction margin dependent on the particular AUC ratio. The method is applicable for assessment of both induction and inhibition-related algorithms. The inclusion of variability into this predictive measure is also considered using midazolam as a case study. Comparison of the traditional two-fold and the new predictive method was performed on a subset of midazolam DDIs collated from previous databases; in each case, DDIs were predicted using the dynamic model in Simcyp simulator. A 21% reduction in prediction accuracy was evident using the new predictive measure, in particular at the level of no/weak interaction (AUC ratio assessed via the new predictive measure. Thus, the study proposes a more logical method for the assessment of prediction success and its application for induction and inhibition DDIs.

  3. Development and application of mental disorders predictive techniques for chinese soldiers

    OpenAIRE

    Zhang, Li-yi

    2011-01-01

    Currently,the incidence rate of mental diseases of soldiers,especially recruits,shows an increasing trend.The early prediction and intervention is the best way to decrease the disability and burden caused by mental diseases.The present study elaborated on the etiology of mental diseases,related risk factors,prodromes,and the status of the study on predictive techniques in evaluating mental diseases.The current study aimed to provide theoretical and practical basis for the research and develop...

  4. Application of ALOGPS 2.1 to predict log D distribution coefficient for Pfizer proprietary compounds.

    Science.gov (United States)

    Tetko, Igor V; Poda, Gennadiy I

    2004-11-01

    Evaluation of the ALOGPS, ACD Labs LogD, and PALLAS PrologD suites to calculate the log D distribution coefficient resulted in high root-mean-squared error (RMSE) of 1.0-1.5 log for two in-house Pfizer's log D data sets of 17,861 and 640 compounds. Inaccuracy in log P prediction was the limiting factor for the overall log D estimation by these algorithms. The self-learning feature of the ALOGPS (LIBRARY mode) remarkably improved the accuracy in log D prediction, and an rmse of 0.64-0.65 was calculated for both data sets.

  5. Application of artificial intelligence methods for prediction of steel mechanical properties

    Directory of Open Access Journals (Sweden)

    Z. Jančíková

    2008-10-01

    Full Text Available The target of the contribution is to outline possibilities of applying artificial neural networks for the prediction of mechanical steel properties after heat treatment and to judge their perspective use in this field. The achieved models enable the prediction of final mechanical material properties on the basis of decisive parameters influencing these properties. By applying artificial intelligence methods in combination with mathematic-physical analysis methods it will be possible to create facilities for designing a system of the continuous rationalization of existing and also newly developing industrial technologies.

  6. Application of system identification modelling to solar hybrid systems for predicting radiation, temperature and load

    Energy Technology Data Exchange (ETDEWEB)

    Sinha, S.; Matsumoto, Tsuyoshi; Kojima, Toshinori [Seikei University, Tokyo (Japan). Dept. of Industrial Chemistry; Sanjay Kumar [Kyoto University (Japan). Dept. of Global Environment Engineering

    2001-03-01

    Uncertainties in local solar radiation, ambient temperature and thermal load data have been one of the major factors limiting the reliability and efficiency of solar thermal hybrid systems. In the present paper, moving average auto regressive erogenous (ARX) model based reasoning has been mooted and modified to include moving average method, as an effective tool for predictions of these data. The results show that the method is quite robust and is capable of predicting fairly accurate results, which would make these systems more viable in areas where meteorological data are not available or vague. (author)

  7. Combining multiple models to generate consensus: Application to radiation-induced pneumonitis prediction

    Science.gov (United States)

    Das, Shiva K.; Chen, Shifeng; Deasy, Joseph O.; Zhou, Sumin; Yin, Fang-Fang; Marks, Lawrence B.

