Science.gov

Sample records for pathway combinations predict

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

    PubMed Central

    Huang, Lu; Jiang, Yuyang; Chen, Yuzong

    2017-01-01

    Synergistic drug combinations enable enhanced therapeutics. Their discovery typically involves the measurement and assessment of drug combination index (CI), which can be facilitated by the development and applications of in-silico CI predictive tools. In this work, we developed and tested the ability of a mathematical model of drug-targeted EGFR-ERK pathway in predicting CIs and in analyzing multiple synergistic drug combinations against observations. Our mathematical model was validated against the literature reported signaling, drug response dynamics, and EGFR-MEK drug combination effect. The predicted CIs and combination therapeutic effects of the EGFR-BRaf, BRaf-MEK, FTI-MEK, and FTI-BRaf inhibitor combinations showed consistent synergism. Our results suggest that existing pathway models may be potentially extended for developing drug-targeted pathway models to predict drug combination CI values, isobolograms, and drug-response surfaces as well as to analyze the dynamics of individual and combinations of drugs. With our model, the efficacy of potential drug combinations can be predicted. Our method complements the developed in-silico methods (e.g. the chemogenomic profile and the statistically-inferenced network models) by predicting drug combination effects from the perspectives of pathway dynamics using experimental or validated molecular kinetic constants, thereby facilitating the collective prediction of drug combination effects in diverse ranges of disease systems. PMID:28102344

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

    NASA Astrophysics Data System (ADS)

    Huang, Lu; Jiang, Yuyang; Chen, Yuzong

    2017-01-01

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

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

    PubMed

    Huang, Lu; Jiang, Yuyang; Chen, Yuzong

    2017-01-19

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

  4. A combined oncogenic pathway signature of BRAF, KRAS and PI3KCA mutation improves colorectal cancer classification and cetuximab treatment prediction

    PubMed Central

    Tian, Sun; Simon, Iris; Moreno, Victor; Roepman, Paul; Tabernero, Josep; Snel, Mireille; van't Veer, Laura; Salazar, Ramon; Bernards, Rene

    2013-01-01

    Objective To develop gene expression profiles that characterise KRAS-, BRAF- or PIK3CA-activated- tumours, and to explore whether these profiles might be helpful in predicting the response to the epidermal growth factor receptor (EGFR) pathway inhibitors better than mutation status alone. Design Fresh frozen tumour samples from 381 colorectal cancer (CRC) patients were collected and mutations in KRAS, BRAF and PIK3CA were assessed. Using microarray data, three individual oncogenic and a combined model were developed and validated in an independent set of 80 CRC patients, and in a dataset from metastatic CRC patients treated with cetuximab. Results 175 tumours (45.9%) harboured oncogenic mutations in KRAS (30.2%), BRAF (11.0%) and PIK3CA (11.5%). Activating mutation signatures for KRAS (75 genes), for BRAF (58 genes,) and for PIK3CA (49 genes) were developed. The development of a combined oncogenic pathway signature-classified tumours as ‘activated oncogenic’, or as ‘wildtype-like’ with a sensitivity of 90.3% and a specificity of 61.7%. The identified signature revealed other mechanisms that can activate ERK/MAPK pathway in KRAS, BRAF and PIK3CA wildtype patients. The combined signature is associated with response to cetuximab treatment in patients with metastatic CRC (HR 2.51, p<0.0009). Conclusion A combined oncogenic pathway signature allows the identification of patients with an active EGFR-signalling pathway that could benefit from downstream pathway inhibition. PMID:22798500

  5. Hormone signaling pathways under stress combinations.

    PubMed

    Suzuki, Nobuhiro

    2016-11-01

    As sessile organisms, plants are continuously exposed to various environmental stresses. In contrast to the controlled conditions employed in many researches, more than one or more abiotic and/or biotic stresses simultaneously occur and highly impact growth of plants and crops in the field environments. Therefore, an urgent need to generate crops with enhanced tolerance to stress combinations exists. Researchers, however, focused on the mechanisms underlying acclimation of plants to combined stresses only in recent studies. Plant hormones might be a key regulator of the tailored responses of plants to different stress combinations. Co-ordination between different hormone signaling, or hormone signaling and other pathways such as ROS regulatory mechanisms could be flexible, being altered by timing and types of stresses, and could be different depending on plant species under the stress combinations. In this review, update on recent studies focusing on complex-mode of hormone signaling under stress combinations will be provided.

  6. Why do personality traits predict divorce? Multiple pathways through satisfaction.

    PubMed

    Solomon, Brittany C; Jackson, Joshua J

    2014-06-01

    While previous studies indicate that personality traits influence the likelihood of divorce, the processes that drive this relationship have yet to be examined. Accordingly, the current study utilized a nationally representative, longitudinal sample (N = 8,206) to test whether relationship satisfaction is a pathway by which personality traits influence relationship dissolution. Specifically, we examined 2 different pathways: the enduring dynamics and emergent distress pathways. The enduring dynamics pathway specifies that the association between personality and relationship satisfaction reflects ongoing relationship dynamics, which are presumed to be stable across a relationship. In contrast, the emergent distress pathway proposes that personality leads to worsening dynamics across the course of a relationship, which is indicated by changes in satisfaction. For each pathway, we assessed actor, partner, and combined effects for the Big Five. Results replicate previous research in that personality traits prospectively predict relationship dissolution. Both the enduring dynamics and emergent distress pathways served to explain this relationship, though the enduring dynamics model evidenced the largest effects. The emergent distress pathway was stronger for couples who experienced certain life events, suggesting that personality plays a role in adapting to changing life circumstances. Moreover, results suggest that the personality of the dyad is important in this process: Above and beyond actor effects, partner effects influenced relationship functioning (although the influence of combined effects was less clear). In sum, the current study demonstrates that personality traits shape the overall quality of one's relationship, which in turn influences the likelihood of relationship dissolution.

  7. Predictive mathematical models of cancer signalling pathways.

    PubMed

    Bachmann, J; Raue, A; Schilling, M; Becker, V; Timmer, J; Klingmüller, U

    2012-02-01

    Complex intracellular signalling networks integrate extracellular signals and convert them into cellular responses. In cancer cells, the tightly regulated and fine-tuned dynamics of information processing in signalling networks is altered, leading to uncontrolled cell proliferation, survival and migration. Systems biology combines mathematical modelling with comprehensive, quantitative, time-resolved data and is most advanced in addressing dynamic properties of intracellular signalling networks. Here, we introduce different modelling approaches and their application to medical systems biology, focusing on the identifiability of parameters in ordinary differential equation models and their importance in network modelling to predict cellular decisions. Two related examples are given, which include processing of ligand-encoded information and dual feedback regulation in erythropoietin (Epo) receptor signalling. Finally, we review the current understanding of how systems biology could foster the development of new treatment strategies in the context of lung cancer and anaemia.

  8. Combining Modeling and Gaming for Predictive Analytics

    SciTech Connect

    Riensche, Roderick M.; Whitney, Paul D.

    2012-08-22

    Many of our most significant challenges involve people. While human behavior has long been studied, there are recent advances in computational modeling of human behavior. With advances in computational capabilities come increases in the volume and complexity of data that humans must understand in order to make sense of and capitalize on these modeling advances. Ultimately, models represent an encapsulation of human knowledge. One inherent challenge in modeling is efficient and accurate transfer of knowledge from humans to models, and subsequent retrieval. The simulated real-world environment of games presents one avenue for these knowledge transfers. In this paper we describe our approach of combining modeling and gaming disciplines to develop predictive capabilities, using formal models to inform game development, and using games to provide data for modeling.

  9. Predicting novel pathways in genome-scale metabolic networks.

    PubMed

    Schuster, Stefan; de Figueiredo, Luís F; Kaleta, Christoph

    2010-10-01

    Elementary-modes analysis has become a well-established theoretical tool in metabolic pathway analysis. It allows one to decompose complex metabolic networks into the smallest functional entities, which can be interpreted as biochemical pathways. This analysis has, in medium-size metabolic networks, led to the successful theoretical prediction of hitherto unknown pathways. For illustration, we discuss the example of the phosphoenolpyruvate-glyoxylate cycle in Escherichia coli. Elementary-modes analysis meets with the problem of combinatorial explosion in the number of pathways with increasing system size, which has hampered scaling it up to genome-wide models. We present a novel approach to overcoming this obstacle. That approach is based on elementary flux patterns, which are defined as sets of reactions representing the basic routes through a particular subsystem that are compatible with admissible fluxes in a (possibly) much larger metabolic network. The subsystem can be made up by reactions in which we are interested in, for example, reactions producing a certain metabolite. This allows one to predict novel metabolic pathways in genome-scale networks.

  10. SPATIAL PREDICTION USING COMBINED SOURCES OF DATA

    EPA Science Inventory

    For improved environmental decision-making, it is important to develop new models for spatial prediction that accurately characterize important spatial and temporal patterns of air pollution. As the U .S. Environmental Protection Agency begins to use spatial prediction in the reg...

  11. Dynamic biochemical reaction process analysis and pathway modification predictions.

    PubMed

    Conejeros, R; Vassiliadis, V S

    2000-05-05

    Recently, the area of model predictive modification of biochemical pathways has received attention with the aim to increase the productivity of microbial systems. In this study, we present a generalization of previous work, where, using a sensitivity study over the fermentation as a dynamic system, the optimal selection of reaction steps for modification (amplification or attenuation) is determined. The influence of metabolites in the activity of enzymes has also been considered (through activation or inhibition). We further introduce a new concept in the dynamic modeling of biochemical reaction systems including a generalized continuous superstructure in which two artificial multiplicative terms are included to account for: (a) enzyme overexpression or underexpression (attenuation or amplification) for the whole enzyme pool; and (b) modification of the apparent order of a kinetic expression with respect to the concentration of a metabolite or any subset of metabolites participating in the pathway. This new formulation allows the prediction of the sensitivity of the pathway performance index (objective function) with respect to the concentration of the enzyme, as well as the interaction of the enzyme with other metabolites. Using this framework, a case study for the production of penicillin V is analyzed, obtaining the most sensitive reaction steps (or bottlenecks) and the most significant regulations of the system, due to the effect of concentration of intracellular metabolites on the activity of each enzyme.

  12. Aquatic pathways model to predict the fate of phenolic compounds

    SciTech Connect

    Aaberg, R.L.; Peloquin, R.A.; Strenge, D.L.; Mellinger, P.J.

    1983-04-01

    Organic materials released from energy-related activities could affect human health and the environment. To better assess possible impacts, we developed a model to predict the fate of spills or discharges of pollutants into flowing or static bodies of fresh water. A computer code, Aquatic Pathways Model (APM), was written to implement the model. The computer programs use compartmental analysis to simulate aquatic ecosystems. The APM estimates the concentrations of chemicals in fish tissue, water and sediment, and is therefore useful for assessing exposure to humans through aquatic pathways. The APM will consider any aquatic pathway for which the user has transport data. Additionally, APM will estimate transport rates from physical and chemical properties of chemicals between several key compartments. The major pathways considered are biodegradation, fish and sediment uptake, photolysis, and evaporation. The model has been implemented with parameters for distribution of phenols, an important class of compounds found in the water-soluble fractions of coal liquids. Current modeling efforts show that, in comparison with many pesticides and polyaromatic hydrocarbons (PAH), the lighter phenolics (the cresols) are not persistent in the environment. The properties of heavier molecular weight phenolics (indanols, naphthols) are not well enough understood at this time to make similar judgements. For the twelve phenolics studied, biodegradation appears to be the major pathway for elimination from aquatic environments. A pond system simulation (using APM) of a spill of solvent refined coal (SRC-II) materials indicates that phenol, cresols, and other single cyclic phenolics are degraded to 16 to 25 percent of their original concentrations within 30 hours. Adsorption of these compounds into sediments and accumulation by fish was minor.

  13. A combination assay for simultaneous assessment of multiple signaling pathways.

    PubMed

    Goetz, A S; Liacos, J; Yingling, J; Ignar, D M

    1999-12-01

    We have developed an assay in which modulation of two or more signaling pathways can be assessed concurrently by combining reporter gene systems with fluorescent probe technology. The validation of this method was achieved by indirect analysis of adenylyl cyclase activation with the use of a cyclic AMP response element (CRE)-luciferase reporter system in combination with the measurement of calcium mobilization by Calcium Green-1 AM fluorescence on a fluorescent imaging plate reader. To demonstrate the utility of the method in studying the pharmacology of receptors that couple to more than one G protein, Chinese hamster ovary (CHO) cells, which stably expressed both the CRE-luciferase reporter gene and the human pituitary adenylyl cyclase-activating peptide (PACAP) receptor, were treated with PACAP 1-27 and 1-38. Calcium mobilization and the induction of adenylyl cyclase activity in response to each concentration of peptide were assessed in individuals wells. This assay may also be used to screen for ligands of two or more unrelated receptors simultaneously without compromising the assessment of either signaling pathway. To illustrate this point, Rat-1 fibroblasts, which expressed human alpha1A receptors, were cocultured with CRE-luciferase CHO cells, which expressed human GLP-1 receptors. Calcium mobilization elicited by phenylephrine agonism of the alpha1A receptor was assessed in the same assay as GLP-1-induced activation of adenylyl cyclase. The pEC(50) for each agonist was similar to that observed when the cell lines were not cocultured. The number of different receptors that can be screened per well is limited only by the ability to distinguish different reporter gene signals and fluorescent indicators.

  14. Future missions studies: Combining Schatten's solar activity prediction model with a chaotic prediction model

    NASA Technical Reports Server (NTRS)

    Ashrafi, S.

    1991-01-01

    K. Schatten (1991) recently developed a method for combining his prediction model with our chaotic model. The philosophy behind this combined model and his method of combination is explained. Because the Schatten solar prediction model (KS) uses a dynamo to mimic solar dynamics, accurate prediction is limited to long-term solar behavior (10 to 20 years). The Chaotic prediction model (SA) uses the recently developed techniques of nonlinear dynamics to predict solar activity. It can be used to predict activity only up to the horizon. In theory, the chaotic prediction should be several orders of magnitude better than statistical predictions up to that horizon; beyond the horizon, chaotic predictions would theoretically be just as good as statistical predictions. Therefore, chaos theory puts a fundamental limit on predictability.

  15. pathDIP: an annotated resource for known and predicted human gene-pathway associations and pathway enrichment analysis

    PubMed Central

    Rahmati, Sara; Abovsky, Mark; Pastrello, Chiara; Jurisica, Igor

    2017-01-01

    Molecular pathway data are essential in current computational and systems biology research. While there are many primary and integrated pathway databases, several challenges remain, including low proteome coverage (57%), low overlap across different databases, unavailability of direct information about underlying physical connectivity of pathway members, and high fraction of protein-coding genes without any pathway annotations, i.e. ‘pathway orphans’. In order to address all these challenges, we developed pathDIP, which integrates data from 20 source pathway databases, ‘core pathways’, with physical protein–protein interactions to predict biologically relevant protein–pathway associations, referred to as ‘extended pathways’. Cross-validation determined 71% recovery rate of our predictions. Data integration and predictions increase coverage of pathway annotations for protein-coding genes to 86%, and provide novel annotations for 5732 pathway orphans. PathDIP (http://ophid.utoronto.ca/pathdip) annotates 17 070 protein-coding genes with 4678 pathways, and provides multiple query, analysis and output options. PMID:27899558

  16. Automatic Reaction Pathway Search via Combined Molecular Dynamics and Coordinate Driving Method.

    PubMed

    Yang, Manyi; Zou, Jingxiang; Wang, Guoqiang; Li, Shuhua

    2017-02-16

    We proposed and implemented a combined molecular dynamics and coordinate driving (MD/CD) method for automatically searching multistep reaction pathways of chemical reactions. In this approach, the molecular dynamic (MD) method at the molecular mechanics (MM) or semiempirical quantum mechanical (QM) level is employed to explore the conformational space of the minimum structures, and the modified coordinate driving (CD) method is used to build reaction pathways for representative conformers. The MD/CD method is first applied to two model reactions (the Claisen rearrangement and the intermolecular aldol reaction). By comparing the obtained results with those of the existing methods, we found that the MD/CD method has a comparable performance in searching low-energy reaction pathways. Then, the MD/CD method is further applied to investigate two reactions: the electrocyclic reaction of benzocyclobutene-7-carboxaldehyde and the intramolecular Diels-Alder reaction of ketothioester with 11 effectively rotatable single bonds. For the first reaction, our results can correctly account for its torquoselectivity. For the second one, our method predicts eight reaction channels, leading to eight different stereo- and regioselective products. The MD/CD method is expected to become an efficient and cost-effective theoretical tool for automatically searching low-energy reaction pathways for relatively complex chemical reactions.

  17. Analysis of Pole Coordinate Data Predictions in the Earth Orientation Parameters Combination of Prediction Pilot Project

    DTIC Science & Technology

    2011-01-01

    ARTIFICIAL SATELLITES , Vol. 46, No. 4 - 2011 DOI: 10.2478/v10018-012-0006-x ANALYSIS OF POLE COORDINATE DATA PREDICTIONS IN THE EARTH ORIENTATION...Warsaw initiated the Earth Orientation Parameters Combination of Prediction Pilot Project, which was accepted by the IERS Directing Board. The goal of...this project is to determine the feasibility of combining Earth Orientation Parameters (EOP) predictions on an operational basis. The ensemble

  18. Airway gene expression of IL-1 pathway mediators predicts exacerbation risk in obstructive airway disease

    PubMed Central

    Baines, Katherine J; Fu, Juan-juan; McDonald, Vanessa M; Gibson, Peter G

    2017-01-01

    Background Exacerbations of asthma and COPD are a major cause of morbidity and mortality and are responsible for significant health care costs. This study further investigates interleukin (IL)-1 pathway activation and its relationship with exacerbations of asthma and COPD. Methods In this prospective cohort study, 95 participants with stable asthma (n=35) or COPD (n=60) were recruited and exacerbations recorded over the following 12 months. Gene expressions of IL-1 pathway biomarkers, including the IL-1 receptors (IL1R1, IL1R2, and IL1RN), and signaling molecules (IRAK2, IRAK3, and PELI1), were measured in sputum using real-time quantitative polymerase chain reaction. Mediators were compared between the frequent (≥2 exacerbations in the 12 months) and infrequent exacerbators, and the predictive relationships investigated using receiver operating characteristic curves and area under the curve (AUC) values. Results Of the 95 participants, 89 completed the exacerbation follow-up, where 30 participants (n=22 COPD, n=8 asthma) had two or more exacerbations. At the baseline visit, expressions of IRAK2, IRAK3, PELI1, and IL1R1 were elevated in participants with frequent exacerbations of both asthma and COPD combined and separately. In the combined population, sputum gene expression of IRAK3 (AUC=75.4%; P<0.001) was the best predictor of future frequent exacerbations, followed by IL1R1 (AUC=72.8%; P<0.001), PELI1 (AUC=71.2%; P<0.001), and IRAK2 (AUC=68.6; P=0.004). High IL-1 pathway gene expression was associated with frequent prior year exacerbations and correlated with the number and severity of exacerbations. Conclusion The upregulation of IL-1 pathway mediators is associated with frequent exacerbations of obstructive airway disease. Further studies should investigate these mediators as both potential diagnostic biomarkers predicting at-risk patients and novel treatment targets. PMID:28223794

  19. Pole coordinates data prediction by combination of least squares extrapolation and double autoregressive prediction

    NASA Astrophysics Data System (ADS)

    Kosek, Wieslaw

    2016-04-01

    Future Earth Orientation Parameters data are needed to compute real time transformation between the celestial and terrestrial reference frames. This transformation is realized by predictions of x, y pole coordinates data, UT1-UTC data and precesion-nutation extrapolation model. This paper is focused on the pole coordinates data prediction by combination of the least-squares (LS) extrapolation and autoregressive (AR) prediction models (LS+AR). The AR prediction which is applied to the LS extrapolation residuals of pole coordinates data does not able to predict all frequency bands of them and it is mostly tuned to predict subseasonal oscillations. The absolute values of differences between pole coordinates data and their LS+AR predictions increase with prediction length and depend mostly on starting prediction epochs, thus time series of these differences for 2, 4 and 8 weeks in the future were analyzed. Time frequency spectra of these differences for different prediction lengths are very similar showing some power in the frequency band corresponding to the prograde Chandler and annual oscillations, which means that the increase of prediction errors is caused by mismodelling of these oscillations by the LS extrapolation model. Thus, the LS+AR prediction method can be modified by taking into additional AR prediction correction computed from time series of these prediction differences for different prediction lengths. This additional AR prediction is mostly tuned to the seasonal frequency band of pole coordinates data.

  20. The Pursuit of Pathways: Combining Rigorous Academics with Career Training

    ERIC Educational Resources Information Center

    Schwartz, Robert B.

    2014-01-01

    In February 2011 author Robert Schwartz, along with with two colleagues--economist Ronald Ferguson and journalist William Symonds--released a report, "Pathways to Prosperity: Meeting the Challenge of Preparing Young Americans for the 21st Century," which was published by Harvard University's Graduate School of Education. When they…

  1. Efficient key pathway mining: combining networks and OMICS data.

    PubMed

    Alcaraz, Nicolas; Friedrich, Tobias; Kötzing, Timo; Krohmer, Anton; Müller, Joachim; Pauling, Josch; Baumbach, Jan

    2012-07-01

    Systems biology has emerged over the last decade. Driven by the advances in sophisticated measurement technology the research community generated huge molecular biology data sets. These comprise rather static data on the interplay of biological entities, for instance protein-protein interaction network data, as well as quite dynamic data collected for studying the behavior of individual cells or tissues in accordance with changing environmental conditions, such as DNA microarrays or RNA sequencing. Here we bring the two different data types together in order to gain higher level knowledge. We introduce a significantly improved version of the KeyPathwayMiner software framework. Given a biological network modelled as a graph and a set of expression studies, KeyPathwayMiner efficiently finds and visualizes connected sub-networks where most components are expressed in most cases. It finds all maximal connected sub-networks where all nodes but k exceptions are expressed in all experimental studies but at most l exceptions. We demonstrate the power of the new approach by comparing it to similar approaches with gene expression data previously used to study Huntington's disease. In addition, we demonstrate KeyPathwayMiner's flexibility and applicability to non-array data by analyzing genome-scale DNA methylation profiles from colorectal tumor cancer patients. KeyPathwayMiner release 2 is available as a Cytoscape plugin and online at http://keypathwayminer.mpi-inf.mpg.de.

  2. Targeting Metabolic Survival Pathways in Lung Cancer via Combination Therapy

    DTIC Science & Technology

    2014-06-01

    critical metabolic pathways necessary for survival of liver kinase B1 (LKB1)- deficient non-small cell lung cancer (NSCLC) cell lines. We have conducted...13C metabolic flux analysis studies in LKB1 proficient or deficient NSCLC cells under nutrient complete or metabolic stress conditions (e.g. hypoxia...derived pyruvate in mitochondria. LKB1- deficient cells also exhibit increased reliance on glutamine metabolism. Treatment with biguanides such as

  3. A binary classifier for prediction of the types of metabolic pathway of chemicals.

    PubMed

    Fang, Yemin; Chen, Lei

    2016-12-15

    The study of metabolic pathway is one of the most important fields in biochemistry. Good comprehension of the metabolic pathway system is helpful to uncover the mechanism of some fundamental biological processes. Because chemicals are part of the main components of the metabolic pathway, correct identification of which metabolic pathways a given chemical can participate in is an important step for understanding the metabolic pathway system. Most previous methods only considered the chemical information, which tried to deal with a multi-label classification problem of assigning chemicals to proper metabolic pathways. In this study, the pathway information was also employed, thereby transforming the problem into a binary classification problem of identifying the pair of chemicals and metabolic pathways, i.e., a chemical and a metabolic pathway was paired as a sample to be considered in this study. To construct the prediction model, the association between chemical pathway type pairs was evaluated by integrating the association between chemicals and association between pathway types. The support vector machine was adopted as the prediction engine. The extensive tests show that the constructed model yields good performance with total prediction accuracy around 0.878. Furthermore, the comparison results indicate that our model is quite effective and suitable for the identification of whether a given chemical can participate in a given metabolic pathway.

