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Sample records for applications predicting drug-target

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

    Directory of Open Access Journals (Sweden)

    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. Large-scale prediction of drug-target relationships

    DEFF Research Database (Denmark)

    Kuhn, Michael; Campillos, Mónica; González, Paula;

    2008-01-01

    also provides a more global view on drug-target relations. Here we review recent attempts to apply large-scale computational analyses to predict novel interactions of drugs and targets from molecular and cellular features. In this context, we quantify the family-dependent probability of two proteins to...

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

    Directory of Open Access Journals (Sweden)

    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.

  4. Optimized shapes of magnetic arrays for drug targeting applications

    OpenAIRE

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

    2016-01-01

    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 ...

  5. Drug-targeting methodologies with applications: A review.

    Science.gov (United States)

    Kleinstreuer, Clement; Feng, Yu; Childress, Emily

    2014-12-16

    Targeted drug delivery to solid tumors is a very active research area, focusing mainly on improved drug formulation and associated best delivery methods/devices. Drug-targeting has the potential to greatly improve drug-delivery efficacy, reduce side effects, and lower the treatment costs. However, the vast majority of drug-targeting studies assume that the drug-particles are already at the target site or at least in its direct vicinity. In this review, drug-delivery methodologies, drug types and drug-delivery devices are discussed with examples in two major application areas: (1) inhaled drug-aerosol delivery into human lung-airways; and (2) intravascular drug-delivery for solid tumor targeting. The major problem addressed is how to deliver efficiently the drug-particles from the entry/infusion point to the target site. So far, most experimental results are based on animal studies. Concerning pulmonary drug delivery, the focus is on the pros and cons of three inhaler types, i.e., pressurized metered dose inhaler, dry powder inhaler and nebulizer, in addition to drug-aerosol formulations. Computational fluid-particle dynamics techniques and the underlying methodology for a smart inhaler system are discussed as well. Concerning intravascular drug-delivery for solid tumor targeting, passive and active targeting are reviewed as well as direct drug-targeting, using optimal delivery of radioactive microspheres to liver tumors as an example. The review concludes with suggestions for future work, considereing both pulmonary drug targeting and direct drug delivery to solid tumors in the vascular system. PMID:25516850

  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-therapy networks and the predictions of novel drug targets

    OpenAIRE

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

    2008-01-01

    Recently, a number of drug-therapy, disease, drug, and drug-target networks have been introduced. Here we suggest novel methods for network-based prediction of novel drug targets and for improvement of drug efficiency by analysing the effects of drugs on the robustness of cellular networks.

  8. An improved approach for predicting drug-target interaction: proteochemometrics to molecular docking.

    Science.gov (United States)

    Shaikh, Naeem; Sharma, Mahesh; Garg, Prabha

    2016-02-23

    Proteochemometric (PCM) methods, which use descriptors of both the interacting species, i.e. drug and the target, are being successfully employed for the prediction of drug-target interactions (DTI). However, unavailability of non-interacting dataset and determining the applicability domain (AD) of model are a main concern in PCM modeling. In the present study, traditional PCM modeling was improved by devising novel methodologies for reliable negative dataset generation and fingerprint based AD analysis. In addition, various types of descriptors and classifiers were evaluated for their performance. The Random Forest and Support Vector Machine models outperformed the other classifiers (accuracies >98% and >89% for 10-fold cross validation and external validation, respectively). The type of protein descriptors had negligible effect on the developed models, encouraging the use of sequence-based descriptors over the structure-based descriptors. To establish the practical utility of built models, targets were predicted for approved anticancer drugs of natural origin. The molecular recognition interactions between the predicted drug-target pair were quantified with the help of a reverse molecular docking approach. The majority of predicted targets are known for anticancer therapy. These results thus correlate well with anticancer potential of the selected drugs. Interestingly, out of all predicted DTIs, thirty were found to be reported in the ChEMBL database, further validating the adopted methodology. The outcome of this study suggests that the proposed approach, involving use of the improved PCM methodology and molecular docking, can be successfully employed to elucidate the intricate mode of action for drug molecules as well as repositioning them for new therapeutic applications. PMID:26822863

  9. 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

  10. Prediction of drug-target interactions and drug repositioning via network-based inference.

    Directory of Open Access Journals (Sweden)

    Feixiong Cheng

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

  11. 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.

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

    KAUST Repository

    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

  13. 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.

  14. 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 . PMID:27167132

  15. 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.

  16. In-silico prediction of drug targets, biological activities, signal pathways and regulating networks of dioscin based on bioinformatics

    OpenAIRE

    Yin, Lianhong; Zheng, Lingli; Xu, Lina; Dong, Deshi; Han, Xu; Qi, Yan; Zhao, Yanyan; Xu, Youwei; Peng, Jinyong

    2015-01-01

    Background Inverse docking technology has been a trend of drug discovery, and bioinformatics approaches have been used to predict target proteins, biological activities, signal pathways and molecular regulating networks affected by drugs for further pharmacodynamic and mechanism studies. Methods In the present paper, inverse docking technology was applied to screen potential targets from potential drug target database (PDTD). Then, the corresponding gene information of the obtained drug-targe...

  17. Prediction of Drug-Target Interactions for Drug Repositioning Only Based on Genomic Expression Similarity

    OpenAIRE

    Kejian Wang; Jiazhi Sun; Shufeng Zhou; Chunling Wan; Shengying Qin; Can Li; Lin He; Lun Yang

    2013-01-01

    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 develop...

  18. Metabolic network analysis predicts efficacy of FDA-approved drugs targeting the causative agent of a neglected tropical disease

    Directory of Open Access Journals (Sweden)

    Chavali Arvind K

    2012-04-01

    Full Text Available Abstract Background Systems biology holds promise as a new approach to drug target identification and drug discovery against neglected tropical diseases. Genome-scale metabolic reconstructions, assembled from annotated genomes and a vast array of bioinformatics/biochemical resources, provide a framework for the interrogation of human pathogens and serve as a platform for generation of future experimental hypotheses. In this article, with the application of selection criteria for both Leishmania major targets (e.g. in silico gene lethality and drugs (e.g. toxicity, a method (MetDP to rationally focus on a subset of low-toxic Food and Drug Administration (FDA-approved drugs is introduced. Results This metabolic network-driven approach identified 15 L. major genes as high-priority targets, 8 high-priority synthetic lethal targets, and 254 FDA-approved drugs. Results were compared to previous literature findings and existing high-throughput screens. Halofantrine, an antimalarial agent that was prioritized using MetDP, showed noticeable antileishmanial activity when experimentally evaluated in vitro against L. major promastigotes. Furthermore, synthetic lethality predictions also aided in the prediction of superadditive drug combinations. For proof-of-concept, double-drug combinations were evaluated in vitro against L. major and four combinations involving the drug disulfiram that showed superadditivity are presented. Conclusions A direct metabolic network-driven method that incorporates single gene essentiality and synthetic lethality predictions is proposed that generates a set of high-priority L. major targets, which are in turn associated with a select number of FDA-approved drugs that are candidate antileishmanials. Additionally, selection of high-priority double-drug combinations might provide for an attractive and alternative avenue for drug discovery against leishmaniasis.

  19. 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.

  20. Magnetic microgels for drug targeting applications: Physical-chemical properties and cytotoxicity evaluation

    Science.gov (United States)

    Turcu, Rodica; Craciunescu, Izabell; Garamus, Vasil M.; Janko, Christina; Lyer, Stefan; Tietze, Rainer; Alexiou, Christoph; Vekas, Ladislau

    2015-04-01

    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 Fe3O4/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.

  1. A new look at drugs targeting malignant melanoma--an application for mass spectrometry imaging.

    Science.gov (United States)

    Sugihara, Yutaka; Végvári, Akos; Welinder, Charlotte; Jönsson, Göran; Ingvar, Christian; Lundgren, Lotta; Olsson, Håkan; Breslin, Thomas; Wieslander, Elisabet; Laurell, Thomas; Rezeli, Melinda; Jansson, Bo; Nishimura, Toshihide; Fehniger, Thomas E; Baldetorp, Bo; Marko-Varga, György

    2014-09-01

    Malignant melanoma (MM) patients are being treated with an increasing number of personalized medicine (PM) drugs, several of which are small molecule drugs developed to treat patients with specific disease genotypes and phenotypes. In particular, the clinical application of protein kinase inhibitors has been highly effective for certain subsets of MM patients. Vemurafenib, a protein kinase inhibitor targeting BRAF-mutated protein, has shown significant efficacy in slowing disease progression. In this paper, we provide an overview of this new generation of targeted drugs, and demonstrate the first data on localization of PM drugs within tumor compartments. In this study, we have introduced MALDI-MS imaging to provide new information on one of the drugs currently used in the PM treatment of MM, vemurafenib. In a proof-of-concept in vitro study, MALDI-MS imaging was used to identify vemurafenib applied to metastatic lymph nodes tumors of subjects attending the regional hospital network of Southern Sweden. The paper provides evidence of BRAF overexpression in tumors isolated from MM patients and localization of the specific drug targeting BRAF, vemurafenib, using MS fragment ion signatures. Our ability to determine drug uptake at the target sites of directed therapy provides important opportunity for increasing our understanding about the mode of action of drug activity within the disease environment. PMID:25044963

  2. Halbach arrays consisting of cubic elements optimised for high field gradients in magnetic drug targeting applications

    International Nuclear Information System (INIS)

    A key challenge in the development of magnetic drug targeting (MDT) as a clinically relevant technique is designing systems that can apply sufficient magnetic force to actuate magnetic drug carriers at useful tissue depths. In this study an optimisation routine was developed to generate designs of Halbach arrays consisting of multiple layers of high grade, cubic, permanent magnet elements, configured to deliver the maximum pull or push force at a position of interest between 5 and 50 mm from the array, resulting in arrays capable of delivering useful magnetic forces to depths past 20 mm. The optimisation routine utilises a numerical model of the magnetic field and force generated by an arbitrary configuration of magnetic elements. Simulated field and force profiles of optimised arrays were evaluated, also taking into account the forces required for assembling the array in practice. The resultant selection for the array, consisting of two layers, was then constructed and characterised to verify the simulations. Finally the array was utilised in a set of in vitro experiments to demonstrate its capacity to separate and retain microbubbles loaded with magnetic nanoparticles against a constant flow. The optimised designs are presented as light-weight, inexpensive options for applying high-gradient, external magnetic fields in MDT applications. (paper)

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

    International Nuclear Information System (INIS)

    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 Fe3O4/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

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

    Science.gov (United States)

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

    2015-07-15

    Predicting drug-target interaction using computational approaches is an important step in drug discovery and repositioning. To predict whether there will be an interaction between a drug and a target, most existing methods identify similar drugs and targets in the database. The prediction is then made based on the known interactions of these drugs and targets. This idea is promising. However, there are two shortcomings that have not yet been addressed appropriately. Firstly, most of the methods only use 2D chemical structures and protein sequences to measure the similarity of drugs and targets respectively. However, this information may not fully capture the characteristics determining whether a drug will interact with a target. Secondly, there are very few known interactions, i.e. many interactions are "missing" in the database. Existing approaches are biased towards known interactions and have no good solutions to handle possibly missing interactions which affect the accuracy of the prediction. In this paper, we enhance the similarity measures to include non-structural (and non-sequence-based) information and introduce the concept of a "super-target" to handle the problem of possibly missing interactions. Based on evaluations on real data, we show that our similarity measure is better than the existing measures and our approach is able to achieve higher accuracy than the two best existing algorithms, WNN-GIP and KBMF2K. Our approach is available at http://web.hku.hk/∼liym1018/projects/drug/drug.html or http://www.bmlnwpu.org/us/tools/PredictingDTI_S2/METHODS.html. PMID:25957673

  5. 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.

  6. GWAS and drug targets

    OpenAIRE

    Cao, Chen; Moult, John

    2014-01-01

    Background Genome wide association studies (GWAS) have revealed a large number of links between genome variation and complex disease. Among other benefits, it is expected that these insights will lead to new therapeutic strategies, particularly the identification of new drug targets. In this paper, we evaluate the power of GWAS studies to find drug targets by examining how many existing drug targets have been directly 'rediscovered' by this technique, and the extent to which GWAS results may ...

  7. Application of CellDesigner to the Selection of Anticancer Drug Targets: Test Case using P53

    OpenAIRE

    Isea, Raul; Hoebeke, Johan; Mayo, Rafael; Alvarez, Fernando; Holmes, David S.

    2013-01-01

    Cancer is a disease involving many genes, consequently it has been difficult to design anticancer drugs that are efficacious over a broad range of cancers. The robustness of cellular responses to gene knockout and the need to reduce undesirable side effects also contribute to the problem of effective anti-cancer drug design. To promote the successful selection of drug targets, each potential target should be subjected to a systems biology scrutiny to locate effective and specific targets whil...

  8. 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

  9. 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.

  10. Analyzing ferrofluid transport for magnetic drug targeting

    International Nuclear Information System (INIS)

    Experimental and numerical investigations of magnetically induced localization of ferrofluid and its subsequent dispersion are performed in a forced flow. The ferrofluid accumulation behaves as a solid obstacle in the flow as the competing magnetic and fluid shear forces give rise to a rigidly bound core region followed by a washaway region at its wake. Results of the analysis provide meaningful information on ferrofluid transport for various magnetic drug targeting applications

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

    Directory of Open Access Journals (Sweden)

    Ce Bian

    2012-11-01

    Full Text Available 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.

  12. 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. PMID:26415215

  13. Drug targeting to the brain.

    Science.gov (United States)

    Pardridge, William M

    2007-09-01

    The goal of brain drug targeting technology is the delivery of therapeutics across the blood-brain barrier (BBB), including the human BBB. This is accomplished by re-engineering pharmaceuticals to cross the BBB via specific endogenous transporters localized within the brain capillary endothelium. Certain endogenous peptides, such as insulin or transferrin, undergo receptor-mediated transport (RMT) across the BBB in vivo. In addition, peptidomimetic monoclonal antibodies (MAb) may also cross the BBB via RMT on the endogenous transporters. The MAb may be used as a molecular Trojan horse to ferry across the BBB large molecule pharmaceuticals, including recombinant proteins, antibodies, RNA interference drugs, or non-viral gene medicines. Fusion proteins of the molecular Trojan horse and either neurotrophins or single chain Fv antibodies have been genetically engineered. The fusion proteins retain bi-functional properties, and both bind the BBB receptor, to trigger transport into brain, and bind the cognate receptor inside brain to induce the pharmacologic effect. Trojan horse liposome technology enables the brain targeting of non-viral plasmid DNA. Molecular Trojan horses may be formulated with fusion protein technology, avidin-biotin technology, or Trojan horse liposomes to target to brain virtually any large molecule pharmaceutical. PMID:17554607

  14. 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. PMID:24717692

  15. Recent discoveries of influenza A drug target sites to combat virus replication.

    Science.gov (United States)

    Patel, Hershna; Kukol, Andreas

    2016-06-15

    Sequence variations in the binding sites of influenza A proteins are known to limit the effectiveness of current antiviral drugs. Clinically, this leads to increased rates of virus transmission and pathogenicity. Potential influenza A inhibitors are continually being discovered as a result of high-throughput cell based screening studies, whereas the application of computational tools to aid drug discovery has further increased the number of predicted inhibitors reported. This review brings together the aspects that relate to the identification of influenza A drug target sites and the findings from recent antiviral drug discovery strategies. PMID:27284062

  16. Nanomechanics of Drug-target Interactions and Antibacterial Resistance Detection

    OpenAIRE

    Ndieyira, J. W.; Watari, M.; McKendry, R. A.

    2013-01-01

    The cantilever sensor, which acts as a transducer of reactions between model bacterial cell wall matrix immobilized on its surface and antibiotic drugs in solution, has shown considerable potential in biochemical sensing applications with unprecedented sensitivity and specificity(1-5). The drug-target interactions generate surface stress, causing the cantilever to bend, and the signal can be analyzed optically when it is illuminated by a laser. The change in surface stress measured with nano-...

  17. Meningococcal disease and future drug targets

    DEFF Research Database (Denmark)

    Gammelgaard, L K; Colding, H; Hartzen, S H; Penkowa, M

    2011-01-01

    recent data and current knowledge on molecular mechanisms of meningococcal disease and explains how host immune responses ultimately may aggravate neuropathology and the clinical prognosis. Within this context, particular importance is paid to the endotoxic components that provide potential drug targets...

  18. 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. The...

  19. Functional genomics and cancer drug target discovery.

    Science.gov (United States)

    Moody, Susan E; Boehm, Jesse S; Barbie, David A; Hahn, William C

    2010-06-01

    The recent development of technologies for whole-genome sequencing, copy number analysis and expression profiling enables the generation of comprehensive descriptions of cancer genomes. However, although the structural analysis and expression profiling of tumors and cancer cell lines can allow the identification of candidate molecules that are altered in the malignant state, functional analyses are necessary to confirm such genes as oncogenes or tumor suppressors. Moreover, recent research suggests that tumor cells also depend on synthetic lethal targets, which are not mutated or amplified in cancer genomes; functional genomics screening can facilitate the discovery of such targets. This review provides an overview of the tools available for the study of functional genomics, and discusses recent research involving the use of these tools to identify potential novel drug targets in cancer. PMID:20521217

  20. 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....

  1. Mining metabolic networks for optimal drug targets.

    Science.gov (United States)

    Sridhar, Padmavati; Song, Bin; Kahveci, Tamer; Ranka, Sanjay

    2008-01-01

    Recent advances in bioinformatics promote drug-design methods that aim to reduce side-effects. Efficient computational methods are required to identify the optimal enzyme-combination (i.e., drug targets) whose inhibition, will achieve the required effect of eliminating a given target set of compounds, while incurring minimal side-effects. We formulate the optimal enzyme-combination identification problem as an optimization problem on metabolic networks. We define a graph based computational damage model that encapsulates the impact of enzymes onto compounds in metabolic networks. We develop a branch-and-bound algorithm, named OPMET, to explore the search space dynamically. We also develop two filtering strategies to prune the search space while still guaranteeing an optimal solution. They compute an upper bound to the number of target compounds eliminated and a lower bound to the side-effect respectively. Our experiments on the human metabolic network demonstrate that the proposed algorithm can accurately identify the target enzymes for known successful drugs in the literature. Our experiments also show that OPMET can reduce the total search time by several orders of magnitude as compared to the exhaustive search. PMID:18229694

  2. Identification of potential drug targets in Helicobacter pylori strain HPAG1 by in silico genome analysis.

    Science.gov (United States)

    Neelapu, Nageswara R R; Mutha, Naresh V R; Akula, Srinivas

    2015-01-01

    Helicobacter pylori colonizes the stomach, causing gastritis, peptic ulcers and gastric carcinoma. Drugs for treatment of H. pylori relieve from gastritis or pain but are not specific to H. pylori. Therefore, there is an immediate requirement for new therapeutic molecules to treat H. pylori. Current study investigates identification of drug targets in the strain HPAG1 of H. pylori by in silico genome analysis. Genome of HPAG1 was reconstructed for metabolic pathways and compared with Homosapien sapiens to identify genes which are unique to H. pylori. These unique genes were subjected to gene property analysis to identify the potentiality of the drug targets. Among the total number of genes analysed in H. pylori strain HPAG1, nearly 542 genes qualified as unique molecules and among them 29 were identified to be potential drug targets. Co/Zn/Cd efflux system membrane fusion protein, Ferric sidephore transport system and biopolymer transport protein EXbB were found to be critical drug targets to H. pylori HPAG1. Five genes (superoxide dismutase, HtrA protease/chaperone protein, Heatinducible transcription repressor HrcA, HspR, transcriptional repressor of DnaK operon, Cobalt-zinccadmium resistance protein CzcA) of the 29 predicted drug targets are already experimentally validated either genetically or biochemically lending credence to our unique approach. PMID:26205802

  3. Nanomechanics of drug-target interactions and antibacterial resistance detection.

    Science.gov (United States)

    Ndieyira, Joseph W; Watari, Moyu; McKendry, Rachel A

    2013-01-01

    The cantilever sensor, which acts as a transducer of reactions between model bacterial cell wall matrix immobilized on its surface and antibiotic drugs in solution, has shown considerable potential in biochemical sensing applications with unprecedented sensitivity and specificity. The drug-target interactions generate surface stress, causing the cantilever to bend, and the signal can be analyzed optically when it is illuminated by a laser. The change in surface stress measured with nano-scale precision allows disruptions of the biomechanics of model bacterial cell wall targets to be tracked in real time. Despite offering considerable advantages, multiple cantilever sensor arrays have never been applied in quantifying drug-target binding interactions. Here, we report on the use of silicon multiple cantilever arrays coated with alkanethiol self-assembled monolayers mimicking bacterial cell wall matrix to quantitatively study antibiotic binding interactions. To understand the impact of vancomycin on the mechanics of bacterial cell wall structures. We developed a new model(1) which proposes that cantilever bending can be described by two independent factors; i) namely a chemical factor, which is given by a classical Langmuir adsorption isotherm, from which we calculate the thermodynamic equilibrium dissociation constant (Kd) and ii) a geometrical factor, essentially a measure of how bacterial peptide receptors are distributed on the cantilever surface. The surface distribution of peptide receptors (p) is used to investigate the dependence of geometry and ligand loading. It is shown that a threshold value of p ~10% is critical to sensing applications. Below which there is no detectable bending signal while above this value, the bending signal increases almost linearly, revealing that stress is a product of a local chemical binding factor and a geometrical factor combined by the mechanical connectivity of reacted regions and provides a new paradigm for design of powerful

  4. The hydrogenosome as a drug target.

    Science.gov (United States)

    Benchimol, Marlene

    2008-01-01

    Hydrogenosomes are spherical or slightly elongated organelles found in non-mitochondrial organisms. In Trichomonas hydrogenosomes measure between 200 to 500 nm, but under drug treatment they can reach 2 microm. Like mitochondria hydrogenosomes: (1) are surrounded by two closely apposed membranes and present a granular matrix: (2) divide in three different ways: segmentation, partition and the heart form; (3) they may divide at any phase of the cell cycle; (4) produce ATP; (5) participate in the metabolism of pyruvate formed during glycolysis; (6) are the site of molecular hydrogen formation; (7) present a relationship with the endoplasmic reticulum; (8) incorporate calcium; (9) import proteins post-translationally; (10) present cardiolipin. However, there are differences, such as: (1) absence of genetic material, at least in trichomonas; (2) lack a respiratory chain and cytochromes; (3) absence of the F(0)-F(1) ATPase; (4) absence of the tricarboxylic acid cycle; (5) lack of oxidative phosphorylation; (6) presence of peripheral vesicles. Hydrogenosomes are considered an excellent drug target since their metabolic pathway is distinct from those found in mitochondria and thus medicines directed to these organelles will probably not affect the host-cell. The main drug used against trichomonads is metronidazole, although other drugs such as beta-Lapachone, colchicine, Taxol, nocodazole, griseofulvin, cytochalasins, hydroxyurea, among others, have been used in trichomonad studies, showing: (1) flagella internalization forming pseudocyst; (2) dysfunctional hydrogenosomes; (3) hydrogenosomes with abnormal sizes and shapes and with an electron dense deposit called nucleoid; (4) intense autophagy in which hydrogenosomes are removed and further digested in lysosomes. PMID:18473836

  5. Drug target mining and analysis of the Chinese tree shrew for pharmacological testing.

    Directory of Open Access Journals (Sweden)

    Feng Zhao

    Full Text Available The discovery of new drugs requires the development of improved animal models for drug testing. The Chinese tree shrew is considered to be a realistic candidate model. To assess the potential of the Chinese tree shrew for pharmacological testing, we performed drug target prediction and analysis on genomic and transcriptomic scales. Using our pipeline, 3,482 proteins were predicted to be drug targets. Of these predicted targets, 446 and 1,049 proteins with the highest rank and total scores, respectively, included homologs of targets for cancer chemotherapy, depression, age-related decline and cardiovascular disease. Based on comparative analyses, more than half of drug target proteins identified from the tree shrew genome were shown to be higher similarity to human targets than in the mouse. Target validation also demonstrated that the constitutive expression of the proteinase-activated receptors of tree shrew platelets is similar to that of human platelets but differs from that of mouse platelets. We developed an effective pipeline and search strategy for drug target prediction and the evaluation of model-based target identification for drug testing. This work provides useful information for future studies of the Chinese tree shrew as a source of novel targets for drug discovery research.

  6. 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.

  7. 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.

  8. Templated in-situ synthesis of gold nanoclusters conjugated to drug target bacterial enoyl-ACP reductase, and their application to the detection of mercury ions using a test stripe

    International Nuclear Information System (INIS)

    Fluorescent gold nanoclusters (AuNCs) were synthesized using a drug target bacterial enoyl-ACP reductase (FabI) as a template. The physical and chemical properties of the AuNCs were studied by UV-vis absorption, fluorescence, X-ray photoelectron spectroscopy and TEM. The AuNCs-FabI conjugate was prepared by in situ reduction of tetrachloroaurate in the presence of FabI. The conjugated particles were loaded onto nylon membranes by taking advantage of the electrostatic interaction between the negatively charged AuNCs-FabI and the nylon film which is positively charged at pH 7.4. This results in the formation of a test stripe with sensor spots that can be used to detect Hg(II) ion in the 1 nM to 10 μM concentration range. The test stripes are simple, convenient, selective, sensitive, and can be quickly read out with bare eyes after illumination with a UV lamp. (author)

  9. Magnetorelaxometric quantification of magnetic nanoparticles in an artery model after ex vivo magnetic drug targeting

    International Nuclear Information System (INIS)

    In magnetic drug targeting a chemotherapeutic agent is bound to coated magnetic nanoparticles, which are administered to the blood vessel system and subsequently focused by an external applied magnetic field. The optimization of intra-arterial magnetic drug targeting (MDT) requires detailed knowledge about the biodistribution of particles in the artery and the respective surrounding after the application. Here, we demonstrate the potential of magnetorelaxometry for quantifying the distribution of magnetic nanoparticles in the artery. To this end, we present a magnetorelaxometry investigation of a MDT study in an artery model. In particular, the absolute magnetic nanoparticle accumulation along the artery as well as the uptake profile along the region around the MDT-magnet position was quantified. (note)

  10. Magnetorelaxometric quantification of magnetic nanoparticles in an artery model after ex vivo magnetic drug targeting

    Energy Technology Data Exchange (ETDEWEB)

    Richter, H; Wiekhorst, F; Schwarz, K; Trahms, L [Physikalisch-Technische Bundesanstalt, Berlin (Germany); Lyer, S; Tietze, R; Alexiou, Ch [Department of Oto-Rhino-Laryngology, Head and Neck Surgery, University of Erlangen-Nuernberg (Germany)], E-mail: heike.richter@ptb.de, E-mail: lutz.trahms@ptb.de

    2009-09-21

    In magnetic drug targeting a chemotherapeutic agent is bound to coated magnetic nanoparticles, which are administered to the blood vessel system and subsequently focused by an external applied magnetic field. The optimization of intra-arterial magnetic drug targeting (MDT) requires detailed knowledge about the biodistribution of particles in the artery and the respective surrounding after the application. Here, we demonstrate the potential of magnetorelaxometry for quantifying the distribution of magnetic nanoparticles in the artery. To this end, we present a magnetorelaxometry investigation of a MDT study in an artery model. In particular, the absolute magnetic nanoparticle accumulation along the artery as well as the uptake profile along the region around the MDT-magnet position was quantified. (note)

  11. 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.

  12. Seizure prediction and its applications.

    Science.gov (United States)

    Iasemidis, Leon D

    2011-10-01

    Epilepsy is characterized by intermittent, paroxysmal, hypersynchronous electrical activity that may remain localized and/or spread and severely disrupt the brain's normal multitask and multiprocessing function. Epileptic seizures are the hallmarks of such activity. The ability to issue warnings in real time of impending seizures may lead to novel diagnostic tools and treatments for epilepsy. Applications may range from a warning to the patient to avert seizure-associated injuries, to automatic timely administration of an appropriate stimulus. Seizure prediction could become an integral part of the treatment of epilepsy through neuromodulation, especially in the new generation of closed-loop seizure control systems. PMID:21939848

  13. Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives.

    Science.gov (United States)

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

    2016-04-01

    The use of Cheminformatics tools is gaining importance in the field of translational research from Medicinal Chemistry to Neuropharmacology. In particular, we need it for the analysis of chemical information on large datasets of bioactive compounds. These compounds form large multi-target complex networks (drug-target interactome network) resulting in a very challenging data analysis problem. Artificial Neural Network (ANN) algorithms may help us predict the interactions of drugs and targets in CNS interactome. In this work, we trained different ANN models able to predict a large number of drug-target interactions. These models predict a dataset of thousands of interactions of central nervous system (CNS) drugs characterized by > 30 different experimental measures in >400 different experimental protocols for >150 molecular and cellular targets present in 11 different organisms (including human). The model was able to classify cases of non-interacting vs. interacting drug-target pairs with satisfactory performance. A second aim focus on two main directions: the synthesis and assay of new derivatives of TVP1022 (S-analogues of rasagiline) and the comparison with other rasagiline derivatives recently reported. Finally, we used the best of our models to predict drug-target interactions for the best new synthesized compound against a large number of CNS protein targets. PMID:26721628

  14. What makes a good anti-inflammatory drug target?

    Science.gov (United States)

    Simmons, David L

    2006-03-01

    This review focuses on the major, 'successful' target families in inflammation and attempts to identify some of the key features of what makes a good anti-inflammatory target. The review is based on a systematic analysis of approved anti-inflammatory drugs grouped according to their drug-target family. The cytokine family is a drug-dense area. They have yielded and continue to yield a rich stream of drugs. As in other therapeutic areas, G-protein-coupled receptors (GPCRs), also known as seven-transmembrane pass receptors, have provided significant drug targets. In addition, the superfamilies of cell adhesion molecules and co-stimulatory molecules, which have special relevance to immune processes, have begun to provide the first approved drugs and might yield many more. The recent, rapid increase in the number of defined targets in the immune system -- leukocyte surface antigens, cytokines, GPCRs, adhesion molecules and co-stimulatory molecules -- will ensure a rich stream of future anti-inflammatory drug targets. PMID:16580598

  15. Gene Ontology and KEGG Pathway Enrichment Analysis of a Drug Target-Based Classification System.

    Directory of Open Access Journals (Sweden)

    Lei Chen

    Full Text Available Drug-target interaction (DTI is a key aspect in pharmaceutical research. With the ever-increasing new drug data resources, computational approaches have emerged as powerful and labor-saving tools in predicting new DTIs. However, so far, most of these predictions have been based on structural similarities rather than biological relevance. In this study, we proposed for the first time a "GO and KEGG enrichment score" method to represent a certain category of drug molecules by further classification and interpretation of the DTI database. A benchmark dataset consisting of 2,015 drugs that are assigned to nine categories ((1 G protein-coupled receptors, (2 cytokine receptors, (3 nuclear receptors, (4 ion channels, (5 transporters, (6 enzymes, (7 protein kinases, (8 cellular antigens and (9 pathogens was constructed by collecting data from KEGG. We analyzed each category and each drug for its contribution in GO terms and KEGG pathways using the popular feature selection "minimum redundancy maximum relevance (mRMR" method, and key GO terms and KEGG pathways were extracted. Our analysis revealed the top enriched GO terms and KEGG pathways of each drug category, which were highly enriched in the literature and clinical trials. Our results provide for the first time the biological relevance among drugs, targets and biological functions, which serves as a new basis for future DTI predictions.

  16. Structural genomics of infectious disease drug targets: the SSGCID

    International Nuclear Information System (INIS)

    An introduction and overview of the focus, goals and overall mission of the Seattle Structural Genomics Center for Infectious Disease (SSGCID) is given. The Seattle Structural Genomics Center for Infectious Disease (SSGCID) is a consortium of researchers at Seattle BioMed, Emerald BioStructures, the University of Washington and Pacific Northwest National Laboratory that was established to apply structural genomics approaches to drug targets from infectious disease organisms. The SSGCID is currently funded over a five-year period by the National Institute of Allergy and Infectious Diseases (NIAID) to determine the three-dimensional structures of 400 proteins from a variety of Category A, B and C pathogens. Target selection engages the infectious disease research and drug-therapy communities to identify drug targets, essential enzymes, virulence factors and vaccine candidates of biomedical relevance to combat infectious diseases. The protein-expression systems, purified proteins, ligand screens and three-dimensional structures produced by SSGCID constitute a valuable resource for drug-discovery research, all of which is made freely available to the greater scientific community. This issue of Acta Crystallographica Section F, entirely devoted to the work of the SSGCID, covers the details of the high-throughput pipeline and presents a series of structures from a broad array of pathogenic organisms. Here, a background is provided on the structural genomics of infectious disease, the essential components of the SSGCID pipeline are discussed and a survey of progress to date is presented

  17. Targeting protein kinases in the malaria parasite: update of an antimalarial drug target.

    Science.gov (United States)

    Zhang, Veronica M; Chavchich, Marina; Waters, Norman C

    2012-01-01

    Millions of deaths each year are attributed to malaria worldwide. Transmitted through the bite of an Anopheles mosquito, infection and subsequent death from the Plasmodium species, most notably P. falciparum, can readily spread through a susceptible population. A malaria vaccine does not exist and resistance to virtually every antimalarial drug predicts that mortality and morbidity associated with this disease will increase. With only a few antimalarial drugs currently in the pipeline, new therapeutic options and novel chemotypes are desperately needed. Hit-to-Lead diversity may successfully provide novel inhibitory scaffolds when essential enzymes are targeted, for example, the plasmodial protein kinases. Throughout the entire life cycle of the malaria parasite, protein kinases are essential for growth and development. Ongoing efforts continue to characterize these kinases, while simultaneously pursuing them as antimalarial drug targets. A collection of structural data, inhibitory profiles and target validation has set the foundation and support for targeting the malarial kinome. Pursuing protein kinases as cancer drug targets has generated a wealth of information on the inhibitory strategies that can be useful for antimalarial drug discovery. In this review, progress on selected protein kinases is described. As the search for novel antimalarials continues, an understanding of the phosphor-regulatory pathways will not only validate protein kinase targets, but also will identify novel chemotypes to thwart malaria drug resistance. PMID:22242850

  18. Melanocortin receptors as drug targets for disorders of energy balance.

    Science.gov (United States)

    Adan, Roger A H; van Dijk, Gertjan

    2006-06-01

    There is overwhelming evidence that the brain melanocortin system is a key regulator of energy balance, and dysregulations in the brain melanocortin system can lead to obesity. The melanocortin system is one of the major downstream leptin signaling pathways in the brain. In contrast to leptin, preclinical studies indicate that diet-induced obese animals are still responsive to the anorectic effects of melanocortin receptor agonists, suggesting the melanocortin system is an interesting therapeutic opportunity. Besides regulating energy balance, melanocortins are involved in a variety of other neuroendocrine processes, including inflammation, blood pressure regulation, addictive and sexual behavior, and sensation of pain. This review evaluates the melanocortin system function from the perspective to use specific melanocortin (MC) receptors as drug targets, with a focus on the treatment of obesity and eating disorders in humans, and the implications this may have on mechanisms beyond the control of energy balance. PMID:16787227

  19. Drug targets for lymphatic filariasis: A bioinformatics approach

    Directory of Open Access Journals (Sweden)

    Om Prakash Sharma

    2013-08-01

    Full Text Available This review article discusses the current scenario of the national and international burden due to lymphatic filariasis (LF and describes the active elimination programmes for LF and their achievements to eradicate this most debilitating disease from the earth. Since, bioinformatics is a rapidly growing field of biological study, and it has an increasingly significant role in various fields of biology. We have reviewed its leading involvement in the filarial research using different approaches of bioinformatics and have summarized available existing drugs and their targets to re-examine and to keep away from the resisting conditions. Moreover, some of the novel drug targets have been assembled for further study to design fresh and better pharmacological therapeutics. Various bioinformatics-based web resources, and databases have been discussed, which may enrich the filarial research.

  20. Protein painting reveals solvent-excluded drug targets hidden within native protein–protein interfaces

    Science.gov (United States)

    Luchini, Alessandra; Espina, Virginia; Liotta, Lance A.

    2014-01-01

    Identifying the contact regions between a protein and its binding partners is essential for creating therapies that block the interaction. Unfortunately, such contact regions are extremely difficult to characterize because they are hidden inside the binding interface. Here we introduce protein painting as a new tool that employs small molecules as molecular paints to tightly coat the surface of protein–protein complexes. The molecular paints, which block trypsin cleavage sites, are excluded from the binding interface. Following mass spectrometry, only peptides hidden in the interface emerge as positive hits, revealing the functional contact regions that are drug targets. We use protein painting to discover contact regions between the three-way interaction of IL1β ligand, the receptor IL1RI and the accessory protein IL1RAcP. We then use this information to create peptides and monoclonal antibodies that block the interaction and abolish IL1β cell signalling. The technology is broadly applicable to discover protein interaction drug targets. PMID:25048602

  1. Phospholipid-Based Prodrugs for Drug Targeting in Inflammatory Bowel Disease: Computational Optimization and In-Vitro Correlation.

    Science.gov (United States)

    Dahan, Arik; Ben-Shabat, Shimon; Cohen, Noa; Keinan, Shahar; Kurnikov, Igor; Aponick, Aaron; Zimmermann, Ellen M

    2016-01-01

    In inflammatory bowel disease (IBD) patients, the enzyme phospholipase A2 (PLA2) is overexpressed in the inflamed intestinal tissue, and hence may be exploited as a prodrug-activating enzyme allowing drug targeting to the site(s) of gut inflammation. The purpose of this work was to develop powerful modern computational approaches, to allow optimized a-priori design of phospholipid (PL) based prodrugs for IBD drug targeting. We performed simulations that predict the activation of PL-drug conjugates by PLA2 with both human and bee venom PLA2. The calculated results correlated well with in-vitro experimental data. In conclusion, a-priori drug design using a computational approach complements and extends experimentally derived data, and may improve resource utilization and speed drug development. PMID:27086789

  2. 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

  3. 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.