    2008-01-01

    The fusion of predictions from disparate models has been used in several fields to obtain a more realistic and robust estimate of the “ground truth” by allowing the models to reinforce each other when consensus exists, or, conversely, negate each other when there is no consensus. Fusion has been shown to be most effective when the models have some complementary strengths arising from different approaches. In this work, we fuse the results from four common but methodologically different nonlinear multivariate models (Decision Trees, Neural Networks, Support Vector Machines, Self-Organizing Maps) that were trained to predict radiation-induced pneumonitis risk on a database of 219 lung cancer patients treated with radiotherapy (34 with Grade 2+ postradiotherapy pneumonitis). Each model independently incorporated a small number of features from the available set of dose and nondose patient variables to predict pneumonitis; no two models had all features in common. Fusion was achieved by simple averaging of the predictions for each patient from all four models. Since a model’s prediction for a patient can be dependent on the patient training set used to build the model, the average of several different predictions from each model was used in the fusion (predictions were made by repeatedly testing each patient with a model built from different cross-validation training sets that excluded the patient being tested). The area under the receiver operating characteristics curve for the fused cross-validated results was 0.79, with lower variance than the individual component models. From the fusion, five features were extracted as the consensus among all four models in predicting radiation pneumonitis. Arranged in order of importance, the features are (1) chemotherapy; (2) equivalent uniform dose (EUD) for exponent a=1.2 to 3; (3) EUD for a=0.5 to 1.2, lung volume receiving >20–30 Gy; (4) female sex; and (5) squamous cell histology. To facilitate ease of interpretation

  8. Combining multiple models to generate consensus: Application to radiation-induced pneumonitis prediction

    Energy Technology Data Exchange (ETDEWEB)

    Das, Shiva K.; Chen Shifeng; Deasy, Joseph O.; Zhou Sumin; Yin Fangfang; Marks, Lawrence B. [Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710 (United States); Department of Radiation Oncology, Washington University School of Medicine, St. Louis, Missouri 63110 (United States); Department of Radiation Oncology, Duke University Medical Center, Durham, North Carolina 27710 (United States); Department of Radiation Oncology, University of North Carolina School of Medicine, Chapel Hill, North Carolina 27599 (United States)

    2008-11-15

    The fusion of predictions from disparate models has been used in several fields to obtain a more realistic and robust estimate of the ''ground truth'' by allowing the models to reinforce each other when consensus exists, or, conversely, negate each other when there is no consensus. Fusion has been shown to be most effective when the models have some complementary strengths arising from different approaches. In this work, we fuse the results from four common but methodologically different nonlinear multivariate models (Decision Trees, Neural Networks, Support Vector Machines, Self-Organizing Maps) that were trained to predict radiation-induced pneumonitis risk on a database of 219 lung cancer patients treated with radiotherapy (34 with Grade 2+ postradiotherapy pneumonitis). Each model independently incorporated a small number of features from the available set of dose and nondose patient variables to predict pneumonitis; no two models had all features in common. Fusion was achieved by simple averaging of the predictions for each patient from all four models. Since a model's prediction for a patient can be dependent on the patient training set used to build the model, the average of several different predictions from each model was used in the fusion (predictions were made by repeatedly testing each patient with a model built from different cross-validation training sets that excluded the patient being tested). The area under the receiver operating characteristics curve for the fused cross-validated results was 0.79, with lower variance than the individual component models. From the fusion, five features were extracted as the consensus among all four models in predicting radiation pneumonitis. Arranged in order of importance, the features are (1) chemotherapy; (2) equivalent uniform dose (EUD) for exponent a=1.2 to 3; (3) EUD for a=0.5 to 1.2, lung volume receiving >20-30 Gy; (4) female sex; and (5) squamous cell histology. To facilitate

  9. Predicting future wind power generation and power demand in France using statistical downscaling methods developed for hydropower applications

    Science.gov (United States)

    Najac, Julien

    2014-05-01

    For many applications in the energy sector, it is crucial to dispose of downscaling methods that enable to conserve space-time dependences at very fine spatial and temporal scales between variables affecting electricity production and consumption. For climate change impact studies, this is an extremely difficult task, particularly as reliable climate information is usually found at regional and monthly scales at best, although many industry oriented applications need further refined information (hydropower production model, wind energy production model, power demand model, power balance model…). Here we thus propose to investigate the question of how to predict and quantify the influence of climate change on climate-related energies and the energy demand. To do so, statistical downscaling methods originally developed for studying climate change impacts on hydrological cycles in France (and which have been used to compute hydropower production in France), have been applied for predicting wind power generation in France and an air temperature indicator commonly used for predicting power demand in France. We show that those methods provide satisfactory results over the recent past and apply this methodology to several climate model runs from the ENSEMBLES project.