  4. Chemical combinations elucidate pathway interactions and regulation relevant to Hepatitis C replication

    PubMed Central

    Owens, Christopher M; Mawhinney, Christina; Grenier, Jill M; Altmeyer, Ralf; Lee, Margaret S; Borisy, Alexis A; Lehár, Joseph; Johansen, Lisa M

    2010-01-01

    The search for effective Hepatitis C antiviral therapies has recently focused on host sterol metabolism and protein prenylation pathways that indirectly affect viral replication. However, inhibition of the sterol pathway with statin drugs has not yielded consistent results in patients. Here, we present a combination chemical genetic study to explore how the sterol and protein prenylation pathways work together to affect hepatitis C viral replication in a replicon assay. In addition to finding novel targets affecting viral replication, our data suggest that the viral replication is strongly affected by sterol pathway regulation. There is a marked transition from antagonistic to synergistic antiviral effects as the combination targets shift downstream along the sterol pathway. We also show how pathway regulation frustrates potential hepatitis C therapies based on the sterol pathway, and reveal novel synergies that selectively inhibit hepatitis C replication over host toxicity. In particular, combinations targeting the downstream sterol pathway enzymes produced robust and selective synergistic inhibition of hepatitis C replication. Our findings show how combination chemical genetics can reveal critical pathway connections relevant to viral replication, and can identify potential treatments with an increased therapeutic window. PMID:20531405

  5. In silico prediction of pharmaceutical degradation pathways: a benchmarking study.

    PubMed

    Kleinman, Mark H; Baertschi, Steven W; Alsante, Karen M; Reid, Darren L; Mowery, Mark D; Shimanovich, Roman; Foti, Chris; Smith, William K; Reynolds, Dan W; Nefliu, Marcela; Ott, Martin A

    2014-11-03

    Zeneth is a new software application capable of predicting degradation products derived from small molecule active pharmaceutical ingredients. This study was aimed at understanding the current status of Zeneth's predictive capabilities and assessing gaps in predictivity. Using data from 27 small molecule drug substances from five pharmaceutical companies, the evolution of Zeneth predictions through knowledge base development since 2009 was evaluated. The experimentally observed degradation products from forced degradation, accelerated, and long-term stability studies were compared to Zeneth predictions. Steady progress in predictive performance was observed as the knowledge bases grew and were refined. Over the course of the development covered within this evaluation, the ability of Zeneth to predict experimentally observed degradants increased from 31% to 54%. In particular, gaps in predictivity were noted in the areas of epimerizations, N-dealkylation of N-alkylheteroaromatic compounds, photochemical decarboxylations, and electrocyclic reactions. The results of this study show that knowledge base development efforts have increased the ability of Zeneth to predict relevant degradation products and aid pharmaceutical research. This study has also provided valuable information to help guide further improvements to Zeneth and its knowledge base.

  6. Combining classifiers for HIV-1 drug resistance prediction.

    PubMed

    Srisawat, Anantaporn; Kijsirikul, Boonserm

    2008-01-01

    This paper applies and studies the behavior of three learning algorithms, i.e. the Support Vector machine (SVM), the Radial Basis Function Network (the RBF network), and k-Nearest Neighbor (k-NN) for predicting HIV-1 drug resistance from genotype data. In addition, a new algorithm for classifier combination is proposed. The results of comparing the predictive performance of three learning algorithms show that, SVM yields the highest average accuracy, the RBF network gives the highest sensitivity, and k-NN yields the best in specificity. Finally, the comparison of the predictive performance of the composite classifier with three learning algorithms demonstrates that the proposed composite classifier provides the highest average accuracy.

  7. Combination of longitudinal biomarkers in predicting binary events.

    PubMed

    Liu, Danping; Albert, Paul S

    2014-10-01

    In disease screening, the combination of multiple biomarkers often substantially improves the diagnostic accuracy over a single marker. This is particularly true for longitudinal biomarkers where individual trajectory may improve the diagnosis. We propose a pattern mixture model (PMM) framework to predict a binary disease status from a longitudinal sequence of biomarkers. The marker distribution given the disease status is estimated from a linear mixed effects model. A likelihood ratio statistic is computed as the combination rule, which is optimal in the sense of the maximum receiver operating characteristic (ROC) curve under the correctly specified mixed effects model. The individual disease risk score is then estimated by Bayes' theorem, and we derive the analytical form of the 95% confidence interval. We show that this PMM is an approximation to the shared random effects (SRE) model proposed by Albert (2012. A linear mixed model for predicting a binary event from longitudinal data under random effects mis-specification. Statistics in Medicine 31: (2), 143-154). Further, with extensive simulation studies, we found that the PMM is more robust than the SRE model under wide classes of models. This new PPM approach for combining biomarkers is motivated by and applied to a fetal growth study, where the interest is in predicting macrosomia using longitudinal ultrasound measurements.

  8. Predicting virological decay in patients starting combination antiretroviral therapy

    PubMed Central

    2016-01-01

    Objective: Model trajectories of viral load measurements from time of starting combination antiretroviral therapy (cART), and use the model to predict whether patients will achieve suppressed viral load (≤200 copies/ml) within 6-months of starting cART. Design: Prospective cohort study including HIV-positive adults (UK Collaborative HIV Cohort Study). Methods: Eligible patients were antiretroviral naive and started cART after 1997. Random effects models were used to estimate viral load trends. Patients were randomly selected to form a validation dataset with those remaining used to fit the model. We evaluated predictions of suppression using indices of diagnostic test performance. Results: Of 9562 eligible patients 6435 were used to fit the model and 3127 for validation. Mean log10 viral load trajectories declined rapidly during the first 2 weeks post-cART, moderately between 2 weeks and 3 months, and more slowly thereafter. Higher pretreatment viral load predicted steeper declines, whereas older age, white ethnicity, and boosted protease inhibitor/non-nucleoside reverse transcriptase inhibitors based cART-regimen predicted a steeper decline from 3 months onwards. Specificity of predictions and the diagnostic odds ratio substantially improved when predictions were based on viral load measurements up to the 4-month visit compared with the 2 or 3-month visits. Diagnostic performance improved when suppression was defined by two consecutive suppressed viral loads compared with one. Conclusions: Viral load measurements can be used to predict if a patient will be suppressed by 6-month post-cART. Graphical presentations of this information could help clinicians decide the optimum time to switch treatment regimen during the first months of cART. PMID:27124894

  9. Prediction of Pathway Activation by Xenobiotic-Responsive Transcription Factors in the Mouse Liver

    EPA Science Inventory

    Many drugs and environmentally-relevant chemicals activate xenobioticresponsive transcription factors (TF). Identification of target genes of these factors would be useful in predicting pathway activation in in vitro chemical screening. Starting with a large compendium of Affymet...

  10. Crosstalk between hedgehog and other signaling pathways as a basis for combination therapies in cancer.

    PubMed

    Brechbiel, Jillian; Miller-Moslin, Karen; Adjei, Alex A

    2014-07-01

    The hedgehog (Hh) pathway is aberrantly activated in a number of tumors. In medulloblastoma, basal cell carcinoma, and rhabdomyosarcoma, mutations in Hh pathway genes lead to ligand-independent pathway activation. In many other tumor types, ligand-dependent activation of Hh signaling is potentiated through crosstalk with other critical molecular signaling pathways. Among such pathways, RAS/RAF/MEK/ERK, PI3K/AKT/mTOR, EGFR, and Notch are of particular interest because agents that selectively inhibit these pathways are available and can be readily combined with agents such as vismodegib, sonidegib (LDE225), and BMS-833923, which target smoothened-a key Hh pathway regulator. Numerous preclinical studies have revealed the ways in which Hh intersects with each of these pathways, and combination therapies have resulted in improved antitumor efficacy and survival in animal models. Hh also plays an important role in hematopoiesis and in the maintenance of BCR-ABL-driven leukemic stem cells. Thus, combined inhibition of the Hh pathway and BCR-ABL has emerged as a promising potential therapeutic strategy in chronic myeloid leukemia (CML). A number of clinical trials evaluating combinations of Hh inhibitors with other targeted agents are now underway in CML and a variety of solid tumors. This review highlights these trials and summarizes preclinical evidence of crosstalk between Hh and four other actionable pathways-RAS/RAF/MEK/ERK, PI3K/AKT/mTOR, EGFR, and Notch-as well as the role of Hh in the maintenance of BCR-ABL-driven leukemic stem cells.

  11. Causal Network Models for Predicting Compound Targets and Driving Pathways in Cancer.

    PubMed

    Jaeger, Savina; Min, Junxia; Nigsch, Florian; Camargo, Miguel; Hutz, Janna; Cornett, Allen; Cleaver, Stephen; Buckler, Alan; Jenkins, Jeremy L

    2014-06-01

    Gene-expression data are often used to infer pathways regulating transcriptional responses. For example, differentially expressed genes (DEGs) induced by compound treatment can help characterize hits from phenotypic screens, either by correlation with known drug signatures or by pathway enrichment. Pathway enrichment is, however, typically computed with DEGs rather than "upstream" nodes that are potentially causal of "downstream" changes. Here, we present graph-based models to predict causal targets from compound-microarray data. We test several approaches to traversing network topology, and show that a consensus minimum-rank score (SigNet) beat individual methods and could highly rank compound targets among all network nodes. In addition, larger, less canonical networks outperformed linear canonical interactions. Importantly, pathway enrichment using causal nodes rather than DEGs recovers relevant pathways more often. To further validate our approach, we used integrated data sets from the Cancer Genome Atlas to identify driving pathways in triple-negative breast cancer. Critical pathways were uncovered, including the epidermal growth factor receptor 2-phosphatidylinositide 3-kinase-AKT-MAPK growth pathway andATR-p53-BRCA DNA damage pathway, in addition to unexpected pathways, such as TGF-WNT cytoskeleton remodeling, IL12-induced interferon gamma production, and TNFR-IAP (inhibitor of apoptosis) apoptosis; the latter was validated by pooled small hairpin RNA profiling in cancer cells. Overall, our approach can bridge transcriptional profiles to compound targets and driving pathways in cancer.

  12. GOPred: GO Molecular Function Prediction by Combined Classifiers

    PubMed Central

    Saraç, Ömer Sinan; Atalay, Volkan; Cetin-Atalay, Rengul

    2010-01-01

    Functional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http://kinaz.fen.bilkent.edu.tr/gopred). PMID:20824206

  13. Hippo Pathway Phylogenetics Predicts Monoubiquitylation of Salvador and Merlin/Nf2

    PubMed Central

    Newfeld, Stuart J.

    2012-01-01

    Recently we employed phylogenetics to predict that the cellular interpretation of TGF-β signals is modulated by monoubiquitylation cycles affecting the Smad4 signal transducer/tumor suppressor. This prediction was subsequently validated by experiments in flies, frogs and mammalian cells. Here we apply a phylogenetic approach to the Hippo pathway and predict that two of its signal transducers, Salvador and Merlin/Nf2 (also a tumor suppressor) are regulated by monoubiquitylation. This regulatory mechanism does not lead to protein degradation but instead serves as a highly efficient “off/on” switch when the protein is subsequently deubiquitylated. Overall, our study shows that the creative application of phylogenetics can predict new roles for pathway components and new mechanisms for regulating intercellular signaling pathways. PMID:23272121

  14. Predicting the types of metabolic pathway of compounds using molecular fragments and sequential minimal optimization.

    PubMed

    Chen, Lei; Chu, Chen; Feng, Kaiyan

    2016-01-01

    A metabolic pathway is a series of biological processes providing necessary molecules and energies for an organism, which could be essential to the lives of the living organisms. Most metabolic pathways require the involvement of compounds and given a compound it is helpful to know what types of metabolic pathways the compound participates in. In this study, compounds are first represented by molecular fragments which are then delivered to a prediction engine called Sequential Minimal Optimization (SMO) for predictions. Maximum relevance and minimum redundancy (mRMR) and incremental feature selection are adopted to extract key features based on which an optimal prediction engine is established. The proposed method is effective comparing to the random forest, Dagging and a popular method that integrating chemical-chemical interactions and chemical-chemical similarities. We also make predictions using some compounds with unknown metabolic pathways and choose 17 compounds for analysis. The results indicate that the method proposed may become a useful tool in predicting and analyzing metabolic pathways.

  15. Robust prediction of protein subcellular localization combining PCA and WSVMs.

    PubMed

    Tian, Jiang; Gu, Hong; Liu, Wenqi; Gao, Chiyang

    2011-08-01

    Automated prediction of protein subcellular localization is an important tool for genome annotation and drug discovery, and Support Vector Machines (SVMs) can effectively solve this problem in a supervised manner. However, the datasets obtained from real experiments are likely to contain outliers or noises, which can lead to poor generalization ability and classification accuracy. To explore this problem, we adopt strategies to lower the effect of outliers. First we design a method based on Weighted SVMs, different weights are assigned to different data points, so the training algorithm will learn the decision boundary according to the relative importance of the data points. Second we analyse the influence of Principal Component Analysis (PCA) on WSVM classification, propose a hybrid classifier combining merits of both PCA and WSVM. After performing dimension reduction operations on the datasets, kernel-based possibilistic c-means algorithm can generate more suitable weights for the training, as PCA transforms the data into a new coordinate system with largest variances affected greatly by the outliers. Experiments on benchmark datasets show promising results, which confirms the effectiveness of the proposed method in terms of prediction accuracy.

  16. Model Predictive Control of Integrated Gasification Combined Cycle Power Plants

    SciTech Connect

    B. Wayne Bequette; Priyadarshi Mahapatra

    2010-08-31

    The primary project objectives were to understand how the process design of an integrated gasification combined cycle (IGCC) power plant affects the dynamic operability and controllability of the process. Steady-state and dynamic simulation models were developed to predict the process behavior during typical transients that occur in plant operation. Advanced control strategies were developed to improve the ability of the process to follow changes in the power load demand, and to improve performance during transitions between power levels. Another objective of the proposed work was to educate graduate and undergraduate students in the application of process systems and control to coal technology. Educational materials were developed for use in engineering courses to further broaden this exposure to many students. ASPENTECH software was used to perform steady-state and dynamic simulations of an IGCC power plant. Linear systems analysis techniques were used to assess the steady-state and dynamic operability of the power plant under various plant operating conditions. Model predictive control (MPC) strategies were developed to improve the dynamic operation of the power plants. MATLAB and SIMULINK software were used for systems analysis and control system design, and the SIMULINK functionality in ASPEN DYNAMICS was used to test the control strategies on the simulated process. Project funds were used to support a Ph.D. student to receive education and training in coal technology and the application of modeling and simulation techniques.

  17. VisANT 3.0: new modules for pathway visualization, editing, prediction and construction.

    PubMed

    Hu, Zhenjun; Ng, David M; Yamada, Takuji; Chen, Chunnuan; Kawashima, Shuichi; Mellor, Joe; Linghu, Bolan; Kanehisa, Minoru; Stuart, Joshua M; DeLisi, Charles

    2007-07-01

    With the integration of the KEGG and Predictome databases as well as two search engines for coexpressed genes/proteins using data sets obtained from the Stanford Microarray Database (SMD) and Gene Expression Omnibus (GEO) database, VisANT 3.0 supports exploratory pathway analysis, which includes multi-scale visualization of multiple pathways, editing and annotating pathways using a KEGG compatible visual notation and visualization of expression data in the context of pathways. Expression levels are represented either by color intensity or by nodes with an embedded expression profile. Multiple experiments can be navigated or animated. Known KEGG pathways can be enriched by querying either coexpressed components of known pathway members or proteins with known physical interactions. Predicted pathways for genes/proteins with unknown functions can be inferred from coexpression or physical interaction data. Pathways produced in VisANT can be saved as computer-readable XML format (VisML), graphic images or high-resolution Scalable Vector Graphics (SVG). Pathways in the format of VisML can be securely shared within an interested group or published online using a simple Web link. VisANT is freely available at http://visant.bu.edu.

  18. Predicting metabolic pathways of small molecules and enzymes based on interaction information of chemicals and proteins.

    PubMed

    Gao, Yu-Fei; Chen, Lei; Cai, Yu-Dong; Feng, Kai-Yan; Huang, Tao; Jiang, Yang

    2012-01-01

    Metabolic pathway analysis, one of the most important fields in biochemistry, is pivotal to understanding the maintenance and modulation of the functions of an organism. Good comprehension of metabolic pathways is critical to understanding the mechanisms of some fundamental biological processes. Given a small molecule or an enzyme, how may one identify the metabolic pathways in which it may participate? Answering such a question is a first important step in understanding a metabolic pathway system. By utilizing the information provided by chemical-chemical interactions, chemical-protein interactions, and protein-protein interactions, a novel method was proposed by which to allocate small molecules and enzymes to 11 major classes of metabolic pathways. A benchmark dataset consisting of 3,348 small molecules and 654 enzymes of yeast was constructed to test the method. It was observed that the first order prediction accuracy evaluated by the jackknife test was 79.56% in identifying the small molecules and enzymes in a benchmark dataset. Our method may become a useful vehicle in predicting the metabolic pathways of small molecules and enzymes, providing a basis for some further analysis of the pathway systems.

  19. Generation of an atlas for commodity chemical production in Escherichia coli and a novel pathway prediction algorithm, GEM-Path.

    PubMed

    Campodonico, Miguel A; Andrews, Barbara A; Asenjo, Juan A; Palsson, Bernhard O; Feist, Adam M

    2014-09-01

    The production of 75% of the current drug molecules and 35% of all chemicals could be achieved through bioprocessing (Arundel and Sawaya, 2009). To accelerate the transition from a petroleum-based chemical industry to a sustainable bio-based industry, systems metabolic engineering has emerged to computationally design metabolic pathways for chemical production. Although algorithms able to provide specific metabolic interventions and heterologous production pathways are available, a systematic analysis for all possible production routes to commodity chemicals in Escherichia coli is lacking. Furthermore, a pathway prediction algorithm that combines direct integration of genome-scale models at each step of the search to reduce the search space does not exist. Previous work (Feist et al., 2010) performed a model-driven evaluation of the growth-coupled production potential for E. coli to produce multiple native compounds from different feedstocks. In this study, we extended this analysis for non-native compounds by using an integrated approach through heterologous pathway integration and growth-coupled metabolite production design. In addition to integration with genome-scale model integration, the GEM-Path algorithm developed in this work also contains a novel approach to address reaction promiscuity. In total, 245 unique synthetic pathways for 20 large volume compounds were predicted. Host metabolism with these synthetic pathways was then analyzed for feasible growth-coupled production and designs could be identified for 1271 of the 6615 conditions evaluated. This study characterizes the potential for E. coli to produce commodity chemicals, and outlines a generic strain design workflow to design production strains.

  20. A new molecular signature method for prediction of driver cancer pathways from transcriptional data

    PubMed Central

    Rykunov, Dmitry; Beckmann, Noam D.; Li, Hui; Uzilov, Andrew; Schadt, Eric E.; Reva, Boris

    2016-01-01

    Assigning cancer patients to the most effective treatments requires an understanding of the molecular basis of their disease. While DNA-based molecular profiling approaches have flourished over the past several years to transform our understanding of driver pathways across a broad range of tumors, a systematic characterization of key driver pathways based on RNA data has not been undertaken. Here we introduce a new approach for predicting the status of driver cancer pathways based on signature functions derived from RNA sequencing data. To identify the driver cancer pathways of interest, we mined DNA variant data from TCGA and nominated driver alterations in seven major cancer pathways in breast, ovarian and colon cancer tumors. The activation status of these driver pathways were then characterized using RNA sequencing data by constructing classification signature functions in training datasets and then testing the accuracy of the signatures in test datasets. The signature functions differentiate well tumors with nominated pathway activation from tumors with no signs of activation: average AUC equals to 0.83. Our results confirm that driver genomic alterations are distinctively displayed at the transcriptional level and that the transcriptional signatures can generally provide an alternative to DNA sequencing methods in detecting specific driver pathways. PMID:27098033

  1. A new molecular signature method for prediction of driver cancer pathways from transcriptional data.

    PubMed

    Rykunov, Dmitry; Beckmann, Noam D; Li, Hui; Uzilov, Andrew; Schadt, Eric E; Reva, Boris

    2016-06-20

    Assigning cancer patients to the most effective treatments requires an understanding of the molecular basis of their disease. While DNA-based molecular profiling approaches have flourished over the past several years to transform our understanding of driver pathways across a broad range of tumors, a systematic characterization of key driver pathways based on RNA data has not been undertaken. Here we introduce a new approach for predicting the status of driver cancer pathways based on signature functions derived from RNA sequencing data. To identify the driver cancer pathways of interest, we mined DNA variant data from TCGA and nominated driver alterations in seven major cancer pathways in breast, ovarian and colon cancer tumors. The activation status of these driver pathways were then characterized using RNA sequencing data by constructing classification signature functions in training datasets and then testing the accuracy of the signatures in test datasets. The signature functions differentiate well tumors with nominated pathway activation from tumors with no signs of activation: average AUC equals to 0.83. Our results confirm that driver genomic alterations are distinctively displayed at the transcriptional level and that the transcriptional signatures can generally provide an alternative to DNA sequencing methods in detecting specific driver pathways.

  2. Simultaneous prediction of enzyme orthologs from chemical transformation patterns for de novo metabolic pathway reconstruction

    PubMed Central

    Tabei, Yasuo; Yamanishi, Yoshihiro; Kotera, Masaaki

    2016-01-01

    Motivation: Metabolic pathways are an important class of molecular networks consisting of compounds, enzymes and their interactions. The understanding of global metabolic pathways is extremely important for various applications in ecology and pharmacology. However, large parts of metabolic pathways remain unknown, and most organism-specific pathways contain many missing enzymes. Results: In this study we propose a novel method to predict the enzyme orthologs that catalyze the putative reactions to facilitate the de novo reconstruction of metabolic pathways from metabolome-scale compound sets. The algorithm detects the chemical transformation patterns of substrate–product pairs using chemical graph alignments, and constructs a set of enzyme-specific classifiers to simultaneously predict all the enzyme orthologs that could catalyze the putative reactions of the substrate–product pairs in the joint learning framework. The originality of the method lies in its ability to make predictions for thousands of enzyme orthologs simultaneously, as well as its extraction of enzyme-specific chemical transformation patterns of substrate–product pairs. We demonstrate the usefulness of the proposed method by applying it to some ten thousands of metabolic compounds, and analyze the extracted chemical transformation patterns that provide insights into the characteristics and specificities of enzymes. The proposed method will open the door to both primary (central) and secondary metabolism in genomics research, increasing research productivity to tackle a wide variety of environmental and public health matters. Availability and Implementation: Contact: maskot@bio.titech.ac.jp PMID:27307627

  3. Caspase-8 activation by TRAIL monotherapy predicts responses to IAPi and TRAIL combination treatment in breast cancer cell lines

    PubMed Central

    Polanski, R; Vincent, J; Polanska, U M; Petreus, T; Tang, E K Y

    2015-01-01

    The discovery of cancer cell-selective tumour necrosis factor-related apoptosis inducing ligand (TRAIL)-induced apoptosis generated broad excitement and development of TRAIL receptor agonists (TRA) as potential cancer therapy. Studies demonstrating the synergistic combination effect of SMAC mimetics and TRA further suggested potentially effective treatment in multiple tumour settings. However, predictive biomarkers allowing identification of patients that could respond to treatment are lacking. Here, we described a high throughput combination screen conducted across a panel of 31 breast cancer cell lines in which we observed highly synergistic activity between TRAIL and the inhibitors of apoptosis proteins (IAP) inhibitor (IAPi) AZD5582 in ~30% of cell lines. We detected no difference in the expression levels of the IAPi or TRAIL-targeted proteins or common modulators of the apoptotic pathway between the sensitive and resistant cell lines. Synergistic combination effect of AZD5582 and TRAIL correlated with sensitivity to TRAIL, but not to AZD5582 as a single agent. TRAIL treatment led to significantly greater activity of Caspase-8 in sensitive than in resistant cell lines (P=0.002). The majority (12/14) of AZD5582+TRAIL-resistant cell lines retained a functional cell death pathway, as they were sensitive to AZD5582+TNFα combination treatment. This suggested that failure of the TRAIL receptor complex to transduce the death signal to Caspase-8 underlies AZD5582+TRAIL resistance. We developed a 3D spheroid assay and demonstrated its suitability for the ex vivo analysis of the Caspase-8 activity as a predictive biomarker. Altogether, our study demonstrated a link between the functionality of the TRAIL receptor pathway and the synergistic activity of the IAPi+TRA combination treatment. It also provided a rationale for development of the Caspase-8 activity assay as a functional predictive biomarker that could allow better prediction of the response to IAPi

  4. Combining pathway analysis with flux balance analysis for the comprehensive study of metabolic systems.

    PubMed

    Schilling, C H; Edwards, J S; Letscher, D; Palsson, B Ø

    The elucidation of organism-scale metabolic networks necessitates the development of integrative methods to analyze and interpret the systemic properties of cellular metabolism. A shift in emphasis from single metabolic reactions to systemically defined pathways is one consequence of such an integrative analysis of metabolic systems. The constraints of systemic stoichiometry, and limited thermodynamics have led to the definition of the flux space within the context of convex analysis. The flux space of the metabolic system, containing all allowable flux distributions, is constrained to a convex polyhedral cone in a high-dimensional space. From metabolic pathway analysis, the edges of the high-dimensional flux cone are vectors that correspond to systemically defined "extreme pathways" spanning the capabilities of the system. The addition of maximum flux capacities of individual metabolic reactions serves to further constrain the flux space and has led to the development of flux balance analysis using linear optimization to calculate optimal flux distributions. Here we provide the precise theoretical connections between pathway analysis and flux balance analysis allowing for their combined application to study integrated metabolic function. Shifts in metabolic behavior are calculated using linear optimization and are then interpreted using the extreme pathways to demonstrate the concept of pathway utilization. Changes to the reaction network, such as the removal of a reaction, can lead to the generation of suboptimal phenotypes that can be directly attributed to the loss of pathway function and capabilities. Optimal growth phenotypes are calculated as a function of environmental variables, such as the availability of substrate and oxygen, leading to the definition of phenotypic phase planes. It is illustrated how optimality properties of the computed flux distributions can be interpreted in terms of the extreme pathways. Together these developments are applied to an

  5. Coregulation of terpenoid pathway genes and prediction of isoprene production in Bacillus subtilis using transcriptomics

    SciTech Connect

    Hess, Becky M.; Xue, Junfeng; Markillie, Lye Meng; Taylor, Ronald C.; Wiley, H. S.; Ahring, Birgitte K.; Linggi, Bryan E.