  4. Structures of Trypanosome Vacuolar Soluble Pyrophosphatases: Antiparasitic Drug Targets.

    Science.gov (United States)

    Yang, Yunyun; Ko, Tzu-Ping; Chen, Chun-Chi; Huang, Guozhong; Zheng, Yingying; Liu, Weidong; Wang, Iren; Ho, Meng-Ru; Hsu, Shang-Te Danny; O'Dowd, Bing; Huff, Hannah C; Huang, Chun-Hsiang; Docampo, Roberto; Oldfield, Eric; Guo, Rey-Ting

    2016-05-20

    Trypanosomatid parasites are the causative agents of many neglected tropical diseases, including the leishmaniases, Chagas disease, and human African trypanosomiasis. They exploit unusual vacuolar soluble pyrophosphatases (VSPs), absent in humans, for cell growth and virulence and, as such, are drug targets. Here, we report the crystal structures of VSP1s from Trypanosoma cruzi and T. brucei, together with that of the T. cruzi protein bound to a bisphosphonate inhibitor. Both VSP1s form a hybrid structure containing an (N-terminal) EF-hand domain fused to a (C-terminal) pyrophosphatase domain. The two domains are connected via an extended loop of about 17 residues. Crystallographic analysis and size exclusion chromatography indicate that the VSP1s form tetramers containing head-to-tail dimers. Phosphate and diphosphate ligands bind in the PPase substrate-binding pocket and interact with several conserved residues, and a bisphosphonate inhibitor (BPH-1260) binds to the same site. On the basis of Cytoscape and other bioinformatics analyses, it is apparent that similar folds will be found in most if not all trypanosomatid VSP1s, including those found in insects (Angomonas deanei, Strigomonas culicis), plant pathogens (Phytomonas spp.), and Leishmania spp. Overall, the results are of general interest since they open the way to structure-based drug design for many of the neglected tropical diseases. PMID:26907161

  5. Structure determination of drug target proteins by neutron crystallography

    International Nuclear Information System (INIS)

    High resolution X-ray crystallography provides information for most of the atoms comprising the proteins, with the exception of hydrogen atoms. Whereas, neutron crystallography, which is a powerful technique for locating hydrogen atoms, enables us to obtain accurate atomic positions within proteins. Neutron diffraction data can provide information of the location of hydrogen atoms to the structural information determined by X-ray crystallography. Here, we show the recent results of the structural determination of drug-target proteins, porcine pancreatic elastase and human immuno-deficiency virus type-1 protease by both X-ray and neutron diffraction. The structure of porcine pancreatic elastase with its potent inhibitor was determined to 0.094 nm resolution by X-ray diffraction and 0.165 nm resolution by neutron diffraction. The structure of HIV-PR with its potent inhibitor was also determined to 0.093 nm resolution by X-ray diffraction and 0.19 nm resolution by neutron diffraction. The ionization state and the location of hydrogen atoms of the catalytic residue in these enzymes were determined by neutron diffraction. Furthermore, collaborative use of both X-ray and neutron crystallography to identify the location of ambiguous hydrogen atoms will be shown. (author)

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

    International Nuclear Information System (INIS)

    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

  7. Bacterial carbonic anhydrases as drug targets: towards novel antibiotics ?

    Directory of Open Access Journals (Sweden)

    ClaudiuT.Supuran

    2011-07-01

    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 a-, b-, and/or g-CA families. In the last decade, the a-CAs from Neisseria spp. and Helicobacter pylori as well as the b-class enzymes from Escherichia coli, H. pylori, Mycobacterium tuberculosis, Brucella spp., Streptococcus pneumoniae, Salmonella enterica and Haemophilus influenzae have been cloned and characterized in detail. For some of these enzymes the X-ray crystal structures were determined, and in vitro and in vivo inhibition studies with various classes of inhibitors, such as anions, sulfonamides and sulfamates reported. Although efficient inhibitors have been reported for many such enzymes, only for Nessseria spp., H. pylori, B. suis and S. pneumoniae enzymes it has been possible to evidence inhibition of bacterial growth in vivo. Thus, bacterial CAs represent promising targets for obtaining antibacterials devoid of the resistance problems of the clinically used such agents but further studies are needed to validate these and other less investigated enzymes as novel drug targets

  8. Essential gene identification and drug target prioritization in Aspergillus fumigatus.

    Science.gov (United States)

    Hu, Wenqi; Sillaots, Susan; Lemieux, Sebastien; Davison, John; Kauffman, Sarah; Breton, Anouk; Linteau, Annie; Xin, Chunlin; Bowman, Joel; Becker, Jeff; Jiang, Bo; Roemer, Terry

    2007-03-01

    Aspergillus fumigatus is the most prevalent airborne filamentous fungal pathogen in humans, causing severe and often fatal invasive infections in immunocompromised patients. Currently available antifungal drugs to treat invasive aspergillosis have limited modes of action, and few are safe and effective. To identify and prioritize antifungal drug targets, we have developed a conditional promoter replacement (CPR) strategy using the nitrogen-regulated A. fumigatus NiiA promoter (pNiiA). The gene essentiality for 35 A. fumigatus genes was directly demonstrated by this pNiiA-CPR strategy from a set of 54 genes representing broad biological functions whose orthologs are confirmed to be essential for growth in Candida albicans and Saccharomyces cerevisiae. Extending this approach, we show that the ERG11 gene family (ERG11A and ERG11B) is essential in A. fumigatus despite neither member being essential individually. In addition, we demonstrate the pNiiA-CPR strategy is suitable for in vivo phenotypic analyses, as a number of conditional mutants, including an ERG11 double mutant (erg11BDelta, pNiiA-ERG11A), failed to establish a terminal infection in an immunocompromised mouse model of systemic aspergillosis. Collectively, the pNiiA-CPR strategy enables a rapid and reliable means to directly identify, phenotypically characterize, and facilitate target-based whole cell assays to screen A. fumigatus essential genes for cognate antifungal inhibitors. PMID:17352532

  9. Predictive Microbiology and Food Safety Applications

    Science.gov (United States)

    Mathematical modeling is the science of systematic study of recurrent events or phenomena. When models are properly developed, their applications may save costs and time. For microbial food safety research and applications, predictive microbiology models may be developed based on the fact that most ...

  10. Chemical Genomics and Emerging DNA Technologies in the Identification of Drug Mechanisms and Drug Targets

    DEFF Research Database (Denmark)

    Olsen, Louise Cathrine Braun; Færgeman, Nils J.

    2012-01-01

    Chemical genomics combines chemistry with molecular biology as a means of exploring the function of unknown proteins or identifying the proteins responsible for a particular phenotype induced by a small cell-permeable bioactive molecule. Chemical genomics therefore has the potential to identify and...... validate therapeutic targets and to discover drug candidates for rapidly and effectively generating new interventions for human diseases. The recent emergence of genomic technologies and their application on genetically tractable model organisms like Drosophila melanogaster,Caenorhabditis elegans and...... Saccharomyces cerevisiae have provided momentum to cell biological and biomedical research, particularly in the functional characterization of gene functions and the identification of novel drug targets. We therefore anticipate that chemical genomics and the vast development of genomic technologies will play...

  11. 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. PMID:26222406

  12. Legionella pneumophila Carbonic Anhydrases: Underexplored Antibacterial Drug Targets.

    Science.gov (United States)

    Supuran, Claudiu T

    2016-01-01

    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. PMID:27322334

  13. 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.

  14. Genetic Approaches To Identifying Novel Osteoporosis Drug Targets.

    Science.gov (United States)

    Brommage, Robert

    2015-10-01

    During the past two decades effective drugs for treating osteoporosis have been developed, including anti-resorptives inhibiting bone resorption (estrogens, the SERM raloxifene, four bisphosphonates, RANKL inhibitor denosumab) and the anabolic bone forming daily injectable peptide teriparatide. Two potential drugs (odanacatib and romosozumab) are in late stage clinical development. The most pressing unmet need is for orally active anabolic drugs. This review describes the basic biological studies involved in developing these drugs, including the animal models employed for osteoporosis drug development. The genomics revolution continues to identify potential novel osteoporosis drug targets. Studies include human GWAS studies and identification of mutant genes in subjects having abnormal bone mass, mouse QTL and gene knockouts, and gene expression studies. Multiple lines of evidence indicate that Wnt signaling plays a major role in regulating bone formation and continued study of this complex pathway is likely to lead to key discoveries. In addition to the classic Wnt signaling targets DKK1 and sclerostin, LRP4, LRP5/LRP6, SFRP4, WNT16, and NOTUM can potentially be targeted to modulate Wnt signaling. Next-generation whole genome and exome sequencing, RNA-sequencing and CRISPR/CAS9 gene editing are new experimental techniques contributing to understanding the genome. The International Knockout Mouse Consortium efforts to knockout and phenotype all mouse genes are poised to accelerate. Accumulating knowledge will focus attention on readily accessible databases (Big Data). Efforts are underway by the International Bone and Mineral Society to develop an annotated Skeletome database providing information on all genes directly influencing bone mass, architecture, mineralization or strength. PMID:25833316

  15. New drugs targeting Th2 lymphocytes in asthma.

    Science.gov (United States)

    Caramori, Gaetano; Groneberg, David; Ito, Kazuhiro; Casolari, Paolo; Adcock, Ian M; Papi, Alberto

    2008-02-27

    Asthma represents a profound worldwide public health problem. The most effective anti-asthmatic drugs currently available include inhaled beta2-agonists and glucocorticoids and control asthma in about 90-95% of patients. The current asthma therapies are not cures and symptoms return soon after treatment is stopped even after long term therapy. Although glucocorticoids are highly effective in controlling the inflammatory process in asthma, they appear to have little effect on the lower airway remodelling processes that appear to play a role in the pathophysiology of asthma at currently prescribed doses. The development of novel drugs may allow resolution of these changes. In addition, severe glucocorticoid-dependent and resistant asthma presents a great clinical burden and reducing the side-effects of glucocorticoids using novel steroid-sparing agents is needed. Furthermore, the mechanisms involved in the persistence of inflammation are poorly understood and the reasons why some patients have severe life threatening asthma and others have very mild disease are still unknown. Drug development for asthma has been directed at improving currently available drugs and findings new compounds that usually target the Th2-driven airway inflammatory response. Considering the apparently central role of T lymphocytes in the pathogenesis of asthma, drugs targeting disease-inducing Th2 cells are promising therapeutic strategies. However, although animal models of asthma suggest that this is feasible, the translation of these types of studies for the treatment of human asthma remains poor due to the limitations of the models currently used. The myriad of new compounds that are in development directed to modulate Th2 cells recruitment and/or activation will clarify in the near future the relative importance of these cells and their mediators in the complex interactions with the other pro-inflammatory/anti-inflammatory cells and mediators responsible of the different asthmatic

  16. Predictive microbiology for food packaging applications

    Science.gov (United States)

    Mathematical modeling has been applied to describe the microbial growth and inactivation in foods for decades and is also known as ‘Predictive microbiology’. When models are developed and validated, their applications may save cost and time. The Pathogen Modeling Program (PMP), a collection of mode...

  17. Prediction of cavitation erosion for marine applications

    Science.gov (United States)

    Maquil, T.; Yakubov, S.; Rung, T.

    2015-12-01

    The paper presents the development of a cavitation erosion prediction method. The approach is tailored to marine applications and embedded into a VoF-based procedure for the simulation of turbulent flows. Supplementary to the frequently employed Euler-Euler models, Euler-Lagrange approaches are employed to simulate cavitation. The study aims to convey the merits of an Euler-Lagrange approach for erosion simulations. Accordingly, the erosion model is able to separate different damage mechanisms, e.g. micro-jets, single and collective bubble collapse, and also quantifies their contribution to the total damage. Emphasis is devoted to the prediction of the cavitation extend, the influence of compressible effects and the performance of the material damage model in practical applications. Examples included refer to 2D validation test cases and reveal a fair predictive accuracy.

  18. In silico analysis of Burkholderia pseudomallei genome sequence for potential drug targets.

    Science.gov (United States)

    Chong, Chan-Eng; Lim, Boon-San; Nathan, Sheila; Mohamed, Rahmah

    2006-01-01

    Recent advances in DNA sequencing technology have enabled elucidation of whole genome information from a plethora of organisms. In parallel with this technology, various bioinformatics tools have driven the comparative analysis of the genome sequences between species and within isolates. While drawing meaningful conclusions from a large amount of raw material, computer-aided identification of suitable targets for further experimental analysis and characterization, has also led to the prediction of non-human homologous essential genes in bacteria as promising candidates for novel drug discovery. Here, we present a comparative genomic analysis to identify essential genes in Burkholderia pseudomallei. Our in silico prediction has identified 312 essential genes which could also be potential drug candidates. These genes encode essential proteins to support the survival of B. pseudomallei including outer-inner membrane and surface structures, regulators, proteins involved in pathogenenicity, adaptation, chaperones as well as degradation of small and macromolecules, energy metabolism, information transfer, central/intermediate/miscellaneous metabolism pathways and some conserved hypothetical proteins of unknown function. Therefore, our in silico approach has enabled rapid screening and identification of potential drug targets for further characterization in the laboratory. PMID:16922696

  19. Collaborative development of predictive toxicology applications.

    Science.gov (United States)

    Hardy, Barry; Douglas, Nicki; Helma, Christoph; Rautenberg, Micha; Jeliazkova, Nina; Jeliazkov, Vedrin; Nikolova, Ivelina; Benigni, Romualdo; Tcheremenskaia, Olga; Kramer, Stefan; Girschick, Tobias; Buchwald, Fabian; Wicker, Joerg; Karwath, Andreas; Gütlein, Martin; Maunz, Andreas; Sarimveis, Haralambos; Melagraki, Georgia; Afantitis, Antreas; Sopasakis, Pantelis; Gallagher, David; Poroikov, Vladimir; Filimonov, Dmitry; Zakharov, Alexey; Lagunin, Alexey; Gloriozova, Tatyana; Novikov, Sergey; Skvortsova, Natalia; Druzhilovsky, Dmitry; Chawla, Sunil; Ghosh, Indira; Ray, Surajit; Patel, Hitesh; Escher, Sylvia

    2010-01-01

    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-activity relationship modelling of REACH

  20. 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

  1. Genetic Validation of Aminoacyl-tRNA Synthetases as Drug Targets in Trypanosoma brucei

    OpenAIRE

    Kalidas, Savitha; Cestari, Igor; Monnerat, Severine; Li, Qiong; Regmi, Sandesh; Hasle, Nicholas; Labaied, Mehdi; Parsons, Marilyn; Stuart, Kenneth; Phillips, Margaret A.

    2014-01-01

    Human African trypanosomiasis (HAT) is an important public health threat in sub-Saharan Africa. Current drugs are unsatisfactory, and new drugs are being sought. Few validated enzyme targets are available to support drug discovery efforts, so our goal was to obtain essentiality data on genes with proven utility as drug targets. Aminoacyl-tRNA synthetases (aaRSs) are known drug targets for bacterial and fungal pathogens and are required for protein synthesis. Here we survey the essentiality of...

  2. A comparison of machine learning techniques for detection of drug target articles

    OpenAIRE

    Danger, Roxana; Segura Bedmar, Isabel; Martínez, Paloma; Rosso, Paolo

    2010-01-01

    Important progress in treating diseases has been possible thanks to the identification of drug targets. Drug targets are the molecular structures whose abnormal activity, associated to a disease, can be modified by drugs, improving the health of patients. Pharmaceutical industry needs to give priority to their identification and validation in order to reduce the long and costly drug development times. In the last two decades, our knowledge about drugs, their mechanisms of action and dru...

  3. 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...

  4. 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

  5. Green Toxicology – Application of predictive toxicology

    DEFF Research Database (Denmark)

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

    2014-01-01

    reduction. This objective is partly achieved through core principles of green chemistry. However, better utilization of existing predictive toxicological tools alongside new inventions is still required. For this, input from toxicologists early in the chemical enterprise is necessary to make informed...... 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...... be applied in chemical risk assessment to a greater extent than is currently the case. Greater focus on these tools, their strengths and weaknesses, should be part of chemistry training at the university level, thus ensuring constant focus on the issue and fostering new inventions into the future....

  6. Targeted Tumor Therapy with "Magnetic Drug Targeting": Therapeutic Efficacy of Ferrofluid Bound Mitoxantrone

    Science.gov (United States)

    Alexiou, Ch.; Schmid, R.; Jurgons, R.; Bergemann, Ch.; Arnold, W.; Parak, F.G.

    The difference between success or failure of chemotherapy depends not only on the drug itself but also on how it is delivered to its target. Biocompatible ferrofluids (FF) are paramagnetic nanoparticles, that may be used as a delivery system for anticancer agents in locoregional tumor therapy, called "magnetic drug targeting". Bound to medical drugs, such magnetic nanoparticles can be enriched in a desired body compartment (tumor) using an external magnetic field, which is focused on the area of the tumor. Through this form of target directed drug application, one attempts to concentrate a pharmacological agent at its site of action in order to minimize unwanted side effects in the organism and to increase its locoregional effectiveness. Tumor bearing rabbits (VX2 squamous cell carcinoma) in the area of the hind limb, were treated by a single intra-arterial injection (A. femoralis) of mitoxantrone bound ferrofluids (FF-MTX), while focusing an external magnetic field (1.7 Tesla) onto the tumor for 60 minutes. Complete tumor remissions could be achieved in these animals in a dose related manner (20% and 50% of the systemic dose of mitoxantrone), without any negative side effects, like e.g. leucocytopenia, alopecia or gastrointestinal disorders. The strong and specific therapeutic efficacy in tumor treatment with mitoxantrone bound ferrofluids may indicate that this system could be used as a delivery system for anticancer agents, like radionuclids, cancer-specific antibodies, anti-angiogenetic factors, genes etc.

  7. Calculation of nanoparticle capture efficiency in magnetic drug targeting

    International Nuclear Information System (INIS)

    The implant assisted magnetic targeted drug delivery system of Aviles, Ebner and Ritter, which uses high gradient magnetic separation (HGMS) is considered. In this 2D model large ferromagnetic particles are implanted as seeds to aid collection of multiple domain nanoparticles (radius ∼200nm). Here, in contrast, single domain magnetic nanoparticles (radius in 20-100 nm) are considered and the Langevin function is used to describe the magnetization. Simulations based on this model were performed using the open source C++ finite volume library OpenFOAM. The simulations indicate that use of the Langevin function predicts greater collection efficiency than might be otherwise expected

  8. Rolling Bearing Life Prediction, Theory, and Application

    Science.gov (United States)

    Zaretsky, Erwin V.

    2013-01-01

    A tutorial is presented outlining the evolution, theory, and application of rolling-element bearing life prediction from that of A. Palmgren, 1924; W. Weibull, 1939; G. Lundberg and A. Palmgren, 1947 and 1952; E. Ioannides and T. Harris, 1985; and E. Zaretsky, 1987. Comparisons are made between these life models. The Ioannides-Harris model without a fatigue limit is identical to the Lundberg-Palmgren model. The Weibull model is similar to that of Zaretsky if the exponents are chosen to be identical. Both the load-life and Hertz stress-life relations of Weibull, Lundberg and Palmgren, and Ioannides and Harris reflect a strong dependence on the Weibull slope. The Zaretsky model decouples the dependence of the critical shear stress-life relation from the Weibull slope. This results in a nominal variation of the Hertz stress-life exponent. For 9th- and 8th-power Hertz stress-life exponents for ball and roller bearings, respectively, the Lundberg- Palmgren model best predicts life. However, for 12th- and 10th-power relations reflected by modern bearing steels, the Zaretsky model based on the Weibull equation is superior. Under the range of stresses examined, the use of a fatigue limit would suggest that (for most operating conditions under which a rolling-element bearing will operate) the bearing will not fail from classical rolling-element fatigue. Realistically, this is not the case. The use of a fatigue limit will significantly overpredict life over a range of normal operating Hertz stresses. Since the predicted lives of rolling-element bearings are high, the problem can become one of undersizing a bearing for a particular application.

  9. 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. PMID:26691755

  10. 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

  11. 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.

  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. Virus-encoded chemokine receptors--putative novel antiviral drug targets

    DEFF Research Database (Denmark)

    Rosenkilde, Mette M

    2005-01-01

    Large DNA viruses, in particular herpes- and poxviruses, have evolved proteins that serve as mimics or decoys for endogenous proteins in the host. The chemokines and their receptors serve key functions in both innate and adaptive immunity through control of leukocyte trafficking, and have as such a...... receptors. The chemokine receptors belong to the superfamily of G-protein coupled 7TM receptors that per se are excellent drug targets. At present, non-peptide antagonists have been developed against many chemokine receptors. The potentials of the virus-encoded chemokine receptors as drug targets--ie. as...

  14. UDP-galactopyranose mutase, a potential drug target against human pathogenic nematode Brugia malayi.

    Science.gov (United States)

    Misra, Sweta; Valicherla, Guru R; Mohd Shahab; Gupta, Jyoti; Gayen, Jiaur R; Misra-Bhattacharya, Shailja

    2016-08-01

    Lymphatic filariasis, a vector-borne neglected tropical disease affects millions of population in tropical and subtropical countries. Vaccine unavailability and emerging drug resistance against standard antifilarial drugs necessitate search of novel drug targets for developing alternate drugs. Recently, UDP-galactopyranose mutases (UGM) have emerged as a promising drug target playing an important role in parasite virulence and survival. This study deals with the cloning and characterization of Brugia malayi UGM and further exploring its antifilarial drug target potential. The recombinant protein was actively involved in conversion of UDP-galactopyranose (substrate) to UDP-galactofuranose (product) revealing Km and Vmax to be ∼51.15 μM and ∼1.27 μM/min, respectively. The purified protein appeared to be decameric in native state and its 3D homology modeling using Aspergillus fumigatus UGM enzyme as template revealed conservation of active site residues. Two specific prokaryotic inhibitors (compounds A and B) of the enzyme inhibited B. malayi UGM enzymatic activity competitively depicting Ki values ∼22.68 and ∼23.0 μM, respectively. These compounds were also active in vitro and in vivo against B. malayi The findings suggest that B. malayi UGM could be a potential antifilarial therapeutic drug target. PMID:27465638

  15. DRUG TARGETING TO THE KIDNEY WITH LOW-MOLECULAR-WEIGHT PROTEINS

    NARCIS (Netherlands)

    FRANSSEN, EJF; MOOLENAAR, F; DEZEEUW, D; MEIJER, DKF

    1993-01-01

    This paper reviews the design of a drug targeting strategy for renal specific delivery and endorenal release of drugs using low-molecular-weight proteins (LMWPs). In general, LMWPs are known to be filtered and subsequently reabsorbed by the proximal tubular cells of the kidneys. Within these cells L

  16. 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...

  17. Characterizing EPR-mediated passive drug targeting using contrast-enhanced functional ultrasound imaging

    Czech Academy of Sciences Publication Activity Database

    Theek, B.; Gremse, F.; Kunjachan, S.; Fokong, S.; Pola, Robert; Pechar, Michal; Deckers, R.; Storm, G.; Ehling, J.; Kiessling, F.; Lammers, T.

    2014-01-01

    Roč. 182, 28 May (2014), s. 83-89. ISSN 0168-3659 R&D Projects: GA ČR GCP207/12/J030 Institutional support: RVO:61389013 Keywords : drug targeting * nanomedicine * theranostics Subject RIV: CD - Macromolecular Chemistry Impact factor: 7.705, year: 2014

  18. Predicting new molecular targets for rhein using network pharmacology

    Directory of Open Access Journals (Sweden)

    Zhang Aihua

    2012-03-01

    Full Text Available Abstract Background Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before. Methods In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline. Results Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges. Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism. Conclusions Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.

  19. Molecular Characterization of Legionellosis Drug Target Candidate Enzyme Phosphoglucosamine Mutase from Legionella pneumophila (strain Paris): An In Silico Approach

    Science.gov (United States)

    Mazumder, Habibul Hasan; Khan, Arif; Hossain, Mohammad Uzzal; Chowdhury, Homaun Kabir

    2014-01-01

    The harshness of legionellosis differs from mild Pontiac fever to potentially fatal Legionnaire's disease. The increasing development of drug resistance against legionellosis has led to explore new novel drug targets. It has been found that phosphoglucosamine mutase, phosphomannomutase, and phosphoglyceromutase enzymes can be used as the most probable therapeutic drug targets through extensive data mining. Phosphoglucosamine mutase is involved in amino sugar and nucleotide sugar metabolism. The purpose of this study was to predict the potential target of that specific drug. For this, the 3D structure of phosphoglucosamine mutase of Legionella pneumophila (strain Paris) was determined by means of homology modeling through Phyre2 and refined by ModRefiner. Then, the designed model was evaluated with a structure validation program, for instance, PROCHECK, ERRAT, Verify3D, and QMEAN, for further structural analysis. Secondary structural features were determined through self-optimized prediction method with alignment (SOPMA) and interacting networks by STRING. Consequently, we performed molecular docking studies. The analytical result of PROCHECK showed that 95.0% of the residues are in the most favored region, 4.50% are in the additional allowed region and 0.50% are in the generously allowed region of the Ramachandran plot. Verify3D graph value indicates a score of 0.71 and 89.791, 1.11 for ERRAT and QMEAN respectively. Arg419, Thr414, Ser412, and Thr9 were found to dock the substrate for the most favorable binding of S-mercaptocysteine. However, these findings from this current study will pave the way for further extensive investigation of this enzyme in wet lab experiments and in that way assist drug design against legionellosis. PMID:25705169

  20. Applications for predictive microbiology to food packaging

    Science.gov (United States)

    Predictive microbiology has been used for several years in the food industry to predict microbial growth, inactivation and survival. Predictive models provide a useful tool in risk assessment, HACCP set-up and GMP for the food industry to enhance microbial food safety. This report introduces the c...

  1. Chinese Companies Distress Prediction: An Application of Data Envelopment Analysis

    OpenAIRE

    Li, Zhiyong; Crook, Jonathan; Andreeva, Galina

    2013-01-01

    Bankruptcy prediction is a key part in corporate credit risk management. Traditional bankruptcy prediction models employ financial ratios or market prices to predict bankruptcy or financial distress prior to its occurrence. We investigate the predictive accuracy of corporate efficiency measures along with standard financial ratios in predicting corporate distress in Chinese companies. Data Envelopment Analysis (DEA) is used to measure corporate efficiency. In contrast to previous applications...

  2. 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.

  3. Prediction of novel drug target Involved in psychos is in Alzheimer Disease: A Computational Network study

    Directory of Open Access Journals (Sweden)

    Mrinal Mirsra

    2014-09-01

    Full Text Available Alzheimer (AD disease is the most frequent form ofdementia. Several structural and functional genomic factors are strongly associated with AD candidate genes, including age of onset, cognitive decline and amyloid depositions. Serotonin (5-TH receptors play an important role in psychosis in AD with cognitive impairment. This study is based on insilco identification and prioritization of the differentially expressed genes of the genetic network involved in AD. Fourteen 5-HT candidate genes interaction network associated with AD was generated using agilent literature search cytoscape plugin. The organic layout shows cross-interaction between the genes set. On merging the genetic network with gene expression profile data, notable changes in interaction patterns were observed. These changes revealed 5-HTR2A, 5-HTR2C and 5-HTR4 as important genes in AD. Further refinement with Enrichment Map indicated 5-HTR2C as novel candidate gene that showed high functional significance in correlation to Alzheimer's disease. Our result will be a crucial factor for better understanding of the genetic pathways involved in causing psychosis in AD and will form a future landmark in developing target based drug therapies against this disease.

  4. 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

  5. Perspective of Cyclin-dependent kinase 9 (CDK9) as a Drug Target

    Czech Academy of Sciences Publication Activity Database

    Kryštof, Vladimír; Baumli, S.; Fürst, R.

    2012-01-01

    Roč. 18, č. 20 (2012), s. 2883-2890. ISSN 1381-6128 R&D Projects: GA ČR GAP305/12/0783 Institutional research plan: CEZ:AV0Z50380511 Keywords : Cancer * inflammation * kinase Subject RIV: ED - Physiology Impact factor: 3.311, year: 2012 http://www.benthamdirect.org/pages/article/1/3177374/perspective-of-cyclin-dependent-kinase-9-cdk9-as-a-drug-target.html

  6. Host-bacterial coevolution and the search for new drug targets

    OpenAIRE

    Zaneveld, Jesse; Turnbaugh, Peter J.; Lozupone, Catherine; Ley, Ruth E.; Hamady, Micah; Gordon, Jeffrey I; Knight, Rob

    2008-01-01

    Understanding coevolution between humans and our microbial symbionts and pathogens requires complementary approaches, ranging from community analysis to in-depth analysis of individual genomes. Here we review the evidence for coevolution between symbionts and their hosts, the role of horizontal gene transfer in coevolution, and genomic and metagenomic approaches to identifying drug targets. Recent studies have shown that our symbiotic microbes confer many metabolic capabilities that our mamma...

  7. Quantitative targeting maps based on experimental investigations for a branched tube model in magnetic drug targeting

    Energy Technology Data Exchange (ETDEWEB)

    Gitter, K., E-mail: kurt.gitter@tu-dresden.de [TU Dresden, Institute of Fluid Mechanics, Chair of Magnetofluiddynamics, 01062 Dresden (Germany); Odenbach, S. [TU Dresden, Institute of Fluid Mechanics, Chair of Magnetofluiddynamics, 01062 Dresden (Germany)

    2011-12-15

    Magnetic drug targeting (MDT), because of its high targeting efficiency, is a promising approach for tumour treatment. Unwanted side effects are considerably reduced, since the nanoparticles are concentrated within the target region due to the influence of a magnetic field. Nevertheless, understanding the transport phenomena of nanoparticles in an artery system is still challenging. This work presents experimental results for a branched tube model. Quantitative results describe, for example, the net amount of nanoparticles that are targeted towards the chosen region due to the influence of a magnetic field. As a result of measurements, novel drug targeting maps, combining, e.g. the magnetic volume force, the position of the magnet and the net amount of targeted nanoparticles, are presented. The targeting maps are valuable for evaluation and comparison of setups and are also helpful for the design and the optimisation of a magnet system with an appropriate strength and distribution of the field gradient. The maps indicate the danger of accretion within the tube and also show the promising result of magnetic drug targeting that up to 97% of the nanoparticles were successfully targeted. - Highlights: > Quantitative targeting maps summarise a series of measurements. > Targeting maps combine quantitative data, magnetic volume forces and magnet position. > Here, up to 97% of injected particles were targeted towards the tumour region. > High concentration of injected ferrofluid brings the danger of accretion. > Low miscibility of ferrofluid by water modelling the blood flow is detected.

  8. 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.

  9. Identification of novel drug targets in HpB38, HpP12, HpG27, Hpshi470, HpSJM180 strains of Helicobacter pylori : an in silico approach for therapeutic intervention.

    Science.gov (United States)

    Neelapu, Nageswara Rao Reddy; Pavani, T

    2013-05-01

    Helicobacter species colonizes the stomach and are associated with the development of gastritis disease. Drugs for treatment of Helicobacter infection relieve pain or gastritis symptoms but they are not targeted specifically to Helicobacter pylori. Therefore, there is dire need for discovery of new drug targets and drugs for the treatment of H. pylori. The main objective of this study is to screen the potential drug targets by in silico analysis for the potent strains of H. pylori which include HpB38, HpP12, HpG27, Hpshi470 and HpSJM180. Genome and metabolic pathways of pathogen H. pylori and the host Homosapien sapiens are compared and genes which were unique to H. pylori were filtered and catalogued. These unique genes were subjected to gene property analysis to identify the potentiality of the drug targets. Among the total number of genes analysed in different strains of H. pylori nearly 558, 569, 539, 569, 567 number of genes in HpB38, HpP12, HpG27, Hpshi470 and HpSJM180 found qualified as unique molecules and among them 17 qualified as potential drug targets. Membrane fusion protein of hefABC efflux system, 50 S ribosomal protein L33, Hydrogenase expression protein/formation of HypD, Cag pathogenecity island protein X, Apolipoprotein N acyl transferase, DNA methyalse, Histone like binding protein, Peptidoglycan-associated lipoprotein OprL were found to be critical drug targets to H. pylori. Three (hefABC efflux system, Hydrogenase expression protein/formation of HypD, Cag pathogenecity island protein X) of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lending credence to our unique approach. PMID:23410125

  10. 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...

  11. Predicting New Materials for Hydrogen Storage Application

    Directory of Open Access Journals (Sweden)

    Helmer Fjellvåg

    2009-12-01

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

  12. Application of optimal prediction to molecular dynamics

    Energy Technology Data Exchange (ETDEWEB)

    Barber IV, John Letherman

    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 {delta}-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.

  13. 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.

  14. 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)

  15. 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

  16. 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...

  17. 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

    transcriptional 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. In a broader perspective, our study provides strong evidence that genomic strategies might be used in a clinical setting to prospectively identify candidate drugs that subsequently are validated in vitro to define the most effective drug combination for individual cancer patients on a...

  18. Pediatric Malignant Bone Tumors: A Review and Update on Current Challenges, and Emerging Drug Targets.

    Science.gov (United States)

    Jackson, Twana M; Bittman, Mark; Granowetter, Linda

    2016-07-01

    Osteosarcoma (OS) and the Ewing sarcoma family of tumors (ESFT) are the most common malignant bone tumors in children and adolescents. While significant improvements in survival have been seen in other pediatric malignancies the treatment and prognosis for pediatric bone tumors has remained unchanged for the past 3 decades. This review and update of pediatric malignant bone tumors will provide a general overview of osteosarcoma and the Ewing sarcoma family of tumors, discuss bone tumor genomics, current challenges, and emerging drug targets. PMID:27265835

  19. Rho, ROCK and actomyosin contractility in metastasis as drug targets [version 1; referees: 2 approved

    Directory of Open Access Journals (Sweden)

    Irene Rodriguez-Hernandez

    2016-04-01

    Full Text Available Metastasis is the spread of cancer cells around the body and the cause of the majority of cancer deaths. Metastasis is a very complex process in which cancer cells need to dramatically modify their cytoskeleton and cope with different environments to successfully colonize a secondary organ. In this review, we discuss recent findings pointing at Rho-ROCK or actomyosin force (or both as major drivers of many of the steps required for metastatic success. We propose that these are important drug targets that need to be considered in the clinic to palliate metastatic disease.

  20. Neoadjuvant Window Studies of Metformin and Biomarker Development for Drugs Targeting Cancer Metabolism.

    Science.gov (United States)

    Lord, Simon R; Patel, Neel; Liu, Dan; Fenwick, John; Gleeson, Fergus; Buffa, Francesca; Harris, Adrian L

    2015-05-01

    There has been growing interest in the potential of the altered metabolic state typical of cancer cells as a drug target. The antidiabetes drug, metformin, is now under intense investigation as a safe method to modify cancer metabolism. Several studies have used window of opportunity in breast cancer patients before neoadjuvant chemotherapy to correlate gene expression analysis, metabolomics, immunohistochemical markers, and metabolic serum markers with those likely to benefit. We review the role metabolite measurement, functional imaging and gene sequencing analysis play in elucidating the effects of metabolically targeted drugs in cancer treatment and determining patient selection. PMID:26063894

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

    OpenAIRE

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

    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 th...

  2. 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

  3. Drug targets for cell cycle dysregulators in leukemogenesis: in silico docking studies.

    Directory of Open Access Journals (Sweden)

    Archana Jayaraman

    Full Text Available Alterations in cell cycle regulating proteins are a key characteristic in neoplastic proliferation of lymphoblast cells in patients with Acute Lymphoblastic Leukemia (ALL. The aim of our study was to investigate whether the routinely administered ALL chemotherapeutic agents would be able to bind and inhibit the key deregulated cell cycle proteins such as--Cyclins E1, D1, D3, A1 and Cyclin Dependent Kinases (CDK 2 and 6. We used Schrödinger Glide docking protocol to dock the chemotherapeutic drugs such as Doxorubicin and Daunorubicin and others which are not very common including Clofarabine, Nelarabine and Flavopiridol, to the crystal structures of these proteins. We observed that the drugs were able to bind and interact with cyclins E1 and A1 and CDKs 2 and 6 while their docking to cyclins D1 and D3 were not successful. This binding proved favorable to interact with the G1/S cell cycle phase proteins that were examined in this study and may lead to the interruption of the growth of leukemic cells. Our observations therefore suggest that these drugs could be explored for use as inhibitors for these cell cycle proteins. Further, we have also highlighted residues which could be important in the designing of pharmacophores against these cell cycle proteins. This is the first report in understanding the mechanism of action of the drugs targeting these cell cycle proteins in leukemia through the visualization of drug-target binding and molecular docking using computational methods.

  4. Parallel shRNA and CRISPR-Cas9 screens enable antiviral drug target identification.

    Science.gov (United States)

    Deans, Richard M; Morgens, David W; Ökesli, Ayşe; Pillay, Sirika; Horlbeck, Max A; Kampmann, Martin; Gilbert, Luke A; Li, Amy; Mateo, Roberto; Smith, Mark; Glenn, Jeffrey S; Carette, Jan E; Khosla, Chaitan; Bassik, Michael C

    2016-05-01

    Broad-spectrum antiviral drugs targeting host processes could potentially treat a wide range of viruses while reducing the likelihood of emergent resistance. Despite great promise as therapeutics, such drugs remain largely elusive. Here we used parallel genome-wide high-coverage short hairpin RNA (shRNA) and clustered regularly interspaced short palindromic repeats (CRISPR)-Cas9 screens to identify the cellular target and mechanism of action of GSK983, a potent broad-spectrum antiviral with unexplained cytotoxicity. We found that GSK983 blocked cell proliferation and dengue virus replication by inhibiting the pyrimidine biosynthesis enzyme dihydroorotate dehydrogenase (DHODH). Guided by mechanistic insights from both genomic screens, we found that exogenous deoxycytidine markedly reduced GSK983 cytotoxicity but not antiviral activity, providing an attractive new approach to improve the therapeutic window of DHODH inhibitors against RNA viruses. Our results highlight the distinct advantages and limitations of each screening method for identifying drug targets, and demonstrate the utility of parallel knockdown and knockout screens for comprehensive probing of drug activity. PMID:27018887

  5. 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.

  6. An Approach to Performance Prediction for Parallel Applications

    Energy Technology Data Exchange (ETDEWEB)

    Ipek, E; de Supinski, B R; Schulz, M; McKee, S A

    2005-05-17

    Accurately modeling and predicting performance for large-scale applications becomes increasingly difficult as system complexity scales dramatically. Analytic predictive models are useful, but are difficult to construct, usually limited in scope, and often fail to capture subtle interactions between architecture and software. In contrast, we employ multilayer neural networks trained on input data from executions on the target platform. This approach is useful for predicting many aspects of performance, and it captures full system complexity. Our models are developed automatically from the training input set, avoiding the difficult and potentially error-prone process required to develop analytic models. This study focuses on the high-performance, parallel application SMG2000, a much studied code whose variations in execution times are still not well understood. Our model predicts performance on two large-scale parallel platforms within 5%-7% error across a large, multi-dimensional parameter space.

  7. 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.