  10. Reduction of SEM noise and extended application to prediction of CD uniformity and its experimental validation

    Science.gov (United States)

    Kim, Hoyeon; Hwang, Chan; Oh, Seok-hwan; Yeo, Jeongho; Kim, Young hee

    2011-03-01

    As the design rule of Integrated Circuits(IC) becomes smaller, the precise measurement of Critical Dimension (CD) of features and minimization of deviation in CD measured becomes a vital issue. In this paper, a simple frequency analysis method to extract the noise from SEM images was used to evaluate the contribution of SEM noise in CD Uniformity. Multiple SEM images of simple Line and Space (L/S) patterns were analyzed and a model of frequency profile (Power Spectrum Density (PSD) model) was made using an offline analyzing tool based on Matlab®. From this profile, white noise and 1/f profile were separated. Noises are eliminated to generate a noise reduced PSD profile to make CD results. The contribution of white noise on CD measurement can be assessed using Line Width Roughness (LWR) measurement. Furthermore, CD uniformity can be also predicted from the model. This prediction is based on an assumption that CD uniformity is equal to LWR if the inspection area is extended to infinity and appropriate sampling method is applied. The results showed that the contribution of white noise on LWR can be up to around 70% (in power) without any noise reduction measures (sum line averaging) after imaging in photo resist image. For experimental validation, CD uniformity is predicted from the model for different measurement conditions and compared with real measurement. For a result, CD uniformity prediction (3sigma) from the model shows within 20% in accuracy with real CD uniformity value measured from the photo resist image.

  11. Application of Neuro-Net Technology to Reservoir Prediction in Chendao Oil Field

    Institute of Scientific and Technical Information of China (English)

    Jiang Suhua

    1996-01-01

    @@ Recently, the Research Institute of Geological Sciences of the Shengli oil region and the University of Petroleum have been cooperated in developing a set of intelligent expert system to predicte reservoir and to estimate sand body thickness using multiple seismic information.

  12. Predicting Dropout Student: An Application of Data Mining Methods in an Online Education Program

    Science.gov (United States)

    Yukselturk, Erman; Ozekes, Serhat; Turel, Yalin Kilic

    2014-01-01

    This study examined the prediction of dropouts through data mining approaches in an online program. The subject of the study was selected from a total of 189 students who registered to the online Information Technologies Certificate Program in 2007-2009. The data was collected through online questionnaires (Demographic Survey, Online Technologies…

  13. Explicit model predictive control applications in power systems: an AGC study for an isolated industrial system

    DEFF Research Database (Denmark)

    Jiang, Hao; Lin, Jin; Song, Yonghua;

    2016-01-01

    Model predictive control (MPC), that can consider system constraints, is one of the most advanced control technology used nowadays. In power systems, MPC is applied in a way that an optimal control sequence is given every step by an online MPC controller. The main drawback is that the control law...

  14. Application of radial basis function neural network to predict soil sorption partition coefficient using topological descriptors.

    Science.gov (United States)

    Sabour, Mohammad Reza; Moftakhari Anasori Movahed, Saman

    2017-02-01

    The soil sorption partition coefficient logKoc is an indispensable parameter that can be used in assessing the environmental risk of organic chemicals. In order to predict soil sorption partition coefficient for different and even unknown compounds in a fast and accurate manner, a radial basis function neural network (RBFNN) model was developed. Eight topological descriptors of 800 organic compounds were used as inputs of the model. These 800 organic compounds were chosen from a large and very diverse data set. Generalized Regression Neural Network (GRNN) was utilized as the function in this neural network model due to its capability to adapt very quickly. Hence, it can be used to predict logKoc for new chemicals, as well. Out of total data set, 560 organic compounds were used for training and 240 to test efficiency of the model. The obtained results indicate that the model performance is very well. The correlation coefficients (R2) for training and test sets were 0.995 and 0.933, respectively. The root-mean square errors (RMSE) were 0.2321 for training set and 0.413 for test set. As the results for both training and test set are extremely satisfactory, the proposed neural network model can be employed not only to predict logKoc of known compounds, but also to be adaptive for prediction of this value precisely for new products that enter the market each year.

  15. Squared exponential covariance function for prediction of hydrocarbon in seabed logging application

    Science.gov (United States)

    Mukhtar, Siti Mariam; Daud, Hanita; Dass, Sarat Chandra

    2016-11-01

    Seabed Logging technology (SBL) has progressively emerged as one of the demanding technologies in Exploration and Production (E&P) industry. Hydrocarbon prediction in deep water areas is crucial task for a driller in any oil and gas company as drilling cost is very expensive. Simulation data generated by Computer Software Technology (CST) is use