    2013-06-19

    The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding the system level regulation and control of the pathway. To address this limitation, we examined Bacillus subtilis grown under multiple conditions and then determined the relationship between altered isoprene production and the pattern of gene expression. We found that terpenoid genes appeared to fall into two distinct subsets with opposing correlations with respect to the amount of isoprene produced. The group whose expression levels positively correlated with isoprene production included dxs, the gene responsible for the commitment step in the pathway, as well as ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. This analysis showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model which accurately predicts production of this secondary metabolite across many simulated environmental conditions.

  6. Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using Transcriptomics

    PubMed Central

    Hess, Becky M.; Xue, Junfeng; Markillie, Lye Meng; Taylor, Ronald C.; Wiley, H. Steven; Ahring, Birgitte K.; Linggi, Bryan

    2013-01-01

    The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes, as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding system level regulation and control of the pathway. To address these limitations, we examined Bacillus subtilis grown under multiple conditions and determined the relationship between altered isoprene production and gene expression patterns. We found that with respect to the amount of isoprene produced, terpenoid genes fall into two distinct subsets with opposing correlations. The group whose expression levels positively correlated with isoprene production included dxs, which is responsible for the commitment step in the pathway, ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome-wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. These analyses showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model that accurately predicts production of this secondary metabolite across many simulated environmental conditions. PMID:23840410

  7. Coregulation of Terpenoid Pathway Genes and Prediction of Isoprene Production in Bacillus subtilis Using Transcriptomics.

    PubMed

    Hess, Becky M; Xue, Junfeng; Markillie, Lye Meng; Taylor, Ronald C; Wiley, H Steven; Ahring, Birgitte K; Linggi, Bryan

    2013-01-01

    The isoprenoid pathway converts pyruvate to isoprene and related isoprenoid compounds in plants and some bacteria. Currently, this pathway is of great interest because of the critical role that isoprenoids play in basic cellular processes, as well as the industrial value of metabolites such as isoprene. Although the regulation of several pathway genes has been described, there is a paucity of information regarding system level regulation and control of the pathway. To address these limitations, we examined Bacillus subtilis grown under multiple conditions and determined the relationship between altered isoprene production and gene expression patterns. We found that with respect to the amount of isoprene produced, terpenoid genes fall into two distinct subsets with opposing correlations. The group whose expression levels positively correlated with isoprene production included dxs, which is responsible for the commitment step in the pathway, ispD, and two genes that participate in the mevalonate pathway, yhfS and pksG. The subset of terpenoid genes that inversely correlated with isoprene production included ispH, ispF, hepS, uppS, ispE, and dxr. A genome-wide partial least squares regression model was created to identify other genes or pathways that contribute to isoprene production. These analyses showed that a subset of 213 regulated genes was sufficient to create a predictive model of isoprene production under different conditions and showed correlations at the transcriptional level. We conclude that gene expression levels alone are sufficiently informative about the metabolic state of a cell that produces increased isoprene and can be used to build a model that accurately predicts production of this secondary metabolite across many simulated environmental conditions.

  8. MCPath: Monte Carlo path generation approach to predict likely allosteric pathways and functional residues

    PubMed Central

    Kaya, Cihan; Armutlulu, Andac; Ekesan, Solen; Haliloglu, Turkan

    2013-01-01

    Allosteric mechanism of proteins is essential in biomolecular signaling. An important aspect underlying this mechanism is the communication pathways connecting functional residues. Here, a Monte Carlo (MC) path generation approach is proposed and implemented to define likely allosteric pathways through generating an ensemble of maximum probability paths. The protein structure is considered as a network of amino acid residues, and inter-residue interactions are described by an atomistic potential function. PDZ domain structures are presented as case studies. The analysis for bovine rhodopsin and three myosin structures are also provided as supplementary case studies. The suggested pathways and the residues constituting the pathways are maximally probable and mostly agree with the previous studies. Overall, it is demonstrated that the communication pathways could be multiple and intrinsically disposed, and the MC path generation approach provides an effective tool for the prediction of key residues that mediate the allosteric communication in an ensemble of pathways and functionally plausible residues. The MCPath server is available at http://safir.prc.boun.edu.tr/clbet_server. PMID:23742907

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

    PubMed Central

    Ouyang, Liang; Cai, Haoyang; Liu, Bo

    2016-01-01

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

  10. Targeting the MAPK and PI3K pathways in combination with PD1 blockade in melanoma

    PubMed Central

    Deken, Marcel A.; Gadiot, Jules; Jordanova, Ekaterina S.; Lacroix, Ruben; van Gool, Melissa; Kroon, Paula; Pineda, Cristina; Geukes Foppen, Marnix H.; Scolyer, Richard; Song, Ji-Ying; Verbrugge, Inge; Hoeller, Christoph; Dummer, Reinhard; Haanen, John B. A. G.; Long, Georgina V.; Blank, Christian U.

    2016-01-01

    ABSTRACT Immunotherapy of advanced melanoma with CTLA-4 or PD-1/PD-L1 checkpoint blockade induces in a proportion of patients long durable responses. In contrast, targeting the MAPK-pathway by selective BRAF and MEK inhibitors induces high response rates, but most patients relapse. Combining targeted therapy with immunotherapy is proposed to improve the long-term outcomes of patients. Preclinical data endorsing this hypothesis are accumulating. Inhibition of the PI3K-Akt-mTOR pathway may be a promising treatment option to overcome resistance to MAPK inhibition and for additional combination with immunotherapy. We therefore evaluated to which extent dual targeting of the MAPK and PI3K-Akt-mTOR pathways affects tumor immune infiltrates and whether it synergizes with PD-1 checkpoint blockade in a BRAFV600E/PTEN−/−-driven melanoma mouse model. Short-term dual BRAF + MEK inhibition enhanced tumor immune infiltration and improved tumor control when combined with PD-1 blockade in a CD8+ T cell dependent manner. Additional PI3K inhibition did not impair tumor control or immune cell infiltration and functionality. Analysis of on-treatment samples from melanoma patients treated with BRAF or BRAF + MEK inhibitors indicates that inhibitor-mediated T cell infiltration occurred in all patients early after treatment initiation but was less frequent found in on-treatment biopsies beyond day 15. Our findings provide a rationale for clinical testing of short-term BRAF + MEK inhibition in combination with immune checkpoint blockade, currently implemented at our institutes. Additional PI3K inhibition could be an option for BRAF + MEK inhibitor resistant patients that receive targeted therapy in combination with immune checkpoint blockade. PMID:28123875

  11. Identification of Novel Biomarkers in Seasonal Allergic Rhinitis by Combining Proteomic, Multivariate and Pathway Analysis

    PubMed Central

    Wang, Hui; Gottfries, Johan; Barrenäs, Fredrik; Benson, Mikael

    2011-01-01

    Background Glucocorticoids (GCs) play a key role in the treatment of seasonal allergic rhinitis (SAR). However, some patients show a low response to GC treatment. We hypothesized that proteins that correlated to discrimination between symptomatic high and low responders (HR and LR) to GC treatment might be regulated by GCs and therefore suitable as biomarkers for GC treatment. Methodology/Principal Findings We identified 953 nasal fluid proteins in symptomatic HR and LR with a LC MS/MS based-quantitative proteomics analysis and performed multivariate analysis to identify a combination of proteins that best separated symptomatic HR and LR. Pathway analysis showed that those proteins were most enriched in the acute phase response pathway. We prioritized candidate biomarkers for GC treatment based on the multivariate and pathway analysis. Next, we tested if those candidate biomarkers differed before and after GC treatment in nasal fluids from 40 patients with SAR using ELISA. Several proteins including ORM (P<0.0001), APOH (P<0.0001), FGA (P<0.01), CTSD (P<0.05) and SERPINB3 (P<0.05) differed significantly before and after GC treatment. Particularly, ORM (P<0.01), FGA (P<0.05) and APOH (P<0.01) that belonged to the acute phase response pathway decreased significantly in HR but not LR before and after GC treatment. Conclusions/Significance We identified several novel biomarkers for GC treatment response in SAR with combined proteomics, multivariate and pathway analysis. The analytical principles may be generally applicable to identify biomarkers in clinical studies of complex diseases. PMID:21887273

  12. Combined C4d and CD3 immunostaining predicts immunoglobulin (Ig)A nephropathy progression

    PubMed Central

    Faria, B; Henriques, C; Matos, A C; Daha, M R; Pestana, M; Seelen, M

    2015-01-01

    A number of molecules have been shown recently to be involved in the pathogenesis and progression of immunoglobulin (Ig)A nephropathy (IgAN). Among these, we have selected C4d (complement lectin pathway involvement), CD3 (T cell marker, traducing interstitial inflammation), transglutaminase 2 (TGase-2, involved in tissue fibrosis development) and p-extracelluar-regulated kinase (ERK)1/2 (protein kinase intracellular signaling molecule) to perform a panel of immunohistological biomarkers and assess its predictive value for disease progression. Immunohistochemical staining of these biomarkers was performed in paraffin sections from 74 renal biopsy cases with the clinical diagnosis of IgAN. Association between score analysis of these parameters and disease course was assessed through univariate and multivariate analysis, including baseline clinical and histological data. Univariate analysis showed that glomerular C4d, tubulointerstitial TGase2 and CD3 scores were associated with baseline proteinuria and disease progression. Multivariate analysis showed that only baseline estimated glomerular filtration rate (eGFR), C4d and CD3 were associated independently with progressive kidney disease (decline of at least 50% in the eGFR or progression to end-stage renal disease (ESRD) during the follow-up period). Establishing an accurate prediction model for IgAN progression is still a matter of research in clinical nephrology. The complement system, particularly lectin pathway activation, and T cell activation, have been shown previously to be potential modifiers of the disease course. Here we show that the combination of two histological biomarkers (C4d and CD3) can be a powerful predictor of IgAN progression and a potential useful tool for the clinical approach of this disease. PMID:25267249

  13. Functional Variants in Notch Pathway Genes NCOR2, NCSTN, and MAML2 Predict Survival of Patients with Cutaneous Melanoma

    PubMed Central

    Zhang, Weikang; Liu, Hongliang; Liu, Zhensheng; Zhu, Dakai; Amos, Christopher I.; Fang, Shenying; Lee, Jeffrey E.; Wei, Qingyi

    2015-01-01

    Background The Notch signaling pathway is constitutively activated in human cutaneous melanoma to promote growth and aggressive metastatic potential of primary melanoma cells. Therefore, genetic variants in Notch pathway genes may affect the prognosis of cutaneous melanoma patients. Methods We identified 6,256 SNPs in 48 Notch genes in 858 cutaneous melanoma patients included in a previously published cutaneous melanoma genome-wide association study dataset. Multivariate and stepwise Cox proportional hazards regression and false-positive report probability corrections were performed to evaluate associations between putative functional SNPs and cutaneous melanoma disease-specific survival. Receiver operating characteristic curve was constructed, and area under the curve was used to assess the classification performance of the model. Results Four putative functional SNPs of Notch pathway genes had independent and joint predictive roles in survival of cutaneous melanoma patients. The most significant variant was NCOR2 rs2342924 T>C (adjusted HR, 2.71; 95% confidence interval, 1.73–4.23; Ptrend = 9.62 × 10−7), followed by NCSTN rs1124379 G>A, NCOR2 rs10846684 G>A, and MAML2 rs7953425 G>A (Ptrend = 0.005, 0.005, and 0.013, respectively). The receiver operating characteristic analysis revealed that area under the curve was significantly increased after adding the combined unfavorable genotype score to the model containing the known clinicopathologic factors. Conclusions Our results suggest that SNPs in Notch pathway genes may be predictors of cutaneous melanoma disease-specific survival. Impact Our discovery offers a translational potential for using genetic variants in Notch pathway genes as a genotype score of biomarkers for developing an improved prognostic assessment and personalized management of cutaneous melanoma patients. PMID:25953768

  14. Combined gene expression and proteomic analysis of EGF induced apoptosis in A431 cells suggests multiple pathways trigger apoptosis.

    PubMed

    Alanazi, Ibrahim; Ebrahimie, Esmaeil; Hoffmann, Peter; Adelson, David L

    2013-11-01

    A431 cells, derived from epidermoid carcinoma, overexpress the epidermal growth factor receptor (EGFR) and when treated with a high dose of EGF will undergo apoptosis. We exploited microarray and proteomics techniques and network prediction to study the regulatory mechanisms of EGF-induced apoptosis in A431 cells. We observed significant changes in gene expression in 162 genes, approximately evenly split between pro-apoptotic and anti-apoptotic genes and identified 30 proteins from the proteomic data that had either pro or anti-apoptotic annotation. Our correlation analysis of gene expression and proteome modeled a number of distinct sub-networks that are associated with the onset of apoptosis, allowing us to identify specific pathways and components. These include components of the interferon signalling pathway, and down stream components, including cytokines and suppressors of cytokine signalling. A central component of almost all gene expression sub-networks identified was TP53, which is mutated in A431 cells, and was down regulated. This down regulation of TP53 appeared to be correlated with proteomic sub-networks of cytoskeletal or cell adhesion components that might induce apoptosis by triggering cytochrome C release. Of the only three genes also differentially expressed as proteins, only serpinb1 had a known association with apoptosis. We confirmed that up regulation and cleavage of serpinb1 into L-DNAaseII was correlated with the induction of apoptosis. It is unlikely that a single pathway, but more likely a combination of pathways is needed to trigger EGF induced apoptosis in A431cells.

  15. Predicting the Toxicity of Adjuvant Breast Cancer Drug Combination Therapy

    DTIC Science & Technology

    2013-03-01

    transport studying docetaxel pharmacokinetics in wild-type FVB and Mdr1a/ b constitutive knockout (KO) mice. For all tissues in both the FVB and KO...16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF PAGES 19a. NAME OF RESPONSIBLE PERSON USAMRMC a. REPORT U b ...the role of PGP in drug PK. Dose mdr1a/ b knockout mice with LAPATINIB, DOCETAXEL, DOXORUBICIN, combination LAPATINIB and DOCETAXEL, and combination

  16. Prediction of Protein–Protein Interactions by Evidence Combining Methods

    PubMed Central

    Chang, Ji-Wei; Zhou, Yan-Qing; Ul Qamar, Muhammad Tahir; Chen, Ling-Ling; Ding, Yu-Duan

    2016-01-01

    Most cellular functions involve proteins’ features based on their physical interactions with other partner proteins. Sketching a map of protein–protein interactions (PPIs) is therefore an important inception step towards understanding the basics of cell functions. Several experimental techniques operating in vivo or in vitro have made significant contributions to screening a large number of protein interaction partners, especially high-throughput experimental methods. However, computational approaches for PPI predication supported by rapid accumulation of data generated from experimental techniques, 3D structure definitions, and genome sequencing have boosted the map sketching of PPIs. In this review, we shed light on in silico PPI prediction methods that integrate evidence from multiple sources, including evolutionary relationship, function annotation, sequence/structure features, network topology and text mining. These methods are developed for integration of multi-dimensional evidence, for designing the strategies to predict novel interactions, and for making the results consistent with the increase of prediction coverage and accuracy. PMID:27879651

  17. Probing the coagulation pathway with aptamers identifies combinations that synergistically inhibit blood clot formation.

    PubMed

    Bompiani, Kristin M; Lohrmann, Jens L; Pitoc, George A; Frederiksen, James W; Mackensen, George B; Sullenger, Bruce A

    2014-08-14

    Coordinated enzymatic reactions regulate blood clot generation. To explore the contributions of various coagulation enzymes in this process, we utilized a panel of aptamers against factors VIIa, IXa, Xa, and prothrombin. Each aptamer dose-dependently inhibited clot formation, yet none was able to completely impede this process in highly procoagulant settings. However, several combinations of two aptamers synergistically impaired clot formation. One extremely potent aptamer combination was able to maintain human blood fluidity even during extracorporeal circulation, a highly procoagulant setting encountered during cardiopulmonary bypass surgery. Moreover, this aptamer cocktail could be rapidly reversed with antidotes to restore normal hemostasis, indicating that even highly potent aptamer combinations can be rapidly controlled. These studies highlight the potential utility of using sets of aptamers to probe the functions of proteins in molecular pathways for research and therapeutic ends.

  18. Managing care pathways combining SNOMED CT, archetypes and an electronic guideline system.

    PubMed

    Bernstein, Knut; Andersen, Ulrich

    2008-01-01

    Today electronic clinical guideline systems exist, but they are not well integrated with electronic health records. This paper thus proposes that the patient's "position" in the pathway during the patient journey should be made visible to all involved healthcare parties and the patient. This requires that the generic knowledge, which is represented in the guidelines, is combined with the patient specific information - and then made accessible for all relevant parties. In addition to the decision support provided by the guideline system documentation support can be provided by templates based on archetypes. This paper provides a proposal for how the guideline system and the EHR can be integrated by the use of archetypes and SNOMED CT. SNOMED CT provides the common reference terminology and the semantic links between the systems. The proposal also includes the use of a National Patient Index for storing data about the patient's position in the pathway and for sharing this information by all involved parties.

  19. Aquatic Pathways Model to predict the fate of phenolic compounds. Appendixes A through D

    SciTech Connect

    Aaberg, R.L.; Peloquin, R.A.; Strenge, D.L.; Mellinger, P.L.

    1983-04-01

    Organic materials released from energy-related activities could affect human health and the environment. We have developed a model to predict the fate of spills or discharges of pollutants into flowing or static bodies of fresh water. A computer code, Aquatic Pathways Model (APM), was written to implement the model. The APM estimates the concentrations of chemicals in fish tissue, water and sediment, and is therefore useful for assessing exposure to humans through aquatic pathways. The major pathways considered are biodegradation, fish and sediment uptake, photolysis, and evaporation. The model has been implemented with parameters for the distribution of phenols, an important class of compounds found in the water-soluble fractions of coal liquids. The model was developed to estimate the fate of liquids derived from coal. Current modeling efforts show that, in comparison with many pesticides and polyaromatic hydrocarbons (PAH), the lighter phenolics (the cresols) are not persistent in the environment. For the twelve phenolics studied, biodegradation appears to be the major pathway for elimination from aquatic environments. A pond system simulation of a spill of solvent-refined coal (SRC-II) materials indicates that phenol, cresols, and other single cyclic phenolics are degraded to 16 to 25 percent of their original concentrations within 30 hours. Adsorption of these compounds into sediments and accumulation by fish was minor. Results of a simulated spill of a coal liquid (SRC-II) into a pond show that APM predicted the allocation of 12 phenolic components among six compartments at 30 hours after a small spill. The simulation indicated that most of the introduced phenolic compounds were biodegraded. The phenolics remaining in the aquatic system partitioned according to their molecular weight and structure. A substantial amount was predicted to remain in the water, with less than 0.01% distributed in sediment or fish.

  20. Quantification of primary motor pathways using diffusion MRI tractography and its application to predict postoperative motor deficits in children with focal epilepsy.

    PubMed

    Jeong, Jeong-Won; Asano, Eishi; Juhász, Csaba; Chugani, Harry T

    2014-07-01

    As a new tool to quantify primary motor pathways and predict postoperative motor deficits in children with focal epilepsy, the present study utilized a maximum a posteriori probability (MAP) classification of diffusion weighted imaging (DWI) tractography combined with Kalman filter. DWI was performed in 31 children with intractable focal epilepsy who underwent epilepsy surgery. Three primary motor pathways associated with "finger," "leg," and "face" were classified using DWI-MAP classifier and compared with the results of invasive electrical stimulation mapping (ESM) via receiver operating characteristic (ROC) curve analysis. The Kalman filter analysis was performed to generate a model to determine the probability of postoperative motor deficits as a function of the proximity between the resection margin and the finger motor pathway. The ROC curve analysis showed that the DWI-MAP achieves high accuracy up to 89% (finger), 88% (leg), 89% (face), in detecting the three motor areas within 20 mm, compared with ESM. Moreover, postoperative reduction of the fiber count of finger pathway was associated with postoperative motor deficits involving the hand. The prediction model revealed an accuracy of 92% in avoiding postoperative deficits if the distance between the resection margin and the finger motor pathway seen on preoperative DWI tractography was 19.5 mm. This study provides evidence that the DWI-MAP combined with Kalman filter can effectively identify the locations of cortical motor areas even in patients whose motor areas are difficult to identify using ESM, and also can serve as a reliable predictor for motor deficits following epilepsy surgery.

  1. HEMET: mathematical model of biochemical pathways for simulation and prediction of HEpatocyte METabolism.

    PubMed

    De Maria, C; Grassini, D; Vozzi, F; Vinci, B; Landi, A; Ahluwalia, A; Vozzi, G

    2008-10-01

    Many computer studies and models have been developed in order to simulate cell biochemical pathways. The difficulty of integrating all the biochemical reactions that occur in a cell in a single model is the main reason for the poor results in the prediction and simulation of cell behaviour under different chemical and physical stimuli. In this paper we have translated biochemical reactions into differential equations for the development of modular model of metabolism of a hepatocyte cultured in static and standard conditions (in a plastic multiwell placed in an incubator at 37 degrees C with 5% of CO(2)). Using biochemical equations and energetic considerations a set of non-linear differential equations has been derived and implemented in Simulink. This set of equations mimics some of the principal metabolic pathways of biomolecules present in the culture medium. The software platform developed is subdivided into separate modules, each one describing a different metabolic pathway; they constitute a library which can be used for developing new modules and models to project, predict and validate cell behaviour in vitro.

  2. Predicting Risk-Mitigating Behaviors From Indecisiveness and Trait Anxiety: Two Cognitive Pathways to Task Avoidance.

    PubMed

    McNeill, Ilona M; Dunlop, Patrick D; Skinner, Timothy C; Morrison, David L

    2016-02-01

    Past research suggests that indecisiveness and trait anxiety may both decrease the likelihood of performing risk-mitigating preparatory behaviors (e.g., preparing for natural hazards) and suggests two cognitive processes (perceived control and worrying) as potential mediators. However, no single study to date has examined the influence of these traits and processes together. Examining them simultaneously is necessary to gain an integrated understanding of their relationship with risk-mitigating behaviors. We therefore examined these traits and mediators in relation to wildfire preparedness in a two-wave field study among residents of wildfire-prone areas in Western Australia (total N = 223). Structural equation modeling results showed that indecisiveness uniquely predicted preparedness, with higher indecisiveness predicting lower preparedness. This relationship was fully mediated by perceived control over wildfire-related outcomes. Trait anxiety did not uniquely predict preparedness or perceived control, but it did uniquely predict worry, with higher trait anxiety predicting more worrying. Also, worry trended toward uniquely predicting preparedness, albeit in an unpredicted positive direction. This shows how the lack of performing risk-mitigating behaviors can result from distinct cognitive processes that are linked to distinct personality traits. It also highlights how simultaneous examination of multiple pathways to behavior creates a fuller understanding of its antecedents.