  8. Drug targeting strategies for the treatment of inflammatory bowel disease: a mechanistic update.

    Science.gov (United States)

    Dahan, Arik; Amidon, Gordon L; Zimmermann, Ellen M

    2010-07-01

    The therapeutic management of inflammatory bowel disease (IBD) represents the perfect scenario for drug targeting to the site(s) of action. While existing formulation-based targeting strategies include rectal dosage forms and oral systems that target the colon by pH-, time-, microflora- and pressure-triggered drug release, novel approaches for site-specific delivery in IBD therapy will target the inflamed intestine per se rather than intestinal region. The purpose of this article is to present a mechanistic update on the strategies employed to achieve minimal systemic exposure accompanied by maximal drug levels in the inflamed intestinal tissue. The introduction of biological agents, micro/nanoparticulate carriers including liposomes, transgenic bacteria, and gene therapy opportunities are discussed, as well as the challenges remaining to be achieved in the targeted treatment of IBD. PMID:20594127

  9. Using mitochondrial sirtuins as drug targets: disease implications and available compounds.

    Science.gov (United States)

    Gertz, Melanie; Steegborn, Clemens

    2016-08-01

    Sirtuins are an evolutionary conserved family of NAD(+)-dependent protein lysine deacylases. Mammals have seven Sirtuin isoforms, Sirt1-7. They contribute to regulation of metabolism, stress responses, and aging processes, and are considered therapeutic targets for metabolic and aging-related diseases. While initial studies were focused on Sirt1 and 2, recent progress on the mitochondrial Sirtuins Sirt3, 4, and 5 has stimulated research and drug development for these isoforms. Here we review the roles of Sirtuins in regulating mitochondrial functions, with a focus on the mitochondrially located isoforms, and on their contributions to disease pathologies. We further summarize the compounds available for modulating the activity of these Sirtuins, again with a focus on mitochondrial isoforms, and we describe recent results important for the further improvement of compounds. This overview illustrates the potential of mitochondrial Sirtuins as drug targets and summarizes the status, progress, and challenges in developing small molecule compounds modulating their activity. PMID:27007507

  10. 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.

  11. Liver X receptor as a drug target for the treatment of breast cancer.

    Science.gov (United States)

    Wu, Ying; Yu, Dan-Dan; Yan, Da-Li; Hu, Yong; Chen, Dan; Liu, Yun; Zhang, He-da; Yu, Shao-Rong; Cao, Hai-Xia; Feng, Ji-Feng

    2016-06-01

    Liver X receptor (LXR) has been exploited widely as a drug target in breast cancer treatment, and various mechanisms underlying the effects of LXR in this area are well studied. The activated LXR plays important roles in estrogen receptor α (ERα) breast cancer cells, such as reducing cell proliferation and arresting cell cycle progression. Different LXR ligands have diverse effects on the development of breast cancer, such as the inhibitory effect of oxysterol, which can return cells to normocholesterol conditions and target other metabolic genes. Moreover, 27-hydroxycholesterol, a locally produced cholesterol metabolite, reportedly promotes the proliferation of ERα breast cancer cells in vitro and facilitates tumor metastasis with other LXR ligands. Moreover, the expression of LXR also exerts potential effects on immune surveillance, tumor immunity, and tumor microenvironment. These advances in breast cancer research indicate that LXR may be a new therapeutic target to treat the refractory or drug-resistant subtypes of breast cancer. PMID:26872310

  12. Orphan G protein-coupled receptors (GPCRs):biological functions and potential drug targets

    Institute of Scientific and Technical Information of China (English)

    Xiao-long TANG; Ying WANG; Da-li LI; Jian LUO; Ming-yao LIU

    2012-01-01

    The superfamily of G protein-coupled receptors (GPCRs) includes at least 800 seven-transmembrane receptors that participate in diverse physiological and pathological functions.GPCRs are the most successful targets of modern medicine,and approximately 36%of marketed pharmaceuticals target human GPCRs.However,the endogenous ligands of more than 140 GPCRs remain unidentified,leaving the natural functions of those GPCRs in doubt.These are the so-called orphan GPCRs,a great source of drug targets.This review focuses on the signaling transduction pathways of the Adhesion GPCR family,the LGR subfamily,and the PSGR subfamily,and their potential functions in immunology,development,and cancers.In this review,we present the current approaches and difficulties of orphan GPCR deorphanization and characterization.

  13. 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.

  14. Inclusion of magnetic dipole-dipole and hydrodynamic interactions in implant-assisted magnetic drug targeting

    International Nuclear Information System (INIS)

    Mathematical modelling of the implant-assisted magnetic drug targeting system of Aviles, Ebner and Ritter is performed. In order to model the agglomeration of particles known to occur in this system, the magnetic dipole-dipole and hydrodynamic interactions are included. Such interactions were calculated previously by Mikkelsen et al. under low magnetic fields (∼0.05 T) in microfluidic systems. Here, a higher magnetic field (0.7 T) is considered and the effect of interactions on two nanoparticles with a seed implant is calculated. The calculations were performed with the open-source software OpenFOAM. Different initial positions are considered and the system performance is assessed in terms of capture cross section. Inclusion of both interactions was seen to alter the capture cross section of the system by up to 7% in absolute terms.

  15. Genetic validation of aminoacyl-tRNA synthetases as drug targets in Trypanosoma brucei.

    Science.gov (United States)

    Kalidas, Savitha; Cestari, Igor; Monnerat, Severine; Li, Qiong; Regmi, Sandesh; Hasle, Nicholas; Labaied, Mehdi; Parsons, Marilyn; Stuart, Kenneth; Phillips, Margaret A

    2014-04-01

    Human African trypanosomiasis (HAT) is an important public health threat in sub-Saharan Africa. Current drugs are unsatisfactory, and new drugs are being sought. Few validated enzyme targets are available to support drug discovery efforts, so our goal was to obtain essentiality data on genes with proven utility as drug targets. Aminoacyl-tRNA synthetases (aaRSs) are known drug targets for bacterial and fungal pathogens and are required for protein synthesis. Here we survey the essentiality of eight Trypanosoma brucei aaRSs by RNA interference (RNAi) gene expression knockdown, covering an enzyme from each major aaRS class: valyl-tRNA synthetase (ValRS) (class Ia), tryptophanyl-tRNA synthetase (TrpRS-1) (class Ib), arginyl-tRNA synthetase (ArgRS) (class Ic), glutamyl-tRNA synthetase (GluRS) (class 1c), threonyl-tRNA synthetase (ThrRS) (class IIa), asparaginyl-tRNA synthetase (AsnRS) (class IIb), and phenylalanyl-tRNA synthetase (α and β) (PheRS) (class IIc). Knockdown of mRNA encoding these enzymes in T. brucei mammalian stage parasites showed that all were essential for parasite growth and survival in vitro. The reduced expression resulted in growth, morphological, cell cycle, and DNA content abnormalities. ThrRS was characterized in greater detail, showing that the purified recombinant enzyme displayed ThrRS activity and that the protein localized to both the cytosol and mitochondrion. Borrelidin, a known inhibitor of ThrRS, was an inhibitor of T. brucei ThrRS and showed antitrypanosomal activity. The data show that aaRSs are essential for T. brucei survival and are likely to be excellent targets for drug discovery efforts. PMID:24562907

  16. 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.

  17. Phytochemical-mediated Protein Expression Profiling and the Potential Applications in Therapeutic Drug Target Identifications.

    Science.gov (United States)

    Wong, Fai-Chu; Tan, Siok-Thing; Chai, Tsun-Thai

    2016-07-29

    Many phytochemicals derived from edible medicinal plants have been investigated intensively for their various bioactivities. However, the detailed mechanism and their corresponding molecular targets frequently remain elusive. In this review, we present a summary of the research works done on phytochemical-mediated molecular targets, identified via proteomic approach. Concurrently, we also highlighted some pharmaceutical drugs which could be traced back to their origins in phytochemicals. For ease of presentation, these identified protein targets were categorized into two important healthcare-related fields, namely anti-bacterial and anti-cancer research. Through this review, we hope to highlight the usefulness of comparative proteomic as a powerful tool in phytochemical-mediated protein target identifications. Likewise, we wish to inspire further investigations on some of these protein targets identified over the last few years. With contributions from all researchers, the accumulative efforts could eventually lead to the discovery of some target-specific, low-toxicity therapeutic agents. PMID:26193174

  18. Conditional nonlinear optimal perturbation: Applications to stability, sensitivity, and predictability

    Institute of Scientific and Technical Information of China (English)

    DUAN WanSuo; MU Mu

    2009-01-01

    Conditional nonlinear optimal perturbation (CNOP) is a nonlinear generalization of linear singular vec-tor (LSV) and features the largest nonlinear evolution at prediction time for the initial perturbations in a given constraint. It was proposed initially for predicting the limitation of predictability of weather or climate. Then CNOP has been applied to the studies of the problems related to predictability for weather and climate. In this paper, we focus on reviewing the recent advances of CNOP's applications,which involves the ones of CNOP in problems of ENSO amplitude asymmetry, block onset, and the sensitivity analysis of ecosystem and ocean's circulations, etc. Especially, CNOP has been primarily used to construct the initial perturbation fields of ensemble forecasting, and to determine the sensitive area of target observation for precipitations. These works extend CNOP'a applications to investigating the nonlinear dynamical behaviors of atmospheric or oceanic systems, even a coupled system, and studying the problem of the transition between the equilibrium states. These contributions not only attack the particular physical problems, but also show the superiority of CNOP to LSV in revealing the effect of nonlinear physical processes. Consequently, CNOP represents the optimal precursors for a weather or climate event; in predictability studies, CNOP stands for the initial error that has the largest negative effect on prediction; and in sensitivity analysis, CNOP is the most unstable (sensitive) mode.In multi-equilibrium state regime, CNOP is the initial perturbation that induces the transition between equilibriums most probably. Furthermore, CNOP has been used to construct ensemble perturbation fields in ensemble forecast studies and to identify sensitive area of target observation. CNOP theory has become more and more substantial. It is expected that CNOP also serves to improve the predict-ability of the realistic predictions for weather and climate events

  19. Towards more accurate and reliable predictions for nuclear applications

    International Nuclear Information System (INIS)

    The need for nuclear data far from the valley of stability, for applications such as nuclear astrophysics or future nuclear facilities, challenges the robustness as well as the predictive power of present nuclear models. Most of the nuclear data evaluation and prediction are still performed on the basis of phenomenological nuclear models. For the last decades, important progress has been achieved in fundamental nuclear physics, making it now feasible to use more reliable, but also more complex microscopic or semi-microscopic models in the evaluation and prediction of nuclear data for practical applications. In the present contribution, the reliability and accuracy of recent nuclear theories are discussed for most of the relevant quantities needed to estimate reaction cross sections and beta-decay rates, namely nuclear masses, nuclear level densities, gamma-ray strength, fission properties and beta-strength functions. It is shown that nowadays, mean-field models can be tuned at the same level of accuracy as the phenomenological models, renormalized on experimental data if needed, and therefore can replace the phenomenogical inputs in the prediction of nuclear data. While fundamental nuclear physicists keep on improving state-of-the-art models, e.g. within the shell model or ab initio models, nuclear applications could make use of their most recent results as quantitative constraints or guides to improve the predictions in energy or mass domain that will remain inaccessible experimentally. (orig.)

  20. NASTRAN application for the prediction of aircraft interior noise

    Science.gov (United States)

    Marulo, Francesco; Beyer, Todd B.

    1987-08-01

    The application of a structural-acoustic analogy within the NASTRAN finite element program for the prediction of aircraft interior noise is presented. Some refinements of the method, which reduce the amount of computation required for large, complex structures, are discussed. Also, further improvements are proposed and preliminary comparisons with structural and acoustic modal data obtained for a large, composite cylinder are presented.

  1. 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.

  2. Conditional nonlinear optimal perturbation: Applications to stability, sensitivity, and predictability

    Institute of Scientific and Technical Information of China (English)

    2009-01-01

    Conditional nonlinear optimal perturbation (CNOP) is a nonlinear generalization of linear singular vector (LSV) and features the largest nonlinear evolution at prediction time for the initial perturbations in a given constraint. It was proposed initially for predicting the limitation of predictability of weather or climate. Then CNOP has been applied to the studies of the problems related to predictability for weather and climate. In this paper, we focus on reviewing the recent advances of CNOP’s applications, which involves the ones of CNOP in problems of ENSO amplitude asymmetry, block onset, and the sensitivity analysis of ecosystem and ocean’s circulations, etc. Especially, CNOP has been primarily used to construct the initial perturbation fields of ensemble forecasting, and to determine the sensitive area of target observation for precipitations. These works extend CNOP’s applications to investigating the nonlinear dynamical behaviors of atmospheric or oceanic systems, even a coupled system, and studying the problem of the transition between the equilibrium states. These contributions not only attack the particular physical problems, but also show the superiority of CNOP to LSV in revealing the effect of nonlinear physical processes. Consequently, CNOP represents the optimal precursors for a weather or climate event; in predictability studies, CNOP stands for the initial error that has the largest negative effect on prediction; and in sensitivity analysis, CNOP is the most unstable (sensitive) mode. In multi-equilibrium state regime, CNOP is the initial perturbation that induces the transition between equilibriums most probably. Furthermore, CNOP has been used to construct ensemble perturbation fields in ensemble forecast studies and to identify sensitive area of target observation. CNOP theory has become more and more substantial. It is expected that CNOP also serves to improve the predictability of the realistic predictions for weather and climate

  3. 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.

  4. 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. PMID:22234525

  5. Mitoxantrone Loaded Superparamagnetic Nanoparticles for Drug Targeting: A Versatile and Sensitive Method for Quantification of Drug Enrichment in Rabbit Tissues Using HPLC-UV

    Directory of Open Access Journals (Sweden)

    Rainer Tietze

    2010-01-01

    Full Text Available In medicine, superparamagnetic nanoparticles bound to chemotherapeutics are currently investigated for their feasibility in local tumor therapy. After intraarterial application, these particles can be accumulated in the targeted area by an external magnetic field to increase the drug concentration in the region of interest (Magnetic-Drug-Targeting. We here present an analytical method (HPLC-UV, to detect pure or ferrofluid-bound mitoxantrone in a complex matrix even in trace amounts in order to perform biodistribution studies. Mitoxantrone could be extracted in high yields from different tissues. Recovery of mitoxantrone in liver tissue (5000 ng/g was 76±2%. The limit of quantification of mitoxantrone standard was 10 ng/mL ±12%. Validation criteria such as linearity, precision, and stability were evaluated in ranges achieving the FDA requirements. As shown for pilot samples, biodistribution studies can easily be performed after application of pure or ferrofluid-bound mitoxantrone.

  6. 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.

  7. 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

  8. Practical application of predictive microbiology software programs to HACCP plans.

    Science.gov (United States)

    Fujikawa, H; Kokubo, Y

    2001-08-01

    We studied how predictive microbiology models could practically be applied to HACCP plans with two predictive software programs that are currently available. The software programs were the Food Micromodel elaborated by the Ministry of Agriculture, Fisheries, and Food, U.K. and the Pathogen Modeling Program of Eastern Regional Research Center, U.S. Department of Agriculture. They successfully provided useful information on (i) the determination of Critical Control Points (CCPs), (ii) the estimation of critical limits at CCPs, (iii) the decision of abused products, (iv) the assessment of equivalence of HACCP plans, and further (v) the development of new products. With the information simulated by the software programs, HACCP teams could make scientific and objective decisions for developing their individual plans. It was also confirmed that microbiological process standards for food processing are indispensable for the application of the predictive programs to HACCP plans. PMID:11817141

  9. 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)...

  10. Leveraging structure determination with fragment screening for infectious disease drug targets: MECP synthase from Burkholderia pseudomallei

    Energy Technology Data Exchange (ETDEWEB)

    Begley, Darren W.; Hartley, Robert C.; Davies, Douglas R.; Edwards, Thomas E.; Leonard, Jess T.; Abendroth, Jan; Burris, Courtney A.; Bhandari, Janhavi; Myler, Peter J.; Staker, Bart L.; Stewart, Lance J. (UWASH); (Emerald)

    2011-09-28

    As part of the Seattle Structural Genomics Center for Infectious Disease, we seek to enhance structural genomics with ligand-bound structure data which can serve as a blueprint for structure-based drug design. We have adapted fragment-based screening methods to our structural genomics pipeline to generate multiple ligand-bound structures of high priority drug targets from pathogenic organisms. In this study, we report fragment screening methods and structure determination results for 2C-methyl-D-erythritol-2,4-cyclo-diphosphate (MECP) synthase from Burkholderia pseudomallei, the gram-negative bacterium which causes melioidosis. Screening by nuclear magnetic resonance spectroscopy as well as crystal soaking followed by X-ray diffraction led to the identification of several small molecules which bind this enzyme in a critical metabolic pathway. A series of complex structures obtained with screening hits reveal distinct binding pockets and a range of small molecules which form complexes with the target. Additional soaks with these compounds further demonstrate a subset of fragments to only bind the protein when present in specific combinations. This ensemble of fragment-bound complexes illuminates several characteristics of MECP synthase, including a previously unknown binding surface external to the catalytic active site. These ligand-bound structures now serve to guide medicinal chemists and structural biologists in rational design of novel inhibitors for this enzyme.

  11. The periplasmic protein TolB as a potential drug target in Pseudomonas aeruginosa.

    Directory of Open Access Journals (Sweden)

    Alessandra Lo Sciuto

    Full Text Available The Gram-negative bacterium Pseudomonas aeruginosa is one of the most dreaded pathogens in the hospital setting, and represents a prototype of multi-drug resistant "superbug" for which effective therapeutic options are very limited. The identification and characterization of new cellular functions that are essential for P. aeruginosa viability and/or virulence could drive the development of anti-Pseudomonas compounds with novel mechanisms of action. In this study we investigated whether TolB, the periplasmic component of the Tol-Pal trans-envelope protein complex of Gram-negative bacteria, represents a potential drug target in P. aeruginosa. By combining conditional mutagenesis with the analysis of specific pathogenicity-related phenotypes, we demonstrated that TolB is essential for P. aeruginosa growth, both in laboratory and clinical strains, and that TolB-depleted P. aeruginosa cells are strongly defective in cell-envelope integrity, resistance to human serum and several antibiotics, as well as in the ability to cause infection and persist in an insect model of P. aeruginosa infection. The essentiality of TolB for P. aeruginosa growth, resistance and pathogenicity highlights the potential of TolB as a novel molecular target for anti-P. aeruginosa drug discovery.

  12. The periplasmic protein TolB as a potential drug target in Pseudomonas aeruginosa.

    Science.gov (United States)

    Lo Sciuto, Alessandra; Fernández-Piñar, Regina; Bertuccini, Lucia; Iosi, Francesca; Superti, Fabiana; Imperi, Francesco

    2014-01-01

    The Gram-negative bacterium Pseudomonas aeruginosa is one of the most dreaded pathogens in the hospital setting, and represents a prototype of multi-drug resistant "superbug" for which effective therapeutic options are very limited. The identification and characterization of new cellular functions that are essential for P. aeruginosa viability and/or virulence could drive the development of anti-Pseudomonas compounds with novel mechanisms of action. In this study we investigated whether TolB, the periplasmic component of the Tol-Pal trans-envelope protein complex of Gram-negative bacteria, represents a potential drug target in P. aeruginosa. By combining conditional mutagenesis with the analysis of specific pathogenicity-related phenotypes, we demonstrated that TolB is essential for P. aeruginosa growth, both in laboratory and clinical strains, and that TolB-depleted P. aeruginosa cells are strongly defective in cell-envelope integrity, resistance to human serum and several antibiotics, as well as in the ability to cause infection and persist in an insect model of P. aeruginosa infection. The essentiality of TolB for P. aeruginosa growth, resistance and pathogenicity highlights the potential of TolB as a novel molecular target for anti-P. aeruginosa drug discovery. PMID:25093328

  13. Cannabinoid receptor 1 is a potential drug target for treatment of translocation-positive rhabdomyosarcoma.

    Science.gov (United States)

    Oesch, Susanne; Walter, Dagmar; Wachtel, Marco; Pretre, Kathya; Salazar, Maria; Guzmán, Manuel; Velasco, Guillermo; Schäfer, Beat W

    2009-07-01

    Gene expression profiling has revealed that the gene coding for cannabinoid receptor 1 (CB1) is highly up-regulated in rhabdomyosarcoma biopsies bearing the typical chromosomal translocations PAX3/FKHR or PAX7/FKHR. Because cannabinoid receptor agonists are capable of reducing proliferation and inducing apoptosis in diverse cancer cells such as glioma, breast cancer, and melanoma, we evaluated whether CB1 is a potential drug target in rhabdomyosarcoma. Our study shows that treatment with the cannabinoid receptor agonists HU210 and Delta(9)-tetrahydrocannabinol lowers the viability of translocation-positive rhabdomyosarcoma cells through the induction of apoptosis. This effect relies on inhibition of AKT signaling and induction of the stress-associated transcription factor p8 because small interfering RNA-mediated down-regulation of p8 rescued cell viability upon cannabinoid treatment. Finally, treatment of xenografts with HU210 led to a significant suppression of tumor growth in vivo. These results support the notion that cannabinoid receptor agonists could represent a novel targeted approach for treatment of translocation-positive rhabdomyosarcoma. PMID:19509271

  14. Cancer therapy with drug loaded magnetic nanoparticles-magnetic drug targeting

    International Nuclear Information System (INIS)

    The aim of magnetic drug targeting (MDT) in cancer therapy is to concentrate chemotherapeutics to a tumor region while simultaneously the overall dose is reduced. This can be achieved with coated superparamagnetic nanoparticles bound to a chemotherapeutic agent. These particles are applied intra arterially close to the tumor region and focused to the tumor by a strong external magnetic field. The interaction of the particles with the field gradient leads to an accumulation in the region of interest (i.e. tumor). The particle enrichment and thereby the drug-load in the tumor during MDT has been proven by several analytical and imaging methods. Moreover, in pilot studies we investigated in an experimental in vivo tumor model the effectiveness of this approach. Complete tumor regressions without any negative side effects could be observed. - Research Highlights: →Iron oxide nanoparticles can be enriched in tumors by external magnetic fields. → Histology evidences the intravasation of particles enter the intracellular space. → Non-invasive imaging techniques can display the spatial arrangement of particles. → HPLC-analysis show outstanding drug enrichment in tumors after MDT.

  15. Cancer therapy with drug loaded magnetic nanoparticles-magnetic drug targeting

    Energy Technology Data Exchange (ETDEWEB)

    Alexiou, Christoph, E-mail: c.alexiou@web.d [Department of Oto-rhino-laryngology, Head and Neck Surgery, University Hospital Erlangen, Section for Experimental Oncology and Nanomedicine at the Else Kroener-Fresenius-Stiftung-Professorship (Germany); Tietze, Rainer; Schreiber, Eveline [Department of Oto-rhino-laryngology, Head and Neck Surgery, University Hospital Erlangen, Section for Experimental Oncology and Nanomedicine at the Else Kroener-Fresenius-Stiftung-Professorship (Germany); Jurgons, Roland [Franz Penzoldt Center, University Hospital Erlangen (Germany); Richter, Heike; Trahms, Lutz [PTB Berlin (Germany); Rahn, Helene; Odenbach, Stefan [TU Dresden, Chair of Magnetofluiddynamics, 01062 Dresden (Germany); Lyer, Stefan [Department of Oto-rhino-laryngology, Head and Neck Surgery, University Hospital Erlangen, Section for Experimental Oncology and Nanomedicine at the Else Kroener-Fresenius-Stiftung-Professorship (Germany)

    2011-05-15

    The aim of magnetic drug targeting (MDT) in cancer therapy is to concentrate chemotherapeutics to a tumor region while simultaneously the overall dose is reduced. This can be achieved with coated superparamagnetic nanoparticles bound to a chemotherapeutic agent. These particles are applied intra arterially close to the tumor region and focused to the tumor by a strong external magnetic field. The interaction of the particles with the field gradient leads to an accumulation in the region of interest (i.e. tumor). The particle enrichment and thereby the drug-load in the tumor during MDT has been proven by several analytical and imaging methods. Moreover, in pilot studies we investigated in an experimental in vivo tumor model the effectiveness of this approach. Complete tumor regressions without any negative side effects could be observed. - Research Highlights: Iron oxide nanoparticles can be enriched in tumors by external magnetic fields. Histology evidences the intravasation of particles enter the intracellular space. Non-invasive imaging techniques can display the spatial arrangement of particles. HPLC-analysis show outstanding drug enrichment in tumors after MDT.

  16. Quadruplex DNA: a promising drug target for the medicinal inorganic chemist.

    Science.gov (United States)

    Ralph, Stephen F

    2011-01-01

    Compounds that can bind to and stabilize quadruplex DNA structures in telomeres, or induce formation of such structures from ssDNA, represent an attractive general approach to the treatment of cancer. Until recently most effort in this area has been directed towards the synthesis of organic compounds for this purpose. More recently there has been growing recognition that metal complexes offer a number of potential advantages for the preparation of lead complexes that bind with high affinity and selectivity for quadruplex DNA. This review seeks to discuss the work that has been reported in this area to date. While most early studies focused on metal complexes of porphyrin ligands, during the past 4 years there has been a dramatic increase in the number of papers in the literature examining the potential of mononuclear complexes of a variety of other ligands, particularly Schiff base ligands and those based on phenanthroline, as quadruplex DNA binders and telomerase inhibitors. In addition, there has been growing interest in exploiting supramolecular chemistry to prepare novel multinuclear complexes that bind to this new drug target. PMID:21189126

  17. 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...

  18. Predictive Modeling of Addiction Lapses in a Mobile Health Application

    OpenAIRE

    Chih, Ming-Yuan; Patton, Timothy; McTavish, Fiona M.; Isham, Andrew; Judkins-Fisher, Chris L.; Atwood, Amy K.; Gustafson, David H.

    2013-01-01

    The chronically relapsing nature of alcoholism leads to substantial personal, family, and societal costs. Addiction-Comprehensive Health Enhancement Support System (A-CHESS) is a smartphone application that aims to reduce relapse. To offer targeted support to patients who are at risk of lapses within the coming week, a Bayesian network model to predict such events was constructed using responses on 2,934 weekly surveys (called the Weekly Check-in) from 152 alcohol-dependent individuals who re...

  19. 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.

  20. 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.

  1. 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.

  2. 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

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

    Science.gov (United States)

    Mdluli, Khisimuzi; Kaneko, Takushi; Upton, Anna

    2015-06-01

    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. PMID:25635061

  4. Structure of drug-target proteins determined by both X-ray and neutron diffraction

    International Nuclear Information System (INIS)

    Crystallography enables us to obtain accurate atomic positions within proteins. High resolution X-ray crystallography provides information for most of the atoms comprising a protein, with the exception of hydrogens. Neutron diffraction data can provide information of the location of hydrogen atoms to the structural information determined by X-ray crystallography. Here, we show the recent of the structural determination of drug-target proteins, porcine pancreatic elastase (PPE) and human immuno-deficiency virus type-1 protease (HIV-PR) by both X-ray and neutron diffraction. The structure of porcine pancreatic elastase with its potent inhibitor (FR13080) was determined to 0.94A resolution by X-ray diffraction and 1.75 A resolution by neutron diffraction. It was found that there are two characteristic hydrogen bonding interactions in which hydrogen atoms were confirmed. One is located between a catalytic aspartate and histidine, another is involved in the inhibitor recognition site. The structure of HIV-PR with its potent inhibitor (KNI-272) was also determined to 0.93 A resolution by X-ray diffraction and 2.3 A resolution by neutron diffraction. The ionization state of the catalytic residues were clarified to show that Asp125 is protonated and Asp25 is deprotonated. The ionization state and the location of hydrogen atoms of the catalytic residue in HIV-PR were firstly determined by neutron diffraction. Furthermore, collaborative use of both X-ray and neutron to identify the location of ambiguous hydrogen atoms will be shown. (author)

  5. Development of Drugs Targeting the PI3K Signalling Pathway in Leukaemias and Lymphomas

    Directory of Open Access Journals (Sweden)

    Alexandre Arcaro

    2015-03-01

    Full Text Available The phosphoinositide 3-kinase (PI3K family of signalling enzymes play a key role in the transduction of signals from activated cell surface receptors controlling cell growth and proliferation, survival, metabolism, and migration. The intracellular signalling pathway from activated receptors to PI3K and its downstream targets v-akt murine thymoma viral oncogene homolog (Akt and mechanistic target of rapamycin (mTOR is very frequently deregulated by genetic and epigenetic mechanisms in human cancer, including leukaemia and lymphoma. In the past decade, an arsenal of small molecule inhibitors of key enzymes in this pathway has been developed and evaluated in pre-clinical studies and clinical trials in cancer patients. These include pharmacological inhibitors of Akt, mTOR, and PI3K, some of which are approved for the treatment of leukaemia and lymphoma. The PI3K family comprises eight different catalytic isoforms in humans, which have been subdivided into three classes. Class I PI3K isoforms have been extensively studied in the context of human cancer, and the isoforms p110α and p110δ are validated drug targets. The recent approval of a p110δ-specific PI3K inhibitor (idelalisib/Zydelig® for the treatment of selected B cell malignancies represents the first success in developing these molecules into anti-cancer drugs. In addition to PI3K inhibitors, mTOR inhibitors are intensively studied in leukaemia and lymphoma, and temsirolimus (Torisel® is approved for the treatment of a type of lymphoma. Based on these promising results it is hoped that additional novel PI3K pathway inhibitors will in the near future be further developed into new drugs for leukaemia and lymphoma.

  6. Molecular and biochemical characterization of methionine aminopeptidase of Babesia bovis as a potent drug target.

    Science.gov (United States)

    Munkhjargal, Tserendorj; Ishizaki, Takahiro; Guswanto, Azirwan; Takemae, Hitoshi; Yokoyama, Naoaki; Igarashi, Ikuo

    2016-05-15

    Aminopeptidases are increasingly being investigated as therapeutic targets in various diseases. In this study, we cloned, expressed, and biochemically characterized a member of the methionine aminopeptidase (MAP) family from Babesia bovis (B. bovis) to develop a potential molecular drug target. Recombinant B. bovis MAP (rBvMAP) was expressed in Escherichia coli (E. coli) as a glutathione S-transferase (GST)-fusion protein, and we found that it was antigenic. An antiserum against the rBvMAP protein was generated in mice, and then a native B. bovis MAP was identified in B. bovis by Western blot assay. Further, an immunolocalization assay showed that MAP is present in the cytoplasm of the B. bovis merozoite. Analysis of the biochemical properties of rBvMAP revealed that it was enzymatically active, with optimum activity at pH 7.5. Enhanced enzymatic activity was observed in the presence of divalent manganese cations and was effectively inhibited by a metal chelator, ethylenediaminetetraacetic acid (EDTA). Moreover, the enzymatic activity of BvMAP was inhibited by amastatin and bestatin as inhibitors of MAP (MAPi) in a dose-dependent manner. Importantly, MAPi was also found to significantly inhibit the growth of Babesia parasites both in vitro and in vivo; additionally, they induced high levels of cytokines and immunoglobulin (IgG) titers in the host. Therefore, our results suggest that BvMAP is a molecular target of amastatin and bestatin, and those inhibitors may be drug candidates for the treatment of babesiosis, though more studies are required to confirm this. PMID:27084466

  7. 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

  8. Application Of Extreme Value Theory To Bursts Prediction

    Directory of Open Access Journals (Sweden)

    Abas bin Md Said

    2009-10-01

    Full Text Available Bursts and extreme events in quantities such as connection durations, file sizes, throughput, etc. may produce undesirable consequences in computer networks. Deterioration in the quality of service is a major consequence. Predicting these extreme events and burst is important. It helps in reserving the right resources for a better quality of service. We applied Extreme value theory (EVT to predict bursts in network traffic. We took a deeper look into the application of EVT by using EVT based Exploratory Data Analysis. We found that traffic is naturally divided into two categories, Internal and external traffic. The internal traffic follows generalized extreme value (GEV model with a negative shape parameter, which is also the same as Weibull distribution. The external traffic follows a GEV with positive shape parameter, which is Frechet distribution. These findings are of great value to the quality of service in data networks, especially when included in service level agreement as traffic descriptor parameters.

  9. Application of Adaptive Predictive Control to a Newborn Incubator

    Directory of Open Access Journals (Sweden)

    Med A. Zermani

    2011-01-01

    Full Text Available Problem statement: This study presents an application of Indirect Adaptive Generalized Predictive Control (IAGPC of an incubator for newborn, in order to improve the performance of temperature control. Approach: Analysis of physical phenomena of incubator was involved together knowledge of the dynamic behavior. Incubator was identified by means of Recursive Least Square (RLS technique associated with a projection of the model parameters for robust system identification. Results: Results showed that mathematical model of neonatal incubator predicted coincide with the measured data. A comparative study was made between ON-OFF, PID and IAGPC control in order to provide the performance of each strategy. Conclusion: Results had proved effectiveness of the IAGPC as a control of incubator system.

  10. 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.

  11. Stochastic Model Predictive Control with Applications in Smart Energy Systems

    DEFF Research Database (Denmark)

    Sokoler, Leo Emil; Edlund, Kristian; Mølbak, Tommy;

    2012-01-01

    cover more than 50% of the total consumption by 2050. Energy systems based on significant amounts of renewable energy sources are subject to uncertainties. To accommodate the need for model predictive control (MPC) of such systems, the effect of the stochastic effects on the constraints must be...... function). This is convenient for energy systems, since some constraints are very important to satisfy with a high probability, whereas violation of others are less prone to have a large economic penalty. In MPC applications the control action is obtained by solving an optimization problem at each sampling...... instant. To make the controller applicable in real-time efficient and reliable algorithms are required. If the uncertainty is assumed to be Gaussian, the optimization problems associated with chance constrained (linear) MPC can be expressed as second order cone programming (SOCP) problems. In this paper...

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

    Science.gov (United States)

    Park, Yoon-Dong; Sun, Wei; Salas, Antonio; Antia, Avan; Carvajal, Cindy; Wang, Amy; Xu, Xin; Meng, Zhaojin; Zhou, Ming; Tawa, Gregory J.; Dehdashti, Jean; Zheng, Wei; Henderson, Christina M.; Zelazny, Adrian M.

    2016-01-01

    ABSTRACT 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. PMID:27486194

  13. Rational design of urea-based glutamate carboxypeptidase II (GCPII) inhibitors as versatile tools for specific drug targeting and delivery

    Czech Academy of Sciences Publication Activity Database

    Tykvart, Jan; Schimer, Jiří; Bařinková, Jitka; Pachl, Petr; Poštová Slavětínská, Lenka; Majer, Pavel; Konvalinka, Jan; Šácha, Pavel

    2014-01-01

    Roč. 22, č. 15 (2014), s. 4099-4108. ISSN 0968-0896 R&D Projects: GA ČR GBP208/12/G016 Grant ostatní: OPPK(CZ) CZ.2.16/3.1.00/24016 Institutional support: RVO:61388963 Keywords : GCPII * PSMA * structure-aided drug design * specific drug targeting Subject RIV: CE - Biochemistry Impact factor: 2.793, year: 2014

  14. Predicting aquifer response time for application in catchment modeling.

    Science.gov (United States)

    Walker, Glen R; Gilfedder, Mat; Dawes, Warrick R; Rassam, David W

    2015-01-01

    It is well established that changes in catchment land use can lead to significant impacts on water resources. Where land-use changes increase evapotranspiration there is a resultant decrease in groundwater recharge, which in turn decreases groundwater discharge to streams. The response time of changes in groundwater discharge to a change in recharge is a key aspect of predicting impacts of land-use change on catchment water yield. Predicting these impacts across the large catchments relevant to water resource planning can require the estimation of groundwater response times from hundreds of aquifers. At this scale, detailed site-specific measured data are often absent, and available spatial data are limited. While numerical models can be applied, there is little advantage if there are no detailed data to parameterize them. Simple analytical methods are useful in this situation, as they allow the variability in groundwater response to be incorporated into catchment hydrological models, with minimal modeling overhead. This paper describes an analytical model which has been developed to capture some of the features of real, sloping aquifer systems. The derived groundwater response timescale can be used to parameterize a groundwater discharge function, allowing groundwater response to be predicted in relation to different broad catchment characteristics at a level of complexity which matches the available data. The results from the analytical model are compared to published field data and numerical model results, and provide an approach with broad application to inform water resource planning in other large, data-scarce catchments. PMID:24842053

  15. Predictive modeling of addiction lapses in a mobile health application.

    Science.gov (United States)

    Chih, Ming-Yuan; Patton, Timothy; McTavish, Fiona M; Isham, Andrew J; Judkins-Fisher, Chris L; Atwood, Amy K; Gustafson, David H

    2014-01-01

    The chronically relapsing nature of alcoholism leads to substantial personal, family, and societal costs. Addiction-comprehensive health enhancement support system (A-CHESS) is a smartphone application that aims to reduce relapse. To offer targeted support to patients who are at risk of lapses within the coming week, a Bayesian network model to predict such events was constructed using responses on 2,934 weekly surveys (called the Weekly Check-in) from 152 alcohol-dependent individuals who recently completed residential treatment. The Weekly Check-in is a self-monitoring service, provided in A-CHESS, to track patients' recovery progress. The model showed good predictability, with the area under receiver operating characteristic curve of 0.829 in the 10-fold cross-validation and 0.912 in the external validation. The sensitivity/specificity table assists the tradeoff decisions necessary to apply the model in practice. This study moves us closer to the goal of providing lapse prediction so that patients might receive more targeted and timely support. PMID:24035143

  16. 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.

  17. Recent advance in life prediction for HTGR applications

    International Nuclear Information System (INIS)

    Key issues in design methods at high temperatures for an HTGR regime are creep constitutive equations. The life in service of structural components is controlled by creep damage. A creep constitutive equation is then needed to calculate inelastic stress-strain components. The method for life prediction, applicable to this temperature regime, has been investigated. The ductility exhaustion rule in conjunction with the creep constitutive equation is confirmed to be useful from the point of view of methodology. Creep-fatigue damage for Hastelloy XRs was assessed by this method in conjunction with the Miner's rule. It is found that the ductility exhaustion for creep damage has a tendency to estimate creep damage larger than the time faction that is often used conventionally. Creep damage under compressive stress should be evaluated at high temperatures. (Author)

  18. Machine learning applications in cancer prognosis and prediction

    Directory of Open Access Journals (Sweden)

    Konstantina Kourou

    2015-01-01

    Full Text Available 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.

  19. Radon monitoring and its application for earthquake prediction

    International Nuclear Information System (INIS)

    Concentrations ofa wide range of terrestrial gases containing radionuclides like 222Rn (Radon), H2 (Hydrogen), Hg (Mercury), CO2 (Carbon dioxide) and He4(Helium) in ground water and soil air have commonly been found to be anomalously high along active faults, suggesting that these faults may be the path for least resistance for the out gassing processes of the solid earth. Among the naturally occurring radionucludes, the 238U decay series has received great attention in connection with the earthquake prediction and monitoring research all over the world. Due to its nearly ubiquitous occurrence, appreciable abundance, chemical inactivity and convenient half-life (3.823 d), 222Rn in the 238U series is the most extensively studied one in this regard. In this report, a brief account of the application of 222Rn monitoring carried out all over the world, studies carried out in India, modeling of earthquake predictions, measurement techniques, measuring equipments, its availability in India, Indian radon monitoring programme and its prospects are presented. (author)

  20. 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.