  3. Predicting the Toxicity of Adjuvant Breast Cancer Drug Combination Therapy

    DTIC Science & Technology

    2012-09-01

    hepatic metabo- lism based on the ratio of total liver:intestinal CYP3A, the major cytochrome P450 enzyme sub-family responsible for lapatinib...combination with cyclosporin A. Alternatively, the increase in exposure was likely more resultant of competitive inhibition of cytochrome P450 enzymes by...sex on the clearance of cytochrome P450 3A substrates. Clin Pharmacokinet 44(1):33–60 27. Bischoff KB, Dedrick RL, Zaharko DS (1970) Preliminary

  4. A Combined Epidemiologic and Metabolomic Approach Improves CKD Prediction

    PubMed Central

    Clish, Clary B.; Ghorbani, Anahita; Larson, Martin G.; Elmariah, Sammy; McCabe, Elizabeth; Yang, Qiong; Cheng, Susan; Pierce, Kerry; Deik, Amy; Souza, Amanda L.; Farrell, Laurie; Domos, Carly; Yeh, Robert W.; Palacios, Igor; Rosenfield, Kenneth; Vasan, Ramachandran S.; Florez, Jose C.; Wang, Thomas J.; Fox, Caroline S.

    2013-01-01

    Metabolomic approaches have begun to catalog the metabolic disturbances that accompany CKD, but whether metabolite alterations can predict future CKD is unknown. We performed liquid chromatography/mass spectrometry–based metabolite profiling on plasma from 1434 participants in the Framingham Heart Study (FHS) who did not have CKD at baseline. During the following 8 years, 123 individuals developed CKD, defined by an estimated GFR of <60 ml/min per 1.73 m2. Numerous metabolites were associated with incident CKD, including 16 that achieved the Bonferroni-adjusted significance threshold of P≤0.00023. To explore how the human kidney modulates these metabolites, we profiled arterial and renal venous plasma from nine individuals. Nine metabolites that predicted CKD in the FHS cohort decreased more than creatinine across the renal circulation, suggesting that they may reflect non–GFR-dependent functions, such as renal metabolism and secretion. Urine isotope dilution studies identified citrulline and choline as markers of renal metabolism and kynurenic acid as a marker of renal secretion. In turn, these analytes remained associated with incident CKD in the FHS cohort, even after adjustment for eGFR, age, sex, diabetes, hypertension, and proteinuria at baseline. Addition of a multimarker metabolite panel to clinical variables significantly increased the c-statistic (0.77–0.83, P<0.0001); net reclassification improvement was 0.78 (95% confidence interval, 0.60 to 0.95; P<0.0001). Thus, the addition of metabolite profiling to clinical data may significantly improve the ability to predict whether an individual will develop CKD by identifying predictors of renal risk that are independent of estimated GFR. PMID:23687356

  5. Cetuximab and Cisplatin Show Different Combination Effect in Nasopharyngeal Carcinoma Cells Lines via Inactivation of EGFR/AKT Signaling Pathway

    PubMed Central

    Gu, Jiajia; Yin, Li; Wu, Jing; Zhang, Nan; Huang, Teng; Ding, Kai; Cao, Haixia; Xu, Lin; He, Xia

    2016-01-01

    Nasopharyngeal carcinoma (NPC) is a common malignant cancer in South China. Cisplatin is a classical chemotherapeutic employed for NPC treatment. Despite the use of cisplatin-based concurrent chemoradiotherapy, distant failure still confuses clinicians and the outcome of metastatic NPC remains disappointing. Hence, a potent systemic therapy is needed for this cancer. Epidermal growth factor receptor (EGFR) represents a promising new therapeutic target in cancer. We predicted that combining the conventional cytotoxic drug cisplatin with the novel molecular-targeted agent cetuximab demonstrates a strong antitumor effect on NPC cells. In this study, we selected HNE1 and CNE2 cells, which have been proved to possess different EGFR expression levels, to validate our conjecture. The two-drug regimen showed a significant synergistic effect in HNE1 cells but an additive effect in CNE2 cells. Our results showed that cisplatin-induced apoptosis was significantly enhanced by cetuximab in the high EGFR-expressing HNE1 cells but not in CNE2 cells. Further molecular mechanism study indicated that the EGFR/AKT pathway may play an important role in cell apoptosis via the mitochondrial-mediated intrinsic pathway and lead to the different antitumor effects of this two-drug regimen between HNE1 and CNE2 cells. Thus, the regimen may be applied in personalized NPC treatments. PMID:27313893

  6. NLLSS: Predicting Synergistic Drug Combinations Based on Semi-supervised Learning

    PubMed Central

    Chen, Ming; Wang, Quanxin; Zhang, Lixin; Yan, Guiying

    2016-01-01

    Fungal infection has become one of the leading causes of hospital-acquired infections with high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases. Synergistic drug combinations could provide an effective strategy to overcome drug resistance. Meanwhile, synergistic drug combinations can increase treatment efficacy and decrease drug dosage to avoid toxicity. Therefore, computational prediction of synergistic drug combinations for fungus-causing diseases becomes attractive. In this study, we proposed similar nature of drug combinations: principal drugs which obtain synergistic effect with similar adjuvant drugs are often similar and vice versa. Furthermore, we developed a novel algorithm termed Network-based Laplacian regularized Least Square Synergistic drug combination prediction (NLLSS) to predict potential synergistic drug combinations by integrating different kinds of information such as known synergistic drug combinations, drug-target interactions, and drug chemical structures. We applied NLLSS to predict antifungal synergistic drug combinations and showed that it achieved excellent performance both in terms of cross validation and independent prediction. Finally, we performed biological experiments for fungal pathogen Candida albicans to confirm 7 out of 13 predicted antifungal synergistic drug combinations. NLLSS provides an efficient strategy to identify potential synergistic antifungal combinations. PMID:27415801

  7. A strategy to combine pathway-targeted low toxicity drugs in ovarian cancer

    PubMed Central

    Delaney, Joe R.; Patel, Chandni; McCabe, Katelyn E.; Lu, Dan; Davis, Mitzie-Ann; Tancioni, Isabelle; von Schalscha, Tami; Bartakova, Alena; Haft, Carley; Schlaepfer, David D.; Stupack, Dwayne G.

    2015-01-01

    Serous Ovarian Cancers (SOC) are frequently resistant to programmed cell death. However, here we describe that these programmed death-resistant cells are nonetheless sensitive to agents that modulate autophagy. Cytotoxicity is not dependent upon apoptosis, necroptosis, or autophagy resolution. A screen of NCBI yielded more than one dozen FDA-approved agents displaying perturbed autophagy in ovarian cancer. The effects were maximized via combinatorial use of the agents that impinged upon distinct points of autophagy regulation. Autophagosome formation correlated with efficacy in vitro and the most cytotoxic two agents gave similar effects to a pentadrug combination that impinged upon five distinct modulators of autophagy. However, in a complex in vivo SOC system, the pentadrug combination outperformed the best two, leaving trace or no disease and with no evidence of systemic toxicity. Targeting the autophagy pathway in a multi-modal fashion might therefore offer a clinical option for treating recalcitrant SOC. PMID:26418751

  8. A strategy to combine pathway-targeted low toxicity drugs in ovarian cancer.

    PubMed

    Delaney, Joe R; Patel, Chandni; McCabe, Katelyn E; Lu, Dan; Davis, Mitzie-Ann; Tancioni, Isabelle; von Schalscha, Tami; Bartakova, Alena; Haft, Carley; Schlaepfer, David D; Stupack, Dwayne G

    2015-10-13

    Serous Ovarian Cancers (SOC) are frequently resistant to programmed cell death. However, here we describe that these programmed death-resistant cells are nonetheless sensitive to agents that modulate autophagy. Cytotoxicity is not dependent upon apoptosis, necroptosis, or autophagy resolution. A screen of NCBI yielded more than one dozen FDA-approved agents displaying perturbed autophagy in ovarian cancer. The effects were maximized via combinatorial use of the agents that impinged upon distinct points of autophagy regulation. Autophagosome formation correlated with efficacy in vitro and the most cytotoxic two agents gave similar effects to a pentadrug combination that impinged upon five distinct modulators of autophagy. However, in a complex in vivo SOC system, the pentadrug combination outperformed the best two, leaving trace or no disease and with no evidence of systemic toxicity. Targeting the autophagy pathway in a multi-modal fashion might therefore offer a clinical option for treating recalcitrant SOC.

  9. Predicting the diagnosis of autism spectrum disorder using gene pathway analysis

    PubMed Central

    Skafidas, E; Testa, R; Zantomio, D; Chana, G; Everall, I P; Pantelis, C

    2014-01-01

    Autism spectrum disorder (ASD) depends on a clinical interview with no biomarkers to aid diagnosis. The current investigation interrogated single-nucleotide polymorphisms (SNPs) of individuals with ASD from the Autism Genetic Resource Exchange (AGRE) database. SNPs were mapped to Kyoto Encyclopedia of Genes and Genomes (KEGG)-derived pathways to identify affected cellular processes and develop a diagnostic test. This test was then applied to two independent samples from the Simons Foundation Autism Research Initiative (SFARI) and Wellcome Trust 1958 normal birth cohort (WTBC) for validation. Using AGRE SNP data from a Central European (CEU) cohort, we created a genetic diagnostic classifier consisting of 237 SNPs in 146 genes that correctly predicted ASD diagnosis in 85.6% of CEU cases. This classifier also predicted 84.3% of cases in an ethnically related Tuscan cohort; however, prediction was less accurate (56.4%) in a genetically dissimilar Han Chinese cohort (HAN). Eight SNPs in three genes (KCNMB4, GNAO1, GRM5) had the largest effect in the classifier with some acting as vulnerability SNPs, whereas others were protective. Prediction accuracy diminished as the number of SNPs analyzed in the model was decreased. Our diagnostic classifier correctly predicted ASD diagnosis with an accuracy of 71.7% in CEU individuals from the SFARI (ASD) and WTBC (controls) validation data sets. In conclusion, we have developed an accurate diagnostic test for a genetically homogeneous group to aid in early detection of ASD. While SNPs differ across ethnic groups, our pathway approach identified cellular processes common to ASD across ethnicities. Our results have wide implications for detection, intervention and prevention of ASD. PMID:22965006

  10. Timing predictability enhances regularity encoding in the human subcortical auditory pathway.

    PubMed

    Gorina-Careta, Natàlia; Zarnowiec, Katarzyna; Costa-Faidella, Jordi; Escera, Carles

    2016-11-17

    The encoding of temporal regularities is a critical property of the auditory system, as short-term neural representations of environmental statistics serve to auditory object formation and detection of potentially relevant novel stimuli. A putative neural mechanism underlying regularity encoding is repetition suppression, the reduction of neural activity to repeated stimulation. Although repetitive stimulation per se has shown to reduce auditory neural activity in animal cortical and subcortical levels and in the human cerebral cortex, other factors such as timing may influence the encoding of statistical regularities. This study was set out to investigate whether temporal predictability in the ongoing auditory input modulates repetition suppression in subcortical stages of the auditory processing hierarchy. Human auditory frequency-following responses (FFR) were recorded to a repeating consonant-vowel stimuli (/wa/) delivered in temporally predictable and unpredictable conditions. FFR amplitude was attenuated by repetition independently of temporal predictability, yet we observed an accentuated suppression when the incoming stimulation was temporally predictable. These findings support the view that regularity encoding spans across the auditory hierarchy and point to temporal predictability as a modulatory factor of regularity encoding in early stages of the auditory pathway.

  11. Timing predictability enhances regularity encoding in the human subcortical auditory pathway

    PubMed Central

    Gorina-Careta, Natàlia; Zarnowiec, Katarzyna; Costa-Faidella, Jordi; Escera, Carles

    2016-01-01

    The encoding of temporal regularities is a critical property of the auditory system, as short-term neural representations of environmental statistics serve to auditory object formation and detection of potentially relevant novel stimuli. A putative neural mechanism underlying regularity encoding is repetition suppression, the reduction of neural activity to repeated stimulation. Although repetitive stimulation per se has shown to reduce auditory neural activity in animal cortical and subcortical levels and in the human cerebral cortex, other factors such as timing may influence the encoding of statistical regularities. This study was set out to investigate whether temporal predictability in the ongoing auditory input modulates repetition suppression in subcortical stages of the auditory processing hierarchy. Human auditory frequency–following responses (FFR) were recorded to a repeating consonant–vowel stimuli (/wa/) delivered in temporally predictable and unpredictable conditions. FFR amplitude was attenuated by repetition independently of temporal predictability, yet we observed an accentuated suppression when the incoming stimulation was temporally predictable. These findings support the view that regularity encoding spans across the auditory hierarchy and point to temporal predictability as a modulatory factor of regularity encoding in early stages of the auditory pathway. PMID:27853313

  12. PD-L1 (B7-H1) and PD-1 Pathway Blockade for Cancer Therapy: Mechanisms, Response Biomarkers and Combinations

    PubMed Central

    Zou, Weiping; Wolchok, Jedd D.; Chen, Lieping

    2016-01-01

    PD-L1 and PD-1 (PD) pathway blockade is a highly promising therapy and has elicited durable anti-tumor responses and long-term remissions in a subset of patients with a broad spectrum of cancers. How to improve, widen, and predict the clinical response to anti-PD therapy is a central theme in the field of cancer immunology and immunotherapy. Oncologic, immunologic, genetic and biological studies focused on the human cancer microenvironment have yielded significant insight into this issue. In this Review, we focus on tumor microenvironment; evaluate several potential therapeutic response markers including the PD-L1 and PD-1 expression pattern, genetic mutations within cancer cells and neoantigens, cancer epigenetics and effector T cell landscape, microbiota, and their mechanisms of action and roles in shaping, being shaped and/or predicting therapeutic responses. We also discuss a variety of combinations with PD pathway blockade and their scientific rationales for cancer treatment. PMID:26936508

  13. Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination

    NASA Astrophysics Data System (ADS)

    Li, Weihua; Sankarasubramanian, A.

    2012-12-01

    Model errors are inevitable in any prediction exercise. One approach that is currently gaining attention in reducing model errors is by combining multiple models to develop improved predictions. The rationale behind this approach primarily lies on the premise that optimal weights could be derived for each model so that the developed multimodel predictions will result in improved predictions. A new dynamic approach (MM-1) to combine multiple hydrological models by evaluating their performance/skill contingent on the predictor state is proposed. We combine two hydrological models, "abcd" model and variable infiltration capacity (VIC) model, to develop multimodel streamflow predictions. To quantify precisely under what conditions the multimodel combination results in improved predictions, we compare multimodel scheme MM-1 with optimal model combination scheme (MM-O) by employing them in predicting the streamflow generated from a known hydrologic model (abcd model orVICmodel) with heteroscedastic error variance as well as from a hydrologic model that exhibits different structure than that of the candidate models (i.e., "abcd" model or VIC model). Results from the study show that streamflow estimated from single models performed better than multimodels under almost no measurement error. However, under increased measurement errors and model structural misspecification, both multimodel schemes (MM-1 and MM-O) consistently performed better than the single model prediction. Overall, MM-1 performs better than MM-O in predicting the monthly flow values as well as in predicting extreme monthly flows. Comparison of the weights obtained from each candidate model reveals that as measurement errors increase, MM-1 assigns weights equally for all the models, whereas MM-O assigns higher weights for always the best-performing candidate model under the calibration period. Applying the multimodel algorithms for predicting streamflows over four different sites revealed that MM-1 performs

  14. Solar wind-magnetosphere interaction: energy transfer pathways and their predictability

    NASA Astrophysics Data System (ADS)

    Vassiliadis, D.

    2001-09-01

    The coupling of the solar wind to planets has been studied for several decades now. Much of the recent progress in understanding the complexity of the interaction is due to the variability of the magnetospheres in the solar system. The interaction with the terrestrial system is evidently the best known, although by far not the simplest one. The geospace and the surrounding solar wind constitute an input-output system where the various parts of the energy budget are measured simultaneously by a fleet of spacecraft. We integrate these measurements by combining plasma physics and simulation models with system analysis methods. The large-scale energy transfer is dominated by magnetic reconnection and its effects which supply electromagnetic and kinetic energy at the rate of ~10^15 W to the magnetosphere with part of it eventually dissipating at the ionospheric boundary. The storage-release of magnetic energy and its transformation to kinetic energy takes place continually through convection and more explosively during magnetospheric substorms. Long known from its effects, ranging from auroral displays to the more recent disruptions of electric power grid operation, this interaction involves many spatial and temporal pathways: The overall disturbance levels are represented by regional and global magnetic indices. Index time series were the first to be reproduced accurately by nonlinear dynamical systems driven by solar wind parameter data. The models have subsequently been used in prediction of the indices based on real-time interplanetary field and plasma parameters from the ACE and WIND spacecraft (http://lep694.gsfc.nasa.gov/RTSM/). The model time scales represent the physical responses, namely the directly driven convection and the less predictable substorm. Currently the approach has been extended to modeling the spatial distribution as well as temporal variations. Data from ground magnetometer arrays have replaced the scalar indices to provide magnetic field maps

  15. Dynamic changes of RNA-sequencing expression for precision medicine: N-of-1-pathways Mahalanobis distance within pathways of single subjects predicts breast cancer survival

    PubMed Central

    Piegorsch, Walter W.; Lussier, Yves A.

    2015-01-01

    Motivation: The conventional approach to personalized medicine relies on molecular data analytics across multiple patients. The path to precision medicine lies with molecular data analytics that can discover interpretable single-subject signals (N-of-1). We developed a global framework, N-of-1-pathways, for a mechanistic-anchored approach to single-subject gene expression data analysis. We previously employed a metric that could prioritize the statistical significance of a deregulated pathway in single subjects, however, it lacked in quantitative interpretability (e.g. the equivalent to a gene expression fold-change). Results: In this study, we extend our previous approach with the application of statistical Mahalanobis distance (MD) to quantify personal pathway-level deregulation. We demonstrate that this approach, N-of-1-pathways Paired Samples MD (N-OF-1-PATHWAYS-MD), detects deregulated pathways (empirical simulations), while not inflating false-positive rate using a study with biological replicates. Finally, we establish that N-OF-1-PATHWAYS-MD scores are, biologically significant, clinically relevant and are predictive of breast cancer survival (P < 0.05, n = 80 invasive carcinoma; TCGA RNA-sequences). Conclusion: N-of-1-pathways MD provides a practical approach towards precision medicine. The method generates the magnitude and the biological significance of personal deregulated pathways results derived solely from the patient’s transcriptome. These pathways offer the opportunities for deriving clinically actionable decisions that have the potential to complement the clinical interpretability of personal polymorphisms obtained from DNA acquired or inherited polymorphisms and mutations. In addition, it offers an opportunity for applicability to diseases in which DNA changes may not be relevant, and thus expand the ‘interpretable ‘omics’ of single subjects (e.g. personalome). Availability and implementation: http://www.lussierlab.net/publications/N-of-1

  16. Titanium α-ω phase transformation pathway and a predicted metastable structure

    SciTech Connect

    Zarkevich, Nickolai A.; Johnson, Duane D.

    2016-01-15

    A titanium is a highly utilized metal for structural lightweighting and its phases, transformation pathways (transition states), and structures have scientific and industrial importance. Using a proper solid-state nudged elastic band method employing two climbing images combined with density functional theory DFT + U methods for accurate energetics, we detail the pressure-induced α (ductile) to ω (brittle) transformation at the coexistence pressure. We also find two transition states along the minimal-enthalpy path and discover a metastable body-centered orthorhombic structure, with stable phonons, a lower density than the end-point phases, and decreasing stability with increasing pressure.

  17. Network-based characterization and prediction of human DNA repair genes and pathways

    PubMed Central

    Li, Yan-Hui; Zhang, Gai-Gai

    2017-01-01

    Network biology is a useful strategy to understand cell’s functional organization. In this study, for the first time, we successfully introduced network approaches to study properties of human DNA repair genes. Compared with non-DNA repair genes, we found distinguishing features for DNA repair genes: (i) they tend to have higher degrees; (ii) they tend to be located at global network center; (iii) they tend to interact directly with each other. Based on these features, we developed the first algorithm to predict new DNA repair genes. We tested several machine-learning models and found that support vector machine with kernel function of radial basis function (RBF) achieve the best performance, with precision = 0.74 and area under curve (AUC) = 0.96. In the end, we applied the algorithm to predict new DNA repair genes and got 32 new candidates. Literature supporting four of the predictions was found. We believe the network approaches introduced here might open a new avenue to understand DNA repair genes and pathways. The suggested algorithm and the predicted genes might be helpful for scientists in the field. PMID:28368026

  18. Combination of rod and cone inputs in parasol ganglion cells of the magnocellular pathway

    PubMed Central

    Cao, Dingcai; Lee, Barry B.; Sun, Hao

    2010-01-01

    This study investigates how rod and cone inputs are combined in the magnocellular (MC) pathway in the mesopic luminance range, when both rods and cones are active. Responses of parafoveal MC ganglion cells from macaque retina were measured as a function of temporal frequency (0.62–20 Hz) or contrast (0.05–0.55) at mesopic light levels (0.2, 2, 20, and 200 td). Stimuli were of three modulation types: (1) isolated rod stimuli (only rod signals were modulated), (2) isolated cone stimuli (only cone luminance signals from long- and middle-wavelength sensitive cones were modulated), and (3) combined rod and cone stimuli (both rod and cone luminance signals were modulated in phase, as with conventional stimuli). The results showed that under mesopic conditions, the relative rod and cone inputs to the MC cells varied with light level and they are combined linearly prior to saturation. Further, rod contrast gain is relatively stable over the mesopic range while cone contrast gain increased with light level. Finally, the measured rod and cone inputs are consistent with the measured human temporal contrast sensitivity functions under comparable stimulation conditions. PMID:20884499

  19. Reconstructing causal pathways and optimal prediction from multivariate time series using the Tigramite package

    NASA Astrophysics Data System (ADS)

    Runge, Jakob

    2016-04-01

    Causal reconstruction techniques from multivariate time series have become a popular approach to analyze interactions in complex systems such as the Earth. These approaches allow to exclude effects of common drivers and indirect influences. Practical applications are, however, especially challenging if nonlinear interactions are taken into account and for typically strongly autocorrelated climate time series. Here we discuss a new reconstruction approach with accompanying software package (Tigramite) and focus on two applications: (1) Information or perturbation transfer along causal pathways. This method allows to detect and quantify which intermediate nodes are important mediators of an interaction mechanism and is illustrated to disentangle pathways of atmospheric flow over Europe and for the ENSO - Indian Monsoon interaction mechanism. (2) A nonlinear model-free prediction technique that efficiently utilizes causal drivers and can be shown to yield information-theoretically optimal predictors avoiding over-fitting. The performance of this framework is illustrated on a climatological index of El Nino Southern Oscillation. References: Runge, J. (2015). Quantifying information transfer and mediation along causal pathways in complex systems. Phys. Rev. E, 92(6), 062829. doi:10.1103/PhysRevE.92.062829 Runge, J., Donner, R. V., & Kurths, J. (2015). Optimal model-free prediction from multivariate time series. Phys. Rev. E, 91(5), 052909. doi:10.1103/PhysRevE.91.052909 Runge, J., Petoukhov, V., Donges, J. F., Hlinka, J., Jajcay, N., Vejmelka, M., … Kurths, J. (2015). Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502. doi:10.1038/ncomms9502

  20. Transcriptional pathway signatures predict MEK addiction and response to selumetinib (AZD6244).

    PubMed

    Dry, Jonathan R; Pavey, Sandra; Pratilas, Christine A; Harbron, Chris; Runswick, Sarah; Hodgson, Darren; Chresta, Christine; McCormack, Rose; Byrne, Natalie; Cockerill, Mark; Graham, Alexander; Beran, Garry; Cassidy, Andrew; Haggerty, Carolyn; Brown, Helen; Ellison, Gillian; Dering, Judy; Taylor, Barry S; Stark, Mitchell; Bonazzi, Vanessa; Ravishankar, Sugandha; Packer, Leisl; Xing, Feng; Solit, David B; Finn, Richard S; Rosen, Neal; Hayward, Nicholas K; French, Tim; Smith, Paul D

    2010-03-15

    Selumetinib (AZD6244, ARRY-142886) is a selective, non-ATP-competitive inhibitor of mitogen-activated protein/extracellular signal-regulated kinase kinase (MEK)-1/2. The range of antitumor activity seen preclinically and in patients highlights the importance of identifying determinants of response to this drug. In large tumor cell panels of diverse lineage, we show that MEK inhibitor response does not have an absolute correlation with mutational or phospho-protein markers of BRAF/MEK, RAS, or phosphoinositide 3-kinase (PI3K) activity. We aimed to enhance predictivity by measuring pathway output through coregulated gene networks displaying differential mRNA expression exclusive to resistant cell subsets and correlated to mutational or dynamic pathway activity. We discovered an 18-gene signature enabling measurement of MEK functional output independent of tumor genotype. Where the MEK pathway is activated but the cells remain resistant to selumetinib, we identified a 13-gene signature that implicates the existence of compensatory signaling from RAS effectors other than PI3K. The ability of these signatures to stratify samples according to functional activation of MEK and/or selumetinib sensitivity was shown in multiple independent melanoma, colon, breast, and lung tumor cell lines and in xenograft models. Furthermore, we were able to measure these signatures in fixed archival melanoma tumor samples using a single RT-qPCR-based test and found intergene correlations and associations with genetic markers of pathway activity to be preserved. These signatures offer useful tools for the study of MEK biology and clinical application of MEK inhibitors, and the novel approaches taken may benefit other targeted therapies.