  1. Application of Machine Learning Algorithms for the Query Performance Prediction

    Directory of Open Access Journals (Sweden)

    MILICEVIC, M.

    2015-08-01

    Full Text Available This paper analyzes the relationship between the system load/throughput and the query response time in a real Online transaction processing (OLTP system environment. Although OLTP systems are characterized by short transactions, which normally entail high availability and consistent short response times, the need for operational reporting may jeopardize these objectives. We suggest a new approach to performance prediction for concurrent database workloads, based on the system state vector which consists of 36 attributes. There is no bias to the importance of certain attributes, but the machine learning methods are used to determine which attributes better describe the behavior of the particular database server and how to model that system. During the learning phase, the system's profile is created using multiple reference queries, which are selected to represent frequent business processes. The possibility of the accurate response time prediction may be a foundation for automated decision-making for database (DB query scheduling. Possible applications of the proposed method include adaptive resource allocation, quality of service (QoS management or real-time dynamic query scheduling (e.g. estimation of the optimal moment for a complex query execution.

  2. A highly accurate predictive-adaptive method for lithium-ion battery remaining discharge energy prediction in electric vehicle applications

    International Nuclear Information System (INIS)

    Highlights: • An energy prediction (EP) method is introduced for battery ERDE determination. • EP determines ERDE through coupled prediction of future states, parameters, and output. • The PAEP combines parameter adaptation and prediction to update model parameters. • The PAEP provides improved ERDE accuracy compared with DC and other EP methods. - Abstract: In order to estimate the remaining driving range (RDR) in electric vehicles, the remaining discharge energy (ERDE) of the applied battery system needs to be precisely predicted. Strongly affected by the load profiles, the available ERDE varies largely in real-world applications and requires specific determination. However, the commonly-used direct calculation (DC) method might result in certain energy prediction errors by relating the ERDE directly to the current state of charge (SOC). To enhance the ERDE accuracy, this paper presents a battery energy prediction (EP) method based on the predictive control theory, in which a coupled prediction of future battery state variation, battery model parameter change, and voltage response, is implemented on the ERDE prediction horizon, and the ERDE is subsequently accumulated and real-timely optimized. Three EP approaches with different model parameter updating routes are introduced, and the predictive-adaptive energy prediction (PAEP) method combining the real-time parameter identification and the future parameter prediction offers the best potential. Based on a large-format lithium-ion battery, the performance of different ERDE calculation methods is compared under various dynamic profiles. Results imply that the EP methods provide much better accuracy than the traditional DC method, and the PAEP could reduce the ERDE error by more than 90% and guarantee the relative energy prediction error under 2%, proving as a proper choice in online ERDE prediction. The correlation of SOC estimation and ERDE calculation is then discussed to illustrate the importance of an

  3. 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.

  4. Dynamical functional prediction and classification, with application to traffic flow prediction

    OpenAIRE

    Chiou, Jeng-Min

    2013-01-01

    Motivated by the need for accurate traffic flow prediction in transportation management, we propose a functional data method to analyze traffic flow patterns and predict future traffic flow. In this study we approach the problem by sampling traffic flow trajectories from a mixture of stochastic processes. The proposed functional mixture prediction approach combines functional prediction with probabilistic functional classification to take distinct traffic flow patterns into account. The proba...

  5. Predicting adverse side effects of drugs

    Directory of Open Access Journals (Sweden)

    Huang Liang-Chin

    2011-12-01

    Full Text Available Abstract Background Studies of toxicity and unintended side effects can lead to improved drug safety and efficacy. One promising form of study comes from molecular systems biology in the form of "systems pharmacology". Systems pharmacology combines data from clinical observation and molecular biology. This approach is new, however, and there are few examples of how it can practically predict adverse reactions (ADRs from an experimental drug with acceptable accuracy. Results We have developed a new and practical computational framework to accurately predict ADRs of trial drugs. We combine clinical observation data with drug target data, protein-protein interaction (PPI networks, and gene ontology (GO annotations. We use cardiotoxicity, one of the major causes for drug withdrawals, as a case study to demonstrate the power of the framework. Our results show that an in silico model built on this framework can achieve a satisfactory cardiotoxicity ADR prediction performance (median AUC = 0.771, Accuracy = 0.675, Sensitivity = 0.632, and Specificity = 0.789. Our results also demonstrate the significance of incorporating prior knowledge, including gene networks and gene annotations, to improve future ADR assessments. Conclusions Biomolecular network and gene annotation information can significantly improve the predictive accuracy of ADR of drugs under development. The use of PPI networks can increase prediction specificity and the use of GO annotations can increase prediction sensitivity. Using cardiotoxicity as an example, we are able to further identify cardiotoxicity-related proteins among drug target expanding PPI networks. The systems pharmacology approach that we developed in this study can be generally applicable to all future developmental drug ADR assessments and predictions.

  6. Echo state network prediction method and its application in flue gas turbine condition prediction

    Science.gov (United States)

    Wang, Shaohong; Chen, Tao; Xu, Xiaoli

    2010-12-01

    On the background of the complex production process of fluid catalytic cracking energy recovery system in large-scale petrochemical refineries, this paper introduced an improved echo state network (ESN) model prediction method which is used to address the condition trend prediction problem of the key power equipment--flue gas turbine. Singular value decomposition method was used to obtain the ESN output weight. Through selecting the appropriate parameters and discarding small singular value, this method overcame the defective solution problem in the prediction by using the linear regression algorithm, improved the prediction performance of echo state network, and gave the network prediction process. In order to solve the problem of noise contained in production data, the translation-invariant wavelet transform analysis method is combined to denoise the noisy time series before prediction. Condition trend prediction results show the effectiveness of the proposed method.

  7. Research and Application of the Beijing Road Traffic Prediction System

    OpenAIRE

    Ruimin Li; Hongliang Ma; Huapu Lu; Min Guo

    2014-01-01

    As an important part of the urban Advanced Traffic Management Systems (ATMS) and Advanced Traveler Information Systems (ATIS), short-term road traffic prediction system has received special attention in recent decades. The success of ATMS and ATIS technology deployment is heavily dependent on the availability of timely and accurate estimation or prediction of prevailing and emerging traffic conditions. We studied a real-time road traffic prediction system developed for Beijing based on variou...

  8. Application of artificial neural networks in critical heat flux prediction

    International Nuclear Information System (INIS)

    The critical heat flux (CHF) are predicted and its parametric trends are analyzed by apply in artificial neural networks (ANNs) to the CHF data base of upward flow water in uniformly heated vertical round tubes. The prediction and analysis are based on the local conditions hypothesis. Groeneveld's CHF Look-up Table is used to train the ANNs, and the trained ANN predicts the CHF better than any other conventional correlations method, with root-mean-square (RMS) error of 14%

  9. Pathogenesis of HIV-associated dementia and its potential drug targets%HIV相关痴呆的发病机制及药物治疗靶点

    Institute of Scientific and Technical Information of China (English)

    余小玲; 姜世勃; 刘叔文

    2011-01-01

    HIV - associated dementia ( HAD ) is a serious complication of AIDS patients. With the wide application of highly active antiretroviral therapy ( HAART ), the replication of HIV is under effective control and the incidence of HAD is also declined. However, a relatively mild HIV -associated symptom of dementia, called minor cognitively motor disorder ( MCMD ), become the problem and can not be neglected in the treatment of AIDS. The neural injury caused by HIV may be mediated mainly by macrophages, microglia and astrocytes, though we can not rule out the direct damage on neurons by HIV proteins. To date, the precise mechanism of neural damage caused by HIV remains unclear. The present review tries to figure out the recent progress of pathogenesis and potential drug targets for HAD.%@@ 随着艾滋病(acquired immune deficiency syndrome, AIDS) 的全球性暴发流行,HIV相关神经系统功能障碍,主要为HIV相关痴呆(HIV-associated dementia, HAD)和更加严重的HIV相关神经认知紊乱(HIV-associated neurocognitive disorders, HAND),逐渐被人们所认识并研究.

  10. 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

    2016-01-01

    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. PMID:27095146

  11. 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.

  12. 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.

  13. The cytoskeleton as a drug target for neuroprotection: the case of the autism- mutated ADNP.

    Science.gov (United States)

    Gozes, Illana

    2016-03-01

    Fifteen years ago we discovered activity-dependent neuroprotective protein (ADNP), and showed that it is essential for brain formation/function. Our protein interaction studies identified ADNP as a member of the chromatin remodeling complex, SWI/SNF also associated with alternative splicing of tau and prediction of tauopathy. Recently, we have identified cytoplasmic ADNP interactions with the autophagy regulating microtubule-associated protein 1 light chain 3 (LC3) and with microtubule end-binding (EB) proteins. The ADNP-EB-binding SIP domain is shared with the ADNP snippet drug candidate, NAPVSIPQ termed NAP (davunetide). Thus, we identified a precise target for ADNP/NAP (davunetide) neuroprotection toward improved drug development. PMID:25955282

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

    Science.gov (United States)

    Guo, Jing; Liu, Hui; Zheng, Jie

    2016-01-01

    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

  15. Applications of Pre-Geological Prediction in Tunnel Construction

    Institute of Scientific and Technical Information of China (English)

    2005-01-01

    Pre-geological prediction(PGP)is defined as the prediction of engineering geologic condition and hydrogeological condition certain distance ahead of the working face. The purpose of this paper is to introduce mainly geologic survey before and in excavation, to clarify their emphasis on PGP. At the same time, the technique is applied to an engineering case, the longest highway tunnel in Gansu province. Data of geological survey of outside tunnels, sound wave detection, and geologic sketch for both tunnel face and sidewalls within the tunnel are analyzed. After analyzing these data, long-term pre-geological prediction forecasting basic geological conditions of fault 4 such as lithology, scope, location, etc., and short-term and more accurate pre-geological prediction are reported.

  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 Neural Network in Prediction of Financial Viability

    OpenAIRE

    Roli Pradhan; K. K. Pathak; V.P. Singh

    2011-01-01

    Bankruptcy prediction is very important for all the organization since it affects the economy and causes a rise in many social problems with incremental high costs. There are large number of techniques that have been developed to predict the bankruptcy of firms, which helps the decision makers such as investors and financial analysts to plan in accordance to the financial position of the firm regarding the terms of credit as well as the recovery of the lent amount. The Altman Model for predic...

  18. Predicting Customer Potential Value: an application in the insurance industry

    OpenAIRE

    Verhoef, Peter; Donkers, Bas

    2001-01-01

    textabstractFor effective Customer Relationship Management (CRM), it is essential to have information on the potential value of customers. Based on the interplay between potential value and realized value, managers can devise customer specific strategies. In this article we introduce a model for predicting the potential value of a current customer. Furthermore, we discuss and apply different modeling strategies for predicting this potential value.

  19. Brain Emotional Learning-Based Prediction Model (For Long-Term Chaotic Prediction Applications)

    OpenAIRE

    Parsapoor, Mahboobeh

    2016-01-01

    This study suggests a new prediction model for chaotic time series inspired by the brain emotional learning of mammals. We describe the structure and function of this model, which is referred to as BELPM (Brain Emotional Learning-Based Prediction Model). Structurally, the model mimics the connection between the regions of the limbic system, and functionally it uses weighted k nearest neighbors to imitate the roles of those regions. The learning algorithm of BELPM is defined using steepest des...

  20. 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.

  1. RNA Structures as Mediators of Neurological Diseases and as Drug Targets.

    Science.gov (United States)

    Bernat, Viachaslau; Disney, Matthew D

    2015-07-01

    RNAs adopt diverse folded structures that are essential for function and thus play critical roles in cellular biology. A striking example of this is the ribosome, a complex, three-dimensionally folded macromolecular machine that orchestrates protein synthesis. Advances in RNA biochemistry, structural and molecular biology, and bioinformatics have revealed other non-coding RNAs whose functions are dictated by their structure. It is not surprising that aberrantly folded RNA structures contribute to disease. In this Review, we provide a brief introduction into RNA structural biology and then describe how RNA structures function in cells and cause or contribute to neurological disease. Finally, we highlight successful applications of rational design principles to provide chemical probes and lead compounds targeting structured RNAs. Based on several examples of well-characterized RNA-driven neurological disorders, we demonstrate how designed small molecules can facilitate the study of RNA dysfunction, elucidating previously unknown roles for RNA in disease, and provide lead therapeutics. PMID:26139368

  2. 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.

  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 Wavelet Random Coupling Model in Monthly Rainfall Prediction

    Institute of Scientific and Technical Information of China (English)

    DONG Lili; XU Shuqin; LIU Yang; WANG Yunhe

    2011-01-01

    A Trous algorithm of wavelet transform was used to decompose wavelet signal, and the cross-correlation analysis was used to analyze the sequence of each wavelet transform, and then the mathematical model correspond with wavelet transform sequence was established, finally wavelet random coupling model was obtained by wavelet reconstruction algorithm. Then, according to the rainfall data in crop growth period of Farm Chahayang from 1956 to 2008, the wavelet random coupling model was established to fit the model prediction test. The results showed that the prediction and fitting accuracy of the model was high, the model could reflect the rainfall variation regulation in the region, and it was a practical prediction model. It was very important for us to determine reasonably irrigation schedule and to use efficiency coefficient of precipitation resource.

  5. Predictive control and identification: Applications to steering dynamics

    DEFF Research Database (Denmark)

    Hansen, Anca Daniela

    1996-01-01

    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...... on the minimization of the mean squares prediction errors of the ship's deviation from the desired track. Chapter 5 is concerned with constrained predictive control. The presented algorithm, which is based on Rosen's gradient projection method, minimizes a multi-step quadratic loss function, taking...... results show that the proposed strategy leads to a significant better control than the ad-hoc control strategy. Chapter 6 gives a survey on the so-called forgetting factor methods designed for tracking slowly drifting system parameters. The goal of this cpapter is to formulate the identification framework...

  6. 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.

  7. Transdermal drug targeting and functional imaging of tumor blood vessels in the mouse auricle.

    Science.gov (United States)

    Schröder, Hannes; Komljenovic, Dorde; Hecker, Markus; Korff, Thomas

    2016-02-01

    Subcutaneously growing tumors are widely utilized to study tumor angiogenesis and the efficacy of antiangiogenic therapies in mice. To additionally assess functional and morphologic alterations of the vasculature in the periphery of a growing tumor, we exploited the easily accessible and hierarchically organized vasculature of the mouse auricle. By site-specific subcutaneous implantation of a defined preformed mouse B16/F0 melanoma aggregate, a solid tumor nodule developed within 14 d. Growth of the tumor nodule was accompanied by a 4-fold increase in its perfusion as well as a 2- to 4-fold elevated diameter and perfusion of peripheral blood vessels that had connected to the tumor capillary microvasculature. By transdermal application of the anticancer drug bortezomib, tumor growth was significantly diminished by about 50% without provoking side effects. Moreover, perfusion and tumor microvessel diameter as well as growth and perfusion of arterial or venous blood vessels supplying or draining the tumor microvasculature were decreased under these conditions by up to 80%. Collectively, we observed that the progressive tumor growth is accompanied by the enlargement of supplying and draining extratumoral blood vessels. This process was effectively suppressed by bortezomib, thereby restricting the perfusion capacity of both extra and intratumoral blood vessels. PMID:26546130

  8. Importance of polar solvation and configurational entropy for design of antiretroviral drugs targeting HIV-1 protease.

    Science.gov (United States)

    Kar, Parimal; Lipowsky, Reinhard; Knecht, Volker

    2013-05-16

    Both KNI-10033 and KNI-10075 are high affinity preclinical HIV-1 protease (PR) inhibitors with affinities in the picomolar range. In this work, the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) method has been used to investigate the potency of these two HIV-1 PR inhibitors against the wild-type and mutated proteases assuming that potency correlates with the affinity of the drugs for the target protein. The decomposition of the binding free energy reveals the origin of binding affinities or mutation-induced affinity changes. Our calculations indicate that the mutation I50V causes drug resistance against both inhibitors. On the other hand, we predict that the mutant I84V causes drug resistance against KNI-10075 while KNI-10033 is more potent against the I84V mutant compared to wild-type protease. Drug resistance arises mainly from unfavorable shifts in van der Waals interactions and configurational entropy. The latter indicates that neglecting changes in configurational entropy in the computation of relative binding affinities as often done is not appropriate in general. For the bound complex PR(I50V)-KNI-10075, an increased polar solvation free energy also contributes to the drug resistance. The importance of polar solvation free energies is revealed when interactions governing the binding of KNI-10033 or KNI-10075 to the wild-type protease are compared to the inhibitors darunavir or GRL-06579A. Although the contributions from intermolecular electrostatic and van der Waals interactions as well as the nonpolar component of the solvation free energy are more favorable for PR-KNI-10033 or PR-KNI-10075 compared to PR-DRV or PR-GRL-06579A, both KNI-10033 and KNI-10075 show a similar affinity as darunavir and a lower binding affinity relative to GRL-06579A. This is because of the polar solvation free energy which is less unfavorable for darunavir or GRL-06579A relative to KNI-10033 or KNI-10075. The importance of the polar solvation as revealed here

  9. 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.

  10. Application of Trend Impact Analysis for predicting future fruit consumption

    NARCIS (Netherlands)

    Hennen, W.H.G.J.; Benninga, J.

    2009-01-01

    Knowledge of those aspects that motivate consumers towards more fruit consumption is necessary in order to implement policies to stimulate fruit consumption. To predict future fruit consumption based on such knowledge from experts, and based on historic consumption data, the method of Trend Impact A

  11. Coated capsules for drug targeting to proximal and distal part of human intestine.

    Science.gov (United States)

    Dvorácková, Katerina; Rabisková, Miloslava; Gajdziok, Jan; Vetchý, David; Muselík, Jan; Bernatoniene, Jurga; Bajerová, Martina; Drottnerová, Pavlína

    2010-01-01

    Coated hard capsules are becoming increasingly important for a number of reasons such as administration of new active ingredients, oral vaccination, colon drug delivery or their use in preclinical and clinical trials. The independency of coating composition on capsules filling is the major advantage of this dosage form. In our study, two types of hard capsules (gelatin and hypromellose) were coated by non-aqueous solutions of Eudragit L and S 12.5, respectively, to achieve intestinal and distal ileic drug delivery. Gelatin hard capsules were coated with Eudragit film either directly or using hydroxypropyl cellulose sub-coating prior to the final coating. Hypromellose capsules were coated directly. Coated capsules were evaluated for coating thickness by optical microscope and for dissolution in different pH media. Gelatin capsules do not seem to be suitable for direct coating with Eudragit due to insufficient film adhesion to the smooth capsule surface and a brittleness of formed films. These problems can be solved by hydroxypropyl celullose interlayer application. Hypromellose hard capsules could be directly easily coated with both Eudragit solutions. Dissolution of caffeine from coated capsules showed the potency for enteric delivery in gelatin capsules with interlayer and Eudragit L film in 7.5 and 10.0% concentrations and in hypromellose capsules coated with EudragitL in 5-17.5% coating levels. Gelatine capsules with interlayer and 10% Eudragit S film and hypromellose capsules only with high coating level (20%) provided potential distal ileum targeting of incorporated drug. Eudragit S film sprayed onto hypromellose capsules surface was brittle especially in the junction zone between capsule cap and body. Better plasticity of Eudragit S coating could be probably achieved using a different plasticizer. PMID:20369797

  12. The Response Regulator BfmR Is a Potential Drug Target for Acinetobacter baumannii.

    Science.gov (United States)

    Russo, Thomas A; Manohar, Akshay; Beanan, Janet M; Olson, Ruth; MacDonald, Ulrike; Graham, Jessica; Umland, Timothy C

    2016-01-01

    Identification and validation is the first phase of target-based antimicrobial development. BfmR (RstA), a response regulator in a two-component signal transduction system (TCS) in Acinetobacter baumannii, is an intriguing potential antimicrobial target. A unique characteristic of BfmR is that its inhibition would have the dual benefit of significantly decreasing in vivo survival and increasing sensitivity to selected antimicrobials. Studies on the clinically relevant strain AB307-0294 have shown BfmR to be essential in vivo. Here, we demonstrate that this phenotype in strains AB307-0294 and AB908 is mediated, in part, by enabling growth in human ascites fluid and serum. Further, BfmR conferred resistance to complement-mediated bactericidal activity that was independent of capsular polysaccharide. Importantly, BfmR also increased resistance to the clinically important antimicrobials meropenem and colistin. BfmR was highly conserved among A. baumannii strains. The crystal structure of the receiver domain of BfmR was determined, lending insight into putative ligand binding sites. This enabled an in silico ligand binding analysis and a blind docking strategy to assess use as a potential druggable target. Predicted binding hot spots exist at the homodimer interface and the phosphorylation site. These data support pursuing the next step in the development process, which includes determining the degree of inhibition needed to impact growth/survival and the development a BfmR activity assay amenable to high-throughput screening for the identification of inhibitors. Such agents would represent a new class of antimicrobials active against A. baumannii which could be active against other Gram-negative bacilli that possess a TCS with shared homology. IMPORTANCE Increasing antibiotic resistance in bacteria, particularly Gram-negative bacilli, has significantly affected the ability of physicians to treat infections, with resultant increased morbidity, mortality, and health

  13. Comparative evaluation of novel biodegradable nanoparticles for the drug targeting to breast cancer cells.

    Science.gov (United States)

    Mattu, C; Pabari, R M; Boffito, M; Sartori, S; Ciardelli, G; Ramtoola, Z

    2013-11-01

    . Interestingly, PUR nps were superior to commercial polyester-based nps in terms of higher cellular internalisation and cytotoxic activity on the tested cell lines. Results obtained warrants further investigation on the application of these PUR nps for controlled drug delivery and targeting. PMID:23916461

  14. 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, 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. 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...

  15. Bloat free genetic programming: application to human oral bioavailability prediction.

    Science.gov (United States)

    Silva, Sara; Vanneschi, Leonardo

    2012-01-01

    Being able to predict the human oral bioavailability for a potential new drug is extremely important for the drug discovery process. This problem has been addressed by several prediction tools, with Genetic Programming providing some of the best results ever achieved. In this paper we use the newest developments of Genetic Programming, in particular the latest bloat control method, Operator Equalisation, to find out how much improvement we can achieve on this problem. We show examples of some actual solutions and discuss their quality, comparing them with previously published results. We identify some unexpected behaviours related to overfitting, and discuss the way for further improving the practical usage of the Genetic Programming approach. PMID:23356009

  16. RFI modeling and prediction approach for SATOPS applications

    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

    2015-05-01

    This paper describes innovative frameworks to develop RFI modeling and prediction models for (i) estimating the RFI characteristics, (ii) evaluating effectiveness of the existing Unified S-Band (USB) command waveforms employed by civil, commercial and military SATOPS ground stations, and (iii) predicting the impacts of RFI on USB command systems. The approach presented here will allow the communications designer to characterize both friendly and unfriendly RFI sources, and evaluate the impacts of RFI on civil, commercial and military USB SATOPS systems. In addition, the proposed frameworks allow the designer to estimate the optimum transmitted signal power to maintain a required USB SATOPS Quality-of-Service (QoS) in the presence of both friendly and unfriendly RFI sources.

  17. Real-time Traffic State Prediction: Modeling and Applications

    OpenAIRE

    Chen, Hao

    2014-01-01

    Travel-time information is essential in Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of the spatiotemporal evolution of roadway traffic state and travel time. From the perspective of travelers, such information can result in better traveler route choice and departure time decisions. From the transportation agency perspective, such data provide enhanced information with which to better manage a...

  18. Predict the emergence - Application to competencies in job offers

    OpenAIRE

    Abboud, Yacine; Boyer, Anne; Brun, Armelle

    2015-01-01

    International audience —Predicting the emergence of an event enables to anticipate and make decisions upstream. For instance, in the employment sector, it becomes necessary to anticipate the emergence of competencies requirements to help job seekers, education and training organization to better match the needs of the job market. Several approaches address the competencies mining with ontologies, we adopt a different point of view by using pattern mining. We propose a new methodology to pr...

  19. 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...

  20. APPLICATION OF MODEL PREDICTIVE CONTROL TO BATCH POLYMERIZATION REACTOR

    OpenAIRE

    N.M. Ghasem; Hussain, M. A.; S. A. Sata

    2006-01-01

    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 chara...

  1. Application of Linear Prediction Technique to Passive Synthetic Aperture Processing

    OpenAIRE

    Hou Yunshan; Huang Jianguo; Jiang Min; Jin Yong

    2010-01-01

    A method for the synthesis of an aperture with improved angular resolution and array gain is described. The proposed method explores the merit of linear prediction technique to improve the performance of conventional ETAM (extended towed array measurements) method. Previous efforts to improve the ETAM method generally focused on how to get more accurate estimation of overlap correlator, with an aim to reduce bearing estimation variance. In this paper, however, we discuss how to further impro...

  2. Development and application of a prediction model for dental caries.

    Science.gov (United States)

    Abernathy, J R; Graves, R C; Bohannan, H M; Stamm, J W; Greenberg, B G; Disney, J A

    1987-02-01

    The development and validation of a caries prediction model comprising 13 sociodemographic and dental examination variables on Grade 1 and Grade 5 children in the National Preventive Dentistry Demonstration Program are described. The objective was to derive a method of predicting children at high risk to caries early in order that preventive measures might be undertaken. True high risk children were defined in two ways: highest 25% of children based on their 4-yr DMFS increment, and their total DMFS score at the end of the study. In both cases, children predicted to be at high risk were defined as the 25% with the highest discriminant score. Discriminant function and logistic regression analyses were used to determine the extent to which the 13 variables collectively discriminated between true high risk and non-high risk children so defined. Sensitivity was approximately 0.50 and specificity around 0.82, using the 4-yr increment as the criterion for defining true high risk, and approximately 0.64 and 0.88, respectively, using the final DMFS score for defining true high risk. PMID:3467890

  3. 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.

  4. Studies of Retroviral Reverse Transcriptase and Flaviviral Protease Enzymes as Antiviral Drug Targets : Applications in Antiviral Drug Discovery & Therapy

    OpenAIRE

    Junaid, Muhammad

    2012-01-01

    Viruses are a major threat to humans due to their unique adaptability, evolvability and  capability to control their hosts as parasites and genetic elements. HIV/AIDS is the third largest cause of death by infectious diseases in the world, and drug resistance due to the viral mutations is still the leading cause of treatment failure. The flaviviruses, such as Dengue virus (DEN) and Japanese encephalitis virus (JEV), represent other major cause of morbidity and mortality, and the areas where t...

  5. 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

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

    Science.gov (United States)

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

    2016-03-01

    In this paper, we implement three scales of fracture integrated prediction study by classifying it to macro- (> 1/4 λ), meso- (> 1/100 λ and Sichuan basin, where limestone reservoir fractures developed, is implemented. The application results in the study area indicates that the proposed multi-scales integrated fracture prediction method and the technique procedureare able to deal with the strong heterogeneity and multi-scales problems in fracture prediction. Moreover, the multi-scale fracture prediction technique integrated with geology, well-logging and seismic multi-information can help improve the reservoir characterization and sweet-spots prediction for the fractured hydrocarbon reservoirs.

  7. 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

  8. Theoretical outdoor noise propagation models: Application to practical predictions

    Science.gov (United States)

    Tuominen, H. T.; Lahti, T.

    1982-02-01

    The theoretical calculation approaches for outdoor noise propagation are reviewed. Possibilities for their application to practical engineering calculations are outlined. A calculation procedure, which is a combination and extension of several theoretical models, is described. Calculation examples are compared with the results of some propagation studies.

  9. 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-predi...

  10. 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.

  11. 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.

  12. Prediction of three sigma maximum dispersed density for aerospace applications

    Science.gov (United States)

    Charles, Terri L.; Nitschke, Michael D.

    1993-01-01

    Free molecular heating (FMH) is caused by the transfer of energy during collisions between the upper atmosphere molecules and a space vehicle. The dispersed free molecular heating on a surface is an important constraint for space vehicle thermal analyses since it can be a significant source of heating. To reduce FMH to a spacecraft, the parking orbit is often designed to a higher altitude at the expense of payload capability. Dispersed FMH is a function of both space vehicle velocity and atmospheric density, however, the space vehicle velocity variations are insignificant when compared to the atmospheric density variations. The density of the upper atmosphere molecules is a function of altitude, but also varies with other environmental factors, such as solar activity, geomagnetic activity, location, and time. A method has been developed to predict three sigma maximum dispersed density for up to 15 years into the future. This method uses a state-of-the-art atmospheric density code, MSIS 86, along with 50 years of solar data, NASA and NOAA solar activity predictions for the next 15 years, and an Aerospace Corporation correlation to account for density code inaccuracies to generate dispersed maximum density ratios denoted as 'K-factors'. The calculated K-factors can be used on a mission unique basis to calculate dispersed density, and hence dispersed free molecular heating rates. These more accurate K-factors can allow lower parking orbit altitudes, resulting in increased payload capability.

  13. Application of α-track counting method in earthquake prediction

    International Nuclear Information System (INIS)

    The measurement of the radon concentration in the underground water provides a useful tool for earthquake prediction. So far, α-particles emitted by radon and its daughter nuclei have been detected with a liquid scintillator and a ZnS (Ag) scintillator. Recently, a solid state track detector has been used for measurement of α-particles instead of these scintillators. As the solid state track detector for detection of α-particle tracks, the LR115-type II plastic film is suitable for detection of α-particles, protons and fission fragments, because it has no sensitivity for light, X-rays, γ-rays and electrons. The plastic film exposed to α-particles emitted by Rn and its daughter nuclei is etched with the 10% NaOH (600C) for 150 minutes and α-particle tracks are analyzed with a optical macroscope. The radon concentration in the underwater and the underground were measured by this detection method. The preliminary experimental data were obtained and they suggest that this method is useful for the earth prediction. (Yoshimori, M.)

  14. Ephrin receptor A10 is a promising drug target potentially useful for breast cancers including triple negative breast cancers.

    Science.gov (United States)

    Nagano, Kazuya; Maeda, Yuka; Kanasaki, So-Ichiro; Watanabe, Takanobu; Yamashita, Takuya; Inoue, Masaki; Higashisaka, Kazuma; Yoshioka, Yasuo; Abe, Yasuhiro; Mukai, Yohei; Kamada, Haruhiko; Tsutsumi, Yasuo; Tsunoda, Shin-ichi

    2014-09-10

    Ephrin receptor A10 (EphA10) is a relatively uncharacterized protein which is expressed in many breast cancers but not expressed in normal breast tissues. Here, we examined the potential of EphA10 as a drug target in breast cancer. Immunohistochemical staining of clinical tissue sections revealed that EphA10 was expressed in various breast cancer subtypes, including triple negative breast cancers (TNBCs), with no expression observed in normal tissues apart from testis. Ligand-dependent proliferation was observed in EphA10-transfected MDA-MB-435 cells (MDA-MB-435(EphA10)) and native TNBC cells (MDA-MB-436). However, this phenomenon was not observed in parental MDA-MB-435 cells which express a low level of EphA10. Finally, tumor growth was significantly suppressed by administration of an anti-EphA10 monoclonal antibody in a xenograft mouse model. These results suggest that inhibition of EphA10 signaling may be a novel therapeutic option for management of breast cancer, including TNBCs which are currently not treated with molecularly targeted agents. PMID:24946238

  15. 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. PMID:21133071

  16. 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.

  17. 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. PMID:27561525

  18. 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.

  19. Genome-scale reconstruction of the Streptococcus pyogenes M49 metabolic network reveals growth requirements and indicates potential drug targets.

    Science.gov (United States)

    Levering, Jennifer; Fiedler, Tomas; Sieg, Antje; van Grinsven, Koen W A; Hering, Silvio; Veith, Nadine; Olivier, Brett G; Klett, Lara; Hugenholtz, Jeroen; Teusink, Bas; Kreikemeyer, Bernd; Kummer, Ursula

    2016-08-20

    Genome-scale metabolic models comprise stoichiometric relations between metabolites, as well as associations between genes and metabolic reactions and facilitate the analysis of metabolism. We computationally reconstructed the metabolic network of the lactic acid bacterium Streptococcus pyogenes M49. Initially, we based the reconstruction on genome annotations and already existing and curated metabolic networks of Bacillus subtilis, Escherichia coli, Lactobacillus plantarum and Lactococcus lactis. This initial draft was manually curated with the final reconstruction accounting for 480 genes associated with 576 reactions and 558 metabolites. In order to constrain the model further, we performed growth experiments of wild type and arcA deletion strains of S. pyogenes M49 in a chemically defined medium and calculated nutrient uptake and production fluxes. We additionally performed amino acid auxotrophy experiments to test the consistency of the model. The established genome-scale model can be used to understand the growth requirements of the human pathogen S. pyogenes and define optimal and suboptimal conditions, but also to describe differences and similarities between S. pyogenes and related lactic acid bacteria such as L. lactis in order to find strategies to reduce the growth of the pathogen and propose drug targets. PMID:26970054

  20. 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…

  1. An overview of the reliability prediction related aspects of high power IGBTs in wind power applications

    DEFF Research Database (Denmark)

    Busca, Christian; Teodorescu, Remus; Blaabjerg, Frede;

    2011-01-01

    high power Insulated Gate Bipolar Transistors (IGBTs) in the context of wind power applications. At first the latest developments and future predictions about wind energy are briefly discussed. Next the dominant failure mechanisms of high power IGBTs are described and the most commonly used lifetime...... prediction models are reviewed. Also the concept of Accelerated Life Testing (ALT) is briefly reviewed....

  2. 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.

  3. 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.

  4. 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...... 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......, consistently top-ranks native ß-topologies. Since the number of potential ß-topologies grows exponentially with the number of ß-strands, it is unrealistic to expect that all potential ß-topologies can be enumerated for large proteins. The second result of this paper is an enumeration scheme of a subset of ß...

  5. Application of multilayer perceptron for prediction of radionuclide migration from catchment area to watercourse

    International Nuclear Information System (INIS)

    In the thesis the results of verification of multilayer perceptron (MLP) {20–41–1} application with sigmoid activation function for prediction of lateral radionuclide migration are presented. The calculated values of Cs 137 and Sr 90 volumetric activity are close to experimental measurement limits, indicating the possibility of MLP application for the solving problem. (authors)

  6. 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.

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

    Science.gov (United States)

    Zheng, Fudan; Robertson, Alan P; Abongwa, Melanie; Yu, Edward W; Martin, Richard J

    2016-04-01

    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. PMID:27054065

  8. Prediction

    OpenAIRE

    Woollard, W.J.

    2006-01-01

    In this chapter we will look at the ways in which you can use ICT in the classroom to support hypothesis and prediction and how modern technology is enabling: pattern seeking, extrapolation and interpolation to meet the challenges of the information explosion of the 21st century.

  9. 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.

  10. Developing Prediction Equations and a Mobile Phone Application to Identify Infants at Risk of Obesity

    OpenAIRE

    Gillian Santorelli; Petherick, Emily S.; John Wright; Brad Wilson; Haider Samiei; Noël Cameron; William Johnson

    2013-01-01

    Background: Advancements in knowledge of obesity aetiology and mobile phone technology have created the opportunity to develop an electronic tool to predict an infant’s risk of childhood obesity. The study aims were to develop and validate equations for the prediction of childhood obesity and integrate them into a mobile phone application (App). Methods and Findings: Anthropometry and childhood obesity risk data were obtained for 1868 UK-born White or South Asian infants in the Born ...

  11. Application of GA–SVM method with parameter optimization for landslide development prediction

    OpenAIRE

    X. Z. LI; Kong, J. M.

    2014-01-01

    Prediction of the landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. The support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of the SVM model. In this study, we present an application of genetic algorithm and support vector machine (GA–...

  12. Structure-Bioactivity Relationship for Benzimidazole Thiophene Inhibitors of Polo-Like Kinase 1 (PLK1), a Potential Drug Target in Schistosoma mansoni

    OpenAIRE

    Long, Thavy; Neitz, R. Jeffrey; Beasley, Rachel; Kalyanaraman, Chakrapani; Suzuki, Brian M.; Jacobson, Matthew P.; Dissous, Colette; McKerrow, James H.; Drewry, David H.; Zuercher, William J; Singh, Rahul; Caffrey, Conor R

    2016-01-01

    Background 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. Methodology/Principal Findings We show that RNA interference (RNAi) of the Schistosom...

  13. in Silico analysis of Escherichia coli polyphosphate kinase (PPK) as a novel antimicrobial drug target and its high throughput virtual screening against PubChem library

    OpenAIRE

    Saha, Saurav Bhaskar; Verma, Vivek

    2013-01-01

    Multiple drug resistance (MDR) in bacteria is a global health challenge that needs urgent attention. The 2011 outbreak caused by Escherichia coli O104:H4 in Europe has exposed the inability of present antibiotic arsenal to tackle the problem of antimicrobial infections. It has further posed a tremendous burden on entire pharmaceutical industry to find novel drugs and/or drug targets. Polyphosphate kinase (PPK) in bacteria plays a crucial role in helping latter to adapt to stringent conditions...

  14. Novel Approach to Meta-Analysis of Microarray Datasets Reveals Muscle Remodeling-related Drug Targets and Biomarkers in Duchenne Muscular Dystrophy

    OpenAIRE

    Kotelnikova, Ekaterina; Shkrob, Maria A.; Pyatnitskiy, Mikhail A.; Ferlini, Alessandra; Daraselia, Nikolai

    2012-01-01

    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 comb...

  15. 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

  16. Interleukin (IL)-1A and IL-6: Applications to the predictive diagnostic testing of radiation pneumonitis

    International Nuclear Information System (INIS)

    Purpose: To explore the application of interleukin (IL)-1α and IL-6 measurements in the predictive diagnostic testing for symptomatic radiation pneumonitis (RP). Methods and materials: In a prospective protocol investigating RP and cytokines, IL-1α and IL-6 values were analyzed by enzyme-linked immunosorbent assay from serial weekly blood samples of patients receiving chest radiation. We analyzed sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) over selected threshold values for both cytokines in the application to diagnostic testing. Results: The average coefficient of variation was 51% of the weekly mean IL-1α level and 39% of the weekly mean IL-6 value. Interleukin 1α and IL-6 became positively correlated with time. Specificity for both cytokines was better than sensitivity. IL-6 globally outperformed IL-1α in predicting RP, with higher PPV and NPV. Conclusions: Our data demonstrate the feasibility of applying IL-1α and IL-6 measurements of blood specimens to predict RP. Interleukin-6 measurements offer stronger positive predictive value than IL-1α. This application might be further explored in a larger sample of patients

  17. Warped Linear Prediction of Physical Model Excitations with Applications in Audio Compression and Instrument Synthesis

    Directory of Open Access Journals (Sweden)

    Alexis Glass

    2004-06-01

    Full Text Available A sound recording of a plucked string instrument is encoded and resynthesized using two stages of prediction. In the first stage of prediction, a simple physical model of a plucked string is estimated and the instrument excitation is obtained. The second stage of prediction compensates for the simplicity of the model in the first stage by encoding either the instrument excitation or the model error using warped linear prediction. These two methods of compensation are compared with each other, and to the case of single-stage warped linear prediction, adjustments are introduced, and their applications to instrument synthesis and MPEG4's audio compression within the structured audio format are discussed.

  18. Embedded prediction in feature extraction: application to single-trial EEG discrimination.

    Science.gov (United States)

    Hsu, Wei-Yen

    2013-01-01

    In this study, an analysis system embedding neuron-fuzzy prediction in feature extraction is proposed for brain-computer interface (BCI) applications. Wavelet-fractal features combined with neuro-fuzzy predictions are applied for feature extraction in motor imagery (MI) discrimination. The features are extracted from the electroencephalography (EEG) signals recorded from participants performing left and right MI. Time-series predictions are performed by training 2 adaptive neuro-fuzzy inference systems (ANFIS) for respective left and right MI data. Features are then calculated from the difference in multi-resolution fractal feature vector (MFFV) between the predicted and actual signals through a window of EEG signals. Finally, the support vector machine is used for classification. The proposed method estimates its performance in comparison with the linear adaptive autoregressive (AAR) model and the AAR time-series prediction of 6 participants from 2 data sets. The results indicate that the proposed method is promising in MI classification. PMID:23248335

  19. Subsurface subsidence prediction model and its potential applications for longwall mining operations

    Institute of Scientific and Technical Information of China (English)

    QIU Biao; LUO Yi

    2011-01-01

    This paper summarizes the development of an enhanced influence function method to predict longwall mining induced subsurface subsidence.This model takes the stratifications of the overburden,particularly the massive hard rock (i.e.,limestone and sandstone) layers,into consideration.A new deformation term,total strain or void intensity,has been introduced and can be determined from the predicted subsurface movements.This term reflects the volumetric expansion of overburden rock under the influence of mine subsidence.A case study has demonstrated the applicability of the enhanced subsurface subsidence prediction model.

  20. 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...

  1. The landscape of host transcriptional response programs commonly perturbed by bacterial pathogens: towards host-oriented broad-spectrum drug targets.

    Directory of Open Access Journals (Sweden)

    Yared H Kidane

    Full Text Available BACKGROUND: The emergence of drug-resistant pathogen strains and new infectious agents pose major challenges to public health. A promising approach to combat these problems is to target the host's genes or proteins, especially to discover targets that are effective against multiple pathogens, i.e., host-oriented broad-spectrum (HOBS drug targets. An important first step in the discovery of such drug targets is the identification of host responses that are commonly perturbed by multiple pathogens. RESULTS: In this paper, we present a methodology to identify common host responses elicited by multiple pathogens. First, we identified host responses perturbed by each pathogen using a gene set enrichment analysis of publicly available genome-wide transcriptional datasets. Then, we used biclustering to identify groups of host pathways and biological processes that were perturbed only by a subset of the analyzed pathogens. Finally, we tested the enrichment of each bicluster in human genes that are known drug targets, on the basis of which we elicited putative HOBS targets for specific groups of bacterial pathogens. We identified 84 up-regulated and three down-regulated statistically significant biclusters. Each bicluster contained a group of pathogens that commonly dysregulated a group of biological processes. We validated our approach by checking whether these biclusters correspond to known hallmarks of bacterial infection. Indeed, these biclusters contained biological process such as inflammation, activation of dendritic cells, pro- and anti- apoptotic responses and other innate immune responses. Next, we identified biclusters containing pathogens that infected the same tissue. After a literature-based analysis of the drug targets contained in these biclusters, we suggested new uses of the drugs Anakinra, Etanercept, and Infliximab for gastrointestinal pathogens Yersinia enterocolitica, Helicobacter pylori kx2 strain, and enterohemorrhagic Escherichia

  2. Validity of the UKCAT in applicant selection and predicting exam performance in UK dental students.

    Science.gov (United States)

    Lala, Rizwana; Wood, Duncan; Baker, Sarah

    2013-09-01

    The United Kingdom's Clinical Aptitude Test (UKCAT) aims to assess candidates' "natural talent" for dentistry. The aim of this study was to determine the validity of the UKCAT for dental school applicant selection. The relationship of the UKCAT with demographic and academic variables was examined, assessing if the likelihood of being offered a place at a UK dental school was predicted by demographic factors and academic selection tools (predicted grades and existing school results). Finally, the validity of these selection tools in predicting first-year dental exam performance was assessed. Correlational and regression analyses showed that females and poorer students were more likely to have lower UKCAT scores. Gender and social class did not, however, predict first-year dental exam performance. UKCAT scores predicted the likelihood of the candidate being offered a place in the dental course; however, they did not predict exam performance during the first year of the course. Indeed, the only predictor of dental exam performance was existing school results. These findings argue against the use of the UKCAT as the sole determinant in dental applicant selection, instead highlighting the value of using existing school results. PMID:24002854

  3. 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.

  4. 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...

  5. 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.

  6. Application of Monte Carlo simulations to the prediction of the effective elastic moduli of hydrated Nafion

    Science.gov (United States)

    Weiland, Lisa Mauck; Lada, Emily K.; Smith, Ralph C.; Leo, Donald J.

    2005-05-01

    Application of Rotational Isomeric State (RIS) theory to the prediction of Young's modulus of a solvated ionomer is considered. RIS theory directly addresses polymer chain conformation as it relates to mechanical response trends. Successful adaptation of this methodology to the prediction of elastic moduli would thus provide a powerful tool for guiding ionomer fabrication. The Mark-Curro Monte Carlo methodology is applied to generate a statistically valid number of end-to-end chain lengths via RIS theory for a solvated Nafion case. The distribution of chain lengths is then fitted to a Probability Density Function by the Johnson Bounded distribution method. The fitting parameters, as they relate to the model predictions and physical structure of the polymer, are studied so that a means to extend RIS theory to the reliable prediction of ionomer stiffness may be identified.

  7. 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.

  8. Technical and economical evaluation of predictive methods applicable to equipment; Evaluacion tecnico-economica de metodos predictivos aplicados a equipos

    Energy Technology Data Exchange (ETDEWEB)

    Pagola, Guillermo Alejandro [TRANSENER S.A. - Companhia de Transporte de Energia Eletrica em Alta Tensao, Buenos Aires (Argentina)]. E-mail: pagolgui@transx.com.ar

    2001-07-01

    This document presents the accomplished evaluation analysis in relation to new real time predictive method applicable to circuit breakers through both economical evaluations and reliability calculations.

  9. Prediction of protein motions from amino acid sequence and its application to protein-protein interaction

    Directory of Open Access Journals (Sweden)

    Wako Hiroshi

    2010-07-01

    Full Text Available Abstract Background Structural flexibility is an important characteristic of proteins because it is often associated with their function. The movement of a polypeptide segment in a protein can be broken down into two types of motions: internal and external ones. The former is deformation of the segment itself, but the latter involves only rotational and translational motions as a rigid body. Normal Model Analysis (NMA can derive these two motions, but its application remains limited because it necessitates the gathering of complete structural information. Results In this work, we present a novel method for predicting two kinds of protein motions in ordered structures. The prediction uses only information from the amino acid sequence. We prepared a dataset of the internal and external motions of segments in many proteins by application of NMA. Subsequently, we analyzed the relation between thermal motion assessed from X-ray crystallographic B-factor and internal/external motions calculated by NMA. Results show that attributes of amino acids related to the internal motion have different features from those related to the B-factors, although those related to the external motion are correlated strongly with the B-factors. Next, we developed a method to predict internal and external motions from amino acid sequences based on the Random Forest algorithm. The proposed method uses information associated with adjacent amino acid residues and secondary structures predicted from the amino acid sequence. The proposed method exhibited moderate correlation between predicted internal and external motions with those calculated by NMA. It has the highest prediction accuracy compared to a naïve model and three published predictors. Conclusions Finally, we applied the proposed method predicting the internal motion to a set of 20 proteins that undergo large conformational change upon protein-protein interaction. Results show significant overlaps between the

  10. Pointing-Vector and Velocity Based Frequency Predicts for Deep-Space Uplink Array Applications

    Science.gov (United States)

    Tsao, P.; Vilnrotter, Victor A.; Jamnejad, V.

    2008-01-01

    Uplink array technology is currently being developed for NASA's Deep Space Network (DSN) to provide greater range and data throughput for future NASA missions, including manned missions to Mars and exploratory missions to the outer planets, the Kuiper belt, and beyond. Here we describe a novel technique for generating the frequency predicts that are used to compensate for relative Doppler, derived from interpolated earth position and spacecraft ephemerides. The method described here guarantees velocity and range estimates that are consistent with each other, hence one can always be recovered from the other. Experimental results have recently proven that these frequency predicts are accurate enough to maintain the phase of a three element array at the EPOXI spacecraft for three hours. Previous methods derive frequency predicts directly from interpolated relative velocities. However, these velocities were found to be inconsistent with the corresponding spacecraft range, meaning that range could not always be recovered accurately from the velocity predicts, and vice versa. Nevertheless, velocity-based predicts are also capable of maintaining uplink array phase calibration for extended periods, as demonstrated with the EPOXI spacecraft, however with these predicts important range and phase information may be lost. A comparison of the steering-vector method with velocity-based techniques for generating precise frequency predicts specifically for uplink array applications is provided in the following sections.

  11. 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.

  12. Operational Rainfall Prediction on Meso-γ Scales for Hydrologic Applications

    Science.gov (United States)

    Lee, Tim H.; Georgakakos, Konstantine P.

    1996-04-01

    Presented is a rainfall prediction methodology for application in operational hydrologic forecasting with forecast lead times of 1-6 hours and spatial-resolution scales of 10-30 km. The essential elements of the prediction methodology are a mathematical model for precipitation prediction from surface and upper air meteorological variables; operational forecasts of temperature, pressure, humidity, and wind fields by large-scale numerical weather prediction models; surface and upper air meteorological observations; remote and on-site rainfall observations; and a state estimator for real-time updating from local frequent rainfall observations and for probabilistic predictions. This paper formulates a class of rainfall models suitable for this prediction methodology. The models are based on the differential equation of conservation of cloud and rainwater equivalent mass and on a newly introduced advection equation for a parameter that determines updraft strength. The latter advection equation is a prognostic equation for the strength of convection in space and time. The innovative features of the model formulated and tested are the inclusion of the prognostic equation for the advection of regions of active convection, the formulation of the state estimator component for state updating and probabilistic forecasts, and the utilization of a numerical solution scheme which reduces artificial numerical diffusion and can be used with the state estimator because of its explicit form. Utilization of the prediction model formulated was exemplified in several case studies of summer convection in Oklahoma using data available during routine forecast operations. The case studies show that when verified with radar rainfall data, the model's hourly precipitation predictions over a 20,000 km2 area with a 100-900 km2 resolution are better than simple persistence and explain more than 60% of the observed hourly rainfall variance. Sensitivity studies quantify dependence of rainfall

  13. 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.

  14. 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.

  15. 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.

  16. Sequential Monte Carlo Traffic Estimation for Intelligent Transportation System: Motorway Travel Time Prediction Application

    OpenAIRE

    BEN-AISSA, A; Sau, J.; EL-FAOUZI, NE; DE MOUZON, O

    2006-01-01

    The aim of this work is the estimation and prediction of travel time based on a state equation modeling of the traffic dynamical system. The first step is the estimation and prediction of the state vector of the model, from which travel time, like any other relevant quantity, can be derived. Kalman filtering, which is the optimal solution for linear system with Gaussian noise, is not applicable here as our dynamic equation (derived from the well known LWR traffic model) is highly nonlinear. S...

  17. 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.

  18. Application Of Support Vector Machines To Global Prediction Of Nuclear Properties

    CERN Document Server

    Clark, J W; Clark, John W.; Li, Haochen

    2006-01-01

    Advances in statistical learning theory present the opportunity to develop statistical models of quantum many-body systems exhibiting remarkable predictive power. The potential of such ``theory-thin'' approaches is illustrated with the application of Support Vector Machines (SVMs) to global prediction of nuclear properties as functions of proton and neutron numbers $Z$ and $N$ across the nuclidic chart. Based on the principle of structural-risk minimization, SVMs learn from examples in the existing database of a given property $Y$, automatically and optimally identify a set of ``support vectors'' corresponding to representative nuclei in the training set, and approximate the mapping $(Z,N) \\to Y$ in terms of these nuclei. Results are reported for nuclear masses, beta-decay lifetimes, and spins/parities of nuclear ground states. These results indicate that SVM models can match or even surpass the predictive performance of the best conventional ``theory-thick'' global models based on nuclear phenomenology.

  19. 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.

  20. 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.

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

    International Nuclear Information System (INIS)

    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

  2. Modeling Page Views Across Multiple Websites with an Application to Internet Reach and Frequency Prediction

    OpenAIRE

    Peter J. Danaher

    2007-01-01

    In this study, we develop a multivariate generalization of the negative binomial distribution (NBD). This new model has potential application to situations where separate NBDs are correlated, such as for page views across multiple websites. In turn, our page view model is used to predict the audience for Internet advertising campaigns. For very large Internet advertising schedules, a simple approximation to the multivariate model is also derived. In a test of nearly 3,000 Internet advertising...

  3. Linear genetic programming application for successive-station monthly streamflow prediction

    Science.gov (United States)

    Danandeh Mehr, Ali; Kahya, Ercan; Yerdelen, Cahit

    2014-09-01

    In recent decades, artificial intelligence (AI) techniques have been pronounced as a branch of computer science to model wide range of hydrological phenomena. A number of researches have been still comparing these techniques in order to find more effective approaches in terms of accuracy and applicability. In this study, we examined the ability of linear genetic programming (LGP) technique to model successive-station monthly streamflow process, as an applied alternative for streamflow prediction. A comparative efficiency study between LGP and three different artificial neural network algorithms, namely feed forward back propagation (FFBP), generalized regression neural networks (GRNN), and radial basis function (RBF), has also been presented in this study. For this aim, firstly, we put forward six different successive-station monthly streamflow prediction scenarios subjected to training by LGP and FFBP using the field data recorded at two gauging stations on Çoruh River, Turkey. Based on Nash-Sutcliffe and root mean squared error measures, we then compared the efficiency of these techniques and selected the best prediction scenario. Eventually, GRNN and RBF algorithms were utilized to restructure the selected scenario and to compare with corresponding FFBP and LGP. Our results indicated the promising role of LGP for successive-station monthly streamflow prediction providing more accurate results than those of all the ANN algorithms. We found an explicit LGP-based expression evolved by only the basic arithmetic functions as the best prediction model for the river, which uses the records of the both target and upstream stations.

  4. 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.

  5. Advances in Searching for Anticoccidial Drugs Target%抗球虫药物靶标筛选的研究进展

    Institute of Scientific and Technical Information of China (English)

    周变华; 王宏伟; 赵振升

    2011-01-01

    At present,coccidiosis is still mainly controlled by the use of chemotherapeutic agents; thus. New drugs are urgently needed due to the rising problem of drug-resistant strains of Eimeria. To discover and select suitable drug target was the precondition of drug development as well as the key point of drug screening and oriented synthesis. This article reviews the progress in research of the drug target screening.%目前,药物防治仍然是防治球虫病的主要手段,随着抗球虫药物的使用,耐药性的产生越来越严重,迫切需要抗球虫新药的研发,发现和选择合适的药物靶点是药物开发的前提,也是药物筛选及药物定向合成的关键因素.文章就近年来抗球虫药物靶标筛选的研究进展作一简单回顾.

  6. AMP-activated protein kinase: an emerging drug target to regulate imbalances in lipid and carbohydrate metabolism to treat cardio-metabolic diseases.

    Science.gov (United States)

    Srivastava, Rai Ajit K; Pinkosky, Stephen L; Filippov, Sergey; Hanselman, Jeffrey C; Cramer, Clay T; Newton, Roger S

    2012-12-01

    The adenosine monophosphate-activated protein kinase (AMPK) is a metabolic sensor of energy metabolism at the cellular as well as whole-body level. It is activated by low energy status that triggers a switch from ATP-consuming anabolic pathways to ATP-producing catabolic pathways. AMPK is involved in a wide range of biological activities that normalizes lipid, glucose, and energy imbalances. These pathways are dysregulated in patients with metabolic syndrome (MetS), which represents a clustering of major cardiovascular risk factors including diabetes, lipid abnormalities, and energy imbalances. Clearly, there is an unmet medical need to find a molecule to treat alarming number of patients with MetS. AMPK, with multifaceted activities in various tissues, has emerged as an attractive drug target to manage lipid and glucose abnormalities and maintain energy homeostasis. A number of AMPK activators have been tested in preclinical models, but many of them have yet to reach to the clinic. This review focuses on the structure-function and role of AMPK in lipid, carbohydrate, and energy metabolism. The mode of action of AMPK activators, mechanism of anti-inflammatory activities, and preclinical and clinical findings as well as future prospects of AMPK as a drug target in treating cardio-metabolic disease are discussed. PMID:22798688

  7. Essential proteins and possible therapeutic targets of Wolbachia endosymbiont and development of FiloBase--a comprehensive drug target database for Lymphatic filariasis.

    Science.gov (United States)

    Sharma, Om Prakash; Kumar, Muthuvel Suresh

    2016-01-01

    Lymphatic filariasis (Lf) is one of the oldest and most debilitating tropical diseases. Millions of people are suffering from this prevalent disease. It is estimated to infect over 120 million people in at least 80 nations of the world through the tropical and subtropical regions. More than one billion people are in danger of getting affected with this life-threatening disease. Several studies were suggested its emerging limitations and resistance towards the available drugs and therapeutic targets for Lf. Therefore, better medicine and drug targets are in demand. We took an initiative to identify the essential proteins of Wolbachia endosymbiont of Brugia malayi, which are indispensable for their survival and non-homologous to human host proteins. In this current study, we have used proteome subtractive approach to screen the possible therapeutic targets for wBm. In addition, numerous literatures were mined in the hunt for potential drug targets, drugs, epitopes, crystal structures, and expressed sequence tag (EST) sequences for filarial causing nematodes. Data obtained from our study were presented in a user friendly database named FiloBase. We hope that information stored in this database may be used for further research and drug development process against filariasis. URL: http://filobase.bicpu.edu.in. PMID:26806463

  8. Essential proteins and possible therapeutic targets of Wolbachia endosymbiont and development of FiloBase-a comprehensive drug target database for Lymphatic filariasis

    Science.gov (United States)

    Sharma, Om Prakash; Kumar, Muthuvel Suresh

    2016-01-01

    Lymphatic filariasis (Lf) is one of the oldest and most debilitating tropical diseases. Millions of people are suffering from this prevalent disease. It is estimated to infect over 120 million people in at least 80 nations of the world through the tropical and subtropical regions. More than one billion people are in danger of getting affected with this life-threatening disease. Several studies were suggested its emerging limitations and resistance towards the available drugs and therapeutic targets for Lf. Therefore, better medicine and drug targets are in demand. We took an initiative to identify the essential proteins of Wolbachia endosymbiont of Brugia malayi, which are indispensable for their survival and non-homologous to human host proteins. In this current study, we have used proteome subtractive approach to screen the possible therapeutic targets for wBm. In addition, numerous literatures were mined in the hunt for potential drug targets, drugs, epitopes, crystal structures, and expressed sequence tag (EST) sequences for filarial causing nematodes. Data obtained from our study were presented in a user friendly database named FiloBase. We hope that information stored in this database may be used for further research and drug development process against filariasis. URL: http://filobase.bicpu.edu.in. PMID:26806463

  9. Applications of a Complimentary Modeling Framework to Improve Regional-Scale Groundwater Prediction

    Science.gov (United States)

    Valocchi, A. J.; Demissie, Y.

    2010-12-01

    Computational models of groundwater flow are important tools that help guide management policies and decisions. Modern inverse modeling techniques lead to improved model calibration and knowledge of parameter sensitivity and uncertainty. However, their effectiveness in real world groundwater model application is often limited because of the complexity and heterogeneity of natural subsurface systems as well as the insufficiency of representative measured data. Models are often used to make predictions to evaluate the impact of future scenarios or management policies quite different from the historical conditions that provided the data used for calibration. Models are normally calibrated to yield a good overall match (e.g., as measured by the least squares error criterion) to all the available data, while predictions often focus upon critical spatial locations with the largest impact upon social or hydro-ecological factors. We present a complementary modeling framework to improve the performance of inverse modeling by integrating a calibrated physically-based groundwater model with error-correcting data-driven models to handle the bias and uncertainties arising mainly from ignored or misrepresented processes in the groundwater model. The feasibility of adopting the framework is enhanced by advances in measurement technology and observation networks that are leading to increased amounts of hydrologic data. We have previously published an application of the framework to a hypothetical problem, showing promising results. We present application of the framework to two complex real-world case studies where calibrated MODFLOW models have been developed: the Spokane Valley Rathdrum Prairie and Republican River Compact Administration models. The MODFLOW and data-driven models are calibrated to a portion of the available data, and prediction accuracy is assessed using the remaining data. We find that in general the prediction accuracy of using the complementary model is

  10. Life Prediction/Reliability Data of Glass-Ceramic Material Determined for Radome Applications

    Science.gov (United States)

    Choi, Sung R.; Gyekenyesi, John P.

    2002-01-01

    Brittle materials, ceramics, are candidate materials for a variety of structural applications for a wide range of temperatures. However, the process of slow crack growth, occurring in any loading configuration, limits the service life of structural components. Therefore, it is important to accurately determine the slow crack growth parameters required for component life prediction using an appropriate test methodology. This test methodology also should be useful in determining the influence of component processing and composition variables on the slow crack growth behavior of newly developed or existing materials, thereby allowing the component processing and composition to be tailored and optimized to specific needs. Through the American Society for Testing and Materials (ASTM), the authors recently developed two test methods to determine the life prediction parameters of ceramics. The two test standards, ASTM 1368 for room temperature and ASTM C 1465 for elevated temperatures, were published in the 2001 Annual Book of ASTM Standards, Vol. 15.01. Briefly, the test method employs constant stress-rate (or dynamic fatigue) testing to determine flexural strengths as a function of the applied stress rate. The merit of this test method lies in its simplicity: strengths are measured in a routine manner in flexure at four or more applied stress rates with an appropriate number of test specimens at each applied stress rate. The slow crack growth parameters necessary for life prediction are then determined from a simple relationship between the strength and the applied stress rate. Extensive life prediction testing was conducted at the NASA Glenn Research Center using the developed ASTM C 1368 test method to determine the life prediction parameters of a glass-ceramic material that the Navy will use for radome applications.

  11. Artificial neural network application for predicting soil distribution coefficient of nickel

    International Nuclear Information System (INIS)

    The distribution (or partition) coefficient (Kd) is an applicable parameter for modeling contaminant and radionuclide transport as well as risk analysis. Selection of this parameter may cause significant error in predicting the impacts of contaminant migration or site-remediation options. In this regards, various models were presented to predict Kd values for different contaminants specially heavy metals and radionuclides. In this study, artificial neural network (ANN) is used to present simplified model for predicting Kd of nickel. The main objective is to develop a more accurate model with a minimal number of parameters, which can be determined experimentally or select by review of different studies. In addition, the effects of training as well as the type of the network are considered. The Kd values of Ni is strongly dependent on pH of the soil and mathematical relationships were presented between pH and Kd of nickel recently. In this study, the same database of these presented models was used to verify that neural network may be more useful tools for predicting of Kd. Two different types of ANN, multilayer perceptron and redial basis function, were used to investigate the effect of the network geometry on the results. In addition, each network was trained by 80 and 90% of the data and tested for 20 and 10% of the rest data. Then the results of the networks compared with the results of the mathematical models. Although the networks trained by 80 and 90% of the data the results show that all the networks predict with higher accuracy relative to mathematical models which were derived by 100% of data. More training of a network increases the accuracy of the network. Multilayer perceptron network used in this study predicts better than redial basis function network. - Highlights: ► Simplified models for predicting Kd of nickel presented using artificial neural networks. ► Multilayer perceptron and redial basis function used to predict Kd of nickel in soil.

  12. Copula based prediction models: an application to an aortic regurgitation study

    Directory of Open Access Journals (Sweden)

    Shoukri Mohamed M

    2007-06-01

    Full Text Available Abstract Background: An important issue in prediction modeling of multivariate data is the measure of dependence structure. The use of Pearson's correlation as a dependence measure has several pitfalls and hence application of regression prediction models based on this correlation may not be an appropriate methodology. As an alternative, a copula based methodology for prediction modeling and an algorithm to simulate data are proposed. Methods: The method consists of introducing copulas as an alternative to the correlation coefficient commonly used as a measure of dependence. An algorithm based on the marginal distributions of random variables is applied to construct the Archimedean copulas. Monte Carlo simulations are carried out to replicate datasets, estimate prediction model parameters and validate them using Lin's concordance measure. Results: We have carried out a correlation-based regression analysis on data from 20 patients aged 17–82 years on pre-operative and post-operative ejection fractions after surgery and estimated the prediction model: Post-operative ejection fraction = - 0.0658 + 0.8403 (Pre-operative ejection fraction; p = 0.0008; 95% confidence interval of the slope coefficient (0.3998, 1.2808. From the exploratory data analysis, it is noted that both the pre-operative and post-operative ejection fractions measurements have slight departures from symmetry and are skewed to the left. It is also noted that the measurements tend to be widely spread and have shorter tails compared to normal distribution. Therefore predictions made from the correlation-based model corresponding to the pre-operative ejection fraction measurements in the lower range may not be accurate. Further it is found that the best approximated marginal distributions of pre-operative and post-operative ejection fractions (using q-q plots are gamma distributions. The copula based prediction model is estimated as: Post -operative ejection fraction = - 0.0933 + 0

  13. Application of cyclic damage accumulation life prediction model to high temperature components

    Science.gov (United States)

    Nelson, Richard S.

    1989-01-01

    A high temperature, low cycle fatigue life prediction method was developed. This method, Cyclic Damage Accumulation (CDA), was developed for use in predicting the crack initiation lifetime of gas turbine engine materials, but it can be applied to other materials as well. The method is designed to account for the effects on creep-fatigue life of complex loading such as thermomechanical fatigue, hold periods, waveshapes, mean stresses, multiaxiality, cumulative damage, coatings, and environmental attack. Several features of this model were developed to make it practical for application to actual component analysis, such as the ability to handle nonisothermal loading (including TMF), arbitrary cycle paths, and multiple damage modes. The CDA life prediction model was derived from extensive specimen tests conducted on cast nickel-base superalloy B1900 + Hf. These included both monotonic tests (tensile and creep) and strain-controlled fatigue experiments (uniaxial, biaxial, TMF, mixed creep-fatigue, and controlled mean stress). Additional specimen tests were conducted on wrought INCO 718 to verify the applicability of the final CDA model to other high-temperature alloys. The model will be available to potential users in the near future in the form of a FORTRAN-77 computer program.

  14. 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.

  15. Application of discrete grey model in settlement prediction of high-speed railway

    Science.gov (United States)

    Nie, Guangyu; Wen, Hongyan; Gao, Hong; Yang, Zhi; Yang, Ming

    2015-12-01

    The GM (1,1) model uses a discrete form equation to estimate the parameters and employ a continuous form equation to fit the model and predict the data sequence. The jump between the two form of equation is the fundamental reason to causing the error of GM (1,1) model. This paper first introduces the theory of the Discrete Grey Model (DGM (1,1) model), the solving method of model parameter and the solving algorithm of simulation value and the predicted value. Then, a modified DGM (1,1) model is proposed after analyzing the problems of Discrete Grey Model exited in the practical application. Finally, some contrast experiments for high speed railway subgrade settlement prediction are carried on by applying the improved DGM (1,1) model, the GM(1,1) model and the DGM(1,1) model respectively. The experimental results show that the improved DGM (1,1) model could acquire better model accuracy and forecasting result in engineering application.

  16. Developing prediction equations and a mobile phone application to identify infants at risk of obesity.

    Directory of Open Access Journals (Sweden)

    Gillian Santorelli

    Full Text Available Advancements in knowledge of obesity aetiology and mobile phone technology have created the opportunity to develop an electronic tool to predict an infant's risk of childhood obesity. The study aims were to develop and validate equations for the prediction of childhood obesity and integrate them into a mobile phone application (App.Anthropometry and childhood obesity risk data were obtained for 1868 UK-born White or South Asian infants in the Born in Bradford cohort. Logistic regression was used to develop prediction equations (at 6 ± 1.5, 9 ± 1.5 and 12 ± 1.5 months for risk of childhood obesity (BMI at 2 years >91(st centile and weight gain from 0-2 years >1 centile band incorporating sex, birth weight, and weight gain as predictors. The discrimination accuracy of the equations was assessed by the area under the curve (AUC; internal validity by comparing area under the curve to those obtained in bootstrapped samples; and external validity by applying the equations to an external sample. An App was built to incorporate six final equations (two at each age, one of which included maternal BMI. The equations had good discrimination (AUCs 86-91%, with the addition of maternal BMI marginally improving prediction. The AUCs in the bootstrapped and external validation samples were similar to those obtained in the development sample. The App is user-friendly, requires a minimum amount of information, and provides a risk assessment of low, medium, or high accompanied by advice and website links to government recommendations.Prediction equations for risk of childhood obesity have been developed and incorporated into a novel App, thereby providing proof of concept that childhood obesity prediction research can be integrated with advancements in technology.

  17. 核酸适体(aptamer):一种具有潜力的肿瘤药物"靶向配基"%Aptamer:A potential antitumor drugs"targeted ligand"

    Institute of Scientific and Technical Information of China (English)

    郝兰; 袁耿彪; 王志刚

    2012-01-01

    核酸适体(aptamer)可描述为化学抗体,是用配体指数富集法系统进化(SELEX)技术筛选获得的单链DNA或RNA,借其自身形成的空间结构与靶标分子特异性识别,具有靶分子广、亲和力高、特异性强、易改造修饰等特点.本文简述核酸适体作为肿瘤药物"靶向配基"的应用研究.%The aptamer is described as chemical antibody- It is single DNA. or RNA. that can be isolated against by an iterative in vi1.ro process called systematic evolution of ligands by exponential enrichment (SELEX) and can identify target molecules specificity through its own space structure. They possess the molecular recognition properties in terms of their high affinity, strong specificity and easy modified. This paper describes the aptamer application research as an antitumor drug ''targeted ligands''.

  18. Application of a pattern recognition technique to the prediction of tire noise

    Science.gov (United States)

    Chiu, Jinn-Tong; Tu, Fu-Yuan

    2015-08-01

    Tire treads are one of the main sources of car noise. To meet the EU's tire noise regulation ECE-R117, a new method using a pattern recognition technique is adopted in this paper to predict noise from tire tread patterns, thus facilitating the design of low-noise tires. When tires come into contact with the road surface, air pumping may occur in the grooves of tire tread patterns. Using the image of a tread pattern, a matrix is constructed by setting the patterns of tire grooves and tread blocks. The length and width of the contact patch are multiplied by weight functions. The resulting sound pressure as a function of time is subjected to a Fourier transform to simulate a 1/3-octave-band sound pressure level. A particle swarm algorithm is adopted to optimize the weighting parameters for the sound pressure in the frequency domain so that simulated values approach the measured noise level. Two sets of optimal weighting parameters associated with the length and width of the contact patch are obtained. Finally, the weight function is used to predict the tread pattern noise of tires in the same series. A comparison of the prediction and experimental results reveals that, in the 1/3-octave band of frequency (800-2000 Hz), average errors in sound pressure are within 2.5 dB. The feasibility of the proposed application of the pattern recognition technique in predicting noise from tire treads is verified.

  19. 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.

  20. The application of cognitive models to the evaluation and prediction of human reliability

    International Nuclear Information System (INIS)

    The first section of the paper provides a brief overview of a number of important principles relevant to human reliability modeling that have emerged from cognitive models, and presents a synthesis of these approaches in the form of a Generic Error Modeling System (GEMS). The next section illustrates the application of GEMS to some well known nuclear power plant (NPP) incidents in which human error was a major contributor. The way in which design recommendations can emerge from analyses of this type is illustrated. The third section describes the use of cognitive models in the classification of human errors for prediction and data collection purposes. The final section addresses the predictive modeling of human error as part of human reliability assessment in Probabilistic Risk Assessment

  1. Predictions of vapor pressures of aqueous desiccants for cooling applications by using artificial neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Gandhidasan, P. [Mechanical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261 (Saudi Arabia)], E-mail: pgandhi@kfupm.edu.sa; Mohandes, Mohamed A. [Electrical Engineering Department, King Fahd University of Petroleum and Minerals, Dhahran 31261 (Saudi Arabia)

    2008-02-15

    This paper presents a new approach based on artificial neural networks (ANNs) to determine the vapor pressure of three widely used inorganic desiccant solutions, namely, calcium chloride, lithium chloride, and lithium bromide. The vapor pressure of liquid desiccants depends on temperature and concentration. Empirical expressions generally provide vapor pressure with limited accuracy. Further, the expressions currently in use are tedious and valid for narrow ranges and must be adjusted constantly. In this paper neural networks were trained to predict vapor pressure of desiccant solutions with a reasonable accuracy without mathematical formulae. Trained neural network models provided wide ranges of vapor pressure for desiccant solutions without the need to cross reference several tables or charts. Results showed potential of using ANNs for the prediction of vapor pressure of desiccant solution for cooling applications.

  2. Predictions of vapor pressures of aqueous desiccants for cooling applications by using artificial neural networks

    International Nuclear Information System (INIS)

    This paper presents a new approach based on artificial neural networks (ANNs) to determine the vapor pressure of three widely used inorganic desiccant solutions, namely, calcium chloride, lithium chloride, and lithium bromide. The vapor pressure of liquid desiccants depends on temperature and concentration. Empirical expressions generally provide vapor pressure with limited accuracy. Further, the expressions currently in use are tedious and valid for narrow ranges and must be adjusted constantly. In this paper neural networks were trained to predict vapor pressure of desiccant solutions with a reasonable accuracy without mathematical formulae. Trained neural network models provided wide ranges of vapor pressure for desiccant solutions without the need to cross reference several tables or charts. Results showed potential of using ANNs for the prediction of vapor pressure of desiccant solution for cooling applications

  3. 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.

  4. 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.

  5. Advanced state prediction of lithium-ion traction batteries in hybrid and battery electric vehicle applications

    Energy Technology Data Exchange (ETDEWEB)

    Jadidi, Yasser

    2011-07-01

    Automotive power trains with high energy efficiencies - particularly to be found in battery and hybrid electric vehicles - find increasing attention in the focus of reduction of exhaust emissions and increase of mileage. The underlying concept, the electrification of the power train, is subject to the traction battery and its battery management system since the capability of the battery permits and restricts electric propulsion. Consequently, the overall vehicle efficiency and in particular the operation strategy performance strongly depends on the quality of information about the battery. Besides battery technology, the key challenges are given by both the accurate prediction of battery behaviour and the electrochemical battery degradation that leads to power and capacity fade of the traction battery. This book provides the methodology for development of a battery state monitoring and prediction algorithm for application in a battery management system that accounts for the effects of electrochemical degradation. (orig.)

  6. 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.

  7. 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. PMID:27017041

  8. 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.

  9. Selecting molecular therapeutic drug targets based on the expression profiles of intrahepatic cholangiocarcinomas and miRNA-mRNA regulatory networks.

    Science.gov (United States)

    Sun, Boshi; Xie, Changming; Zheng, Tongsen; Yin, Dalong; Wang, Jiabei; Liang, Yingjian; Li, Yuejin; Yang, Guangchao; Shi, Huawen; Pei, Tiemin; Han, Jihua; Liu, Lianxin

    2016-01-01

    The incidence of intrahepatic cholangiocarcinoma (ICC) is increasing yearly, making it the second most common carcinoma after hepatocellular carcinoma among primary malignant liver tumors. Integrated miRNA and mRNA analysis is becoming more frequently used in antitumor ICC treatment. However, this approach generates vast amounts of data, which leads to difficulties performing comprehensive analyses to identify specific therapeutic drug targets. In this study, we provide an in-depth analysis of ICC function, identifying potential highly potent antitumor drugs for antitumor therapy. Two sets of whole genome expression profiles were obtained from the Gene Expression Omnibus (GEO) database. Using modular bioinformatic analysis, six core functional modules were identified for ICC. Based on a Fisher's test of the Cmap small molecule drug database, 65 drug components were identified that regulated the genes of these six core modules. Literature mining was then used to identify 15 new potential antitumor drugs. PMID:26498995

  10. 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.

  11. Novel drug target identification on UDP-Glucose 4-epimerase enzyme in Catharanthus roseus by insilico model

    Institute of Scientific and Technical Information of China (English)

    Ramachandran M; Elumalai EK

    2012-01-01

    Objective: To investigate the definite crystal structure of UDP-glucose 4-epimerase enzymes (EC 5.1.3.2) from Catharanthus roseus (C. roseus) for further research activities. Methods: The structure was modeled using homologous templates. The model validated under PROCHECK and WHAT-IF. Results: The model constructed using Modeller9v7 was validated. Moreover, 89% of residues lie in the most favored region. The model was checked for its grand average of hydropathicity and three binding sites were predicted using Molsoft ICM Pro v3.5. Conclusions:The model was suggested to be the good model. The constructed model can be used for further pharmacological studies and it can act as potential target against novel inhibitors.

  12. Prediction of druggable proteins using machine learning and systems biology: a mini-review

    Directory of Open Access Journals (Sweden)

    Gaurav eKandoi

    2015-12-01

    Full Text Available The emergence of -omics technologies has allowed the collection of vast amounts of data on biological systems. Although the pace of such collection has been exponential, the impact of these data remains small on many critical biomedical applications such as drug development. Limited resources, high costs and low hit-to-lead ratio have led researchers to search for more cost effective methodologies. A possible alternative is to incorporate computational methods of potential drug target prediction early during drug discovery workflow. Computational methods based on systems approaches have the advantage of taking into account the global properties of a molecule not limited to its sequence, structure or function. Machine learning techniques are powerful tools that can extract relevant information from massive and noisy data sets. In recent years the scientific community has explored the combined power of these fields to propose increasingly accurate and low cost methods to propose interesting drug targets. In this mini-review, we describe promising approaches based on the simultaneous use of systems biology and machine learning to access gene and protein druggability. Moreover, we discuss the state-of-the-art of this emerging and interdisciplinary field, discussing data sources, algorithms and the performance of the different methodologies. Finally, we indicate interesting avenues of research and some remaining open challenges.