  1. Prediction of folding pathway and kinetics among plant hemoglobins using an average distance map method.

    PubMed

    Nakajima, Shunsuke; Alvarez-Salgado, Emma; Kikuchi, Takeshi; Arredondo-Peter, Raúl

    2005-11-15

    Computational methods, such as the ADM (average distance map) method, have been developed to predict folding of homologous proteins. In this work we used the ADM method to predict the folding pathway and kinetics among selected plant nonsymbiotic (nsHb), symbiotic (Lb), and truncated (tHb) hemoglobins (Hbs). Results predicted that (1) folding of plant Hbs occurs throughout the formation of compact folding modules mostly formed by helices A, B, and C, and E, F, G, and H (folding modules A/C and E/H, respectively), and (2) primitive (moss) nsHbs fold in the C-->N direction, evolved (monocot and dicot) nsHbs fold either in the C-->N or N-->C direction, and Lbs and plant tHbs fold in the C-->N direction. We also predicted relative folding rates of plant Hbs from qualitative analyses of the stability of subdomains and classified plant Hbs into fast and moderate folding. ADM analysis of nsHbs predicted that prehelix A plays a role during folding of the N-terminal domain of Ceratodon nsHb, and that CD-loop plays a role in folding of primitive (Physcomitrella and Ceratodon) but not evolved nsHbs. Modeling of the rice Hb1 A/C and E/H modules showed that module E/H overlaps to the Mycobacterium tuberculosis HbO two-on-two folding. This observation suggests that module E/H is an ancient tertiary structure in plant Hbs.

  2. Combination effect of therapies targeting the PI3K- and AR-signaling pathways in prostate cancer

    PubMed Central

    Stockert, Jennifer A.; O'Connor, James; Herzog, Bryan; Elaiho, Cordelia; Galsky, Matthew D.; Tewari, Ashutosh Kumar; Yadav, Kamlesh Kumar

    2016-01-01

    Several promising targeted-therapeutics for prostate cancer (PCa), primarily affecting the androgen receptor (AR) and the PI3K/AKT/mTOR-pathway, are in various phases of development. However, despite promise, single-agent inhibitors targeting the two pathways have not shown long-term benefits, perhaps due to a complex compensatory cross talk that exists between the two pathways. Combination therapy has thus been proposed to maximize benefit. We have carried out a systematic study of two-drug combination effect of MDV3100 (AR antagonist), BKM120 (PI3K inhibitor), TKI258 (pan RTK inhibitor) and RAD001 (mTOR inhibitor) using various PCa cell lines. We observed strong synergy when AR-positive cells are treated with MDV3100 in combination with any one of the PI3K-pathway inhibitors: TKI258, BKM120, or RAD001. Growth curve based synergy determination combined with Western blot analysis suggested MDV3100+BKM120 to be the most effective in inducing cell death in such conditions. In the case of dual targeting of the PI3K-pathway BKM120+TKI258 combination displayed exquisite sensitivity in all the 5 cell lines tested irrespective of androgen sensitivity, (LNCaP, VCaP, 22Rv1, PC3 and Du145). The effect of blockade with BKM120+TKI258 in PC3 cells was similar to a combination of BKM120 with chemotherapy drug cabazitaxel. Taken together, our observation supports earlier observations that a combination of AR-inhibitor and PI3K-inhibitor is highly synergistic. Furthermore, combining BKM120 with TKI258 has better synergy than BKM120+RAD001 or RAD001+TKI258 in all the lines, irrespective of androgen sensitivity. Finally, BKM120 also displayed synergy when combined with chemotherapy drug cabazitaxel. No antagonism however was observed with any of the drug combinations. PMID:27783994

  3. Combination of serum angiopoietin-2 and uterine artery Doppler for prediction of preeclampsia.

    PubMed

    Puttapitakpong, Ploynin; Phupong, Vorapong

    2016-02-01

    The aim of this study was to determine the predictive value of the combination of serum angiopoietin-2 (Ang-2) levels and uterine artery Doppler for the detection of preeclampsia in women at 16-18 weeks of gestation and to identify other pregnancy complications that could be predicted with these combined tests. Maternal serum Ang-2 levels were measured, and uterine artery Doppler was performed in 400 pregnant women. The main outcome was preeclampsia. The predictive values of this combination were calculated. Twenty-five women (6.3%) developed preeclampsia. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of uterine artery Doppler combined with serum Ang-2 levels for the prediction of preeclampsia were 24.0%, 94.4%, 22.2% and 94.9%, respectively. For the prediction of early-onset preeclampsia, the sensitivity, specificity, PPV and NPV were 57.1%, 94.1%, 14.8% and 99.2%, respectively. Patients with abnormal uterine artery Doppler and abnormal serum Ang-2 levels (above 19.5 ng ml(-1)) were at higher risk for preterm delivery (relative risk=2.7, 95% confidence interval 1.2-5.8). Our findings revealed that the combination of uterine artery Doppler and serum Ang-2 levels at 16-18 weeks of gestation can be used to predict early-onset preeclampsia but not overall preeclampsia. Thus, this combination may be a useful early second trimester screening test for the prediction of early-onset preeclampsia.

  4. Functional reorganization of the auditory pathways (or lack thereof) in callosal agenesis is predicted by monaural sound localization performance.

    PubMed

    Paiement, Philippe; Champoux, François; Bacon, Benoit A; Lassonde, Maryse; Mensour, Boualem; Leroux, Jean-Maxime; Lepore, Franco

    2010-01-01

    Neuroimaging studies show that permanent peripheral lesions such as unilateral deafness cause functional reorganization in the auditory pathways. However, functional reorganization of the auditory pathways as a result of higher-level damage or abnormalities remains poorly investigated. A relatively recent behavioural study points to functional changes in the auditory pathways in some, but interestingly not in all, of the acallosal individuals that were tested. The present study uses fMRI to investigate auditory activities in both cerebral hemispheres in those same acallosal subjects in order to directly investigate the contributions of ipsilateral and contralateral functional pathways reorganization. Predictions were made that functional reorganization could be predicted from behavioural performance. As reported previously in a number of neuroimaging studies, results showed that in neurologically intact subjects, binaural stimulation induced balanced activities between both hemispheres, while monaural stimulation induced strong contralateral activities and weak ipsilateral activities. In accordance with behavioural predictions, some acallosal subjects showed patterns of auditory cortical activities that were similar to those observed in neurologically intact subjects while others showed functional reorganization of the auditory pathways. Essentially they showed a significant increase and a significant decrease of neural activities in the contralateral and/or ipsilateral pathways, respectively. These findings indicate that at least in some acallosal subjects, functional reorganization inside the auditory pathways does contribute to compensate for the absence of the corpus callosum.

  5. Is More Better? Combining Actuarial Risk Scales to Predict Recidivism among Adult Sex Offenders

    ERIC Educational Resources Information Center

    Seto, Michael C.

    2005-01-01

    The present study was conducted to determine whether combining the results of multiple actuarial risk scales increases accuracy in predicting sex offender recidivism. Multiple methods of combining 4 validated actuarial risk scales--the Violence Risk Appraisal Guide, the Sex Offender Risk Appraisal Guide, the Rapid Risk Assessment for Sexual…

  6. Bacterial community structure and predicted alginate metabolic pathway in an alginate-degrading bacterial consortium.

    PubMed

    Kita, Akihisa; Miura, Toyokazu; Kawata, Satoshi; Yamaguchi, Takeshi; Okamura, Yoshiko; Aki, Tsunehiro; Matsumura, Yukihiko; Tajima, Takahisa; Kato, Junichi; Nishio, Naomichi; Nakashimada, Yutaka

    2016-03-01

    Methane fermentation is one of the effective approaches for utilization of brown algae; however, this process is limited by the microbial capability to degrade alginate, a main polysaccharide found in these algae. Despite its potential, little is known about anaerobic microbial degradation of alginate. Here we constructed a bacterial consortium able to anaerobically degrade alginate. Taxonomic classification of 16S rRNA gene, based on high-throughput sequencing data, revealed that this consortium included two dominant strains, designated HUA-1 and HUA-2; these strains were related to Clostridiaceae bacterium SK082 (99%) and Dysgonomonas capnocytophagoides (95%), respectively. Alginate lyase activity and metagenomic analyses, based on high-throughput sequencing data, revealed that this bacterial consortium possessed putative genes related to a predicted alginate metabolic pathway. However, HUA-1 and 2 did not grow on agar medium with alginate by using roll-tube method, suggesting the existence of bacterial interactions like symbiosis for anaerobic alginate degradation.

  7. Thermochemistry for silicic acid formation reaction: Prediction of new reaction pathway

    NASA Astrophysics Data System (ADS)

    Mondal, Bhaskar; Ghosh, Deepanwita; Das, Abhijit K.

    2009-08-01

    Reaction between SiO 2 and water has been studied extensively using ab initio methods. The mechanism for formation of metasilicic acid SiO(OH) 2 and orthosilicic acid Si(OH) 4 has been explored and a new pathway for formation of Si(OH) 4 is predicted. Heats of reaction ( ΔrH298∘) and heats of formation ( ΔfH298∘) at 298 K for the related reactions and species calculated at two different theoretical levels G3B3 and G3MP2B3 agree well with the literature values. It is found that when SiO 2 reacts simultaneously with two water molecules, the thermodynamic as well as kinetic feasibility of the process is much greater than that when SiO 2 reacts with one molecule of water.

  8. Boolean Network Model for Cancer Pathways: Predicting Carcinogenesis and Targeted Therapy Outcomes

    PubMed Central

    Fumiã, Herman F.; Martins, Marcelo L.

    2013-01-01

    A Boolean dynamical system integrating the main signaling pathways involved in cancer is constructed based on the currently known protein-protein interaction network. This system exhibits stationary protein activation patterns – attractors – dependent on the cell's microenvironment. These dynamical attractors were determined through simulations and their stabilities against mutations were tested. In a higher hierarchical level, it was possible to group the network attractors into distinct cell phenotypes and determine driver mutations that promote phenotypic transitions. We find that driver nodes are not necessarily central in the network topology, but at least they are direct regulators of central components towards which converge or through which crosstalk distinct cancer signaling pathways. The predicted drivers are in agreement with those pointed out by diverse census of cancer genes recently performed for several human cancers. Furthermore, our results demonstrate that cell phenotypes can evolve towards full malignancy through distinct sequences of accumulated mutations. In particular, the network model supports routes of carcinogenesis known for some tumor types. Finally, the Boolean network model is employed to evaluate the outcome of molecularly targeted cancer therapies. The major find is that monotherapies were additive in their effects and that the association of targeted drugs is necessary for cancer eradication. PMID:23922675

  9. Predictive Validity of National Basketball Association Draft Combine on Future Performance.

    PubMed

    Teramoto, Masaru; Cross, Chad L; Rieger, Randall H; Maak, Travis G; Willick, Stuart E

    2017-01-20

    The National Basketball Association (NBA) Draft Combine is an annual event where prospective players are evaluated in terms of their athletic abilities and basketball skills. Data collected at the Combine should help NBA teams select right the players for the upcoming NBA Draft, however its value for predicting future performance of players has not been examined. This study investigated predictive validity of the NBA Draft Combine on future performance of basketball players. We performed a principal component analysis (PCA) on the 2010-2015 Combine data to reduce correlated variables (N = 234), a correlation analysis on the Combine data and future on-court performance to examine relationships (maximum pairwise N = 217), and a robust principal component regression (PCR) analysis to predict first-year and three-year on-court performance from the Combine measures (N = 148 and 127, respectively). Three components were identified within the Combine data via PCA (= Combine subscales): length-size, power-quickness, and upper-body strength. Per the correlation analysis, the individual Combine items for anthropometrics, including height without shoes, standing reach, weight, wingspan, and hand length, as well as the Combine subscale of length-size, had positive, medium-to-large sized correlations (r = 0.313-0.545) with defensive performance quantified by Defensive Box Plus/Minus. The robust PCR analysis showed that the Combine subscale of length-size was a predictor most significantly associated with future on-court performance (p < 0.05), including Win Shares, Box Plus/Minus, and Value Over Replacement Player, followed by upper-body strength. In conclusion, the NBA Draft Combine has value for predicting future performance of players.

  10. Combining natural and man-made DNA tracers to advance understanding of hydrologic flow pathway evolution

    NASA Astrophysics Data System (ADS)

    Dahlke, H. E.; Walter, M. T.; Lyon, S. W.; Rosqvist, G. N.

    2014-12-01

    Identifying and characterizing the sources, pathways and residence times of water and associated constituents is critical to developing improved understanding of watershed-stream connections and hydrological/ecological/biogeochemical models. To date the most robust information is obtained from integrated studies that combine natural tracers (e.g. isotopes, geochemical tracers) with controlled chemical tracer (e.g., bromide, dyes) or colloidal tracer (e.g., carboxilated microspheres, tagged clay particles, microorganisms) applications. In the presented study we explore how understanding of sources and flow pathways of water derived from natural tracer studies can be improved and expanded in space and time by simultaneously introducing man-made, synthetic DNA-based microtracers. The microtracer used were composed of polylactic acid (PLA) microspheres into which short strands of synthetic DNA and paramagnetic iron oxide nanoparticles are incorporated. Tracer experiments using both natural tracers and the DNA-based microtracers were conducted in the sub-arctic, glacierized Tarfala (21.7 km2) catchment in northern Sweden. Isotopic hydrograph separations revealed that even though storm runoff was dominated by pre-event water the event water (i.e. rainfall) contributions to streamflow increased throughout the summer season as glacial snow cover decreased. This suggests that glaciers are a major source of the rainwater fraction in streamflow. Simultaneous injections of ten unique DNA-based microtracers confirmed this hypothesis and revealed that the transit time of water traveling from the glacier surface to the stream decreased fourfold over the summer season leading to instantaneous rainwater contributions during storm events. These results highlight that integrating simultaneous tracer injections (injecting tracers at multiple places at one time) with traditional tracer methods (sampling multiple times at one place) rather than using either approach in isolation can

  11. Combined Gene Expression and RNAi Screening to Identify Alkylation Damage Survival Pathways from Fly to Human.

    PubMed

    Zanotto-Filho, Alfeu; Dashnamoorthy, Ravi; Loranc, Eva; de Souza, Luis H T; Moreira, José C F; Suresh, Uthra; Chen, Yidong; Bishop, Alexander J R

    2016-01-01

    Alkylating agents are a key component of cancer chemotherapy. Several cellular mechanisms are known to be important for its survival, particularly DNA repair and xenobiotic detoxification, yet genomic screens indicate that additional cellular components may be involved. Elucidating these components has value in either identifying key processes that can be modulated to improve chemotherapeutic efficacy or may be altered in some cancers to confer chemoresistance. We therefore set out to reevaluate our prior Drosophila RNAi screening data by comparison to gene expression arrays in order to determine if we could identify any novel processes in alkylation damage survival. We noted a consistent conservation of alkylation survival pathways across platforms and species when the analysis was conducted on a pathway/process level rather than at an individual gene level. Better results were obtained when combining gene lists from two datasets (RNAi screen plus microarray) prior to analysis. In addition to previously identified DNA damage responses (p53 signaling and Nucleotide Excision Repair), DNA-mRNA-protein metabolism (transcription/translation) and proteasome machinery, we also noted a highly conserved cross-species requirement for NRF2, glutathione (GSH)-mediated drug detoxification and Endoplasmic Reticulum stress (ER stress)/Unfolded Protein Responses (UPR) in cells exposed to alkylation. The requirement for GSH, NRF2 and UPR in alkylation survival was validated by metabolomics, protein studies and functional cell assays. From this we conclude that RNAi/gene expression fusion is a valid strategy to rapidly identify key processes that may be extendable to other contexts beyond damage survival.

  12. Systematic prediction of drug combinations based on clinical side-effects.

    PubMed

    Huang, Hui; Zhang, Ping; Qu, Xiaoyan A; Sanseau, Philippe; Yang, Lun

    2014-11-24

    Drug co-prescription (or drug combination) is a therapeutic strategy widely used as it may improve efficacy and reduce side-effect (SE). Since it is impractical to screen all possible drug combinations for every indication, computational methods have been developed to predict new combinations. In this study, we describe a novel approach that utilizes clinical SEs from post-marketing surveillance and the drug label to predict 1,508 novel drug-drug combinations. It outperforms other prediction methods, achieving an AUC of 0.92 compared to an AUC of 0.69 in a previous method, on a much larger drug combination set (245 drug combinations in our dataset compared to 75 in previous work.). We further found from the feature selection that three FDA black-box warned serious SEs, namely pneumonia, haemorrhage rectum, and retinal bleeding, contributed mostly to the predictions and a model only using these three SEs can achieve an average area under curve (AUC) at 0.80 and accuracy at 0.91, potentially with its simplicity being recognized as a practical rule-of-three in drug co-prescription or making fixed-dose drug combination. We also demonstrate this performance is less likely to be influenced by confounding factors such as biased disease indications or chemical structures.

  13. Systematic prediction of drug combinations based on clinical side-effects

    PubMed Central

    Huang, Hui; Zhang, Ping; Qu, Xiaoyan A.; Sanseau, Philippe; Yang, Lun

    2014-01-01

    Drug co-prescription (or drug combination) is a therapeutic strategy widely used as it may improve efficacy and reduce side-effect (SE). Since it is impractical to screen all possible drug combinations for every indication, computational methods have been developed to predict new combinations. In this study, we describe a novel approach that utilizes clinical SEs from post-marketing surveillance and the drug label to predict 1,508 novel drug-drug combinations. It outperforms other prediction methods, achieving an AUC of 0.92 compared to an AUC of 0.69 in a previous method, on a much larger drug combination set (245 drug combinations in our dataset compared to 75 in previous work.). We further found from the feature selection that three FDA black-box warned serious SEs, namely pneumonia, haemorrhage rectum, and retinal bleeding, contributed mostly to the predictions and a model only using these three SEs can achieve an average area under curve (AUC) at 0.80 and accuracy at 0.91, potentially with its simplicity being recognized as a practical rule-of-three in drug co-prescription or making fixed-dose drug combination. We also demonstrate this performance is less likely to be influenced by confounding factors such as biased disease indications or chemical structures. PMID:25418113

  14. Sub-seismic Deformation Prediction of Potential Pathways and Seismic Validation - The Joint Project PROTECT

    NASA Astrophysics Data System (ADS)

    Krawczyk, C. M.; Kolditz, O.

    2013-12-01

    The joint project PROTECT (PRediction Of deformation To Ensure Carbon Traps) aims to determine the existence and characteristics of sub-seismic structures that can potentially link deep reservoirs with the surface in the framework of CO2 underground storage. The research provides a new approach of assessing the long-term integrity of storage reservoirs. The objective is predicting and quantifying the distribution and the amount of sub-/seismic strain caused by fault movement in the proximity of a CO2 storage reservoir. The study is developing tools and workflows which will be tested at the CO2CRC Otway Project Site in the Otway Basin in south-western Victoria, Australia. For this purpose, we are building a geometrical kinematic 3-D model based on 2-D and 3-D seismic data that are provided by the Australian project partner, the CO2CRC Consortium. By retro-deforming the modeled subsurface faults in the inspected subsurface volume we can determine the accumulated sub-seismic deformation and thus the strain variation around the faults. Depending on lithology, the calculated strain magnitude and its orientation can be used as an indicator for fracture density. Furthermore, from the complete 3D strain tensor we can predict the orientation of fractures at sub-seismic scale. In areas where we have preliminary predicted critical deformation, we will acquire in November this year new near- surface, high resolution P- and S-wave 2-D seismic data in order to verify and calibrate our model results. Here, novel and parameter-based model building will especially benefit from extracting velocities and elastic parameters from VSP and other seismic data. Our goal is to obtain a better overview of possible fluid migration pathways and communication between reservoir and overburden. Thereby, we will provide a tool for prediction and adapted time-dependent monitoring strategies for subsurface storage in general including scientific visualization capabilities. Acknowledgement This work

  15. Combined geophysical methods for mapping infiltration pathways at the Aurora Water Aquifer recharge and recovery site

    NASA Astrophysics Data System (ADS)

    Jasper, Cameron A.

    Although aquifer recharge and recovery systems are a sustainable, decentralized, low cost, and low energy approach for the reclamation, treatment, and storage of post- treatment wastewater, they can suffer from poor infiltration rates and the development of a near-surface clogging layer within infiltration ponds. One such aquifer recharge and recovery system, the Aurora Water site in Colorado, U.S.A, functions at about 25% of its predicted capacity to recharge floodplain deposits by flooding infiltration ponds with post-treatment wastewater extracted from river bank aquifers along the South Platte River. The underwater self-potential method was developed to survey self-potential signals at the ground surface in a flooded infiltration pond for mapping infiltration pathways. A method for using heat as a groundwater tracer within the infiltration pond used an array of in situ high-resolution temperature sensing probes. Both relatively positive and negative underwater self-potential anomalies are consistent with observed recovery well pumping rates and specific discharge estimates from temperature data. Results from electrical resistivity tomography and electromagnetics surveys provide consistent electrical conductivity distributions associated with sediment textures. A lab method was developed for resistivity tests of near-surface sediment samples. Forward numerical modeling synthesizes the geophysical information to best match observed self- potential anomalies and provide permeability distributions, which is important for effective aquifer recharge and recovery system design, and optimization strategy development.

  16. A Phosphorus Index that Combines Critical Source Areas and Transport Pathways using a Travel Time Approach

    NASA Astrophysics Data System (ADS)

    Buchanan, B. P.; Walter, T.; Shaw, S. B.; Easton, Z. M.

    2012-12-01

    Spatially distributed nonpoint source (NPS) pollution indices are used to identify areas in a watershed where potential pollutant loading coincides with runoff generating areas. However, most such indices either ignore the degree of hydrologic connectivity to the stream network or they estimate it based simply on the distance of the pollution generating area from an open channel. We propose an NPS pollution index based on runoff travel times from saturated variable source areas (VSA) to the natural stream network as a means for including hydrologic connectivity between source areas and streams. Although this method could be generalized to any pollutant transported by storm runoff, here we focus on phosphorus and refer to the index as the travel-time phosphorus index (TTPI). The TTPI was applied to a 38 km2 agricultural watershed in central New York and shown to yield realistic, spatially explicit predictions of critical phosphorus loading areas and routing pathways. One interesting finding is the potential role of man-made drainage networks (e.g., road- or agricultural-ditches) in NPS pollution and the possibilities of targeting water quality protection practices around or within these networks. Because the technique is GIS-based, relatively simple to apply, uses readily available geospatial data, and the theoretical underpinnings are transparent, it can provide a useful screening tool for water resource managers charged with the identification and remediation of critical NPS pollution source areas.

  17. A phosphorus index that combines critical source areas and transport pathways using a travel time approach

    NASA Astrophysics Data System (ADS)

    Buchanan, Brian P.; Archibald, Josephine A.; Easton, Zachary M.; Shaw, Stephen B.; Schneider, Rebecca L.; Todd Walter, M.

    2013-04-01

    SummarySpatially distributed nonpoint source (NPS) pollution indices are used to identify areas in a watershed where potential pollutant loading coincides with runoff generating areas. However, most such indices either ignore the degree of hydrologic connectivity to the stream network or they estimate it based simply on the distance of the pollution generating area from an open channel. We propose an NPS pollution index based on runoff travel times from saturated variable source areas (VSAs) to the natural stream network as a means for including hydrologic connectivity between source areas and streams. Although this method could be generalized to any pollutant transported by storm runoff, here we focus on phosphorus and refer to the index as the travel-time phosphorus index (TTPI). The TTPI was applied to a 38 km2 agricultural watershed in central New York and shown to yield realistic, spatially explicit predictions of critical phosphorus loading areas and routing pathways. One interesting finding is the potential role of man-made drainage networks (e.g., road- or agricultural-ditches) in NPS pollution and the possibilities of targeting water quality protection practices around or within these networks. Because the technique is GIS-based, relatively simple to apply, uses readily available geospatial data, and the theoretical underpinnings are transparent, it can provide a useful screening tool for water resource managers charged with the identification and remediation of critical NPS pollution source areas.