  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. Ampicillin/penicillin-binding protein interactions as a model drug-target system to optimize affinity pull-down and mass spectrometric strategies for target and pathway identification.

    Science.gov (United States)

    von Rechenberg, Moritz; Blake, Brian Kelly; Ho, Yew-Seng J; Zhen, Yuejun; Chepanoske, Cindy Lou; Richardson, Bonnie E; Xu, Nafei; Kery, Vladimir

    2005-05-01

    The identification and validation of the targets of active compounds identified in cell-based assays is an important step in preclinical drug development. New analytical approaches that combine drug affinity pull-down assays with mass spectrometry (MS) could lead to the identification of new targets and druggable pathways. In this work, we investigate a drug-target system consisting of ampicillin- and penicillin-binding proteins (PBPs) to evaluate and compare different amino-reactive resins for the immobilization of the affinity compound and mass spectrometric methods to identify proteins from drug affinity pull-down assays. First, ampicillin was immobilized onto various amino-reactive resins, which were compared in the ampicillin-PBP model with respect to their nonspecific binding of proteins from an Escherichia coli membrane extract. Dynal M-270 magnetic beads were chosen to further study the system as a model for capturing and identifying the targets of ampicillin, PBPs that were specifically and covalently bound to the immobilized ampicillin. The PBPs were identified, after in situ digestion of proteins bound to ampicillin directly on the beads, by using either one-dimensional (1-D) or two-dimensional (2-D) liquid chromatography (LC) separation techniques followed by tandem mass spectrometry (MS/MS) analysis. Alternatively, an elution with N-lauroylsarcosine (sarcosyl) from the ampicillin beads followed by in situ digestion and 2-D LC-MS/MS analysis identified proteins potentially interacting noncovalently with the PBPs or the ampicillin. The in situ approach required only little time, resources, and sample for the analysis. The combination of drug affinity pull-down assays with in situ digestion and 2-D LC-MS/MS analysis is a useful tool in obtaining complex information about a primary drug target as well as its protein interactors. PMID:15761956

  15. 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.

  16. Application of Data Mining Techniques in Weather Prediction and Climate Change Studies

    Directory of Open Access Journals (Sweden)

    Folorunsho Olaiya

    2012-02-01

    Full Text Available Weather forecasting is a vital application in meteorology and has been one of the most scientifically and technologically challenging problems around the world in the last century. In this paper, we investigate the use of data mining techniques in forecasting maximum temperature, rainfall, evaporation and wind speed. This was carried out using Artificial Neural Network and Decision Tree algorithms and meteorological data collected between 2000 and 2009 from the city of Ibadan, Nigeria. A data model for the meteorological data was developed and this was used to train the classifier algorithms. The performances of these algorithms were compared using standard performance metrics, and the algorithm which gave the best results used to generate classification rules for the mean weather variables. A predictive Neural Network model was also developed for the weather prediction program and the results compared with actual weather data for the predicted periods. The results show that given enough case data, Data Mining techniques can be used for weather forecasting and climate change studies.

  17. A MITgcm/DART Ocean Analysis and Prediction System with Application to the Gulf of Mexico

    Science.gov (United States)

    Hoteit, I.; Hoar, T.; Collins, N.; Anderson, J.; Cornuelle, B.; Heimbach, P.

    2008-12-01

    The ECCO system is a new generation of ocean assimilation systems based on the Massachusetts Institute of Technology general circulation model (MITgcm) and its adjoint. The system has been used to produce the first global 1° ocean state estimates. It is now also used for regional and coastal MITgcm applications. To improve the predictive capabilities of the ECCO system, the Data Assimilation Research Testbed (DART), which is an ensemble Kalman filter (EnKF)-based data assimilation package, has been recently integrated to the ECCO system. DART is a software facility employing different EnKFs and advanced inflation/localization schemes. It has been developed at the National Center of Atmospheric Research (NCAR) and is now used for different operational weather forecasting problems. This contribution describes the integration of DART and the MITgcm, and discusses how this ensemble-based system can complement the existing adjoint-based assimilation system. An example of a 1/10° MITgcm/DART application for predicting the evolution of the loop current in the Gulf of Mexico is presented.

  18. Application of flow-induced vibration predictive techniques to operating steam generators

    International Nuclear Information System (INIS)

    Analytical techniques for flow-induced vibration (FIV), such as those incorporated in available design tools, are routinely applied to process equipment at the initial design stage. Unfortunately, this does not always apply to the situation when problems, related to FIV, develop in crucial operating equipment, since design uses conservative methods, whereas in-service applications require more realistic assessments. Usually these problems appear in the form of severe through wall fret flaws or fatigue cracks that compromise the integrity of the tubes and possibly the complete unit. It is here where a somewhat different approach must be taken in the evaluation of tube response to FIV. Tube damage from fretting wear or fatigue crack growth must be estimated from actual in situ operating conditions. In this paper, an overview of the predictive methods used in the development and/or qualification of remedial measures for problems that occur in operating process equipment along with applications are described. The steps in the evaluation procedure, from the prediction of flow regimes, the development of the nonlinear computer models and associated fluid forcing functions through to the estimates of tube damage in operating heat exchangers and steam generators are presented. A probabilistic (i.e. Monte Carlo simulation) FIV approach that readily accommodates uncertainties associated with damage predictions is summarized. The efficacy of this approach comes from the fact that probabilistic methods facilitate the incorporation of field data, and that a large number of tubes and possible variations in geometry, process and support conditions, usually present in such equipment, can be addressed effectively. (author)

  19. A prediction tool for real-time application in the disruption protection system at JET

    International Nuclear Information System (INIS)

    A disruption prediction system, based on neural networks, is presented in this paper. The system is ideally suitable for on-line application in the disruption avoidance and/or mitigation scheme at the JET tokamak. A multi-layer perceptron (MLP) predictor module has been trained on nine plasma diagnostic signals extracted from 86 disruptive pulses, selected from four years of JET experiments in the pulse range 47830-57346 (from 1999 to 2002). The disruption class of the disruptive pulses is available. In particular, the selected pulses belong to four classes (density limit/high radiated power, internal transport barrier, mode lock and h-mode/l-mode). A self-organizing map has been used to select the samples of the pulses to train the MLP predictor module and to determine its target, increasing the prediction capability of the system. The prediction performance has been tested over 86 disruptive and 102 non-disruptive pulses. The test has been performed presenting to the network all the samples of each pulse sampled every 20 ms. The missed alarm rate and the false alarm rate of the predictor, up to 100 ms prior to the disruption time, are 23% and 1%, respectively. Recent plasma configurations might present features different from those observed in the experiments used in the training set. This 'novelty' can lead to incorrect behaviour of the predictor. To improve the robustness and reliability of the system, a novelty detection module has been integrated in the prediction system, increasing the system performance and resulting in a missed alarm rate reduced to 7% and a false alarm rate reduced to 0%

  20. Chemical Genetic Analysis and Functional Characterization of Staphylococcal Wall Teichoic Acid 2-Epimerases Reveals Unconventional Antibiotic Drug Targets

    Science.gov (United States)

    Mann, Paul A.; Müller, Anna; Wolff, Kerstin A.; Fischmann, Thierry; Wang, Hao; Reed, Patricia; Hou, Yan; Li, Wenjin; Müller, Christa E.; Xiao, Jianying; Murgolo, Nicholas; Sher, Xinwei; Mayhood, Todd; Sheth, Payal R.; Mirza, Asra; Labroli, Marc; Xiao, Li; McCoy, Mark; Gill, Charles J.; Pinho, Mariana G.; Schneider, Tanja; Roemer, Terry

    2016-01-01

    Here we describe a chemical biology strategy performed in Staphylococcus aureus and Staphylococcus epidermidis to identify MnaA, a 2-epimerase that we demonstrate interconverts UDP-GlcNAc and UDP-ManNAc to modulate substrate levels of TarO and TarA wall teichoic acid (WTA) biosynthesis enzymes. Genetic inactivation of mnaA results in complete loss of WTA and dramatic in vitro β-lactam hypersensitivity in methicillin-resistant S. aureus (MRSA) and S. epidermidis (MRSE). Likewise, the β-lactam antibiotic imipenem exhibits restored bactericidal activity against mnaA mutants in vitro and concomitant efficacy against 2-epimerase defective strains in a mouse thigh model of MRSA and MRSE infection. Interestingly, whereas MnaA serves as the sole 2-epimerase required for WTA biosynthesis in S. epidermidis, MnaA and Cap5P provide compensatory WTA functional roles in S. aureus. We also demonstrate that MnaA and other enzymes of WTA biosynthesis are required for biofilm formation in MRSA and MRSE. We further determine the 1.9Å crystal structure of S. aureus MnaA and identify critical residues for enzymatic dimerization, stability, and substrate binding. Finally, the natural product antibiotic tunicamycin is shown to physically bind MnaA and Cap5P and inhibit 2-epimerase activity, demonstrating that it inhibits a previously unanticipated step in WTA biosynthesis. In summary, MnaA serves as a new Staphylococcal antibiotic target with cognate inhibitors predicted to possess dual therapeutic benefit: as combination agents to restore β-lactam efficacy against MRSA and MRSE and as non-bioactive prophylactic agents to prevent Staphylococcal biofilm formation. PMID:27144276

  1. Comparative Proteomic Analysis of Aminoglycosides Resistant and Susceptible Mycobacterium tuberculosis Clinical Isolates for Exploring Potential Drug Targets.

    Directory of Open Access Journals (Sweden)

    Divakar Sharma

    Full Text Available 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.

  2. 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. PMID:26436944

  3. 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

    diagnosis of Alzheimer's disease. Methods: Baseline data from 140 patients with mild cognitive impairment were selected from the Alzheimer's Disease Neuroimaging Study. Three clinical raters classified patients into 6 categories of confidence in the prediction of early Alzheimer's disease, in 4 phases of...

  4. Survey of prediction capabilities of three nuclear data libraries for a PWR application

    International Nuclear Information System (INIS)

    Highlights: • State of uncertainty quantification of three NDLs for a PWR application. • Identification of several order-of-magnitude differences in important contributors. • Impact of cross-material correlations. - Abstract: We survey prediction capabilities of ENDF/B-VII.1, JEFF-3.2 and JENDL-4.0u nuclear data libraries (NDLs) for the application of generating two-group homogenized assembly constants for a steady state diffusion model in the context of UAM-LWR (Uncertainty Analysis in Best-Estimate Modeling for Design, Operation and Safety Analysis of LWRs) Benchmark. We consider two different fuel assembly test cases representing a PWR. State of uncertainty quantification in each NDLs is presented for the application. We expect small differences between the NDLs due to the use of expert judgment in the evaluation processes, and identify several order-of-magnitude differences between the NDLs for significant contributors to uncertainty. We also quantify the contribution from cross-material correlations to the uncertainties

  5. 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...

  6. Traffic Flow Prediction Using MI Algorithm and Considering Noisy and Data Loss Conditions: An Application to Minnesota Traffic Flow Prediction

    OpenAIRE

    Hosseini, Seyed Hadi; Moshiri, Behzad; Rahimi-Kian, Ashkan; Nadjar Araabi, Babak

    2014-01-01

    Traffic flow forecasting is useful for controlling traffic flow, traffic lights, and travel times. This study uses a multi-layer perceptron neural network and the mutual information (MI) technique to forecast traffic flow and compares the prediction results with conventional traffic flow forecasting methods. The MI method is used to calculate the interdependency of historical traffic data and future traffic flow. In numerical case studies, the proposed traffic flow forecasting method was test...

  7. Optimal Design of Low-Density SNP Arrays for Genomic Prediction: Algorithm and Applications.

    Science.gov (United States)

    Wu, Xiao-Lin; Xu, Jiaqi; Feng, Guofei; Wiggans, George R; Taylor, Jeremy F; He, Jun; Qian, Changsong; Qiu, Jiansheng; Simpson, Barry; Walker, Jeremy; Bauck, Stewart

    2016-01-01

    utility of this MOLO algorithm was also demonstrated in a real application, in which a 6K SNP panel was optimized conditional on 5,260 obligatory SNP selected based on SNP-trait association in U.S. Holstein animals. With this MOLO algorithm, both imputation error rate and genomic prediction error rate were minimal. PMID:27583971

  8. 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

  9. Applying predictive analytics to develop an intelligent risk detection application for healthcare contexts.

    Science.gov (United States)

    Moghimi, Fatemeh Hoda; Cheung, Michael; Wickramasinghe, Nilmini

    2013-01-01

    Healthcare is an information rich industry where successful outcomes require the processing of multi-spectral data and sound decision making. The exponential growth of data and big data issues coupled with a rapid increase of service demands in healthcare contexts today, requires a robust framework enabled by IT (information technology) solutions as well as real-time service handling in order to ensure superior decision making and successful healthcare outcomes. Such a context is appropriate for the application of real time intelligent risk detection decision support systems using predictive analytic techniques such as data mining. To illustrate the power and potential of data science technologies in healthcare decision making scenarios, the use of an intelligent risk detection (IRD) model is proffered for the context of Congenital Heart Disease (CHD) in children, an area which requires complex high risk decisions that need to be made expeditiously and accurately in order to ensure successful healthcare outcomes. PMID:23920700

  10. Application of GIS based data driven evidential belief function model to predict groundwater potential zonation

    Science.gov (United States)

    Nampak, Haleh; Pradhan, Biswajeet; Manap, Mohammad Abd

    2014-05-01

    The objective of this paper is to exploit potential application of an evidential belief function (EBF) model for spatial prediction of groundwater productivity at Langat basin area, Malaysia using geographic information system (GIS) technique. About 125 groundwater yield data were collected from well locations. Subsequently, the groundwater yield was divided into high (⩾11 m3/h) and low yields (validation purpose. To perform cross validation, the frequency ratio (FR) approach was applied into remaining groundwater wells with low yield to show the spatial correlation between the low potential zones of groundwater productivity. A total of twelve groundwater conditioning factors that affect the storage of groundwater occurrences were derived from various data sources such as satellite based imagery, topographic maps and associated database. Those twelve groundwater conditioning factors are elevation, slope, curvature, stream power index (SPI), topographic wetness index (TWI), drainage density, lithology, lineament density, land use, normalized difference vegetation index (NDVI), soil and rainfall. Subsequently, the Dempster-Shafer theory of evidence model was applied to prepare the groundwater potential map. Finally, the result of groundwater potential map derived from belief map was validated using testing data. Furthermore, to compare the performance of the EBF result, logistic regression model was applied. The success-rate and prediction-rate curves were computed to estimate the efficiency of the employed EBF model compared to LR method. The validation results demonstrated that the success-rate for EBF and LR methods were 83% and 82% respectively. The area under the curve for prediction-rate of EBF and LR methods were calculated 78% and 72% respectively. The outputs achieved from the current research proved the efficiency of EBF in groundwater potential mapping.

  11. Prediction of noise in ships by the application of “statistical energy analysis.”

    DEFF Research Database (Denmark)

    Jensen, John Ødegaard

    1979-01-01

    If it will be possible effectively to reduce the noise level in the accomodation on board ships, by introducing appropriate noise abatement measures already at an early design stage, it is quite essential that sufficiently accurate prediction methods are available for the naval architects. In gen......, partly through a hull section consisting of several stiffened plate sections. The results of the SEA calculations are compared with corresponding results of vibration measurements on the structures. ©1979 Acoustical Society of America...... special noise abatement measure, e.g., increased structural damping. The paper discusses whether it might be possible to derive an alternative calculation model based on the “statistical energy analysis” approach (SEA). By considering the hull of a ship to be constructed from plate elements connected by...... combination of L junctions, T junctions, and cross junctions, a SEA-calculation model has been derived. Examples on application of the SEA model for prediction of the structure-borne sound transmission are given, partly through simple two-element structures consisting of stiffened and unstiffened plate panels...

  12. A measure of statistical complexity based on predictive information with application to finite spin systems

    Energy Technology Data Exchange (ETDEWEB)

    Abdallah, Samer A., E-mail: samer.abdallah@eecs.qmul.ac.uk [School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS (United Kingdom); Plumbley, Mark D., E-mail: mark.plumbley@eecs.qmul.ac.uk [School of Electronic Engineering and Computer Science, Queen Mary University of London, London E1 4NS (United Kingdom)

    2012-01-09

    We propose the binding information as an information theoretic measure of complexity between multiple random variables, such as those found in the Ising or Potts models of interacting spins, and compare it with several previously proposed measures of statistical complexity, including excess entropy, Bialek et al.'s predictive information, and the multi-information. We discuss and prove some of the properties of binding information, particularly in relation to multi-information and entropy, and show that, in the case of binary random variables, the processes which maximise binding information are the ‘parity’ processes. The computation of binding information is demonstrated on Ising models of finite spin systems, showing that various upper and lower bounds are respected and also that there is a strong relationship between the introduction of high-order interactions and an increase of binding-information. Finally we discuss some of the implications this has for the use of the binding information as a measure of complexity. -- Highlights: ► We introduce ‘binding information’ as a entropic/statistical measure of complexity. ► Binding information (BI) is related to earlier notions of predictive information. ► We derive upper and lower bounds of BI relation to entropy and multi-information. ► Parity processes found to maximise BI in finite sets of binary random variables. ► Application to spin glasses shows highest BI obtained with high-order interactions.

  13. Neurobiological markers predicting treatment response in anxiety disorders: A systematic review and implications for clinical application.

    Science.gov (United States)

    Lueken, Ulrike; Zierhut, Kathrin C; Hahn, Tim; Straube, Benjamin; Kircher, Tilo; Reif, Andreas; Richter, Jan; Hamm, Alfons; Wittchen, Hans-Ulrich; Domschke, Katharina

    2016-07-01

    Anxiety disorders constitute the largest group of mental disorders with a high individual and societal burden. Neurobiological markers of treatment response bear potential to improve response rates by informing stratified medicine approaches. A systematic review was performed on the current evidence of the predictive value of genetic, neuroimaging and other physiological markers for treatment response (pharmacological and/or psychotherapeutic treatment) in anxiety disorders. Studies published until March 2015 were selected through search in PubMed, Web of Science, PsycINFO, Embase, and CENTRAL. Sixty studies were included, among them 27 on genetic, 17 on neuroimaging and 16 on other markers. Preliminary evidence was found for the functional 5-HTTLPR/rs25531 genotypes, anterior cingulate cortex function and cardiovascular flexibility to modulate treatment outcome. Studies varied considerably in methodological quality. Application of more stringent study methodology, predictions on the individual patient level and cross-validation in independent samples are recommended to set the next stage of biomarker research and to avoid flawed conclusions in the emerging field of "Mental Health Predictomics". PMID:27168345

  14. Application of Petri nets to reliability prediction of occupant safety systems with partial detection and repair

    International Nuclear Information System (INIS)

    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 (Pfd), can be a better alternative to reliability prediction. The process of estimating the Pfd 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 Pfd, 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.

  15. 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.

  16. Predicting Nitrogen in Streams: A Comparison of Two Estimates of Fertilizer Application

    Science.gov (United States)

    Mehaffey, M.; Neale, A.

    2011-12-01

    and uptake helping offset the impacts to water. To test the accuracy of our finer resolution nitrogen application data, we compare its ability to predict nitrogen concentrations in streams with the ability of the county sales data to do the same.

  17. Predicting new molecular targets for known drugs

    OpenAIRE

    Keiser, Michael J.; Setola, Vincent; Irwin, John J.; Laggner, Christian; Abbas, Atheir; Hufeisen, Sandra J.; Jensen, Niels H.; Kuijer, Michael B.; Matos, Roberto C.; Tran, Thuy B.; Whaley, Ryan; Glennon, Richard A.; Hert, Jérôme; THOMAS, KELAN L. H.; Edwards, Douglas D.

    2009-01-01

    Whereas drugs are intended to be selective, at least some bind to several physiologic targets, explaining both side effects and efficacy. As many drug-target combinations exist, it would be useful to explore possible interactions computationally. Here, we compared 3,665 FDA-approved and investigational drugs against hundreds of targets, defining each target by its ligands. Chemical similarities between drugs and ligand sets predicted thousands of unanticipated associations. Thirty were tested...

  18. 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.

  19. Cloud Condensation Nuclei Prediction Error from Application of Kohler Theory: Importance for the Aerosol Indirect Effect

    Science.gov (United States)

    Sotiropoulou, Rafaella-Eleni P.; Nenes, Athanasios; Adams, Peter J.; Seinfeld, John H.

    2007-01-01

    In situ observations of aerosol and cloud condensation nuclei (CCN) and the GISS GCM Model II' with an online aerosol simulation and explicit aerosol-cloud interactions are used to quantify the uncertainty in radiative forcing and autoconversion rate from application of Kohler theory. Simulations suggest that application of Koehler theory introduces a 10-20% uncertainty in global average indirect forcing and 2-11% uncertainty in autoconversion. Regionally, the uncertainty in indirect forcing ranges between 10-20%, and 5-50% for autoconversion. These results are insensitive to the range of updraft velocity and water vapor uptake coefficient considered. This study suggests that Koehler theory (as implemented in climate models) is not a significant source of uncertainty for aerosol indirect forcing but can be substantial for assessments of aerosol effects on the hydrological cycle in climatically sensitive regions of the globe. This implies that improvements in the representation of GCM subgrid processes and aerosol size distribution will mostly benefit indirect forcing assessments. Predictions of autoconversion, by nature, will be subject to considerable uncertainty; its reduction may require explicit representation of size-resolved aerosol composition and mixing state.

  20. Autonomous Reactor Control Using Model Based Predictive Control for Space Propulsion Applications

    International Nuclear Information System (INIS)

    Reliable reactor control is important to reactor safety, both in terrestrial and space systems. For a space system, where the time for communication to Earth is significant, autonomous control is imperative. Based on feedback from reactor diagnostics, a controller must be able to automatically adjust to changes in reactor temperature and power level to maintain nominal operation without user intervention. Model-based predictive control (MBPC) (Clarke 1994; Morari 1994) is investigated as a potential control methodology for reactor start-up and transient operation in the presence of an external source. Bragg-Sitton and Holloway (2004) assessed the applicability of MBPC to reactor start-up from a cold, zero-power condition in the presence of a time-varying external radiation source, where large fluctuations in the external radiation source can significantly impact a reactor during start-up operations. The MBPC algorithm applied the point kinetics model to describe the reactor dynamics, using a single group of delayed neutrons; initial application considered a fast neutron lifetime (10-3 sec) to simplify calculations during initial controller analysis. The present study will more accurately specify the dynamics of a fast reactor, using a more appropriate fast neutron lifetime (10-7 sec) than in the previous work. Controller stability will also be assessed by carefully considering the dependencies of each component in the defined cost (objective) function and its subsequent effect on the selected 'optimal' control maneuvers

  1. Micronucleus assay prediction and application optimized by cytochalasin B-induced binucleated tumor cells

    International Nuclear Information System (INIS)

    Improvement in the predictive assertion of the micronucleus assay was achieved by treating human malignant melanoma cells (Mewo) with cytochalasin B (CB), generating binucleated cells (BNC) representing cells after a single karyokinesis. Optimal cell binucleation was determined by testing several cytochalasin B concentrations and different incubation times. On average, 56% binucleated cells were found after incubation with 2 to 3 μg/ml cytochalasin B for 48 h. Cells with at least one micronucleus (Mn) were defined as fraction of cells with micronuclei and describes the degree of damaged cells. We found in binucleated cells 2.2fold the fraction of cells with micronuclei than in mononucleated cells (MNC), as expected assuming that an induced micronucleus is associated with only one single daughter cell after mitosis. The mean of micronuclei per binucleated cells, however, was enhanced about 2.9fold in relation to that of micronuclei per mononucleated cells and is related to the nucelar damage per cell. The application of cytochalasin B did not enhance the fraction of damaged cells although the degree of the injury per cell is intensified. A micronuclei promoting or inhibiting effect of the experimental design due to changes in cell proliferation was excluded by cytofluorometric investigations of DNA content and synthesis after cytochalasin B application. A comparison of the modified with the conventional micronucleus assay shows the superiority of the former. (orig.)

  2. Verification of Numerical Weather Prediction Model Results for Energy Applications in Latvia

    Science.gov (United States)

    Sīle, Tija; Cepite-Frisfelde, Daiga; Sennikovs, Juris; Bethers, Uldis

    2014-05-01

    A resolution to increase the production and consumption of renewable energy has been made by EU governments. Most of the renewable energy in Latvia is produced by Hydroelectric Power Plants (HPP), followed by bio-gas, wind power and bio-mass energy production. Wind and HPP power production is sensitive to meteorological conditions. Currently the basis of weather forecasting is Numerical Weather Prediction (NWP) models. There are numerous methodologies concerning the evaluation of quality of NWP results (Wilks 2011) and their application can be conditional on the forecast end user. The goal of this study is to evaluate the performance of Weather Research and Forecast model (Skamarock 2008) implementation over the territory of Latvia, focusing on forecasting of wind speed and quantitative precipitation forecasts. The target spatial resolution is 3 km. Observational data from Latvian Environment, Geology and Meteorology Centre are used. A number of standard verification metrics are calculated. The sensitivity to the model output interpretation (output spatial interpolation versus nearest gridpoint) is investigated. For the precipitation verification the dichotomous verification metrics are used. Sensitivity to different precipitation accumulation intervals is examined. Skamarock, William C. and Klemp, Joseph B. A time-split nonhydrostatic atmospheric model for weather research and forecasting applications. Journal of Computational Physics. 227, 2008, pp. 3465-3485. Wilks, Daniel S. Statistical Methods in the Atmospheric Sciences. Third Edition. Academic Press, 2011.

  3. 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.

  4. Simulation of magnetic drug targeting through tracheobronchial airways in the presence of an external non-uniform magnetic field using Lagrangian magnetic particle tracking

    International Nuclear Information System (INIS)

    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

  5. Simulation of magnetic drug targeting through tracheobronchial airways in the presence of an external non-uniform magnetic field using Lagrangian magnetic particle tracking

    Science.gov (United States)

    Pourmehran, O.; Rahimi-Gorji, M.; Gorji-Bandpy, M.; 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).

  6. 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.

  7. Predicting enzyme targets for cancer drugs by profiling human Metabolic reactions in NCI-60 cell lines

    Directory of Open Access Journals (Sweden)

    Ching Wai-Ki

    2010-10-01

    Full Text Available Abstract Background Drugs can influence the whole metabolic system by targeting enzymes which catalyze metabolic reactions. The existence of interactions between drugs and metabolic reactions suggests a potential way to discover drug targets. Results In this paper, we present a computational method to predict new targets for approved anti-cancer drugs by exploring drug-reaction interactions. We construct a Drug-Reaction Network to provide a global view of drug-reaction interactions and drug-pathway interactions. The recent reconstruction of the human metabolic network and development of flux analysis approaches make it possible to predict each metabolic reaction's cell line-specific flux state based on the cell line-specific gene expressions. We first profile each reaction by its flux states in NCI-60 cancer cell lines, and then propose a kernel k-nearest neighbor model to predict related metabolic reactions and enzyme targets for approved cancer drugs. We also integrate the target structure data with reaction flux profiles to predict drug targets and the area under curves can reach 0.92. Conclusions The cross validations using the methods with and without metabolic network indicate that the former method is significantly better than the latter. Further experiments show the synergism of reaction flux profiles and target structure for drug target prediction. It also implies the significant contribution of metabolic network to predict drug targets. Finally, we apply our method to predict new reactions and possible enzyme targets for cancer drugs.

  8. Drug targeting of leptin resistance.

    Science.gov (United States)

    Santoro, Anna; Mattace Raso, Giuseppina; Meli, Rosaria

    2015-11-01

    Leptin regulates glucose, lipid and energy homeostasis as well as feeding behavior, serving as a bridge between peripheral metabolically active tissues and the central nervous system (CNS). Indeed, this adipocyte-derived hormone, whose circulating levels mirror fat mass, not only exerts its anti-obesity effects mainly modulating the activity of specific hypothalamic neurons expressing the long form of the leptin receptor (Ob-Rb), but it also shows pleiotropic functions due to the activation of Ob-Rb in peripheral tissues. Nevertheless, several mechanisms have been suggested to mediate leptin resistance, including obesity-associated hyperleptinemia, impairment of leptin access to CNS and the reduction in Ob-Rb signal transduction effectiveness, among others. During the onset and progression of obesity, the dampening of leptin sensitivity often occurs, preventing the efficacy of leptin replacement therapy from overcoming obesity and/or its comorbidities. This review focuses on obesity-associated leptin resistance and the mechanisms underpinning this condition, to highlight the relevance of leptin sensitivity restoration as a useful therapeutic strategy to treat common obesity and its complications. Interestingly, although promising strategies to counteract leptin resistance have been proposed, these pharmacological approaches have shown limited efficacy or even relevant adverse effects in preclinical and clinical studies. Therefore, the numerous findings from this review clearly indicate a lack of a single and efficacious treatment for leptin resistance, highlighting the necessity to find new therapeutic tools to improve leptin sensitivity, especially in patients with most severe disease profiles. PMID:26071010

  9. Drug targeting in cancer therapy

    Czech Academy of Sciences Publication Activity Database

    Říhová, Blanka; Strohalm, Jiří; Hoste, K.; Jelínková, Markéta; Hovorka, Ondřej; Kovář, Marek; Plocová, Daniela; Šírová, Milada; Šťastný, Marek; Ulbrich, Karel

    Chiang Mai : Chiang Mai University, 2000, s. 35-40. [Takeo Wada Cancer Research Symposium. Chiang Mai (TH), 30.11.2000-01.12.2000] R&D Projects: GA ČR GV307/96/K226; GA MZd NC5050 Institutional research plan: CEZ:AV0Z5020903 Keywords : monoclonal antibodies * antitumor immunity Subject RIV: EC - Immunology

  10. 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. PMID:19663765

  11. Application Of Data Mining Techniques For Student Success And Failure Prediction The Case Of DebreMarkos University

    OpenAIRE

    Muluken Alemu Yehuala

    2015-01-01

    Abstract This research work has investigated the potential applicability of data mining technology to predict student success and failure cases on University students datasets. CRISP-DM Cross Industry Standard Process for Data mining is a data mining methodology to be used by the research. Classification and prediction data mining functionalities are used to extract hidden patterns from students data. These patterns can be seen in relation to different variables in the students records. The ...

  12. Computational Redox Potential Predictions: Applications to Inorganic and Organic Aqueous Complexes, and Complexes Adsorbed to Mineral Surfaces

    OpenAIRE

    Krishnamoorthy Arumugam; Udo Becker

    2014-01-01

    Applications of redox processes range over a number of scientific fields. This review article summarizes the theory behind the calculation of redox potentials in solution for species such as organic compounds, inorganic complexes, actinides, battery materials, and mineral surface-bound-species. Different computational approaches to predict and determine redox potentials of electron transitions are discussed along with their respective pros and cons for the prediction of redox potentials. Subs...

  13. Accurate First-Principles Spectra Predictions for Planetological and Astrophysical Applications at Various T-Conditions

    Science.gov (United States)

    Rey, M.; Nikitin, A. V.; Tyuterev, V.

    2014-06-01

    Knowledge of near infrared intensities of rovibrational transitions of polyatomic molecules is essential for the modeling of various planetary atmospheres, brown dwarfs and for other astrophysical applications 1,2,3. For example, to analyze exoplanets, atmospheric models have been developed, thus making the need to provide accurate spectroscopic data. Consequently, the spectral characterization of such planetary objects relies on the necessity of having adequate and reliable molecular data in extreme conditions (temperature, optical path length, pressure). On the other hand, in the modeling of astrophysical opacities, millions of lines are generally involved and the line-by-line extraction is clearly not feasible in laboratory measurements. It is thus suggested that this large amount of data could be interpreted only by reliable theoretical predictions. There exists essentially two theoretical approaches for the computation and prediction of spectra. The first one is based on empirically-fitted effective spectroscopic models. Another way for computing energies, line positions and intensities is based on global variational calculations using ab initio surfaces. They do not yet reach the spectroscopic accuracy stricto sensu but implicitly account for all intramolecular interactions including resonance couplings in a wide spectral range. The final aim of this work is to provide reliable predictions which could be quantitatively accurate with respect to the precision of available observations and as complete as possible. All this thus requires extensive first-principles quantum mechanical calculations essentially based on three necessary ingredients which are (i) accurate intramolecular potential energy surface and dipole moment surface components well-defined in a large range of vibrational displacements and (ii) efficient computational methods combined with suitable choices of coordinates to account for molecular symmetry properties and to achieve a good numerical

  14. Intermediate-term medium-range earthquake prediction algorithm M8: A new spatially stabilized application in Italy

    International Nuclear Information System (INIS)

    A series of experiments, based on the intermediate-term earthquake prediction algorithm M8, has been performed for the retrospective simulation of forward predictions in the Italian territory, with the aim to design an experimental routine for real-time predictions. These experiments evidenced two main difficulties for the application of M8 in Italy. The first one is due to the fact that regional catalogues are usually limited in space. The second one concerns certain arbitrariness and instability, with respect to the positioning of the circles of investigation. Here we design a new scheme for the application of the algorithm M8, which is less subjective and less sensitive to the position of the circles of investigation. To perform this test, we consider a recent revision of the Italian catalogue, named UCI2001, composed by CCI1996, NEIC and ALPOR data for the period 1900-1985, and updated with the NEIC reduces the spatial heterogeneity of the data at the boundaries of Italy. The new variant of the M8 algorithm application reduces the number of spurious alarms and increases the reliability of predictions. As a result, three out of four earthquakes with magnitude Mmax larger than 6.0 are predicted in the retrospective simulation of the forward prediction, during the period 1972-2001, with a space-time volume of alarms comparable to that obtained with the non-stabilized variant of the M8 algorithm in Italy. (author)

  15. 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

  16. Application of the MIT two-channel model to predict flow recirculation in WARD 61-pin blanket tests

    International Nuclear Information System (INIS)

    The preliminary application of MIT TWO-CHANNEL MODEL to WARD sodium blanket tests was presented in this report. Our criterion was employed to predict the recirculation for selected completed (transient and steady state) and proposed (transient only) tests. The heat loss was correlated from the results of the WARD zero power tests. The calculational results show that our criterion agrees with the WARD tests except for WARD RUN 718 for which the criterion predicts a different result from WARD data under bundle heat loss condition. However, if the test assembly is adiabatic, the calculations predict an operating point which is marginally close to the mixed-to-recirculation transition regime

  17. Application of the MIT two-channel model to predict flow recirculation in WARD 61-pin blanket tests

    International Nuclear Information System (INIS)

    The preliminary application of MIT two-channel model to WARD sodium blanket tests was presented in this report. The criterion was employed to predict the recirculation for selected completed (transient and steady state) and proposed (transient only) tests. The heat loss was correlated from the results of the WARD zero power tests. The calculational results show that the criterion agrees with the WARD tests except for WARD RUN 718 for which the criterion predicts a different result from WARD data under bundle heat loss condition. However, if the test assembly is adiabatic, the calculations predict an operating point which is marginally close to the mixed-to-recirculation transition regime

  18. 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

  19. 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.

  20. 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.

  1. A Computational Model for Predicting RNase H Domain of Retrovirus.

    Science.gov (United States)

    Wu, Sijia; Zhang, Xinman; Han, Jiuqiang

    2016-01-01

    RNase H (RNH) is a pivotal domain in retrovirus to cleave the DNA-RNA hybrid for continuing retroviral replication. The crucial role indicates that RNH is a promising drug target for therapeutic intervention. However, annotated RNHs in UniProtKB database have still been insufficient for a good understanding of their statistical characteristics so far. In this work, a computational RNH model was proposed to annotate new putative RNHs (np-RNHs) in the retroviruses. It basically predicts RNH domains through recognizing their start and end sites separately with SVM method. The classification accuracy rates are 100%, 99.01% and 97.52% respectively corresponding to jack-knife, 10-fold cross-validation and 5-fold cross-validation test. Subsequently, this model discovered 14,033 np-RNHs after scanning sequences without RNH annotations. All these predicted np-RNHs and annotated RNHs were employed to analyze the length, hydrophobicity and evolutionary relationship of RNH domains. They are all related to retroviral genera, which validates the classification of retroviruses to a certain degree. In the end, a software tool was designed for the application of our prediction model. The software together with datasets involved in this paper can be available for free download at https://sourceforge.net/projects/rhtool/files/?source=navbar. PMID:27574780

  2. Research on bearing life prediction based on support vector machine and its application

    International Nuclear Information System (INIS)

    Life prediction of rolling element bearing is the urgent demand in engineering practice, and the effective life prediction technique is beneficial to predictive maintenance. Support vector machine (SVM) is a novel machine learning method based on statistical learning theory, and is of advantage in prediction. This paper develops SVM-based model for bearing life prediction. The inputs of the model are features of bearing vibration signal and the output is the bearing running time-bearing failure time ratio. The model is built base on a few failed bearing data, and it can fuse information of the predicted bearing. So it is of advantage to bearing life prediction in practice. The model is applied to life prediction of a bearing, and the result shows the proposed model is of high precision.

  3. Generalized Nonlinear Irreducible Auto-Correlation and Its Applications in Nonlinear Prediction Models Identification

    Institute of Scientific and Technical Information of China (English)

    HOU Yuexian; HE Pilian

    2005-01-01

    There is still an obstacle to prevent neural network from wider and more effective applications, i.e., the lack of effective theories of models identification. Based on information theory and its generalization, this paper introduces a universal method to achieve nonlinear models identification. Two key quantities, which are called nonlinear irreducible auto-correlation (NIAC) and generalized nonlinear irreducible auto-correlation (GNIAC), are defined and discussed. NIAC and GNIAC correspond with intrinstic irreducible auto-dependency (IAD) and generalized irreducible auto-dependency (GIAD) of time series respectively. By investigating the evolving trend of NIAC and GNIAC, the optimal auto-regressive order of nonlinear auto-regressive models could be determined naturally. Subsequently, an efficient algorithm computing NIAC and GNIAC is discussed. Experiments on simulating data sets and typical nonlinear prediction models indicate remarkable correlation between optimal auto-regressive order and the highest order that NIAC-GNIAC have a remarkable non-zero value, therefore demonstrate the validity of the proposal in this paper.

  4. Predicting a quaternary tungsten oxide for sustainable photovoltaic application by density functional theory

    International Nuclear Information System (INIS)

    A quaternary oxide, CuSnW2O8 (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 Cu2ZnSnS4 and CuInxGa1−xSe2 (x = 0.5). In addition, CTTO exhibits rigorous thermodynamic stability comparable to WO3, 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

  5. 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.

  6. 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.

  7. 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.

  8. Application of mental disorders predictive scale in evaluating pathogenic factors and healing efficacy of depression

    Directory of Open Access Journals (Sweden)

    Li-yi ZHANG

    2011-11-01

    Full Text Available Objective To explore the application of Mental Disorders Predictive Scale(MDPS in evaluating the pathogenic factors and healing efficacy of depression.Methods MDPS was adopted for detection in 58 depressive outpatients and 63 normal controls.The outcomes of the measurement and evaluation were compared and analyzed after a re-examination of depressive patients after 6 weeks.Results After a six-week treatment,the clinical symptoms of the depressive patients improved.The depression factor score(2.07±2.87 was significantly lower than the score(8.90±2.05 prior to treatment,(P < 0.01.However,the depression factor score was still significantly higher than the control group score(1.77±2.13,(P < 0.05.The correlation analysis of MDPS risk and depression factors showed that introversion,stressor,unhealthy defense mechanism,and lower social support had a significant correlation with the depression factor r=0.442-0.642,P < 0.05 or P < 0.01.If α=0.10,the order for entering the regression equation was as follows: unhealthy defense mechanism and lower social support.The standard regression coefficients were 0.489 and 0.371.Conclusion MDPS can be used as an index for evaluating the pathogenic factors and healing efficacy of depression.

  9. Predicting a quaternary tungsten oxide for sustainable photovoltaic application by density functional theory

    Science.gov (United States)

    Sarker, Pranab; Al-Jassim, Mowafak M.; Huda, Muhammad N.

    2015-12-01

    A quaternary oxide, CuSnW2O8 (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 Cu2ZnSnS4 and CuInxGa1-xSe2 (x = 0.5). In addition, CTTO exhibits rigorous thermodynamic stability comparable to WO3, 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.

  10. Curcumin binds in silico to anti-cancer drug target enzyme MMP-3 (human stromelysin-1) with affinity comparable to two known inhibitors of the enzyme.

    Science.gov (United States)

    Jerah, Ahmed; Hobani, Yahya; Kumar, B Vinod; Bidwai, Anil

    2015-01-01

    In silico interaction of curcumin with the enzyme MMP-3 (human stromelysin-1) was studied by molecular docking using AutoDock 4.2 as the docking software application. AutoDock 4.2 software serves as a valid and acceptable docking application to study the interactions of small compounds with proteins. Interactions of curcumin with MMP-3 were compared to those of two known inhibitors of the enzyme, PBSA and MPPT. The calculated free energy of binding (ΔG binding) shows that curcumin binds with affinity comparable to or better than the two known inhibitors. Binding interactions of curcumin with active site residues of the enzyme are also predicted. Curcumin appears to bind in an extendended conformation making extensive VDW contacts in the active site of the enzyme. Hydrogen bonding and pi-pi interactions with key active site residues is also observed. Thus, curcumin can be considered as a good lead compound in the development of new inhibitors of MMP-3 which is a potential target of anticancer drugs. The results of these studies can serve as a starting point for further computational and experimental studies. PMID:26420919

  11. Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection

    Science.gov (United States)

    Kumar, Sricharan; Srivistava, Ashok N.

    2012-01-01

    Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.

  12. Predictability of Competing Measures of Core Inflation: An Application for Peru Predictability of Competing Measures of Core Inflation: An Application for Peru

    Directory of Open Access Journals (Sweden)

    Luis F. Zegarra

    1999-03-01

    Full Text Available A central element of an inflation targeting approach to monetary policy is a proper measure of inflation. The international evidence suggests the use of core inflation measures. In this paper we claim that core inflation should be measured as the underlying trend of inflation that comes from nominal shocks that have no real effect in the long term. However, most of the time core inflation is computed zero weighting observations at the tail of the inflation distribution. Quah and Vahey (1996 proposed a method of computing core inflation imposing theory restrictions to a SVAR specification. In this paper we present estimation for Peruvian data and compare the predictability properties of competing measures of inflation following an idea of Diebold and Killian (1997. A central element of an inflation targeting approach to monetary policy is a proper measure of inflation. The international evidence suggests the use of core inflation measures. In this paper we claim that core inflation should be measured as the underlying trend of inflation that comes from nominal shocks that have no real effect in the long term. However, most of the time core inflation is computed zero weighting observations at the tail of the inflation distribution. Quah and Vahey (1996 proposed a method of computing core inflation imposing theory restrictions to a SVAR specification. In this paper we present estimation for Peruvian data and compare the predictability properties of competing measures of inflation following an idea of Diebold and Killian (1997.

  13. 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. PMID:27282231

  14. Application of Suomi-NPP Green Vegetation Fraction and NUCAPS for Improving Regional Numerical Weather Prediction

    Science.gov (United States)

    Case, Jonathan L.; Berndt, Emily B.; Srikishen, Jayanthi; Zavodsky, Bradley T.

    2014-01-01

    The NASA SPoRT Center is working to incorporate Suomi-NPP products into its research and transition activities to improve regional numerical weather prediction (NWP). Specifically, SPoRT seeks to utilize two data products from NOAA/NESDIS: (1) daily global VIIRS green vegetation fraction (GVF), and (2) NOAA Unique CrIS and ATMS Processing System (NUCAPS) temperature and moisture retrieved profiles. The goal of (1) is to improve the representation of vegetation in the Noah land surface model (LSM) over existing climatological GVF datasets in order to improve the land-atmosphere energy exchanges in NWP models and produce better temperature, moisture, and precipitation forecasts. The goal of (2) is to assimilate NUCAPS retrieved profiles into the Gridpoint Statistical Interpolation (GSI) data assimilation system to assess the impact on a summer pre-frontal convection case. Most regional NWP applications make use of a monthly GVF climatology for use in the Noah LSM within the Weather Research and Forecasting (WRF) model. The GVF partitions incoming energy into direct surface heating/evaporation over bare soil versus evapotranspiration processes over vegetated surfaces. Misrepresentations of the fractional coverage of vegetation during anomalous weather/climate regimes (e.g., early/late bloom or freeze; drought) can lead to poor NWP model results when land-atmosphere feedback is important. SPoRT has been producing a daily MODIS GVF product based on the University of Wisconsin Direct Broadcast swaths of Normalized Difference Vegetation Index (NDVI). While positive impacts have been demonstrated in the WRF model for some cases, the reflectances composing these NDVI do not correct for atmospheric aerosols nor satellite view angle, resulting in temporal noisiness at certain locations (especially heavy vegetation). The method behind the NESDIS VIIRS GVF is expected to alleviate the issues seen in the MODIS GVF real-time product, thereby offering a higher-quality dataset for

  15. 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. PMID:26513561

  16. Multi-scale mechanism based life prediction of polymer matrix composites for high temperature airframe applications

    Science.gov (United States)

    Upadhyaya, Priyank

    A multi-scale mechanism-based life prediction model is developed for high-temperature polymer matrix composites (HTPMC) for high temperature airframe applications. In the first part of this dissertation the effect of Cloisite 20A (C20A) nano-clay compounding on the thermo-oxidative weight loss and the residual stresses due to thermal oxidation for a thermoset polymer bismaleimide (BMI) are investigated. A three-dimensional (3-D) micro-mechanics based finite element analysis (FEA) was conducted to investigate the residual stresses due to thermal oxidation using an in-house FEA code (NOVA-3D). In the second part of this dissertation, a novel numerical-experimental methodology is outlined to determine cohesive stress and damage evolution parameters for pristine as well as isothermally aged (in air) polymer matrix composites. A rate-dependent viscoelastic cohesive layer model was implemented in an in-house FEA code to simulate the delamination initiation and propagation in unidirectional polymer composites before and after aging. Double cantilever beam (DCB) experiments were conducted (at UT-Dallas) on both pristine and isothermally aged IM-7/BMI composite specimens to determine the model parameters. The J-Integral based approach was adapted to extract cohesive stresses near the crack tip. Once the damage parameters had been characterized, the test-bed FEA code employed a micromechanics based viscoelastic cohesive layer model to numerically simulate the DCB experiment. FEA simulation accurately captures the macro-scale behavior (load-displacement history) simultaneously with the micro-scale behavior (crack-growth history).

  17. Use of predictions in temperature control in buildings: A passive climate system application. Doctoral thesis

    Energy Technology Data Exchange (ETDEWEB)

    Lute, P.J.

    1992-01-01

    The thesis consists of two parts. The first part is a general part about predictive control in the indoor climate field. The second part deals with the control system implementation in the passive indoor climate system. The fundamentals, general principles and mechanisms of the class of predictive controllers and control strategies with a linear objective function are described in chapter 2. Chapter 3 deals with the typical characteristics of the indoor and outdoor climate, that have to be incorporated in a predictive control system to make it a successful and robust predictive indoor climate control system. A general scheme for a predictive indoor climate system, that has self-learning features, is described in Chapter 4. Chapter 5 introduces the passive indoor climate system to which a predictive control strategy is applied. Finally, in Chapter 6, a test facility for passive climate systems, referred to as the TU Delft test cell, is described.

  18. 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.

  19. Application of Chaos Theory in the Prediction of Motorised Traffic Flows on Urban Networks

    OpenAIRE

    Aderemi Adewumi; Jimmy Kagamba; Alex Alochukwu

    2016-01-01

    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. No...

  20. Novel applications of multitask learning and multiple output regression to multiple genetic trait prediction

    OpenAIRE

    He, Dan; 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...

  1. Comparison of measured and predicted airfoil self-noise with application to wind turbine noise reduction

    International Nuclear Information System (INIS)

    In the ongoing JOULE-III project 'Development of Design Tools for Reduced Aerodynamic Noise Wind Turbines (DRAW)', prediction codes for inflow-turbulence (IT) noise and turbulent boundary layer trailing-edge (TE) noise, are developed and validated. It is shown that the differences in IT noise radiation between airfoils having a different shape, are correctly predicted. The first, preliminary comparison made between predicted and measured TE noise spectra yields satisfactory results. 17 refs

  2. An application of Auto-regressive (AR) model in predicting Aeroelastic Effectsof Lekki Cable Stayed Bridge

    OpenAIRE

    Hassan Abba Musa; Dr. A. Mohammed

    2016-01-01

    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 fluc...

  3. 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...

  4. 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.

  5. Structure-Based Prediction of Subtype Selectivity of Histamine H3 Receptor Selective Antagonists in Clinical Trials

    DEFF Research Database (Denmark)

    Kim, Soo-Kyung; Fristrup, Peter; Abrol, Ravinder; Goddard, William A., III

    2011-01-01

    applications, including treatment of Alzheimer’s disease, attention deficit hyperactivity disorder (ADHD), epilepsy, and obesity.(1) However, many of these drug candidates cause undesired side effects through the cross-reactivity with other histamine receptor subtypes. In order to develop improved selectivity......Histamine receptors (HRs) are excellent drug targets for the treatment of diseases, such as schizophrenia, psychosis, depression, migraine, allergies, asthma, ulcers, and hypertension. Among them, the human H3 histamine receptor (hH3HR) antagonists have been proposed for specific therapeutic...... and activity for such treatments, it would be useful to have the three-dimensional structures for all four HRs. We report here the predicted structures of four HR subtypes (H1, H2, H3, and H4) using the GEnSeMBLE (GPCR ensemble of structures in membrane bilayer environment) Monte Carlo protocol,(2...

  6. 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.

  7. Operational prediction of air quality for the United States: applications of satellite observations

    Science.gov (United States)

    Stajner, Ivanka; Lee, Pius; Tong, Daniel; Pan, Li; McQueen, Jeff; Huang, Jianping; Huang, Ho-Chun; Draxler, Roland; Kondragunta, Shobha; Upadhayay, Sikchya

    2015-04-01

    Operational predictions of ozone and wildfire smoke over United States (U.S.) and predictions of airborne dust over the contiguous 48 states are provided by NOAA at http://airquality.weather.gov/. North American Mesoscale (NAM) weather predictions with inventory based emissions estimates from the U.S. Environmental Protection Agency (EPA) and chemical processes within the Community Multiscale Air Quality (CMAQ) model are combined together to produce ozone predictions. Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model is used to predict wildfire smoke and dust storm predictions. Routine verification of ozone predictions relies on AIRNow compilation of observations from surface monitors. Retrievals of smoke column integrals from GOES satellites and dust column integrals from MODIS satellite instruments are used for verification of smoke and dust predictions. Recent updates of NOAA's operational air quality predictions have focused on mobile emissions using the projections of mobile sources for 2012. Since emission inventories are complex and take years to assemble and evaluate causing a lag of information, we recently began combing inventory information with projections of mobile sources. In order to evaluate this emission update, these changes in projected NOx emissions from 2005-2012 were compared with observed changes in Ozone Monitoring Instrument (OMI) NO2 observations and NOx measured by surface monitors over large U.S. cities over the same period. Comparisons indicate that projected decreases in NOx emissions from 2005 to 2012 are similar, but not as strong as the decreases in the observed NOx concentrations and in OMI NO2 retrievals. Nevertheless, the use of projected mobile NOx emissions in the predictions reduced biases in predicted NOx concentrations, with the largest improvement in the urban areas. Ozone biases are reduced as well, with the largest improvement seen in rural areas. Recent testing of PM2.5 predictions is relying on

  8. Predicting non-fickian moisture diffusion in EMCs for application in micro-electronic devices

    NARCIS (Netherlands)

    Barink, M.; Mavinkurve, A.; Janssen, J.

    2015-01-01

    This study made an attempt to predict the temperature-dependent moisture diffusion of an epoxy molding compound with 3 different diffusion models: Fickian, dual stage and Langmuir diffusion. The Langmuir model provided the best prediction of the moisture diffusion when simulating the input experimen

  9. Ensemble framework based real-time respiratory motion prediction for adaptive radiotherapy applications.

    Science.gov (United States)

    Tatinati, Sivanagaraja; Nazarpour, Kianoush; Tech Ang, Wei; Veluvolu, Kalyana C

    2016-08-01

    Successful treatment of tumors with motion-adaptive radiotherapy requires accurate prediction of respiratory motion, ideally with a prediction horizon larger than the latency in radiotherapy system. Accurate prediction of respiratory motion is however a non-trivial task due to the presence of irregularities and intra-trace variabilities, such as baseline drift and temporal changes in fundamental frequency pattern. In this paper, to enhance the accuracy of the respiratory motion prediction, we propose a stacked regression ensemble framework that integrates heterogeneous respiratory motion prediction algorithms. We further address two crucial issues for developing a successful ensemble framework: (1) selection of appropriate prediction methods to ensemble (level-0 methods) among the best existing prediction methods; and (2) finding a suitable generalization approach that can successfully exploit the relative advantages of the chosen level-0 methods. The efficacy of the developed ensemble framework is assessed with real respiratory motion traces acquired from 31 patients undergoing treatment. Results show that the developed ensemble framework improves the prediction performance significantly compared to the best existing methods. PMID:27238760

  10. Conditional nonlinear optimal perturbation and its applications to the studies of weather and climate predictability

    Institute of Scientific and Technical Information of China (English)

    MU Mu; DUAN Wansuo

    2005-01-01

    Conditional nonlinear optimal perturbation (CNOP) is the initial perturbation that has the largest nonlinear evolution at prediction time for initial perturbations satisfying certain physical constraint condition. It does not only represent the optimal precursor of certain weather or climate event, but also stand for the initial error which has largest effect on the prediction uncertainties at the prediction time. In sensitivity and stability analyses of fluid motion, CNOP also describes the most unstable (or most sensitive) mode. CNOP has been used to estimate the upper bound of the prediction error. These physical characteristics of CNOP are examined by applying respectively them to ENSO predictability studies and ocean's thermohaline circulation (THC) sensitivity analysis. In ENSO predictability studies, CNOP, rather than linear singular vector (LSV), represents the initial patterns that evolve into ENSO events most potentially, i.e. the optimal precursors for ENSO events. When initial perturbation is considered to be the initial error of ENSO, CNOP plays the role of the initial error that has largest effect on the prediction of ENSO. CNOP also derives the upper bound of prediction error of ENSO events. In the THC sensitivity and stability studies, by calculating the CNOP (most unstable perturbation) of THC, it is found that there is an asymmetric nonlinear response of ocean's THC to the finite amplitude perturbations. Finally, attention is paid to the feasibility of CNOP in more complicated model. It is shown that in a model with higher dimensions, CNOP can be computed successfully. The corresponding optimization algorithm is also shown to be efficient.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. 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.

  17. Application of fuzzy inference system in the prediction of wave parameters

    Energy Technology Data Exchange (ETDEWEB)

    Kazeminezhad, M.H.; Etemad-Shahidi, A.; Mousavi, S.J. [Iran Univ. of Science and Technology, Structure and Hydrostructure Research Center, Tehran (Iran)

    2005-10-01

    Wave prediction is one of the most important issues in coastal and ocean engineering studies. In this study, the performance of Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Coastal Engineering Manual (CEM) methods for predicting wave parameters were investigated. The data set used in this study comprises fetch-limited wave data and over water wind data gathered from deep-water location in Lake Ontario. The data set of year 2002 was used to develop the ANFIS models as wave predictor models. The data set of year 2003 was then used to test the developed ANFIS models and also the CEM method. Results indicate that ANFIS outperforms the CEM method in terms of prediction capability as the scatter index of predictions of ANFIS is less than that of CEM method. In particular, the CEM method overestimates the significant wave height and underestimates the peak spectral period, while ANFIS results in more accurate predictions. (Author)

  18. 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.

  19. Differential Expression and Function of PDE8 and PDE4 in Effector T cells: Implications for PDE8 as a Drug Target in Inflammation.

    Science.gov (United States)

    Vang, Amanda G; Basole, Chaitali; Dong, Hongli; Nguyen, Rebecca K; Housley, William; Guernsey, Linda; Adami, Alexander J; Thrall, Roger S; Clark, Robert B; Epstein, Paul M; Brocke, Stefan

    2016-01-01

    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 vs. 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 (q)RT-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 ~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 selectively regulates Teff

  20. Application of dimensional analysis to predict the performance of rockfall barrier

    Science.gov (United States)

    Spadari, M.; Giacomini, A.; Buzzi, O.; Hambleton, J.

    2012-04-01

    Natural hazards involving rocks or rock slopes are responsible for loss of life and damage to infrastructure and are consequently widely studied. Rock fall barriers are a common type of protection structures that is usually designed on the basis of total impact energy. However, the systems are usually tested in free fall where the predominant component of energy is kinematic and it has been shown that there is not a unique relationship between the response of a barrier and the kinetic energy of the impacting block. In particular, recent studies have discussed the so called "bullet effect" i.e. relatively small blocks traveling at high speed can perforate the barriers yet having acceptable level of energy. This effect compromises the use of kinetic energy as an adequate design criterion since there is not a threshold value defining clearly acceptable and unacceptable values of energy. This issue can be addressed empirically by using different block sizes when it comes to test a system. However, the literature still lacks a characterization of a rockfall barrier performance regarding the bullet effect. This note presents the results of the application of dimensional analysis to the physical problem of the bullet effect. This latter has been formulated as a function involving eight key variables: v = f(ρ, K, σy, H, A, Db,Dw) where v is the minimum speed of a given block to break the barrier, ρgs the density of the block, Kis the stiffness of the system, σy is the strength of the wires, H is the height of the barrier, A is the aperture of the mesh, Db is the dimension of the block and Dw is the diameter of the wire. Applying the Buckingham Pi theorem allows reducing the equation above to a simpler problem involving only three dimensionless parameters: E*=F(S*, G*) Where E* is the performance parameter, S* is the strength-stiffness parameter and G* is the geometrical parameters defined as: E*= (ρ.v2.H)/K S*=K/(H.gσy) And G*=A-0.25.Db-0.75.Db F in the simplified

  1. 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.

  2. 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.

  3. 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

  4. 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

  5. Analysis and Application of GC Plus Models for Property Prediction of Organic Chemical Systems

    DEFF Research Database (Denmark)

    Mustaffa, Azizul Azri; Kontogeorgis, Georgios; Gani, Rafiqul

    2011-01-01

    In this paper, a detailed analysis of the performance and trends of predictions of vapour–liquid phase equilibrium with the UNIFAC-CI model, employing a method to predict missing group interaction parameters (GIPs) through the use of connectivity indices, are presented. The cases where the model...... using the predicted GIPs perform well and cases where the performance is unreliable are investigated. The causes for the unreliable performance of the UNIFAC-CI model are explained and results from one of the remedies that gave very good results are presented. The extrapolation features of the UNIFAC...

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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...

  12. Application of fracture mechanics and half-cycle theory to the prediction of fatigue life of aerospace structural components

    Science.gov (United States)

    Ko, William L.

    1989-01-01

    The service life of aircraft structural components undergoing random stress cycling was analyzed by the application of fracture mechanics. The initial crack sizes at the critical stress points for the fatigue crack growth analysis were established through proof load tests. The fatigue crack growth rates for random stress cycles were calculated using the half-cycle method. A new equation was developed for calculating the number of remaining flights for the structural components. The number of remaining flights predicted by the new equation is much lower than that predicted by the conventional equation. This report describes the application of fracture mechanics and the half-cycle method to calculate the number of remaining flights for aircraft structural components.

  13. Predicting Aggression among Male Adolescents: an Application of the Theory of Planned Behavior

    OpenAIRE

    ZinatMotlagh, Fazel; Ataee, Mari; Jalilian, Farzad; MirzaeiAlavijeh, Mehdi; Aghaei, Abbas; Karimzadeh Shirazi, Kambiz

    2013-01-01

    Background: Aggressive behaviorin adolescencecan be expressed asa predictorfor crime, substanceabuse, depression and academic failure. The purpose of this study was to determine the prediction of aggression among Iranian adolescent based on theory of planned behavior (TPB) as a theoretical framework.

  14. 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.

  15. 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.

  16. Time series predictions with neural nets: Application to airborne pollen forecasting

    Science.gov (United States)

    Arizmendi, C. M.; Sanchez, J. R.; Ramos, N. E.; Ramos, G. I.

    1993-09-01

    Pollen allergy is a common disease causing rhinoconjunctivitis (hay fever) in 5 10% of the population. Medical studies have indicated that pollen related diseases could be highly reduced if future pollen contents in the air could be predicted. In this work we have developed a new forecasting method that applies the ability of neural nets to predict the future behaviour of chaotic systems in order to make accurate predictions of the airborne pollen concentration. The method requires that the neural net be fed with non-zero values, which restricts the method predictions to the period following the start of pollen flight. The operational method outlined here constitutes a different point of view with respect to the more generally used forecasts of time series analysis, which require input of many meteorological parameters. Excellent forecasts were obtained training a neural net by using only the time series pollen concentration values.

  17. 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.

  18. Mortality Risk Prediction by Application of PRISM Scoring System in Pediatric Intensive Care Unit

    OpenAIRE

    Mahdi Mohammadi; Afshin Fayyazi; Mohsen Raeisi; Noor Mohammad Noori; Ali Khajeh; Ghasem Miri-Aliabad

    2013-01-01

    Objective: The Pediatric Risk of Mortality (PRISM) score is one of the scores used by many pediatricians for prediction of the mortality risk in the pediatric intensive care unit (PICU). Herein, evaluate the efficacy of PRISM score in prediction of mortality rate in PICU.Methods: In this cohort study, 221 children admitted during an 18-month period to PICU, were enrolled. PRISM score and mortality risk were calculated. Follow up was noted as death or discharge. Results were analyzed by Kaplan...

  19. CPU-GPU hybrid accelerating the Zuker algorithm for RNA secondary structure prediction applications

    OpenAIRE

    2012-01-01

    Background Prediction of ribonucleic acid (RNA) secondary structure remains one of the most important research areas in bioinformatics. The Zuker algorithm is one of the most popular methods of free energy minimization for RNA secondary structure prediction. Thus far, few studies have been reported on the acceleration of the Zuker algorithm on general-purpose processors or on extra accelerators such as Field Programmable Gate-Array (FPGA) and Graphics Processing Units (GPU). To the best of ou...

  20. 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.

  1. Application of Through-Flow Calculation to Design and Performance Prediction of Centrifugal Compressor

    OpenAIRE

    Choi, Y. S.; Kang, S.H.

    1999-01-01

    A computer code predicting the flows through the centrifugal compressor with the radial vaneless diffuser was developed and applied to investigate the detailed flowfields, i.e., secondary flows and jet-wake type flow pattern in design and off-design conditions. Various parameters such as slip factors, aerodynamic blockages, entropy generation and two-zone modeling which are widely used in design and performance prediction, were discussed.A control volume method based on a general curvilinear ...

  2. Predicting Pharmacokinetics of Drugs Using Physiologically Based Modeling—Application to Food Effects

    OpenAIRE

    Parrott, N.; Lukacova, V.; Fraczkiewicz, G.; Bolger, M. B.

    2009-01-01

    Our knowledge of the major mechanisms underlying the effect of food on drug absorption allows reliable qualitative prediction based on biopharmaceutical properties, which can be assessed during the pre-clinical phase of drug discovery. Furthermore, several recent examples have shown that physiologically based absorption models incorporating biorelevant drug solubility measurements can provide quite accurate quantitative prediction of food effect. However, many molecules currently in developme...

  3. Application of a noninhibitory growth model to predict the transient response in a chemostat

    Energy Technology Data Exchange (ETDEWEB)

    Chiam, H.F.; Harris, I.J.

    1983-06-01

    A method of adapting a kinetic model based on steady-state chemostat data to predict the transient performance of a chemostat culture is presented. The proposal provides for a time delay which can be considered equivalent to a period of reduced activity of the organism subsequent to the introduction of a step change in operating conditions. The adapted kinetic model gives substantially better performance in predicting the transient response of an experimental system than the unmodified kinetic model.

  4. AIDS awareness and VCT behaviour: An application of the integrated model of behaviour prediction

    OpenAIRE

    Hilde Diteweg; Adinda van Oostwaard; Hugo Tempelman; Adri Vermeer; Melanie Appels; Marieke F. van der Schaaf; David J.F. Maree

    2013-01-01

    In order to limit the expansion of the HIV and AIDS epidemic in South Africa, it is important to develop targeted prevention strategies. The voluntary HIV counselling and testing (VCT) programme appears to be effective for preventing the spread of the HI virus. This study adapted guidelines of the integrated model of behaviour prediction (IMBP) into a questionnaire and examined the extent to which it predicts behaviour. A sample of 92 sports team members from Limpopo ranging from 14 to 30 yea...

  5. Applications of Semi-parametric Estimation Methods in Causal Inference and Prediction

    OpenAIRE

    Jamshidian, Farid

    2011-01-01

    In this thesis, we argue for the use of loss-based semi-parametric estimation methods as an alternative to traditional parametric models in causal inference and prediction. We present a brief discussion on "black box" epidemiology in the first chapter and argue that risk factor epidemiology can be improved by using semi-parametric estimation methods. We demonstrate the use of semi-parametric methods by applying them to two different problems: one in causal inference and another in prediction....

  6. Super Learning: An Application to the Prediction of HIV-1 Drug Resistance*

    OpenAIRE

    2007-01-01

    Many alternative data-adaptive algorithms can be used to learn a predictor based on observed data. Examples of such learners include decision trees, neural networks, support vector regression, least angle regression, logic regression, and the Deletion/Substitution/Addition algorithm. The optimal learner for prediction will vary depending on the underlying data-generating distribution. In this article we introduce the “super learner”, a prediction algorithm that applies any set of candidate le...

  7. Application of artificial neural network in bundle critical heat flux prediction

    International Nuclear Information System (INIS)

    A bundle critical heat flux (CHF) database based on subchannel local condition is obtained by analyzing existing bundle experimental database with COBRA-IV code. Artificial neural network is then applied to train the database and a bundle CHF prediction model is finally obtained. The prediction accuracy of the obtained model is much better than that from general empiric formula, and the root-mean-square of predicated value is 5.63%. (authors)

  8. A new tool to control meat products safety: a web based application of predictive microbiology models

    OpenAIRE

    Delhalle, Laurent; Adolphe, Ysabelle; Crevecoeur, Sébastien; Didimo Imazaki, Pedro Henrique; Daube, Georges; Clinquart, Antoine

    2011-01-01

    Predictive microbiology is considered by the European legislation as a tool to control food safety. Meat and meat products are particularly sensitive to contamination with pathogens. However, development of predictive microbiology models and interpretation of results require specific knowledge. A free web based model has been developed for an easy use by people who are not experts in this field as industries and public authorities. The model can simulate the growth of Salmonella spp, Listeria...

  9. Are Preoperative Kattan and Stephenson Nomograms Predicting Biochemical Recurrence after Radical Prostatectomy Applicable in the Chinese Population?

    OpenAIRE

    Victor H. W. Yeung; Yi Chiu; Sylvia S. Y. Yu; Au, W. H.; Chan, Steve W.H.

    2013-01-01

    Purpose. Kattan and Stephenson nomograms are based on the outcomes of patients with prostate cancer recruited in the USA, but their applicability to Chinese patients is yet to be validated. We aim at studying the predictive accuracy of these nomograms in the Chinese population. Patients and Methods. A total of 408 patients who underwent laparoscopic or open radical resection of prostate from 1995 to 2009 were recruited. The preoperative clinical parameters of these patients were collected, an...

  10. Aircraft noise prediction via aeroacoustic hybrid methods - development and application of onera tools over the last decade : some examples.

    OpenAIRE

    Redonnet, S.

    2014-01-01

    International audience This article focuses on advanced noise prediction methodologies, in regard to aircraft noise mitigation. More precisely, the so-called aeroacoustic hybrid methodology is first recalled here, before illustrating its potentialities through several examples of application to realistic aircraft noise problems. Among other things, this paper highlights how Onera has contributed to the development of reliable computational methodologies over the last decade, which can now ...

  11. Testing an application of a biotic ligand model to predict acute toxicity of metal mixtures to rainbow trout.

    Science.gov (United States)

    Iwasaki, Yuichi; Kamo, Masashi; Naito, Wataru

    2015-04-01

    The authors tested the applicability of a previously developed biotic ligand model (BLM) to predict acute toxicity of single metals and metal mixtures (cadmium, lead, and zinc) to rainbow trout fry (Oncorhynchus mykiss) from a single available dataset. The BLM used in the present study hypothesizes that metals inhibit an essential cation (calcium) and organisms die as a result of its deficiency, leading to an assumption that the proportion of metal-binding ligand (f) is responsible for the toxic effects of metals on the survival of rainbow trout. The f value is a function of free-ion concentrations of metals computed by a chemical speciation model, and the function has affinity constants as model parameters. First, the survival effects of single metals were statistically modeled separately (i.e., f-survival relationship) by using the generalized linear mixed model with binomial distribution. The modeled responses of survival rates to f overlapped reasonably irrespective of metals tested, supporting the theoretical prediction from the BLM that f-survival relationships are comparable regardless of metal species. The authors thus developed the generalized linear mixed model based on all data pooled across the single-metal tests. The best-fitted model well predicted the survival responses observed in mixture tests (r = 0.97), providing support for the applicability of the BLM to predict effects of metal mixtures. PMID:25323464

  12. Gaussian process prediction of the stress-free configuration of pre-deformed soft tissues: Application to the human cornea.

    Science.gov (United States)

    Businaro, Elena; Studer, Harald; Pajic, Bojan; Büchler, Philippe

    2016-04-01

    Image-based modeling is a popular approach to perform patient-specific biomechanical simulations. One constraint of this technique is that the shape of soft tissues acquired in-vivo is deformed by the physiological loads. Accurate simulations require determining the existing stress in the tissues or their stress-free configurations. This process is time consuming, which is a limitation to the dissemination of numerical planning solutions to clinical practice. In this study, we propose a method to determine the stress-free configuration of soft tissues using a Gaussian process (GP) regression. The prediction relies on a database of pre-calculated results to enable real time predictions. The application of this technique to the human cornea showed a level of accuracy five to ten times higher than the accuracy of the topographic device used to obtain the patients' anatomy; results showed that for almost all optical indices, the predicted curvature error did not exceed 0.025D, while the wavefront aberration percentage error did not overcome 5%. In this context, we believe that GP models are suitable for predicting the stress free configuration of the cornea and can be used in planning tools based on patient-specific finite element simulations. Due to the high level of accuracy required in ophthalmology, this approach is likely to be appropriate for other applications requiring the definition of the relaxed shape of soft tissues. PMID:26920075

  13. Application of Artificial Neural Network in Predicting the Survival Rate of Gastric Cancer Patients

    Directory of Open Access Journals (Sweden)

    A Biglarian

    2011-06-01

    Full Text Available "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 cancers, Tehran, Iran, to predict the survival time using Cox proportional hazard and artificial neural network techniques. "nResults: The estimated one-year, two-year, three-year, four-year and five-year survival rates of the patients were 77.9%, 53.1%, 40.8%, 32.0%, and 17.4%, respectively. The Cox regression analysis revealed that the age at diag-nosis, high-risk behaviors, extent of wall penetration, distant metastasis and tumor stage were significantly associated with the survival rate of the patients. The true prediction of neural network was 83.1%, and for Cox regression model, 75.0%."nConclusion: The present study shows that neural network model is a more powerful statistical tool in predicting the survival rate of the gastric cancer patients compared to Cox proportional hazard regression model. Therefore, this model recommended for the predicting the survival rate of these patients.

  14. 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. PMID:27424056

  15. A Case Study of Software Product Line for Business Applications Changeability Prediction

    OpenAIRE

    Roško, Zdravko; Strahonja, Vjeran

    2014-01-01

    The changeability, a sub-characteristic of maintainability, refers to the level of effort which is required to do modifications to a software product line (SPL) application component. Assuming dependencies between SPL application components and reference architecture implementation (a platform), this paper empirically investigates the relationship between 7 design metrics and changeability of 46 server components of a product line for business applications. In addition, we investigated the us...

  16. DNA-binding protein prediction using plant specific support vector machines: validation and application of a new genome annotation tool.

    Science.gov (United States)

    Motion, Graham B; Howden, Andrew J M; Huitema, Edgar; Jones, Susan

    2015-12-15

    There are currently 151 plants with draft genomes available but levels of functional annotation for putative protein products are low. Therefore, accurate computational predictions are essential to annotate genomes in the first instance, and to provide focus for the more costly and time consuming functional assays that follow. DNA-binding proteins are an important class of proteins that require annotation, but current computational methods are not applicable for genome wide predictions in plant species. Here, we explore the use of species and lineage specific models for the prediction of DNA-binding proteins in plants. We show that a species specific support vector machine model based on Arabidopsis sequence data is more accurate (accuracy 81%) than a generic model (74%), and based on this we develop a plant specific model for predicting DNA-binding proteins. We apply this model to the tomato proteome and demonstrate its ability to perform accurate high-throughput prediction of DNA-binding proteins. In doing so, we have annotated 36 currently uncharacterised proteins by assigning a putative DNA-binding function. Our model is publically available and we propose it be used in combination with existing tools to help increase annotation levels of DNA-binding proteins encoded in plant genomes. PMID:26304539

  17. Application of artificial neural networks as a tool for moisture prediction in microbially colonized halite in the Atacama Desert

    Science.gov (United States)

    Wierzchos, K.; Cancilla, J. C.; Torrecilla, J. S.; Díaz-Rodríguez, P.; Davila, A. F.; Ascaso, C.; Nienow, J.; McKay, C. P.; Wierzchos, J.

    2015-06-01

    The Atacama Desert is the driest and one of the most life-limiting places on Earth. Despite the extreme conditions, microbial endolithic communities have been found inside halite rocks. The presence of these microbial communities is possible due to the hygroscopic properties of evaporitic rocks composed of sodium chloride. It is important to elucidate every possible water source in such a hyperarid environment. Therefore, in the present study, an artificial neural network (ANN) based model has been designed to predict the presence of liquid water on the surface of halite pinnacles. The model predicts the moisture formation using two basic meteorological variables, air temperature, and air relative humidity. ANNs have been successfully employed for the first time as a tool for predicting the appearance of liquid water, a key factor for the endolithic microbial communities living in the driest part of the Atacama Desert. The model developed is able to correctly predict the formation of water on the surface of the halite pinnacles 83% of the cases. We anticipate the future application of this model as an important tool for the prediction of the water availability and therefore potential habitability of lithic substrates in extreme environments on Earth and perhaps elsewhere.

  18. 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.

  19. Application of ann for predicting water quality parameters in the mediterranean sea along gaza-palestine

    International Nuclear Information System (INIS)

    Seawater pollution problems are gaining interest world wide because of their health impacts and other environmental issues. Intense human activities in areas surrounding enclosed and semi-enclosed seas such as the Mediterranean Sea always produce in the long term a strong environmental impact in the form of coastal and marine degradation. This paper is concerned with the use of ANNs (Artificial Neural Networks) MLP ( Multilayer Perceptron) model for the prediction of pH and EC (Electrical Conductivity) in water quality parameters along Gaza city coast. MLP neural networks are trained and developed with reference to three major oceanographic parameters (water temperature, wind speed and turbidity) to predict the values of pH and EC; these parameters are considered as inputs of the neural network. The data collected comprised of four years and collected from nine locations along Gaza coastline. Results show that the model has high capability and accuracy in predicting both parameters. The network performance has been validated with different data sets and the results show satisfactory performance. Results of the developed model have been compared with multiple regression statistical models and found that MLP predictions are slightly better than the conventional methods. Prediction results prove that the proposed approach is suitable for modeling the water quality in the Mediterranean Sea along Gaza. (author)

  20. Estimation of uncertainties in predictions of environmental transfer models: evaluation of methods and application to CHERPAC

    International Nuclear Information System (INIS)

    Models used to simulate environmental transfer of radionuclides typically include many parameters, the values of which are uncertain. An estimation of the uncertainty associated with the predictions is therefore essential. Difference methods to quantify the uncertainty in the prediction parameter uncertainties are reviewed. A statistical approach using random sampling techniques is recommended for complex models with many uncertain parameters. In this approach, the probability density function of the model output is obtained from multiple realizations of the model according to a multivariate random sample of the different input parameters. Sampling efficiency can be improved by using a stratified scheme (Latin Hypercube Sampling). Sample size can also be restricted when statistical tolerance limits needs to be estimated. Methods to rank parameters according to their contribution to uncertainty in the model prediction are also reviewed. Recommended are measures of sensitivity, correlation and regression coefficients that can be calculated on values of input and output variables generated during the propagation of uncertainties through the model. A parameter uncertainty analysis is performed for the CHERPAC food chain model which estimates subjective confidence limits and intervals on the predictions at a 95% confidence level. A sensitivity analysis is also carried out using partial rank correlation coefficients. This identified and ranks the parameters which are the main contributors to uncertainty in the predictions, thereby guiding further research efforts. (author). 44 refs., 2 tabs., 4 figs

  1. 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.

  2. 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.

  3. 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...