  18. New in vitro system to predict chemotherapeutic efficacy of drug combinations in fresh tumor samples

    PubMed Central

    Weidemüller, Paula; Krapfl, Jens; Yassin-Kelepir, Rauaa; Job, Laura; Fraefel, Marius; Braicu, Ioana; Kopp-Schneider, Annette; Sehouli, Jalid; De Wilde, Rudy Leon

    2017-01-01

    Background To find the best individual chemotherapy for cancer patients, the efficacy of different chemotherapeutic drugs can be predicted by pretesting tumor samples in vitro via the chemotherapy-resistance (CTR)-Test®. Although drug combinations are widely used among cancer therapy, so far only single drugs are tested by this and other tests. However, several first line chemotherapies are combining two or more chemotherapeutics, leading to the necessity of drug combination testing methods. Methods We established a system to measure and predict the efficacy of chemotherapeutic drug combinations with the help of the Loewe additivity concept in combination with the CTR-test. A combination is measured by using half of the monotherapy’s concentration of both drugs simultaneously. With this method, the efficacy of a combination can also be calculated based on single drug measurements. Results The established system was tested on a data set of ovarian carcinoma samples using the combination carboplatin and paclitaxel and confirmed by using other tumor species and chemotherapeutics. Comparing the measured and the calculated values of the combination testings revealed a high correlation. Additionally, in 70% of the cases the measured and the calculated values lead to the same chemotherapeutic resistance category of the tumor. Conclusion Our data suggest that the best drug combination consists of the most efficient single drugs and the worst drug combination of the least efficient single drugs. Our results showed that single measurements are sufficient to predict combinations in specific cases but there are exceptions in which it is necessary to measure combinations, which is possible with the presented system. PMID:28265509

  19. Predicting the equilibrium protein folding pathway: structure-based analysis of staphylococcal nuclease.

    PubMed

    Hilser, V J; Freire, E

    1997-02-01

    The equilibrium folding pathway of staphylococcal nuclease (SNase) has been approximated using a statistical thermodynamic formalism that utilizes the high-resolution structure of the native state as a template to generate a large ensemble of partially folded states. Close to 400,000 different states ranging from the native to the completely unfolded states were included in the analysis. The probability of each state was estimated using an empirical structural parametrization of the folding energetics. It is shown that this formalism predicts accurately the stability of the protein, the cooperativity of the folding/unfolding transition observed by differential scanning calorimetry (DSC) or urea denaturation and the thermodynamic parameters for unfolding. More importantly, this formalism provides a quantitative account of the experimental hydrogen exchange protection factors measured under native conditions for SNase. These results suggest that the computer-generated distribution of states approximates well the ensemble of conformations existing in solution. Furthermore, this formalism represents the first model capable of quantitatively predicting within a unified framework the probability distribution of states seen under native conditions and its change upon unfolding.

  20. Two pathways through adversity: Predicting well-being and housing outcomes among homeless service users.

    PubMed

    Walter, Zoe C; Jetten, Jolanda; Dingle, Genevieve A; Parsell, Cameron; Johnstone, Melissa

    2016-06-01

    People who experience homelessness face many challenges and disadvantages that negatively impact health and well-being and form barriers to achieving stable housing. Further, people who are homeless often have limited social connections and support. Building on previous research that has shown the beneficial effect of group identification on health and well-being, the current study explores the relationship between two social identity processes - multiple group memberships and service identification - and well-being and positive housing outcomes. Measures were collected from 76 participants while they were residing in a homeless accommodation service (T1) and again 2-4 weeks after leaving the service (or 3 months after T1 if participants had not left the service). Mediation analyses revealed that multiple group memberships and service identification at T1 independently predicted well-being at T2 indirectly, via social support. Further, both social identity processes also indirectly predicted housing outcomes via social support. The implications of these findings are twofold. First, while belonging to multiple social groups may provide a pathway to gaining social support and well-being, group belonging may not necessarily be beneficial to achieve stable housing. Second, fostering identification with homeless services may be particularly important as a source of support that contributes to well-being.

  1. Comparison between different tests and their combination for prediction of difficult intubation: An analytical study

    PubMed Central

    Basunia, Sandip Roy; Ghosh, Sarmila; Bhattacharya, Susmita; Saha, Indranil; Biswas, Atanu; Prasad, Anu

    2013-01-01

    Context: There is an impelling need for accurate tests to predict difficult intubation, as failure to achieve endotracheal intubation causes significant morbidity and mortality in anesthetic practice. Aim: To calculate the validity of the different tests along with their combination and agreement when compared with endotracheal intubation in predicting difficult intubation. Settings and Design: Operation theaters, analytical study. Materials and Methods: Three hundred patients aged between 16 and 60 years of American society of anesthesiologist (ASA) physical status I and II, scheduled for elective surgical procedures requiring endotracheal intubation were studied during January-July 2012. Mallampati grade (MP), sternomental distance (SMD), thyromental distance (TMD), and Delilkan and Calder test were recorded for every patient. Endotracheal intubation was performed by an experienced anesthesiologist blinded to the measurements and recorded grading of intubation. Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), likelihood ratio (LR), odds ratio (OR), and kappa coefficient of tests individually and in combination were calculated. Statistical Analysis Used: IBM SPSS software (version 16.0) and Epi-info software (version 3.2). Results: Difficult and failed intubation was 13.3% and 0.6%, respectively. Difficult intubation increased with age. TMD and Calder test showed highest sensitivity individually and Dellilkan's test showed least sensitivity. Among the combination of tests, MP with SMD and MP with Calder test had the highest sensitivity. Conclusion: Among individual test TMD and Calder are better predictive tests in terms of sensitivity. Combination of tests increases the chance of prediction of difficult intubation. PMID:25885730

  2. The National Football League (NFL) combine: does normalized data better predict performance in the NFL draft?

    PubMed

    Robbins, Daniel W

    2010-11-01

    The objective of this study was to investigate the predictive ability of National Football League (NFL) combine physical test data to predict draft order over the years 2005-2009. The NFL combine provides a setting in which NFL personnel can evaluate top draft prospects. The predictive ability of combine data in its raw form and when normalized in both a ratio and allometric manner was examined for 17 positions. Data from 8 combine physical performance tests were correlated with draft order to determine the direction and strength of relationship between the various combine measures and draft order. Players invited to the combine and subsequently drafted in the same year (n = 1,155) were included in the study. The primary finding was that performance in the combine physical test battery, whether normalized or not, has little association with draft success. In terms of predicting draft order from outcomes of the 8 tests making up the combine battery, normalized data provided no advantage over raw data. Of the 8 performance measures investigated, straight sprint time and jumping ability seem to hold the most weight with NFL personnel responsible for draft decisions. The NFL should consider revising the combine test battery to reflect the physical characteristics it deems important. It may be that NFL teams are more interested in attributes other than the purely physical traits reflected in the combine test battery. Players with aspirations of entering the NFL may be well advised to develop mental and technical skills in addition to developing the physical characteristics necessary to optimize performance.

  3. Advantages of combined transmembrane topology and signal peptide prediction--the Phobius web server.

    PubMed

    Käll, Lukas; Krogh, Anders; Sonnhammer, Erik L L

    2007-07-01

    When using conventional transmembrane topology and signal peptide predictors, such as TMHMM and SignalP, there is a substantial overlap between these two types of predictions. Applying these methods to five complete proteomes, we found that 30-65% of all predicted signal peptides and 25-35% of all predicted transmembrane topologies overlap. This impairs predictions of 5-10% of the proteome, hence this is an important issue in protein annotation. To address this problem, we previously designed a hidden Markov model, Phobius, that combines transmembrane topology and signal peptide predictions. The method makes an optimal choice between transmembrane segments and signal peptides, and also allows constrained and homology-enriched predictions. We here present a web interface (http://phobius.cgb.ki.se and http://phobius.binf.ku.dk) to access Phobius.

  4. Prediction of hydrolysis pathways and kinetics for antibiotics under environmental pH conditions: a quantum chemical study on cephradine.

    PubMed

    Zhang, Haiqin; Xie, Hongbin; Chen, Jingwen; Zhang, Shushen

    2015-02-03

    Understanding hydrolysis pathways and kinetics of many antibiotics that have multiple hydrolyzable functional groups is important for their fate assessment. However, experimental determination of hydrolysis encounters difficulties due to time and cost restraint. We employed the density functional theory and transition state theory to predict the hydrolysis pathways and kinetics of cephradine, a model of cephalosporin with two hydrolyzable groups, two ionization states, two isomers and two nucleophilic attack directions. Results showed that the hydrolysis of cephradine at pH = 8.0 proceeds via opening of the β-lactam ring followed by intramolecular amidation. The predicted rate constants at different pH conditions are of the same order of magnitude as the experimental values, and the predicted products are confirmed by experiment. This study identified a catalytic role of the carboxyl group in the hydrolysis, and implies that the carboxyl group also plays a catalytic role in the hydrolysis of other cephalosporin and penicillin antibiotics. This is a first attempt to quantum chemically predict hydrolysis of an antibiotic with complex pathways, and indicates that to predict hydrolysis products under the environmental pH conditions, the variation of the rate constants for different pathways with pH should be evaluated.

  5. Prediction Uncertainty Analyses for the Combined Physically-Based and Data-Driven Models

    NASA Astrophysics Data System (ADS)

    Demissie, Y. K.; Valocchi, A. J.; Minsker, B. S.; Bailey, B. A.

    2007-12-01

    The unavoidable simplification associated with physically-based mathematical models can result in biased parameter estimates and correlated model calibration errors, which in return affect the accuracy of model predictions and the corresponding uncertainty analyses. In this work, a physically-based groundwater model (MODFLOW) together with error-correcting artificial neural networks (ANN) are used in a complementary fashion to obtain an improved prediction (i.e. prediction with reduced bias and error correlation). The associated prediction uncertainty of the coupled MODFLOW-ANN model is then assessed using three alternative methods. The first method estimates the combined model confidence and prediction intervals using first-order least- squares regression approximation theory. The second method uses Monte Carlo and bootstrap techniques for MODFLOW and ANN, respectively, to construct the combined model confidence and prediction intervals. The third method relies on a Bayesian approach that uses analytical or Monte Carlo methods to derive the intervals. The performance of these approaches is compared with Generalized Likelihood Uncertainty Estimation (GLUE) and Calibration-Constrained Monte Carlo (CCMC) intervals of the MODFLOW predictions alone. The results are demonstrated for a hypothetical case study developed based on a phytoremediation site at the Argonne National Laboratory. This case study comprises structural, parameter, and measurement uncertainties. The preliminary results indicate that the proposed three approaches yield comparable confidence and prediction intervals, thus making the computationally efficient first-order least-squares regression approach attractive for estimating the coupled model uncertainty. These results will be compared with GLUE and CCMC results.

  6. A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions.

    PubMed

    Birlutiu, Adriana; d'Alché-Buc, Florence; Heskes, Tom

    2015-01-01

    Computational methods for predicting protein-protein interactions are important tools that can complement high-throughput technologies and guide biologists in designing new laboratory experiments. The proteins and the interactions between them can be described by a network which is characterized by several topological properties. Information about proteins and interactions between them, in combination with knowledge about topological properties of the network, can be used for developing computational methods that can accurately predict unknown protein-protein interactions. This paper presents a supervised learning framework based on Bayesian inference for combining two types of information: i) network topology information, and ii) information related to proteins and the interactions between them. The motivation of our model is that by combining these two types of information one can achieve a better accuracy in predicting protein-protein interactions, than by using models constructed from these two types of information independently.

  7. Combined migration velocity model-building and its application in tunnel seismic prediction

    NASA Astrophysics Data System (ADS)

    Gong, Xiang-Bo; Han, Li-Guo; Niu, Jian-Jun; Zhang, Xiao-Pei; Wang, De-Li; Du, Li-Zhi

    2010-09-01

    We propose a combined migration velocity analysis and imaging method based on Kirchhoff integral migration and reverse time migration, using the residual curvature analysis and layer stripping strategy to build the velocity model. This method improves the image resolution of Kirchhoff integral migration and reduces the computations of the reverse time migration. It combines the advantages of efficiency and accuracy of the two migration methods. Its application in tunnel seismic prediction shows good results. Numerical experiments show that the imaging results of reverse time migration are better than the imaging results of Kirchhoff integral migration in many aspects of tunnel prediction. Field data show that this method has efficient computations and can establish a reasonable velocity model and a high quality imaging section. Combination with geological information can make an accurate prediction of the front of the tunnel geological structure.

  8. An Ancient Pathway Combining Carbon Dioxide Fixation with the Generation and Utilization of a Sodium Ion Gradient for ATP Synthesis

    PubMed Central

    Poehlein, Anja; Schmidt, Silke; Kaster, Anne-Kristin; Goenrich, Meike; Vollmers, John; Thürmer, Andrea; Bertsch, Johannes; Schuchmann, Kai; Voigt, Birgit; Hecker, Michael; Daniel, Rolf; Thauer, Rudolf K.; Gottschalk, Gerhard; Müller, Volker

    2012-01-01

    Synthesis of acetate from carbon dioxide and molecular hydrogen is considered to be the first carbon assimilation pathway on earth. It combines carbon dioxide fixation into acetyl-CoA with the production of ATP via an energized cell membrane. How the pathway is coupled with the net synthesis of ATP has been an enigma. The anaerobic, acetogenic bacterium Acetobacterium woodii uses an ancient version of this pathway without cytochromes and quinones. It generates a sodium ion potential across the cell membrane by the sodium-motive ferredoxin:NAD oxidoreductase (Rnf). The genome sequence of A. woodii solves the enigma: it uncovers Rnf as the only ion-motive enzyme coupled to the pathway and unravels a metabolism designed to produce reduced ferredoxin and overcome energetic barriers by virtue of electron-bifurcating, soluble enzymes. PMID:22479398

  9. Combined AKT and MEK Pathway Blockade in Pre-Clinical Models of Enzalutamide-Resistant Prostate Cancer

    PubMed Central

    Toren, Paul; Kim, Soojin; Johnson, Fraser; Zoubeidi, Amina

    2016-01-01

    Despite recent improvements in patient outcomes using newer androgen receptor (AR) pathway inhibitors, treatment resistance in castrate resistant prostate cancer (CRPC) continues to remain a clinical problem. Co-targeting alternate resistance pathways are of significant interest to treat CRPC and delay the onset of resistance. Both the AKT and MEK signaling pathways become activated as prostate cancer develops resistance to AR-targeted therapies. This pre-clinical study explores co-targeting these pathways in AR-positive prostate cancer models. Using various in vitro models of prostate cancer disease states including androgen dependent (LNCaP), CRPC (V16D and 22RV1) and ENZ-resistant prostate cancer (MR49C and MR49F), we evaluate the relevance of targeting both AKT and MEK pathways. Our data reveal that AKT inhibition induces apoptosis and inhibits cell growth in PTEN null cell lines independently of their sensitivity to hormone therapy; however, AKT inhibition had no effect on the PTEN positive 22RV1 cell line. Interestingly, we found that MEK inhibition had greater effect on 22RV1 cells compared to LNCaP, V16D or ENZ-resistant cells MR49C and MR49F cells. In vitro, combination AKT and MEK blockade had evidence of synergy observed in some cell lines and assays, but this was not consistent across all results. In vivo, the combination of AKT and MEK inhibition resulted in more consistent tumor growth inhibition of MR49F xenografts and longer disease specific survival compared to AKT inhibitor monotherapy. As in our in vitro study, 22RV1 xenografts were more resistant to AKT inhibition while they were more sensitive to MEK inhibition. Our results suggest that targeting AKT and MEK in combination may be a valuable strategy in prostate cancer when both pathways are activated and further support the importance of characterizing the dominant oncogenic pathway in each patient’s tumor in order to select optimal therapy. PMID:27046225

  10. Induction of ATM/ATR pathway combined with Vγ2Vδ2 T cells enhances cytotoxicity of ovarian cancer cells.

    PubMed

    Lu, Jingwei; Das, Manjusri; Kanji, Suman; Aggarwal, Reeva; Joseph, Matthew; Ray, Alo; Shapiro, Charles L; Pompili, Vincent J; Das, Hiranmoy

    2014-07-01

    Many ovarian cancer cells express stress-related molecule MICA/B on their surface that is recognized by Vγ2Vδ2 T cells through their NKG2D receptor, which is transmitted to downstream stress-signaling pathway. However, it is yet to be established how Vγ2Vδ2 T cell-mediated recognition of MICA/B signal is transmitted to downstream stress-related molecules. Identifying targeted molecules would be critical to develop a better therapy for ovarian cancer cells. It is well established that ATM/ATR signal transduction pathways, which is modulated by DNA damage, replication stress, and oxidative stress play central role in stress signaling pathway regulating cell cycle checkpoint and apoptosis. We investigated whether ATM/ATR and its down stream molecules affect Vγ2Vδ2 T cell-mediated cytotoxicity. Herein, we show that ATM/ATR pathway is modulated in ovarian cancer cells in the presence of Vγ2Vδ2 T cells. Furthermore, downregulation of ATM pathway resulted downregulation of MICA, and reduced Vγ2Vδ2 T cell-mediated cytotoxicity. Alternately, stimulating ATM pathway enhanced expression of MICA, and sensitized ovarian cancer cells for cytotoxic lysis by Vγ2Vδ2 T cells. We further show that combining currently approved chemotherapeutic drugs, which induced ATM signal transduction, along with Vγ2Vδ2 T cells enhanced cytotoxicity of resistant ovarian cancer cells. These findings indicate that ATM/ATR pathway plays an important role in tumor recognition, and drugs promoting ATM signaling pathway might be considered as a combination therapy together with Vγ2Vδ2 T cells for effectively treating resistant ovarian cancer cells.

  11. Ras pathway activation in hepatocellular carcinoma and anti-tumoral effect of combined sorafenib and rapamycin in vivo☆

    PubMed Central

    Newell, Pippa; Toffanin, Sara; Villanueva, Augusto; Chiang, Derek Y.; Minguez, Beatriz; Cabellos, Laia; Savic, Radoslav; Hoshida, Yujin; Lim, Kiat Hon; Melgar-Lesmes, Pedro; Yea, Steven; Peix, Judit; Deniz, Kemal; Fiel, M. Isabel; Thung, Swan; Alsinet, Clara; Tovar, Victoria; Mazzaferro, Vincenzo; Bruix, Jordi; Roayaie, Sasan; Schwartz, Myron; Friedman, Scott L.; Llovet, Josep M.

    2010-01-01

    Background/Aims The success of sorafenib in the treatment of advanced hepatocellular carcinoma (HCC) has focused interest on the role of Ras signaling in this malignancy. We investigated the molecular alterations of the Ras pathway in HCC and the antineoplastic effects of sorafenib in combination with rapamycin, an inhibitor of mTOR pathway, in experimental models. Methods Gene expression (qRT-PCR, oligonucleotide microarray), DNA copy number changes (SNP-array), methylation of tumor suppressor genes (methylation-specific PCR) and protein activation (immunohistochemistry) were analysed in 351 samples. Anti-tumoral effects of combined therapy targeting the Ras and mTOR pathways were evaluated in cell lines and HCC xenografts. Results Different mechanisms accounted for Ras pathway activation in HCC. H-ras was up-regulated during different steps of hepatocarcinogenesis. B-raf was overexpressed in advanced tumors and its expression was associated with genomic amplification. Partial methylation of RASSF1A and NORE1A was detected in 89% and 44% of tumors respectively, and complete methylation was found in 11 and 4% of HCCs. Activation of the pathway (pERK immunostaining) was identified in 10.3% of HCC. Blockade of Ras and mTOR pathways with sorafenib and rapamycin reduced cell proliferation and induced apoptosis in cell lines. In vivo, the combination of both compounds enhanced tumor necrosis and ulceration when compared with sorafenib alone. Conclusions Ras activation results from several molecular alterations, such as methylation of tumor suppressors and amplification of oncogenes (B-raf). Sorafenib blocks signaling and synergizes with rapamycin in vivo, preventing tumor progression. These data provide the rationale for testing this combination in clinical studies. PMID:19665249

  12. A hadoop-based method to predict potential effective drug combination.

    PubMed

    Sun, Yifan; Xiong, Yi; Xu, Qian; Wei, Dongqing

    2014-01-01

    Combination drugs that impact multiple targets simultaneously are promising candidates for combating complex diseases due to their improved efficacy and reduced side effects. However, exhaustive screening of all possible drug combinations is extremely time-consuming and impractical. Here, we present a novel Hadoop-based approach to predict drug combinations by taking advantage of the MapReduce programming model, which leads to an improvement of scalability of the prediction algorithm. By integrating the gene expression data of multiple drugs, we constructed data preprocessing and the support vector machines and naïve Bayesian classifiers on Hadoop for prediction of drug combinations. The experimental results suggest that our Hadoop-based model achieves much higher efficiency in the big data processing steps with satisfactory performance. We believed that our proposed approach can help accelerate the prediction of potential effective drugs with the increasing of the combination number at an exponential rate in future. The source code and datasets are available upon request.

  13. Combining Traditional Cyber Security Audit Data with Psychosocial Data: Towards Predictive Modeling for Insider Threat Mitigation

    SciTech Connect

    Greitzer, Frank L.; Frincke, Deborah A.

    2010-09-01

    The purpose of this chapter is to motivate the combination of traditional cyber security audit data with psychosocial data, so as to move from an insider threat detection stance to one that enables prediction of potential insider presence. Two distinctive aspects of the approach are the objective of predicting or anticipating potential risks and the use of organizational data in addition to cyber data to support the analysis. The chapter describes the challenges of this endeavor and progress in defining a usable set of predictive indicators, developing a framework for integrating the analysis of organizational and cyber security data to yield predictions about possible insider exploits, and developing the knowledge base and reasoning capability of the system. We also outline the types of errors that one expects in a predictive system versus a detection system and discuss how those errors can affect the usefulness of the results.

  14. Towards direct synthesis of alane: A predicted defect-mediated pathway confirmed experimentally

    SciTech Connect

    Wang, Lin -Lin; Herwadkar, Aditi; Reich, Jason M.; Johnson, Duane D.; House, Stephen D.; Pena-Martin, Pamela; Rockett, Angus A.; Robertson, Ian M.; Gupta, Shalabh; Pecharsky, Vitalij K.

    2016-08-18

    Here, alane (AlH3) is a unique energetic material that has not found a broad practical use for over 70 years because it is difficult to synthesize directly from its elements. Using density functional theory, we examine the defect-mediated formation of alane monomers on Al(111) in a two-step process: (1) dissociative adsorption of H2 and (2) alane formation, which are both endothermic on a clean surface. Only with Ti dopant to facilitate H2 dissociation and vacancies to provide Al adatoms, both processes become exothermic. In agreement, in situ scanning tunneling microscopy showed that during H2 exposure, alane monomers and clusters form primarily in the vicinity of Al vacancies and Ti atoms. Moreover, ball milling of the Al samples with Ti (providing necessary defects) showed a 10 % conversion of Al into AlH3 or closely related species at 344 bar H2, indicating that the predicted pathway may lead to the direct synthesis of alane from elements at pressures much lower than the 104 bar expected from bulk thermodynamics.

  15. Towards direct synthesis of alane: A predicted defect-mediated pathway confirmed experimentally

    DOE PAGES

    Wang, Lin -Lin; Herwadkar, Aditi; Reich, Jason M.; ...

    2016-08-18

    Here, alane (AlH3) is a unique energetic material that has not found a broad practical use for over 70 years because it is difficult to synthesize directly from its elements. Using density functional theory, we examine the defect-mediated formation of alane monomers on Al(111) in a two-step process: (1) dissociative adsorption of H2 and (2) alane formation, which are both endothermic on a clean surface. Only with Ti dopant to facilitate H2 dissociation and vacancies to provide Al adatoms, both processes become exothermic. In agreement, in situ scanning tunneling microscopy showed that during H2 exposure, alane monomers and clusters formmore » primarily in the vicinity of Al vacancies and Ti atoms. Moreover, ball milling of the Al samples with Ti (providing necessary defects) showed a 10 % conversion of Al into AlH3 or closely related species at 344 bar H2, indicating that the predicted pathway may lead to the direct synthesis of alane from elements at pressures much lower than the 104 bar expected from bulk thermodynamics.« less

  16. A Global Genomic and Genetic Strategy to Predict Pathway Activation of Xenobiotic Responsive Transcription Factors in the Mouse Liver

    EPA Science Inventory

    Many drugs and environmentally-relevant chemicals activate xenobiotic-responsive transcription factors(TF). Identification of target genes of these factors would be useful in predicting pathway activation in in vitro chemical screening. Starting with a large compendium of Affymet...