  4. Hybrid optimization model and its application in prediction of gas emission

    Institute of Scientific and Technical Information of China (English)

    FU Hua; SHU Dan-dan; KANG Hai-chao; YANG Yi-kui

    2012-01-01

    According to the complex nonlinear relationship between gas emission and its effect factors,and the shortcomings that basic colony algorithm is slow,prone to early maturity and stagnation during the search,we introduced a hybrid optimization strategy into a max-min ant colony algorithm,then use this improved ant colony algorithm to estimate the scope of RBF network parameters.According to the amount of pheromone of discrete points,the authors obtained from the interval of network parameters,ants optimize network parameters.Finally,local spatial expansion is introduced to get further optimization of the network.Therefore,we obtain a better time efficiency and solution efficiency optimization model called hybrid improved max-min ant system (HI-MMAS).Simulation experiments,using these theory to predict the gas emission from the working face,show that the proposed method have high prediction feasibility and it is an effective method to predict gas emission.

  5. 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.

  6. Prediction of fracture in the transition regime: application to an A533B pressure vessel steel

    International Nuclear Information System (INIS)

    In order to model the fracture behaviour of pressure vessel steels in the transition regime, a new model has been developped in 1991 at EDF R and D Division in the framework of local approach to fracture. This approach couples the damage mechanics model developped by Rousselier which is accounting for ductile propagation of a crack, and the Beremin's model based on Weibull's statistics which stands for cleavage. It is possible to predict, by this coupled approach, safe lower bound transition curves for a temperature range up to RTNDT+50 C. It has been shown that the predictions of the model agree well with the experimental data, both in terms of fracture toughness at cleavage instability and the amount of pre-cleavage tearing. Those predicted curves have also been compared to the ASME design curve, and substantial safety margins have been exhibited. (orig.)

  7. 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.

  8. Mathcad computer applications predicting antenna parameters from antenna physical dimensions and ground characteristics

    OpenAIRE

    Gerry, Donald D.

    1993-01-01

    Approved for public release; distribution is unlimited. This report provides the documentation for a set of computer applications for the evaluation of antenna parameters. The applications are written for the Mathcad personal computer software for various antenna types listed in the thesis index. Antenna dimen Lieutenant Commander, United States Navy

  9. 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

  10. 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

  11. 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.

  12. Combining multiple models to generate consensus: Application to radiation-induced pneumonitis prediction

    International Nuclear Information System (INIS)

    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 and

  13. Application of Artificial Neural Network For Path Loss Prediction In Urban Macrocellular Environment

    Directory of Open Access Journals (Sweden)

    Joseph M. Mom

    2016-07-01

    Full Text Available An artificial neural network model for the prediction of path loss in urban macrocellular environment is presented. The model consists of a multilayer perceptron trained with measured data using Scaled Conjugate Gradient algorithm. Comparison between the proposed model on one hand, and the free space, Hata and Egli models on the other hand shows a better prediction result. With the proposed ANN model a good generalization is achieved, and it is accurate in environments different from the one used in training the network.

  14. 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.

  15. Improvement of Virtual Screening Predictions using Computational Intelligence Methods

    OpenAIRE

    Cano, Gaspar; García Rodríguez, José; Pérez Sánchez, Horacio

    2014-01-01

    Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of scoring functions used in most VS methods we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SV...

  16. Predictive validity of behavioural animal models for chronic pain

    OpenAIRE

    Berge, Odd-Geir

    2011-01-01

    Rodent models of chronic pain may elucidate pathophysiological mechanisms and identify potential drug targets, but whether they predict clinical efficacy of novel compounds is controversial. Several potential analgesics have failed in clinical trials, in spite of strong animal modelling support for efficacy, but there are also examples of successful modelling. Significant differences in how methods are implemented and results are reported means that a literature-based comparison between precl...

  17. 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.

  18. 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…

  19. Predictive optimal control of sewer networks using CORAL tool: application to Riera Blanca catchment in Barcelona.

    Science.gov (United States)

    Puig, V; Cembrano, G; Romera, J; Quevedo, J; Aznar, B; Ramón, G; Cabot, J

    2009-01-01

    This paper deals with the global control of the Riera Blanca catchment in the Barcelona sewer network using a predictive optimal control approach. This catchment has been modelled using a conceptual modelling approach based on decomposing the catchments in subcatchments and representing them as virtual tanks. This conceptual modelling approach allows real-time model calibration and control of the sewer network. The global control problem of the Riera Blanca catchment is solved using a optimal/predictive control algorithm. To implement the predictive optimal control of the Riera Blanca catchment, a software tool named CORAL is used. The on-line control is simulated by interfacing CORAL with a high fidelity simulator of sewer networks (MOUSE). CORAL interchanges readings from the limnimeters and gate commands with MOUSE as if it was connected with the real SCADA system. Finally, the global control results obtained using the predictive optimal control are presented and compared against the results obtained using current local control system. The results obtained using the global control are very satisfactory compared to those obtained using the local control. PMID:19700825

  20. Application of Fuzzy Regression Model to the Prediction of Field Mouse Occurrence Rate

    Institute of Scientific and Technical Information of China (English)

    XU Fei

    2009-01-01

    Expressions were given to describe the closeness between the estimated value and observed value for two asymmetric exponential fuzzy numbers. Based on that, the model was given to solve the question of fuzzy multivariable regression with fuzzy input, fuzzy output and crisp coefficients. Finally, with this model, the prediction of field mouse occurrence rate had been done and the satisfied result was obtained.

  1. Prediction of First-Order Vessel Responses with Applications to Decision Support Systems

    DEFF Research Database (Denmark)

    Nielsen, Ulrik D.; Iseki, Toshio

    2015-01-01

    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....

  2. Nonlinear modeling and predictive functional control of Hammerstein system with application to the turntable servo system

    Science.gov (United States)

    Zhang, Qian; Wang, Qunjing; Li, Guoli

    2016-05-01

    This article deals with the identification of nonlinear model and Nonlinear Predictive Functional Controller (NPFC) design based on the Hammerstein structure for the turntable servo system. As a mechanism with multi-mass rotational system, nonlinearities significantly influence the system operation, especially when the turntable is in the states of zero-crossing distortion or rapid acceleration/deceleration, etc. The field data from identification experiments are processed by Comprehensive Learning Particle Swarm Optimization (CLPSO). As a result, Hammerstein model can be derived to describe the input-output relationship globally, considering all the linear and nonlinear factors of the turntable system. Cross validation results demonstrate good correspondence between the real equipment and the identified model. In the second part of this manuscript, a nonlinear control strategy based on the genetic algorithm and predictive control is developed. The global nonlinear predictive controller is carried out by two steps: (i) build the linear predictive functional controller with state space equations for the linear subsystem of Hammerstein model, and (ii) optimize the global control variable by minimizing the cost function through genetic algorithm. On the basis of distinguish model for turntable and the effectiveness of NPFC, the good performance of tracking ability is achieved in the simulation results.

  3. Prediction of antimicrobial peptides based on the adaptive neuro-fuzzy inference system application.

    Science.gov (United States)

    Fernandes, Fabiano C; Rigden, Daniel J; Franco, Octavio L

    2012-01-01

    Antimicrobial peptides (AMPs) are widely distributed defense molecules and represent a promising alternative for solving the problem of antibiotic resistance. Nevertheless, the experimental time required to screen putative AMPs makes computational simulations based on peptide sequence analysis and/or molecular modeling extremely attractive. Artificial intelligence methods acting as simulation and prediction tools are of great importance in helping to efficiently discover and design novel AMPs. In the present study, state-of-the-art published outcomes using different prediction methods and databases were compared to an adaptive neuro-fuzzy inference system (ANFIS) model. Data from our study showed that ANFIS obtained an accuracy of 96.7% and a Matthew's Correlation Coefficient (MCC) of0.936, which proved it to be an efficient model for pattern recognition in antimicrobial peptide prediction. Furthermore, a lower number of input parameters were needed for the ANFIS model, improving the speed and ease of prediction. In summary, due to the fuzzy nature ofAMP physicochemical properties, the ANFIS approach presented here can provide an efficient solution for screening putative AMP sequences and for exploration of properties characteristic of AMPs. PMID:23193592

  4. Progress on the Key Technology Development in Application of Ensemble Prediction Products Associated with TIGGE

    Institute of Scientific and Technical Information of China (English)

    JIAO Meiyan

    2010-01-01

    @@ This project is supported by the 2007 R & D Special Fund for Public Welfare by Ministry of Science and Technology and Ministry of Finance.Research tasks in this project are proposed based on the implementation plan of the "THORPEX (The Observing System Research and Predictability Experiment) Interactive Grand Global Ensemble (TIGGE)",a sub-project of the THORPEX international program.

  5. The Prediction of College Student Academic Performance and Retention: Application of Expectancy and Goal Setting Theories

    Science.gov (United States)

    Friedman, Barry A.; Mandel, Rhonda G.

    2010-01-01

    Student retention and performance in higher education are important issues for educators, students, and the nation facing critical professional labor shortages. Expectancy and goal setting theories were used to predict academic performance and college student retention. Students' academic expectancy motivation at the start of the college…

  6. Application of the artificial neural networks for prediction of magnetic saturation of metallic amorphous alloys

    Directory of Open Access Journals (Sweden)

    J. Konieczny

    2008-04-01

    Full Text Available Purpose: The aim of the work is to employ the artificial neural networks for prediction of magnetic saturation ofthe amorphous alloys with the iron and cobalt matrix.Design/methodology/approach: It has been assumed that the artificial neural networks can be used toassign the relationship between the chemical compositions of amorphous alloys, temperature of heat treatment andmagnetic saturation. In order to determine the relationship it has been necessary to work out a suitable calculationmodel. It has been proved that employment of genetic algorithm to selection of input neurons can be very usefultool to improve artificial neural network calculation results. The attempt to use the artificial neural networks forpredicting the effect of the chemical composition and temperature of heat treatment on the magnetic saturation BSsucceeded, as the level of the obtained results was acceptable.Findings: Artificial neural networks, can be applied for predicting the effect of the chemical composition andtemperature of heat treatment on the magnetic saturation.Research limitations/implications: Worked out model should be used for prediction of magnetic saturationonly in particular groups of amorphous alloys, mostly because of the discontinuous character of input data.Practical implications: The results of research make it possible to calculate with a certain admissible error the magneticsaturation Bs value basing on combinations of concentrations of the particular elements and heat treatment temperature.Originality/value: In this paper it has been presented an original trial of prediction of the required magneticproperties of the iron and cobalt amorphous alloys.

  7. Application of GIS in prediction and assessment system of off-site accident consequence for NPP

    International Nuclear Information System (INIS)

    The assessment and prediction software system of off-site accident consequence for Guangdong Nuclear Power Plant (GNARD2.0) is a GIS-based software system. The spatial analysis of radioactive materials and doses with geographic information is available in this system. The structure and functions of the GNARD system and the method of applying ArcView GIS are presented

  8. Super Learning: An Application to the Prediction of HIV-1 Drug Resistance*

    Science.gov (United States)

    Sinisi, Sandra E.; Polley, Eric C.; Petersen, Maya L.; Rhee, Soo-Yon; van der Laan, Mark J.

    2008-01-01

    Many alternative data-adaptive algorithms can be used to learn a predictor based on observed data. Examples of such learners include decision trees, neural networks, support vector regression, least angle regression, logic regression, and the Deletion/Substitution/Addition algorithm. The optimal learner for prediction will vary depending on the underlying data-generating distribution. In this article we introduce the “super learner”, a prediction algorithm that applies any set of candidate learners and uses cross-validation to select between them. Theory shows that asymptotically the super learner performs essentially as well as or better than any of the candidate learners. In this article we present the theory behind the super learner, and illustrate its performance using simulations. We further apply the super learner to a data example, in which we predict the phenotypic antiretroviral susceptibility of HIV based on viral genotype. Specifically, we apply the super learner to predict susceptibility to a specific protease inhibitor, nelfinavir, using a set of database-derived non-polymorphic treatment-selected mutations. PMID:17402922

  9. Super learning: an application to the prediction of HIV-1 drug resistance.

    Science.gov (United States)

    Sinisi, Sandra E; Polley, Eric C; Petersen, Maya L; Rhee, Soo-Yon; van der Laan, Mark J

    2007-01-01

    Many alternative data-adaptive algorithms can be used to learn a predictor based on observed data. Examples of such learners include decision trees, neural networks, support vector regression, least angle regression, logic regression, and the Deletion/Substitution/Addition algorithm. The optimal learner for prediction will vary depending on the underlying data-generating distribution. In this article we introduce the "super learner", a prediction algorithm that applies any set of candidate learners and uses cross-validation to select between them. Theory shows that asymptotically the super learner performs essentially as well as or better than any of the candidate learners. In this article we present the theory behind the super learner, and illustrate its performance using simulations. We further apply the super learner to a data example, in which we predict the phenotypic antiretroviral susceptibility of HIV based on viral genotype. Specifically, we apply the super learner to predict susceptibility to a specific protease inhibitor, nelfinavir, using a set of database-derived non-polymorphic treatment-selected mutations. PMID:17402922

  10. Thermodynamics of grain boundary segregation and applications to anisotropy, compensation effect and prediction

    Czech Academy of Sciences Publication Activity Database

    Lejček, Pavel; Hofmann, S.

    2008-01-01

    Roč. 33, č. 2 (2008), s. 133-163. ISSN 1040-8436 R&D Projects: GA ČR(CZ) GA106/05/0134 Institutional research plan: CEZ:AV0Z10100520 Keywords : anisotropy * compensation effect * gtrain boundaries * prediction * excess Gibbs energy Subject RIV: BM - Solid Matter Physics ; Magnetism Impact factor: 6.300, year: 2008

  11. Application of Simulation-based Reliability Assessment, SBRA, for Lifetime Prediction of Concrete Structures

    Czech Academy of Sciences Publication Activity Database

    Bradáč, J.; Marek, Pavel

    Bratislava : Expertcentrum, 1999 - (Jávor, T.), s. 67-72 [Life Prediction and Aging Managment of Concrete Structures. Bratislava (SK), 06.07.1999-08.07.1999] R&D Projects: GA ČR GA103/98/0215 Keywords : reliability * limit states * failure * probability * loading * resistance * service ability * Monte Carlo Subject RIV: JM - Building Engineering

  12. Development and application of Geobacillus stearothermophilus growth model for predicting spoilage of evaporated milk.

    Science.gov (United States)

    Kakagianni, Myrsini; Gougouli, Maria; Koutsoumanis, Konstantinos P

    2016-08-01

    The presence of Geobacillus stearothermophilus spores in evaporated milk constitutes an important quality problem for the milk industry. This study was undertaken to provide an approach in modelling the effect of temperature on G. stearothermophilus ATCC 7953 growth and in predicting spoilage of evaporated milk. The growth of G. stearothermophilus was monitored in tryptone soy broth at isothermal conditions (35-67 °C). The data derived were used to model the effect of temperature on G. stearothermophilus growth with a cardinal type model. The cardinal values of the model for the maximum specific growth rate were Tmin = 33.76 °C, Tmax = 68.14 °C, Topt = 61.82 °C and μopt = 2.068/h. The growth of G. stearothermophilus was assessed in evaporated milk at Topt in order to adjust the model to milk. The efficiency of the model in predicting G. stearothermophilus growth at non-isothermal conditions was evaluated by comparing predictions with observed growth under dynamic conditions and the results showed a good performance of the model. The model was further used to predict the time-to-spoilage (tts) of evaporated milk. The spoilage of this product caused by acid coagulation when the pH approached a level around 5.2, eight generations after G. stearothermophilus reached the maximum population density (Nmax). Based on the above, the tts was predicted from the growth model as the sum of the time required for the microorganism to multiply from the initial to the maximum level ( [Formula: see text] ), plus the time required after the [Formula: see text] to complete eight generations. The observed tts was very close to the predicted one indicating that the model is able to describe satisfactorily the growth of G. stearothermophilus and to provide realistic predictions for evaporated milk spoilage. PMID:27052699

  13. An application of the biotic ligand model to predict the toxic effects of metal mixtures.

    Science.gov (United States)

    Kamo, Masashi; Nagai, Takashi

    2008-07-01

    The rapidly developing biotic ligand model (BLM) allows us to predict the toxicity of heavy metals in water of various chemistries; however, the current BLM predicts the toxicity of a single metal and not the toxic effects of metal mixtures. The toxic mechanisms of heavy metals are not yet completely understood, but hypocalcemia is suggested to be the most likely toxic mechanism for some metals. The BLM, which predicts the toxicity of metals by the amount of metals binding to ligand, is modified to predict the toxicity by the proportion of nonmetal binding ligand that is available for calcium uptake under the assumption that the organisms die because of hypocalcemia when so few ligands are available for calcium uptake. Because the proportion can be computed when multiple metals are present, the toxic effects of metal mixtures can be predicted. Zinc, copper, and cadmium toxicity to rainbow trout (Oncorhynchus mykiss) are considered. All data are collected from the literature, and a meta-analysis using the modified version of the BLM is conducted. The present study found that the proportion of nonmetal binding ligand is a constant value for any test condition. The proportion is not influenced by water chemistry or by metal species. Using the nature of constant proportion, toxicities of metals are well estimated. In addition, the toxic effects of metal mixtures are the simple sum of the toxicities of each metal (additive effect) corresponding to the bioavailable form of the metals. In terms of total concentration of metals in water, however, nonadditive effects, such as antagonism and synergism, are possible. PMID:18260697

  14. Prediction of enantiomeric selectivity in chromatography. Application of conformation-dependent and conformation-independent descriptors of molecular chirality.

    Science.gov (United States)

    Aires-de-Sousa, João; Gasteiger, Johann

    2002-03-01

    In order to process molecular chirality by computational methods and to obtain predictions for properties that are influenced by chirality, a fixed-length conformation-dependent chirality code is introduced. The code consists of a set of molecular descriptors representing the chirality of a 3D molecular structure. It includes information about molecular geometry and atomic properties, and can distinguish between enantiomers, even if chirality does not result from chiral centers. The new molecular transform was applied to two datasets of chiral compounds, each of them containing pairs of enantiomers that had been separated by chiral chromatography. The elution order within each pair of isomers was predicted by means of Kohonen neural networks (NN) using the chirality codes as input. A previously described conformation-independent chirality code was also applied and the results were compared. In both applications clustering of the two classes of enantiomers (first eluted and last eluted enantiomers) could be successfully achieved by NN and accurate predictions could be obtained for independent test sets. The chirality code described here has a potential for a broad range of applications from stereoselective reactions to analytical chemistry and to the study of biological activity of chiral compounds. PMID:11885960

  15. Applications of Machine learning in Prediction of Breast Cancer Incidence and Mortality

    International Nuclear Information System (INIS)

    Breast cancer is one of the leading causes of cancer deaths for the female population in both developed and developing countries. In this work we have used the baseline descriptive data about the incidence (new cancer cases) of in situ breast cancer among Wisconsin females. The documented data were from the most recent 12-years period for which data are available. Wiscons in cancer incidence and mortality (deaths due to cancer) that occurred were also considered in this work. Artificial Neural network (ANN) have been successfully applied to problems in the prediction of the number of new cancer cases and mortality. Using artificial intelligence (AI) in this study, the numbers of new cancer cases and mortality that may occur are predicted.

  16. Empirical models for predicting wind potential for wind energy applications in rural locations of Nigeria

    Directory of Open Access Journals (Sweden)

    F. C. Odo, G. U. Akubue, S. U. Offiah, P. E. Ugwuoke

    2013-01-01

    Full Text Available In this paper, we use the correlation between the average wind speed and ambient temperature to develop models for predicting wind potentials for two Nigerian locations. Assuming that the troposphere is a typical heterogeneous mixture of ideal gases, we find that for the studied locations, wind speed clearly correlates with ambient temperature in a simple polynomial of 3rd degree. The coefficient of determination and root-mean-square error of the models are 0.81; 0.0024 and 0.56; 0.0041, respectively, for Enugu (6.40N; 7.50E and Owerri (5.50N; 7.00E. These results suggest that the temperature-based model can be used, with acceptable accuracy, in predicting wind potentials needed for preliminary design assessment of wind energy conversion devices for the locations and others with similar meteorological conditions.

  17. Linguistic Modeling of Pressure Signal in Compressor and Application in Aerodynamic Instability Prediction

    Directory of Open Access Journals (Sweden)

    Hanlin Sheng

    2014-01-01

    Full Text Available Using conditional fuzzy clustering, a linguistic model for static pressure signal of compressor outlet in aeroengine was established. The modeling process and the validation result demonstrated unique advances of linguistic modeling in the analysis of complex systems. The linguistic model was used to predict the pressure signal before the engine entered instability. The prediction result showed that the linguistic model could effectively recognize the sudden changes of pressure signal features. The detected change of signal might not necessarily be the commonly considered initial disturbance of compressor instability; however, the pattern recognition ability of linguistic model was still very attractive. At last, it pointed out that setting up a database containing experiment data and historical experience about engine aerodynamic instability and utilizing advanced intelligent computing technology in the database to develop knowledge discovery provide a new idea for the solution to the problem of aerodynamic instability in aeroengine.

  18. On the applicability of hybrid functionals for predicting fundamental properties of metals

    Science.gov (United States)

    Gao, Weiwei; Abtew, Tesfaye A.; Cai, Tianyi; Sun, Yi-Yang; Zhang, Shengbai; Zhang, Peihong

    2016-05-01

    The repercussions of an inaccurate account of electronic states near the Fermi level by hybrid functionals in predicting several important metallic properties are investigated. The difficulties include a vanishing or severely suppressed density of states (DOS) at EF, significantly widened valence bandwidth, greatly enhanced electron-phonon (el-ph) deformation potentials, and an overestimate of magnetic moment in transition metals. The erroneously enhanced el-ph coupling calculated by hybrid functionals may lead to a false prediction of lattice instability. The main culprit of the problem comes from the simplistic treatment of the exchange functional rooted in the original Fock exchange energy. The use of a short-ranged Coulomb interaction alleviates some of the drawbacks but the fundamental issues remain unchanged.

  19. Application of the Grey topological method to predict the effects of ship pitching

    Institute of Scientific and Technical Information of China (English)

    SUN Li-hong; SHEN Ji-hong

    2008-01-01

    Ship motion,with six degrees of freedom,is a complex stochastic process. Sea wind and waves are the primary influencing factors. Prediction of ship motion is significant for ship navigation. To eliminate errors,a path prediction model incorporating ship pitching was developed using the Gray topological method,after analyzing ship pitching motions. With the help of simple introduction to Gray system theory,we selected a group of threshold values. Based on an analysis of ship pitch angle sequences over 40 second intervals,a Grey metabolism GM(1,1) model was established according to the time-series which every threshold corresponded to. Forecasting future ship motion with the GM (1,1) model allowed drawing of the forecast curve with effective forecasting points. The precision of the test results show that the model is accurate,and the forecast results are reliable.

  20. Application of neural networks and sensitivity analysis to improved prediction of trauma survival.

    Science.gov (United States)

    Hunter, A; Kennedy, L; Henry, J; Ferguson, I

    2000-05-01

    The performance of trauma departments is widely audited by applying predictive models that assess probability of survival, and examining the rate of unexpected survivals and deaths. Although the TRISS methodology, a logistic regression modelling technique, is still the de facto standard, it is known that neural network models perform better. A key issue when applying neural network models is the selection of input variables. This paper proposes a novel form of sensitivity analysis, which is simpler to apply than existing techniques, and can be used for both numeric and nominal input variables. The technique is applied to the audit survival problem, and used to analyse the TRISS variables. The conclusions discuss the implications for the design of further improved scoring schemes and predictive models. PMID:10699681

  1. Application of Physics Model in prediction of the Hellas Euro election results

    Directory of Open Access Journals (Sweden)

    L. Magafas

    2009-01-01

    Full Text Available In this paper we use chaos theory to predict the Hellenic Euro election results in the form of time series for Hellenic political parties New Democracy (ND, Panhellenic Socialistic Movement (PASOK, Hellenic Communistic Party (KKE , Coalition of the Radical Left (SYRIZA and (Popular Orthodox Rally LAOS, using the properties of the reconstructed strange attrac-tor of the corresponding non linear system, creating a new scientific field called “DemoscopoPhysics”. For this purpose we found the optimal delay time, the correlation and embedding dimension with the method of Grassberger and Procassia. With the help of topological properties of the corresponding strange attractor we achieved up to a 60 time steps out of sample pre-diction of the public survey.

  2. Application of genetic algorithm - multiple linear regressions to predict the activity of RSK inhibitors

    Directory of Open Access Journals (Sweden)

    Avval Zhila Mohajeri

    2015-01-01

    Full Text Available This paper deals with developing a linear quantitative structure-activity relationship (QSAR model for predicting the RSK inhibition activity of some new compounds. A dataset consisting of 62 pyrazino [1,2-α] indole, diazepino [1,2-α] indole, and imidazole derivatives with known inhibitory activities was used. Multiple linear regressions (MLR technique combined with the stepwise (SW and the genetic algorithm (GA methods as variable selection tools was employed. For more checking stability, robustness and predictability of the proposed models, internal and external validation techniques were used. Comparison of the results obtained, indicate that the GA-MLR model is superior to the SW-MLR model and that it isapplicable for designing novel RSK inhibitors.

  3. Model predictive control algorithms and their application to a continuous fermenter

    Directory of Open Access Journals (Sweden)

    R. G. SILVA

    1999-06-01

    Full Text Available In many continuous fermentation processes, the control objective is to maximize productivity per unit time. The optimum operational point in the steady state can be obtained by maximizing the productivity rate using feed substrate concentration as the independent variable with the equations of the static model as constraints. In the present study, three model-based control schemes have been developed and implemented for a continuous fermenter. The first method modifies the well-known dynamic matrix control (DMC algorithm by making it adaptive. The other two use nonlinear model predictive control algorithms (NMPC, nonlinear model predictive control for calculation of control actions. The NMPC1 algorithm, which uses orthogonal collocation in finite elements, acted similar to NMPC2, which uses equidistant collocation. These algorithms are compared with DMC. The results obtained show the good performance of nonlinear algorithms.

  4. Thalamic functional connectivity predicts seizure laterality in individual TLE patients: Application of a biomarker development strategy

    Directory of Open Access Journals (Sweden)

    Daniel S. Barron

    2015-01-01

    No significant differences in functional connection strength in patient and control groups were observed with Mann-Whitney Tests (corrected for multiple comparisons. Notwithstanding the lack of group differences, individual patient difference scores (from control mean connection strength successfully predicted seizure onset zone as shown in ROC curves: discriminant analysis (two-dimensional predicted seizure onset zone with 85% sensitivity and 91% specificity; logistic regression (four-dimensional achieved 86% sensitivity and 100% specificity. The strongest markers in both analyses were left thalamo-hippocampal and right thalamo-entorhinal cortex functional connection strength. Thus, this study shows that thalamic functional connections are sensitive and specific markers of seizure onset laterality in individual temporal lobe epilepsy patients. This study also advances an overall strategy for the programmatic development of neuroimaging biomarkers in clinical and genetic populations: a disease model informed by coordinate-based meta-analysis was used to anatomically constrain individual patient analyses.

  5. Risk of hypertension in Yozgat Province, Central Anatolia: application of Framingham Hypertension Prediction Risk Score.

    Science.gov (United States)

    Kilic, M; Ede, H; Kilic, A I

    2016-04-01

    The aim of this cross-sectional study was to estimate the risk of hypertension in 1106 Caucasian individuals aged 20-69 years in Yozgat Province, using the Framingham Hypertension Risk Prediction Score (FHRPS). According to FHRPS, average risk of developing hypertension over 4 years was 6.2%. The participants were classified into low- (10%) risk groups. The percentage of participants that fell into these groups was 59.4%, 19.8% and 20.8% respectively. The proportion of participants in the high-risk group was similar to the 4-year incidence of hypertension (21.3%) in the Turkish population. Regression analysis showed that high salt consumption and low educational level significantly increased the risk of hypertension. Economic level, fat consumption, life satisfaction, physical activity, and fruit and vegetable consumption were not correlated with risk of hypertension. This study shows that FHRPS can also be used for predicting risk of hypertension in Central Anatolia. PMID:27432406

  6. Development and Application of Advanced Weather Prediction Technologies for the Wind Energy Industry (Invited)

    Science.gov (United States)

    Mahoney, W. P.; Wiener, G.; Liu, Y.; Myers, W.; Johnson, D.

    2010-12-01

    Wind energy decision makers are required to make critical judgments on a daily basis with regard to energy generation, distribution, demand, storage, and integration. Accurate knowledge of the present and future state of the atmosphere is vital in making these decisions. As wind energy portfolios expand, this forecast problem is taking on new urgency because wind forecast inaccuracies frequently lead to substantial economic losses and constrain the national expansion of renewable energy. Improved weather prediction and precise spatial analysis of small-scale weather events are crucial for renewable energy management. In early 2009, the National Center for Atmospheric Research (NCAR) began a collaborative project with Xcel Energy Services, Inc. to perform research and develop technologies to improve Xcel Energy's ability to increase the amount of wind energy in their generation portfolio. The agreement and scope of work was designed to provide highly detailed, localized wind energy forecasts to enable Xcel Energy to more efficiently integrate electricity generated from wind into the power grid. The wind prediction technologies are designed to help Xcel Energy operators make critical decisions about powering down traditional coal and natural gas-powered plants when sufficient wind energy is predicted. The wind prediction technologies have been designed to cover Xcel Energy wind resources spanning a region from Wisconsin to New Mexico. The goal of the project is not only to improve Xcel Energy’s wind energy prediction capabilities, but also to make technological advancements in wind and wind energy prediction, expand our knowledge of boundary layer meteorology, and share the results across the renewable energy industry. To generate wind energy forecasts, NCAR is incorporating observations of current atmospheric conditions from a variety of sources including satellites, aircraft, weather radars, ground-based weather stations, wind profilers, and even wind sensors on

  7. The application of ductile-fracture analysis to predictions of pressure-tube failure

    International Nuclear Information System (INIS)

    Progress during the past six years towards establishing a method for predicting critical crack length in a reactor pressure tube, based on data from tests on small fracture-mechanics specimens, is reviewed. The disadvantages of relying on data from burst tests alone are described along with the benefits of a small-specimen method. It is clear from the work reviewed that only an approach that can account for the ability of the presssure tube material to increase its crack-growth resistance during stable crack extension is suitable for the prediction of critical crack length. A method that utilizes crack-growth resistance curves based on crack-opening displacement, or the J integral, is described, along with a large body of experimental data. It is concluded that the resistance curve approach provides a viable method for the analysis of fracture in pressure tubes that can greatly improve our understanding of the material's behaviour

  8. Modelling flexible thrust performance for trajectory prediction applications in air traffic management

    OpenAIRE

    Matamoros Cid, Ismael

    2015-01-01

    The Air Traffic Management (ATM) paradigm is shifting towards a scenario where Trajectory Predictors (TP) play a key role. They rely on Aircraft Performance Models (APM), mathematical models of the performance related characteristics of aircraft. The widespread use of non-coventional take-off procedures, such as the flexible thrust method, has arose the necessity of modelling them to keep fidelity in take-off trajectory predictions. This project, carried out with Boeing Research & Technology ...

  9. Fuzzy Sets Method of Reliability Prediction and Its Application to a Turbocharger of Diesel Engines

    OpenAIRE

    Yan-Feng Li; Hong-Zhong Huang

    2013-01-01

    Diesel engine is a complex electromechanical system which must operate reliably in harsh working environments. Reliability analysis and prediction play an important role during the design and development of diesel engines. However, in the traditional reliability methods, the analytical result obtained from the conventional failure mode, effects, and criticality analysis (FMECA) is not sufficient, which not only increases the workload of designers in charge of reliability, but also prolongs th...

  10. Application of Partial Least-Squares Regression Model on Temperature Analysis and Prediction of RCCD

    OpenAIRE

    Yuqing Zhao; Zhenxian Xing

    2013-01-01

    This study, based on the temperature monitoring data of jiangya RCCD, uses principle and method of partial least-squares regression to analyze and predict temperature variation of RCCD. By founding partial least-squares regression model, multiple correlations of independent variables is overcome, organic combination on multiple linear regressions, multiple linear regression and canonical correlation analysis is achieved. Compared with general least-squares regression model result, it is more ...

  11. Online Prediction under Model Uncertainty Via Dynamic Model Averaging: Application to a Cold Rolling Mill

    Czech Academy of Sciences Publication Activity Database

    Raftery, A. E.; Kárný, Miroslav; Ettler, P.

    Volume 52, Number 1 (2010), s. 52-66. ISSN 0040-1706 R&D Projects: GA MŠk 1M0572; GA MŠk(CZ) 7D09008 Institutional research plan: CEZ:AV0Z10750506 Keywords : prediction * rolling mills * Bayesian Dynamic Averaging Subject RIV: BC - Control Systems Theory Impact factor: 1.560, year: 2010 http://library.utia.cas.cz/separaty/2010/AS/karny-0342595.pdf

  12. Application of conditional nonlinear optimal perturbation method to the predictability study of the Kuroshio large meander

    OpenAIRE

    Wang, Qiang; Mu, M.; Dijkstra, H. A.

    2012-01-01

    A reduced-gravity barotropic shallow-water model was used to simulate the Kuroshio path variations. The results show that the model was able to capture the essential features of these path variations. We used one simulation of the model as the reference state and investigated the effects of errors in model parameters on the prediction of the transition to the Kuroshio large meander (KLM) state using the conditional nonlinear optimal parameter perturbation (CNOP-P) method. Because of their rel...

  13. Data-based fault-tolerant model predictive controller an application to a complex dearomatization process

    OpenAIRE

    Kettunen, Markus

    2010-01-01

    The tightening global competition during the last few decades has been the driving force for the optimisation of industrial plant operations through the use of advanced control methods, such as model predictive control (MPC). As the occurrence of faults in the process measurements and actuators has become more common due to the increase in the complexity of the control systems, the need for fault-tolerant control (FTC) to prevent the degradation of the controller performance, and therefore th...

  14. Application of Laguerre based adaptive predictive control to Shape Memory Alloy (SMA) actuators

    OpenAIRE

    S Kannan; GIRAUD-ONDINE, Christophe; PATOOR, Etienne

    2013-01-01

    This paper discusses the use of an existing adaptive predictive controller to control some Shape Memory Alloy (SMA) linear actuators. The model consists in a truncated linear combination of Laguerre filters identified online. The controller stability is studied in details. It is proven that the tracking error is asymptotically stable under some conditions on the modelling error. Moreover, the tracking error converge toward zero for step references, even if the identified model is inaccurate. ...

  15. A review on the Application of Empirical Models to Hydrate Formation Prediction

    OpenAIRE

    Abbasi Aijaz; Hashim Fakhruldin Mohd

    2014-01-01

    In deepwater hydrocarbon transportation pipeline, the production may decrease and operational cost and time are increasing due to the growth rate of hydrate. The pressure of deepwater pipeline is comparatively high, so it is entirely possible to form the hydrate formation conditions and pose a major operational and safety challenge. This work provides a review on empirical models for hydrate formation prediction in deepwater gas pipeline. The correlation and empirical models are presented wit...

  16. Application of Soft Computing for the Prediction of Warpage of Plastic Injection

    OpenAIRE

    Vijaya Kumar Reddy; J.Suresh Kumar*; B. Sidda Reddy; G. Padmanabhan

    2009-01-01

    This paper deals with the development of accurate warpage prediction model for plastic injection molded parts using softcomputing tools namely, artificial neural networks and support vector machines. For training, validating and testing of thewarpage model, a number of MoldFlow (FE) analyses have been carried out using Taguchi’s orthogonal array in the designof experimental technique by considering the process parameters such as mold temperature, melt temperature, packing pressure,packing tim...

  17. Application of Artificial Intelligence to the Prediction of the Antimicrobial Activity of Essential Oils

    OpenAIRE

    Mathieu Daynac; Alvaro Cortes-Cabrera; 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 extract...

  18. Diesel engine emissions and combustion predictions using advanced mixing models applicable to fuel sprays

    Science.gov (United States)

    Abani, Neerav; Reitz, Rolf D.

    2010-09-01

    An advanced mixing model was applied to study engine emissions and combustion with different injection strategies ranging from multiple injections, early injection and grouped-hole nozzle injection in light and heavy duty diesel engines. The model was implemented in the KIVA-CHEMKIN engine combustion code and simulations were conducted at different mesh resolutions. The model was compared with the standard KIVA spray model that uses the Lagrangian-Drop and Eulerian-Fluid (LDEF) approach, and a Gas Jet spray model that improves predictions of liquid sprays. A Vapor Particle Method (VPM) is introduced that accounts for sub-grid scale mixing of fuel vapor and more accurately and predicts the mixing of fuel-vapor over a range of mesh resolutions. The fuel vapor is transported as particles until a certain distance from nozzle is reached where the local jet half-width is adequately resolved by the local mesh scale. Within this distance the vapor particle is transported while releasing fuel vapor locally, as determined by a weighting factor. The VPM model more accurately predicts fuel-vapor penetrations for early cycle injections and flame lift-off lengths for late cycle injections. Engine combustion computations show that as compared to the standard KIVA and Gas Jet spray models, the VPM spray model improves predictions of in-cylinder pressure, heat released rate and engine emissions of NOx, CO and soot with coarse mesh resolutions. The VPM spray model is thus a good tool for efficiently investigating diesel engine combustion with practical mesh resolutions, thereby saving computer time.

  19. Empirical models for end-use properties prediction of LDPE: application in the flexible plastic packaging industry

    Directory of Open Access Journals (Sweden)

    Maria Carolina Burgos Costa

    2008-03-01

    Full Text Available The objective of this work is to develop empirical models to predict end use properties of low density polyethylene (LDPE resins as functions of two intrinsic properties easily measured in the polymers industry. The most important properties for application in the flexible plastic packaging industry were evaluated experimentally for seven commercial polymer grades. Statistical correlation analysis was performed for all variables and used as the basis for proper choice of inputs to each model output. Intrinsic properties selected for resin characterization are fluidity index (FI, which is essentially an indirect measurement of viscosity and weight average molecular weight (MW, and density. In general, models developed are able to reproduce and predict experimental data within experimental accuracy and show that a significant number of end use properties improve as the MW and density increase. Optical properties are mainly determined by the polymer morphology.

  20. Prediction of Instability Separation Modes and Its Application in Practical Dynamic Security Region

    Institute of Scientific and Technical Information of China (English)

    2007-01-01

    The transient critical boundary of dynamic security region (DSR) can be approximated by a few hyper planes correlated with instability separation modes. A method to fast predict instability separation modes is proposed for DSR calculation in power injection space. The method identifies coherent generation groups by the developed K-medoids algorithm, taking a similarity matrix derived from the reachability Grammian as the index. As an experimental result, reachability Grammian matrices under local injections are approximately invariant. It indicates that the generator coherency identifications are nearly consistent for different injections. Then instability separation modes can be predicted at the normal operating point, while average initial acceleration is considered as the measure of the critical generator group to amend the error. Moreover, based on these predicted instability separation modes, a critical point search strategy for DSR calculation is illustrated in the reduced injection space of the critical generators. The proposed method was evaluated using New England Test System, and the computation accuracy and speed in determining the practical DSR were improve.