  17. Untargeted metabolomics analysis reveals key pathways responsible for the synergistic killing of colistin and doripenem combination against Acinetobacter baumannii

    PubMed Central

    Maifiah, Mohd Hafidz Mahamad; Creek, Darren J.; Nation, Roger L.; Forrest, Alan; Tsuji, Brian T.; Velkov, Tony; Li, Jian

    2017-01-01

    Combination therapy is deployed for the treatment of multidrug-resistant Acinetobacter baumannii, as it can rapidly develop resistance to current antibiotics. This is the first study to investigate the synergistic effect of colistin/doripenem combination on the metabolome of A. baumannii. The metabolite levels were measured using LC-MS following treatment with colistin (2 mg/L) or doripenem (25 mg/L) alone, and their combination at 15 min, 1 hr and 4 hr (n = 4). Colistin caused early (15 min and 1 hr) disruption of the bacterial outer membrane and cell wall, as demonstrated by perturbation of glycerophospholipids and fatty acids. Concentrations of peptidoglycan biosynthesis metabolites decreased at 4 hr by doripenem alone, reflecting its mechanism of action. The combination induced significant changes to more key metabolic pathways relative to either monotherapy. Down-regulation of cell wall biosynthesis (via D-sedoheptulose 7-phosphate) and nucleotide metabolism (via D-ribose 5-phosphate) was associated with perturbations in the pentose phosphate pathway induced initially by colistin (15 min and 1 hr) and later by doripenem (4 hr). We discovered that the combination synergistically killed A. baumannii via time-dependent inhibition of different key metabolic pathways. Our study highlights the significant potential of systems pharmacology in elucidating the mechanism of synergy and optimizing antibiotic pharmacokinetics/pharmacodynamics. PMID:28358014

  18. Combinative in vitro studies and computational model to predict 3D cell migration response to drug insult.

    PubMed

    Maffei, Joseph S; Srivastava, Jaya; Fallica, Brian; Zaman, Muhammad H

    2014-10-01

    The development of drugs to counter diseases related to cell migration has resulted in a multi-billion dollar endeavor. Unfortunately, few drugs have emerged from this effort highlighting the need for new methods to enhance assays to study, analyze and control cell migration. In response to this complex process, computational models have emerged as potent tools to describe migration providing a high throughput and low cost method. However, most models are unable to predict migration response to drug with direct application to in vitro experiments. In addition to this, no model to date has attempted to describe migration in response to drugs while incorporating simultaneously protein signaling, proteolytic activity, and 3D culture. In this paper, we describe an integrated computational approach, in conjunction with in vitro observations, to serve as a platform to accurately predict migration in 3D matrices incorporating the function of matrix metalloproteinases (MMPs) and their interaction with the Extracellular signal-related kinase (ERK) signaling pathway. Our results provide biological insight into how matrix density, MMP activity, integrin adhesions, and p-ERK expression all affect speed and persistence in 3D. Predictions from the model provide insight toward improving drug combinations to more effectively reduce both speed and persistence during migration and the role of integrin adhesions in motility. In this way our integrated platform provides future potential to streamline and improve throughput toward the testing and development of migration targeting drugs with tangible application to current in vitro assays.

  19. Combined eye tracking and fMRI reveals neural basis of linguistic predictions during sentence comprehension.

    PubMed

    Bonhage, Corinna E; Mueller, Jutta L; Friederici, Angela D; Fiebach, Christian J

    2015-07-01

    It is widely agreed upon that linguistic predictions are an integral part of language comprehension. Yet, experimental proof of their existence remains challenging. Here, we introduce a new predictive eye gaze reading task combining eye tracking and functional magnetic resonance imaging (fMRI) that allows us to infer the existence and timing of linguistic predictions via anticipatory eye-movements. Participants read different types of word sequences (i.e., regular sentences, meaningless jabberwocky sentences, non-word lists) up to the pre-final word. The final target word was displayed with a temporal delay and its screen position was dependent on the syntactic word category (nouns vs verbs). During the delay, anticipatory eye-movements into the correct target word area were indicative of linguistic predictions. For fMRI analysis, the predictive sentence conditions were contrasted to the non-word condition, with the anticipatory eye-movements specifying differences in timing across conditions. A conjunction analysis of both sentence conditions revealed the neural substrate of word category prediction, namely a distributed network of cortical and subcortical brain regions including language systems, basal ganglia, thalamus, and hippocampus. Direct contrasts between the regular sentence condition and the jabberwocky condition indicate that prediction of word category in meaningless jabberwocky sentences relies on classical left-hemispheric language systems involving Brodman's area 44/45 in the left inferior frontal gyrus, left superior temporal areas, and the dorsal caudate nucleus. Regular sentences, in contrast, allowed for the prediction of specific words. Word-specific predictions were specifically associated with more widely distributed temporal and parietal cortical systems, most prominently in the right hemisphere. Our results support the presence of linguistic predictions during sentence processing and demonstrate the validity of the predictive eye gaze

  20. Combined blockade of AKT/mTOR pathway inhibits growth of human hemangioma via downregulation of proliferating cell nuclear antigen.

    PubMed

    Ou, J M; Qui, M-K; Dai, Y-X; Dong, Q; Shen, J; Dong, P; Wang, X-F; Liu, Y-B; Fei, Z-W

    2012-01-01

    Protein kinase B (AKT)/mammalian target of rapamycin (mTOR) signaling pathway plays a crucial role in the tumorigenesis and progression of multiple tumors, and has been shown to be important therapeutic targets for cancer. The present study aimed to explore the role and molecular mechanisms of AKT/mTOR pathway in human hemangioma (HA). Twenty-five cases of human HA tissues were collected. The expression of AKT, mTOR and proliferating cell nuclear antigen (PCNA) proteins was evaluated using semi-quantitative immunohistochemistry in biopsy samples in different phases of HA. AKT/mTOR pathway was blocked by recombinant small hairpin RNA adenovirus vector rAd5-AKT+mTOR (rAd5-Am), used for infecting proliferating phase HA-derived endothelial cells (HDEC). The expression of AKT, mTOR and PCNA was detected by Real-time PCR and Western blot assays. Cell proliferative activities were determined by MTT assay, and cell cycle distribution and apoptosis were analyzed by flow cytometry. As a consequence, the expression of AKT, mTOR and PCNA was significantly increased in proliferative phase HA, while that was decreased in involutive phase. Combined blockade of AKT/mTOR pathway by rAd5-Am diminished cell proliferative activities, and induced cell apoptosis and cycle arrest with the decreased expression of AKT, mTOR and PCNA in proliferative phase HDEC. In conclusion, the activity of AKT/mTOR pathway was increased in proliferative phase HA, while it was decreased in involutive phase. Combined blockade of AKT/mTOR pathway might suppress cell proliferation via down-regulation of PCNA expression, and induce apoptosis and cycle arrest in proliferative phase HDEC, suggesting that AKT/mTOR pathway might represent the important therapeutic targets for human HA.

  1. Combined targeting of EGFR-dependent and VEGF-dependent pathways: rationale, preclinical studies and clinical applications.

    PubMed

    Tortora, Giampaolo; Ciardiello, Fortunato; Gasparini, Giampietro

    2008-09-01

    Cellular heterogeneity, redundancy of molecular pathways and effects of the microenvironment contribute to the survival, motility and metastasis of cells in solid tumors. It is unlikely that tumors are entirely dependent on only one abnormally activated signaling pathway; consequently, treatment with an agent that interferes with a single target may be insufficient. Combined blockade of functionally linked and relevant multiple targets has become an attractive therapeutic strategy. The EGFR and ERBB2 (HER2) pathways and VEGF-dependent angiogenesis have a pivotal role in cancer pathogenesis and progression. Robust experimental evidence has shown that these pathways are functionally linked and has demonstrated a suggested role for VEGF in the acquired resistance to anti-ERBB drugs when these receptors are pharmacologically blocked. Combined inhibition of ERBB and VEGF signaling interferes with a molecular feedback loop responsible for acquired resistance to anti-ERBB agents and promotes apoptosis while ablating tumor-induced angiogenesis. To this aim, either two agents highly selective against VEGF and ERBB respectively, or, alternatively, a single multitargeted agent, can be used. Preclinical studies have proven the efficacy of both these approaches and early clinical studies have provided encouraging results. This Review discusses the experimental rationale for, preclinical studies of and clinical trials on combined blockade of ERBB and VEGF signaling.

  2. Taking the Next Step: Combining Incrementally Valid Indicators to Improve Recidivism Prediction

    ERIC Educational Resources Information Center

    Walters, Glenn D.

    2011-01-01

    The possibility of combining indicators to improve recidivism prediction was evaluated in a sample of released federal prisoners randomly divided into a derivation subsample (n = 550) and a cross-validation subsample (n = 551). Five incrementally valid indicators were selected from five domains: demographic (age), historical (prior convictions),…

  3. Predicting combined sewer overflows chamber depth using artificial neural networks with rainfall radar data.

    PubMed

    Mounce, S R; Shepherd, W; Sailor, G; Shucksmith, J; Saul, A J

    2014-01-01

    Combined sewer overflows (CSOs) represent a common feature in combined urban drainage systems and are used to discharge excess water to the environment during heavy storms. To better understand the performance of CSOs, the UK water industry has installed a large number of monitoring systems that provide data for these assets. This paper presents research into the prediction of the hydraulic performance of CSOs using artificial neural networks (ANN) as an alternative to hydraulic models. Previous work has explored using an ANN model for the prediction of chamber depth using time series for depth and rain gauge data. Rainfall intensity data that can be provided by rainfall radar devices can be used to improve on this approach. Results are presented using real data from a CSO for a catchment in the North of England, UK. An ANN model trained with the pseudo-inverse rule was shown to be capable of predicting CSO depth with less than 5% error for predictions more than 1 hour ahead for unseen data. Such predictive approaches are important to the future management of combined sewer systems.

  4. MicroRNA-Related Polymorphisms in PI3K/Akt/mTOR Pathway Genes Are Predictive of Limited-Disease Small Cell Lung Cancer Treatment Outcomes

    PubMed Central

    Zhang, Wenjue; Wu, Lihong; Liu, Lipin; Men, Yu; Wang, Jingbo; Liang, Jun; Zhou, Zongmei

    2017-01-01

    The phosphoinositide-3 kinase (PI3K)/Akt/mammalian target of rapamycin (mTOR) signaling pathway plays an important role in cancer progression and treatment, including that of small cell lung cancer (SCLC), a disease with traditionally poor prognosis. Given the regulatory role of microRNA (miRNA) in gene expression, we examined the association of single nucleotide polymorphisms (SNPs) at miRNA-binding sites of genes in the mTOR pathway with the prognosis of patients with limited-disease SCLC. A retrospective study was conducted of 146 patients with limited-disease SCLC treated with chemoradiotherapy. Nine SNPs of six mTOR pathway genes were genotyped using blood samples. Cox proportional hazard regression modeling and recursive partitioning analysis were performed to identify SNPs significantly associated with overall survival. Three SNPs, MTOR: rs2536 (T>C), PIK3R1: rs3756668 (A>G), and PIK3R1: rs12755 (A>C), were associated with longer overall survival. Recursive partitioning analysis based on unfavorable genotype combinations of the rs2536 and rs3756668 SNPs classified patients into three risk subgroups and was internally validated with 1000 bootstrap samples. These findings suggest that miRNA-related polymorphisms in the PI3K/Akt/mTOR pathway may be valuable biomarkers to complement clinicopathological variables in predicting prognosis of limited-disease SCLC and to facilitate selection of patients likely to benefit from chemoradiotherapy. PMID:28280736

  5. Combining Satellite Observations of Fire Activity and Numerical Weather Prediction to Improve the Prediction of Smoke Emissions

    NASA Astrophysics Data System (ADS)

    Peterson, D. A.; Wang, J.; Hyer, E. J.; Ichoku, C. M.

    2012-12-01

    Smoke emissions estimates used in air quality and visibility forecasting applications are currently limited by the information content of satellite fire observations, and the lack of a skillful short-term forecast of changes in fire activity. This study explores the potential benefits of a recently developed sub-pixel-based calculation of fire radiative power (FRPf) from the MODerate Resolution Imaging Spectroradiometer (MODIS), which provides more precise estimates of the radiant energy (over the retrieved fire area) that in turn, improves estimates of the thermal buoyancy of smoke plumes and may be helpful characterizing the meteorological effects on fire activity for large fire events. Results show that unlike the current FRP product, the incorporation of FRPf produces a statistically significant correlation (R = 0.42) with smoke plume height data provided by the Multi-angle Imaging SpectroRadiometer (MISR) and several meteorological variables, such as surface wind speed and temperature, which may be useful for discerning cases where smoke was injected above the boundary layer. Drawing from recent advances in numerical weather prediction (NWP), this study also examines the meteorological conditions characteristic of fire ignition, growth, decay, and extinction, which are used to develop an automated, 24-hour prediction of satellite fire activity. Satellite fire observations from MODIS and geostationary sensors show that the fire prediction model is an improvement (RMSE reduction of 13 - 20%) over the forecast of persistence commonly used by near-real-time fire emission inventories. The ultimate goal is to combine NWP data and satellite fire observations to improve both analysis and prediction of biomass-burning emissions, through improved understanding of the interactions between fire activity and weather at scales appropriate for operational modeling. This is a critical step toward producing a global fire prediction model and improving operational forecasts of

  6. The Combination of Three Components Derived from Sheng MaiSan Protects Myocardial Ischemic Diseases and Inhibits Oxidative Stress via Modulating MAPKs and JAK2-STAT3 Signaling Pathways Based on Bioinformatics Approach

    PubMed Central

    Li, Fang; Zhang, Yu; Zeng, Donglin; Xia, Yu; Fan, Xiaoxue; Tan, Yisha; Kou, Junping; Yu, Boyang

    2017-01-01

    GRS is a drug combination of three components including ginsenoside Rb1, ruscogenin and schisandrin. It derived from the well-known TCM formula Sheng MaiSan, a widely used traditional Chinese medicine for the treatment of cardiovascular diseases in clinic. The present study illuminates its underlying mechanisms against myocardial ischemic diseases based on the combined methods of bioinformatic prediction and experimental verification. A protein database was established through constructing the drug-protein network. And the target-pathway interaction network clustered the potential signaling pathways and targets of GRS in treatment of myocardial ischemic diseases. Several target proteins, such as NFKB1, STAT3 and MAPK14, were identified as the candidate key proteins, and MAPKs and JAK-STAT signaling pathway were suggested as the most related pathways, which were in accordance with the gene ontology analysis. Then, the predictive results were further validated and we found that GRS treatment alleviated hypoxia/reoxygenation (H/R)-induced cardiomyocytes injury via suppression of MDA levels and ROS generation, and potential mechanisms might related to the suppression of activation of MAPKs and JAK2-STAT3 signaling pathways. Conclusively, our results offer the evidence that GRS attenuates myocardial ischemia injury via regulating oxidative stress and MAPKs and JAK2-STAT3 signaling pathways, which supplied some new insights for its prevention and treatment of myocardial ischemia diseases. PMID:28197101

  7. Combining Traditional Cyber Security Audit Data with Psychosocial Data: Towards Predictive Modeling for Insider Threat Mitigation

    NASA Astrophysics Data System (ADS)

    Greitzer, Frank L.; Frincke, Deborah A.

    The purpose of this chapter is to motivate the combination of traditional cyber security audit data with psychosocial data, to support a move from an insider threat detection stance to one that enables prediction of potential insider presence. Twodistinctiveaspects of the approach are the objectiveof predicting or anticipating potential risksandthe useoforganizational datain additiontocyber datato support the analysis. The chapter describes the challenges of this endeavor and reports on progressin definingausablesetof predictiveindicators,developingaframeworkfor integratingthe analysisoforganizationalandcyber securitydatatoyield predictions about possible insider exploits, and developing the knowledge base and reasoning capabilityof the system.We also outline the typesof errors that oneexpectsina predictive system versus a detection system and discuss how those errors can affect the usefulness of the results.

  8. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    PubMed

    Illias, Hazlee Azil; Chai, Xin Rui; Abu Bakar, Ab Halim; Mokhlis, Hazlie

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

  9. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

    PubMed Central

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works. PMID:26103634

  10. Music-induced emotions can be predicted from a combination of brain activity and acoustic features.

    PubMed

    Daly, Ian; Williams, Duncan; Hallowell, James; Hwang, Faustina; Kirke, Alexis; Malik, Asad; Weaver, James; Miranda, Eduardo; Nasuto, Slawomir J

    2015-12-01

    It is widely acknowledged that music can communicate and induce a wide range of emotions in the listener. However, music is a highly-complex audio signal composed of a wide range of complex time- and frequency-varying components. Additionally, music-induced emotions are known to differ greatly between listeners. Therefore, it is not immediately clear what emotions will be induced in a given individual by a piece of music. We attempt to predict the music-induced emotional response in a listener by measuring the activity in the listeners electroencephalogram (EEG). We combine these measures with acoustic descriptors of the music, an approach that allows us to consider music as a complex set of time-varying acoustic features, independently of any specific music theory. Regression models are found which allow us to predict the music-induced emotions of our participants with a correlation between the actual and predicted responses of up to r=0.234,p<0.001. This regression fit suggests that over 20% of the variance of the participant's music induced emotions can be predicted by their neural activity and the properties of the music. Given the large amount of noise, non-stationarity, and non-linearity in both EEG and music, this is an encouraging result. Additionally, the combination of measures of brain activity and acoustic features describing the music played to our participants allows us to predict music-induced emotions with significantly higher accuracies than either feature type alone (p<0.01).

  11. The MORPH-R web server and software tool for predicting missing genes in biological pathways.

    PubMed

    Amar, David; Frades, Itziar; Diels, Tim; Zaltzman, David; Ghatan, Netanel; Hedley, Pete E; Alexandersson, Erik; Tzfadia, Oren; Shamir, Ron

    2015-09-01

    A biological pathway is the set of molecular entities involved in a given biological process and the interrelations among them. Even though biological pathways have been studied extensively, discovering missing genes in pathways remains a fundamental challenge. Here, we present an easy-to-use tool that allows users to run MORPH (MOdule-guided Ranking of candidate PatHway genes), an algorithm for revealing missing genes in biological pathways, and demonstrate its capabilities. MORPH supports the analysis in tomato, Arabidopsis and the two new species: rice and the newly sequenced potato genome. The new tool, called MORPH-R, is available both as a web server (at http://bioinformatics.psb.ugent.be/webtools/morph/) and as standalone software that can be used locally. In the standalone version, the user can apply the tool to new organisms using any proprietary and public data sources.

  12. A Consensus Data Mining secondary structure prediction by combining GOR V and Fragment Database Mining.

    PubMed

    Sen, Taner Z; Cheng, Haitao; Kloczkowski, Andrzej; Jernigan, Robert L

    2006-11-01

    The major aim of tertiary structure prediction is to obtain protein models with the highest possible accuracy. Fold recognition, homology modeling, and de novo prediction methods typically use predicted secondary structures as input, and all of these methods may significantly benefit from more accurate secondary structure predictions. Although there are many different secondary structure prediction methods available in the literature, their cross-validated prediction accuracy is generally <80%. In order to increase the prediction accuracy, we developed a novel hybrid algorithm called Consensus Data Mining (CDM) that combines our two previous successful methods: (1) Fragment Database Mining (FDM), which exploits the Protein Data Bank structures, and (2) GOR V, which is based on information theory, Bayesian statistics, and multiple sequence alignments (MSA). In CDM, the target sequence is dissected into smaller fragments that are compared with fragments obtained from related sequences in the PDB. For fragments with a sequence identity above a certain sequence identity threshold, the FDM method is applied for the prediction. The remainder of the fragments are predicted by GOR V. The results of the CDM are provided as a function of the upper sequence identities of aligned fragments and the sequence identity threshold. We observe that the value 50% is the optimum sequence identity threshold, and that the accuracy of the CDM method measured by Q(3) ranges from 67.5% to 93.2%, depending on the availability of known structural fragments with sufficiently high sequence identity. As the Protein Data Bank grows, it is anticipated that this consensus method will improve because it will rely more upon the structural fragments.

  13. Combined prediction model of death toll for road traffic accidents based on independent and dependent variables.

    PubMed

    Feng, Zhong-xiang; Lu, Shi-sheng; Zhang, Wei-hua; Zhang, Nan-nan

    2014-01-01

    In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

  14. Data-Driven Prediction of Beneficial Drug Combinations in Spontaneous Reporting Systems

    PubMed Central

    Li, Ying; Zhang, Ping; Sun, Zhaonan; Hu, Jianying

    2016-01-01

    Post-market withdrawal of medications because of adverse drug reactions (ADRs) could result in loss of effective compounds which are effective for treating a specific disease but have unfavorable benefit-to- harm ratio. Recent therapeutic successes have renewed interest in drug combinations, which could work synergistically to improve therapeutic efficacy or work antagonistically to alleviate the risk of the ADRs. However, experimental screening approaches are costly and often can identify only a small number of drug combinations. Spontaneous reporting systems (SRSs) routinely collect adverse drug events (ADEs) from patients on complex combinations of medications and provide an empirical and cost-effective source to detect unexpected beneficial drug combinations. In this paper, we proposed a novel data-driven method for the prediction of drug combinations where one drug could reduce the ADRs of the other, based on data from SRSs. The predictive model was shown to be effective using a commonly used evaluation approach in pharmacovigilance by constructing a known drug-drug interaction (DDI) reference standard. The method was applied to perform large-scale screening on SRS data for drug-ADR-drug triples where polypharmacy could potentially reduce the ADR. Analysis of the top ranking candidates showed high level of clinical validity. PMID:28269877

  15. Assessing Long-Term Wind Conditions by Combining Different Measure-Correlate-Predict Algorithms: Preprint

    SciTech Connect

    Zhang, J.; Chowdhury, S.; Messac, A.; Hodge, B. M.

    2013-08-01

    This paper significantly advances the hybrid measure-correlate-predict (MCP) methodology, enabling it to account for variations of both wind speed and direction. The advanced hybrid MCP method uses the recorded data of multiple reference stations to estimate the long-term wind condition at a target wind plant site. The results show that the accuracy of the hybrid MCP method is highly sensitive to the combination of the individual MCP algorithms and reference stations. It was also found that the best combination of MCP algorithms varies based on the length of the correlation period.

  16. Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4.

    PubMed

    Voet, Arnout R D; Kumar, Ashutosh; Berenger, Francois; Zhang, Kam Y J

    2014-04-01

    The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.

  17. Combining in silico and in cerebro approaches for virtual screening and pose prediction in SAMPL4

    NASA Astrophysics Data System (ADS)

    Voet, Arnout R. D.; Kumar, Ashutosh; Berenger, Francois; Zhang, Kam Y. J.

    2014-04-01

    The SAMPL challenges provide an ideal opportunity for unbiased evaluation and comparison of different approaches used in computational drug design. During the fourth round of this SAMPL challenge, we participated in the virtual screening and binding pose prediction on inhibitors targeting the HIV-1 integrase enzyme. For virtual screening, we used well known and widely used in silico methods combined with personal in cerebro insights and experience. Regular docking only performed slightly better than random selection, but the performance was significantly improved upon incorporation of additional filters based on pharmacophore queries and electrostatic similarities. The best performance was achieved when logical selection was added. For the pose prediction, we utilized a similar consensus approach that amalgamated the results of the Glide-XP docking with structural knowledge and rescoring. The pose prediction results revealed that docking displayed reasonable performance in predicting the binding poses. However, prediction performance can be improved utilizing scientific experience and rescoring approaches. In both the virtual screening and pose prediction challenges, the top performance was achieved by our approaches. Here we describe the methods and strategies used in our approaches and discuss the rationale of their performances.

  18. Model predictive control of a combined heat and power plant using local linear models

    SciTech Connect

    Kikstra, J.F.; Roffel, B.; Schoen, P.

    1998-10-01

    Model predictive control has been applied to control of a combined heat and power plant. One of the main features of this plant is that it exhibits nonlinear process behavior due to large throughput swings. In this application, the operating window of the plant has been divided into a number of smaller windows in which the nonlinear process behavior has been approximated by linear behavior. For each operating window, linear step weight models were developed from a detailed nonlinear first principles model, and the model prediction is calculated based on interpolation between these linear models. The model output at each operating point can then be calculated from four basic linear models, and the required control action can subsequently be calculated with the standard model predictive control approach using quadratic programming.

  19. Hybrid predictions of railway induced ground vibration using a combination of experimental measurements and numerical modelling

    NASA Astrophysics Data System (ADS)

    Kuo, K. A.; Verbraken, H.; Degrande, G.; Lombaert, G.

    2016-07-01

    Along with the rapid expansion of urban rail networks comes the need for accurate predictions of railway induced vibration levels at grade and in buildings. Current computational methods for making predictions of railway induced ground vibration rely on simplifying modelling assumptions and require detailed parameter inputs, which lead to high levels of uncertainty. It is possible to mitigate against these issues using a combination of field measurements and state-of-the-art numerical methods, known as a hybrid model. In this paper, two hybrid models are developed, based on the use of separate source and propagation terms that are quantified using in situ measurements or modelling results. These models are implemented using term definitions proposed by the Federal Railroad Administration and assessed using the specific illustration of a surface railway. It is shown that the limitations of numerical and empirical methods can be addressed in a hybrid procedure without compromising prediction accuracy.

  20. Deciphering Combinations of PI3K/AKT/mTOR Pathway Drugs Augmenting Anti-Angiogenic Efficacy In Vivo

    PubMed Central

    Sasore, Temitope; Kennedy, Breandán

    2014-01-01

    Ocular neovascularization is a common pathology associated with human eye diseases e.g. age-related macular degeneration and proliferative diabetic retinopathy. Blindness represents one of the most feared disabilities and remains a major burden to health-care systems. Current approaches to treat ocular neovascularisation include laser photocoagulation, photodynamic therapy and anti-VEGF therapies: Ranibizumab (Lucentis) and Aflibercept (Eylea). However, high clinical costs, frequent intraocular injections, and increased risk of infections are challenges related with these standards of care. Thus, there is a clinical need to develop more effective drugs that overcome these challenges. Here, we focus on an alternative approach by quantifying the in vivo anti-angiogenic efficacy of combinations of phosphatidylinositol-3-kinase (PI3K) pathway inhibitors. The PI3K/AKT/mTOR pathway is a complex signalling pathway involved in crucial cellular functions such as cell proliferation, migration and angiogenesis. RT-PCR confirms the expression of PI3K target genes (pik3ca, pik3r1, mtor and akt1) in zebrafish trunks from 6 hours post fertilisation (hpf) and in eyes from 2 days post fertilisation (dpf). Using both the zebrafish intersegmental vessel and hyaloid vessel assays to measure the in vivo anti-angiogenic efficacy of PI3K/Akt/mTOR pathway inhibitors, we identified 5 µM combinations of i) NVP-BEZ235 (dual PI3K-mTOR inhibitor) + PI-103 (dual PI3K-mTOR inhibitor); or ii) LY-294002 (pan-PI3K inhibitor) + NVP-BEZ235; or iii) NVP-BEZ235 + rapamycin (mTOR inhibitor); or iv) LY-294002 + rapamycin as the most anti-angiogenic. Treatment of developing larvae from 2–5 dpf with 5 µM NVP-BEZ235 plus PI-103 resulted in an essentially intact ocular morphology and visual behaviour, whereas other combinations severely disrupted the developing retinal morphology and visual function. In human ARPE19 retinal pigment epithelium cells, however, no significant difference in cell number was

  1. Combined heat transfer and kinetic models to predict cooking loss during heat treatment of beef meat.

    PubMed

    Kondjoyan, Alain; Oillic, Samuel; Portanguen, Stéphane; Gros, Jean-Bernard

    2013-10-01

    A heat transfer model was used to simulate the temperature in 3 dimensions inside the meat. This model was combined with a first-order kinetic models to predict cooking losses. Identification of the parameters of the kinetic models and first validations were performed in a water bath. Afterwards, the performance of the combined model was determined in a fan-assisted oven under different air/steam conditions. Accurate knowledge of the heat transfer coefficient values and consideration of the retraction of the meat pieces are needed for the prediction of meat temperature. This is important since the temperature at the center of the product is often used to determine the cooking time. The combined model was also able to predict cooking losses from meat pieces of different sizes and subjected to different air/steam conditions. It was found that under the studied conditions, most of the water loss comes from the juice expelled by protein denaturation and contraction and not from evaporation.

  2. Predicting Alzheimer's Disease Using Combined Imaging-Whole Genome SNP Data.

    PubMed

    Kong, Dehan; Giovanello, Kelly S; Wang, Yalin; Lin, Weili; Lee, Eunjee; Fan, Yong; Murali Doraiswamy, P; Zhu, Hongtu

    2015-01-01

    The growing public threat of Alzheimer's disease (AD) has raised the urgency to discover and validate prognostic biomarkers in order to predicting time to onset of AD. It is anticipated that both whole genome single nucleotide polymorphism (SNP) data and high dimensional whole brain imaging data offer predictive values to identify subjects at risk for progressing to AD. The aim of this paper is to test whether both whole genome SNP data and whole brain imaging data offer predictive values to identify subjects at risk for progressing to AD. In 343 subjects with mild cognitive impairment (MCI) enrolled in the Alzheimer's Disease Neuroimaging Initiative (ADNI-1), we extracted high dimensional MR imaging (volumetric data on 93 brain regions plus a surface fluid registration based hippocampal subregion and surface data), and whole genome data (504,095 SNPs from GWAS), as well as routine neurocognitive and clinical data at baseline. MCI patients were then followed over 48 months, with 150 participants progressing to AD. Combining information from whole brain MR imaging and whole genome data was substantially superior to the standard model for predicting time to onset of AD in a 48-month national study of subjects at risk. Our findings demonstrate the promise of combined imaging-whole genome prognostic markers in people with mild memory impairment.

  3. Shunting normal pressure hydrocephalus: the predictive value of combined clinical and CT data.

    PubMed Central

    Vanneste, J; Augustijn, P; Tan, W F; Dirven, C

    1993-01-01

    The value of an ordinal global scale derived from combined clinical and CT data (clin/CT scale) to predict the clinical outcome in 112 patients shunted for presumed normal pressure hydrocephalus (NPH) was analysed. The clinical data were retrospectively collected, all CT scans were re-evaluated, and the clin/CT scale was determined blind to the results of further ancillary tests and to the post-surgical outcome. The scale ranked three classes of prediction: on the basis of clinical and CT characteristics, improvement after shunting was probable, possible, or improbable. The predictive value of the clin/CT scale for the subgroup of communicating NPH was established for two different strategies, depending on the strictness of selection criteria for shunting. In the subgroup of patients with presumed communicating NPH, the prevalence of shunt responsiveness was 29%; the best strategy was to shunt only patients with probable shunt-responsive NPH: the sensitivity was 0.54, the specificity 0.84, and the predictive accuracy 0.75, with a limited number of ineffective shunts (11%) and missed improvements (13%). The study illustrates its need to assess the pre-test probability of NPH based on combined clinical and CT data, before establishing the clinical usefulness of an ancillary test. PMID:8459240

  4. Brain injury prediction: assessing the combined probability of concussion using linear and rotational head acceleration.

    PubMed

    Rowson, Steven; Duma, Stefan M

    2013-05-01

    Recent research has suggested possible long term effects due to repetitive concussions, highlighting the importance of developing methods to accurately quantify concussion risk. This study introduces a new injury metric, the combined probability of concussion, which computes the overall risk of concussion based on the peak linear and rotational accelerations experienced by the head during impact. The combined probability of concussion is unique in that it determines the likelihood of sustaining a concussion for a given impact, regardless of whether the injury would be reported or not. The risk curve was derived from data collected from instrumented football players (63,011 impacts including 37 concussions), which was adjusted to account for the underreporting of concussion. The predictive capability of this new metric is compared to that of single biomechanical parameters. The capabilities of these parameters to accurately predict concussion incidence were evaluated using two separate datasets: the Head Impact Telemetry System (HITS) data and National Football League (NFL) data collected from impact reconstructions using dummies (58 impacts including 25 concussions). Receiver operating characteristic curves were generated, and all parameters were significantly better at predicting injury than random guessing. The combined probability of concussion had the greatest area under the curve for all datasets. In the HITS dataset, the combined probability of concussion and linear acceleration were significantly better predictors of concussion than rotational acceleration alone, but not different from each other. In the NFL dataset, there were no significant differences between parameters. The combined probability of concussion is a valuable method to assess concussion risk in a laboratory setting for evaluating product safety.

  5. Prediction of human drug clearance by multiple metabolic pathways: integration of hepatic and intestinal microsomal and cytosolic data.

    PubMed

    Cubitt, Helen E; Houston, J Brian; Galetin, Aleksandra

    2011-05-01

    The current study assesses hepatic and intestinal glucuronidation, sulfation, and cytochrome P450 (P450) metabolism of raloxifene, quercetin, salbutamol, and troglitazone using different in vitro systems. The fraction metabolized by conjugation and P450 metabolism was estimated in liver and intestine, and the importance of multiple metabolic pathways on accuracy of clearance prediction was assessed. In vitro intrinsic sulfation clearance (CL(int, SULT)) was determined in human intestinal and hepatic cytosol and compared with hepatic and intestinal microsomal glucuronidation (CL(int, UGT)) and P450 clearance (CL(int, CYP)) expressed per gram of tissue. Hepatic and intestinal cytosolic scaling factors of 80.7 mg/g liver and 18 mg/g intestine were estimated from published data. Scaled CL(int, SULT) ranged between 0.7 and 11.4 ml · min(-1) · g(-1) liver and 0.1 and 3.3 ml · min(-1) · g(-1) intestine (salbutamol and quercetin were the extremes). Salbutamol was the only compound with a high extent of sulfation (51 and 28% of total CL(int) for liver and intestine, respectively) and also significant renal clearance (26-57% of observed plasma clearance). In contrast, the clearance of quercetin was largely accounted for by glucuronidation. Drugs metabolized by multiple pathways (raloxifene and troglitazone) demonstrated improved prediction of intravenous clearance using data from all hepatic pathways (44-86% of observed clearance) compared with predictions based only on the primary pathway (22-36%). The assumption of no intestinal first pass resulted in underprediction of oral clearance for raloxifene, troglitazone, and quercetin (3-22% of observed, respectively). Accounting for the intestinal contribution to oral clearance via estimated intestinal availability improved prediction accuracy for raloxifene and troglitazone (within 2.5-fold of observed). Current findings emphasize the importance of both hepatic and intestinal conjugation for in vitro-in vivo extrapolation

  6. Accelerating Adverse Outcome Pathway (AOP) development via computationally predicted AOP networks

    EPA Science Inventory

    The Adverse Outcome Pathway (AOP) framework is increasingly being adopted as a tool for organizing and summarizing the mechanistic information connecting molecular perturbations by environmental stressors with adverse outcomes relevant for ecological and human health outcomes. Ho...

  7. Ethylene and Abscisic Acid Signaling Pathways Differentially Influence Tomato Resistance to Combined Powdery Mildew and Salt Stress

    PubMed Central

    Kissoudis, Christos; Seifi, Alireza; Yan, Zhe; Islam, A. T. M. Tanjimul; van der Schoot, Hanneke; van de Wiel, Clemens C. M.; Visser, Richard G. F.; van der Linden, C. G.; Bai, Yuling

    2017-01-01

    There is currently limited knowledge on the role of hormones in plants responses to combinations of abiotic and pathogen stress factors. This study focused on the response of tomato near-isogenic lines (NILs) that carry the Ol-1, ol-2, and Ol-4 loci, conferring resistance to tomato powdery mildew (PM) caused by Oidium neolycopersici, to combined PM and salt stress. These NILs were crossed with the notabilis (ABA-deficient), defenceless1 (JA-deficient), and epinastic (ET overproducer) tomato mutants to investigate possible roles of hormone signaling in response to combined stresses. In the NILs, marker genes for hormonal pathways showed differential expression patterns upon PM infection. The epinastic mutation resulted in breakdown of resistance in NIL-Ol-1 and NIL-ol-2. This was accompanied by reduced callose deposition, and was more pronounced under combined salt stress. The notabilis mutation resulted in H2O2 overproduction and reduced susceptibility to PM in NIL-Ol-1 under combined stress, but lead to higher plant growth reduction under salinity and combined stress. Resistance in NIL-ol-2 was compromised by the notabilis mutation, which was potentially caused by reduction of callose deposition. Under combined stress the compromised resistance in NIL-ol-2 was restored. PM resistance in NIL-Ol-4 remained robust across all mutant and treatment combinations. Hormone signaling is critical to the response to combined stress and PM, in terms of resistance and plant fitness. ABA appears to be at the crossroads of disease susceptibility/senescence and plant performance under combined stress These gained insights can aid in narrowing down targets for improving crop performance under stress combinations. PMID:28119708

  8. Ethylene and Abscisic Acid Signaling Pathways Differentially Influence Tomato Resistance to Combined Powdery Mildew and Salt Stress.

    PubMed

    Kissoudis, Christos; Seifi, Alireza; Yan, Zhe; Islam, A T M Tanjimul; van der Schoot, Hanneke; van de Wiel, Clemens C M; Visser, Richard G F; van der Linden, C G; Bai, Yuling

    2016-01-01

    There is currently limited knowledge on the role of hormones in plants responses to combinations of abiotic and pathogen stress factors. This study focused on the response of tomato near-isogenic lines (NILs) that carry the Ol-1, ol-2, and Ol-4 loci, conferring resistance to tomato powdery mildew (PM) caused by Oidium neolycopersici, to combined PM and salt stress. These NILs were crossed with the notabilis (ABA-deficient), defenceless1 (JA-deficient), and epinastic (ET overproducer) tomato mutants to investigate possible roles of hormone signaling in response to combined stresses. In the NILs, marker genes for hormonal pathways showed differential expression patterns upon PM infection. The epinastic mutation resulted in breakdown of resistance in NIL-Ol-1 and NIL-ol-2. This was accompanied by reduced callose deposition, and was more pronounced under combined salt stress. The notabilis mutation resulted in H2O2 overproduction and reduced susceptibility to PM in NIL-Ol-1 under combined stress, but lead to higher plant growth reduction under salinity and combined stress. Resistance in NIL-ol-2 was compromised by the notabilis mutation, which was potentially caused by reduction of callose deposition. Under combined stress the compromised resistance in NIL-ol-2 was restored. PM resistance in NIL-Ol-4 remained robust across all mutant and treatment combinations. Hormone signaling is critical to the response to combined stress and PM, in terms of resistance and plant fitness. ABA appears to be at the crossroads of disease susceptibility/senescence and plant performance under combined stress These gained insights can aid in narrowing down targets for improving crop performance under stress combinations.

  9. Combination of transcriptomic and metabolomic analyses reveals a JAZ repressor in the jasmonate signaling pathway of Salvia miltiorrhiza

    PubMed Central

    Ge, Qian; Zhang, Yuan; Hua, Wen-Ping; Wu, Yu-Cui; Jin, Xin-Xin; Song, Shuang-Hong; Wang, Zhe-Zhi

    2015-01-01

    Jasmonates (JAs) are plant-specific key signaling molecules that respond to various stimuli and are involved in the synthesis of secondary metabolites. However, little is known about the JA signal pathway, especially in economically significant medicinal plants. To determine the functions of novel genes that participate in the JA-mediated accumulation of secondary metabolites, we examined the metabolomic and transcriptomic signatures from Salvia miltiorrhiza. For the metabolome, 35 representative metabolites showing significant changes in rates of accumulation were extracted and identified. We also screened out 2131 differentially expressed unigenes, of which 30 were involeved in the phenolic secondary metabolic pathway, while 25 were in the JA biosynthesis and signal pathways. Among several MeJA-induced novel genes, SmJAZ8 was selected for detailed functional analysis. Transgenic plants over-expressing SmJAZ8 exhibited a JA-insensitive phenotype, suggesting that the gene is a transcriptional regulator in the JA signal pathway of S. miltiorrhiza. Furthermore, this transgenic tool revealed that JAZ genes have novel function in the constitutive accumulation of secondary metabolites. Based on these findings, we propose that the combined strategy of transcriptomic and metabolomic analyses is valuable for efficient discovery of novel genes in plants. PMID:26388160

  10. Dynamic Causal Modelling of epileptic seizure propagation pathways: a combined EEG-fMRI study.

    PubMed

    Murta, Teresa; Leal, Alberto; Garrido, Marta I; Figueiredo, Patrícia

    2012-09-01

    Simultaneous EEG-fMRI offers the possibility of non-invasively studying the spatiotemporal dynamics of epileptic activity propagation from the focus towards an extended brain network, through the identification of the haemodynamic correlates of ictal electrical discharges. In epilepsy associated with hypothalamic hamartomas (HH), seizures are known to originate in the HH but different propagation pathways have been proposed. Here, Dynamic Causal Modelling (DCM) was employed to estimate the seizure propagation pathway from fMRI data recorded in a HH patient, by testing a set of clinically plausible network connectivity models of discharge propagation. The model consistent with early propagation from the HH to the temporal-occipital lobe followed by the frontal lobe was selected as the most likely model to explain the data. Our results demonstrate the applicability of DCM to investigate patient-specific effective connectivity in epileptic networks identified with EEG-fMRI. In this way, it is possible to study the propagation pathway of seizure activity, which has potentially great impact in the decision of the surgical approach for epilepsy treatment.

  11. Advanced validation of CFD-FDTD combined method using highly applicable solver for reentry blackout prediction

    NASA Astrophysics Data System (ADS)

    Takahashi, Yusuke

    2016-01-01

    An analysis model of plasma flow and electromagnetic waves around a reentry vehicle for radio frequency blackout prediction during aerodynamic heating was developed in this study. The model was validated based on experimental results from the radio attenuation measurement program. The plasma flow properties, such as electron number density, in the shock layer and wake region were obtained using a newly developed unstructured grid solver that incorporated real gas effect models and could treat thermochemically non-equilibrium flow. To predict the electromagnetic waves in plasma, a frequency-dependent finite-difference time-domain method was used. Moreover, the complicated behaviour of electromagnetic waves in the plasma layer during atmospheric reentry was clarified at several altitudes. The prediction performance of the combined model was evaluated with profiles and peak values of the electron number density in the plasma layer. In addition, to validate the models, the signal losses measured during communication with the reentry vehicle were directly compared with the predicted results. Based on the study, it was suggested that the present analysis model accurately predicts the radio frequency blackout and plasma attenuation of electromagnetic waves in plasma in communication.

  12. Transmission of Predictable Sensory Signals to the Cerebellum via Climbing Fiber Pathways Is Gated during Exploratory Behavior

    PubMed Central

    Lawrenson, Charlotte L.; Watson, Thomas C.

    2016-01-01

    Pathways arising from the periphery that target the inferior olive [spino-olivocerebellar pathways (SOCPs)] are a vital source of information to the cerebellum and are modulated (gated) during active movements. This limits their ability to forward signals to climbing fibers in the cerebellar cortex. We tested the hypothesis that the temporal pattern of gating is related to the predictability of a sensory signal. Low-intensity electrical stimulation of the ipsilateral hindlimb in awake rats evoked field potentials in the C1 zone in the copula pyramidis of the cerebellar cortex. Responses had an onset latency of 12.5 ± 0.3 ms and were either short or long duration (8.7 ± 0.1 vs 31.2 ± 0.3 ms, respectively). Both types of response were shown to be mainly climbing fiber in origin and therefore evoked by transmission in hindlimb SOCPs. Changes in response size (area of field, millivolts per millisecond) were used to monitor differences in transmission during rest and three phases of rearing: phase 1, rearing up; phase 2, upright; and phase 3, rearing down. Responses evoked during phase 2 were similar in size to rest but were smaller during phases 1 and 3, i.e., transmission was reduced during active movement when self-generated (predictable) sensory signals from the hindlimbs are likely to occur. To test whether the pattern of gating was related to the predictability of the sensory signal, some animals received the hindlimb stimulation only during phase 2. Over ∼10 d, the responses became progressively smaller in size, consistent with gating-out transmission of predictable sensory signals relayed via SOCPs. SIGNIFICANCE STATEMENT A major route for peripheral information to gain access to the cerebellum is via ascending climbing fiber pathways. During active movements, gating of transmission in these pathways controls when climbing fiber signals can modify cerebellar activity. We investigated this phenomenon in rats during their exploratory behavior of rearing

  13. Potentiated suppression of Dickkopf-1 in breast cancer by combined administration of the mevalonate pathway inhibitors zoledronic acid and statins.

    PubMed

    Göbel, Andy; Browne, Andrew J; Thiele, Stefanie; Rauner, Martina; Hofbauer, Lorenz C; Rachner, Tilman D

    2015-12-01

    The Wnt-inhibitor dickkopf-1 (DKK-1) promotes cancer-induced osteolytic bone lesions by direct inhibition of osteoblast differentiation and indirect activation of osteoclasts. DKK-1 is highly expressed in human breast cancer cells and can be suppressed by inhibitors of the mevalonate pathway such as statins and amino-bisphosphonates. However, supraphysiological concentrations are required to suppress DKK-1. We show that a sequential mevalonate pathway blockade using statins and amino-bisphosphonates suppresses DKK-1 more significantly than the individual agents alone. Thus, the reduction of the DKK-1 expression and secretion in the human osteotropic tumor cell lines MDA-MB-231, MDA-MET, and MDA-BONE by zoledronic acid was potentiated by the combination with low concentrations of statins (atorvastatin, simvastatin, and rosuvastatin) by up to 75% (p < 0.05). The specific rescue of prenylation using farnesyl pyrophosphate or geranylgeranyl pyrophosphate revealed that these effects were mediated by suppressed geranylgeranylation rather than by suppressed farnesylation. Moreover, combining low concentrations of statins (1 µM atorvastatin or 0.25 µM simvastatin) and zoledronic acid at low concentrations resulted in an at least 50% reversal of breast cancer-derived DKK-1-mediated inhibition of osteogenic markers in C2C12 cells (p < 0.05). Finally, the intratumoral injection of atorvastatin and zoledronic acid in as subcutaneous MDA-MB-231 mouse model reduced the serum level of human DKK-1 by 25% compared to untreated mice. Hence our study reveals that a sequential mevalonate pathway blockade allows for the combined use of low concentration of statins and amino-bisphosphonates. This combination still significantly suppresses breast cancer-derived DKK-1 to levels where it can no longer inhibit Wnt-mediated osteoblast differentiation.

  14. Antibody structure determination using a combination of homology modeling, energy-based refinement, and loop prediction.

    PubMed

    Zhu, Kai; Day, Tyler; Warshaviak, Dora; Murrett, Colleen; Friesner, Richard; Pearlman, David

    2014-08-01

    We present the blinded prediction results in the Second Antibody Modeling Assessment (AMA-II) using a fully automatic antibody structure prediction method implemented in the programs BioLuminate and Prime. We have developed a novel knowledge based approach to model the CDR loops, using a combination of sequence similarity, geometry matching, and the clustering of database structures. The homology models are further optimized with a physics-based energy function (VSGB2.0), which improves the model quality significantly. H3 loop modeling remains the most challenging task. Our ab initio loop prediction performs well for the H3 loop in the crystal structure context, and allows improved results when refining the H3 loops in the context of homology models. For the 10 human and mouse derived antibodies in this assessment, the average RMSDs for the homology model Fv and framework regions are 1.19 Å and 0.74 Å, respectively. The average RMSDs for five non-H3 CDR loops range from 0.61 Å to 1.05 Å, and the H3 loop average RMSD is 2.91 Å using our knowledge-based loop prediction approach. The ab initio H3 loop predictions yield an average RMSD of 1.28 Å when performed in the context of the crystal structure and 2.67 Å in the context of the homology modeled structure. Notably, our method for predicting the H3 loop in the crystal structure environment ranked first among the seven participating groups in AMA-II, and our method made the best prediction among all participants for seven of the ten targets.

  15. The genome characteristics and predicted function of methyl-group oxidation pathway in the obligate aceticlastic methanogens, Methanosaeta spp.

    PubMed

    Zhu, Jinxing; Zheng, Huajun; Ai, Guomin; Zhang, Guishan; Liu, Di; Liu, Xiaoli; Dong, Xiuzhu

    2012-01-01

    In this work, we report the complete genome sequence of an obligate aceticlastic methanogen, Methanosaeta harundinacea 6Ac. Genome comparison indicated that the three cultured Methanosaeta spp., M. thermophila, M. concilii and M. harundinacea 6Ac, each carry an entire suite of genes encoding the proteins involved in the methyl-group oxidation pathway, a pathway whose function is not well documented in the obligately aceticlastic methanogens. Phylogenetic analysis showed that the methyl-group oxidation-involving proteins, Fwd, Mtd, Mch, and Mer from Methanosaeta strains cluster with the methylotrophic methanogens, and were not closely related to those from the hydrogenotrophic methanogens. Quantitative PCR detected the expression of all genes for this pathway, albeit ten times lower than the genes for aceticlastic methanogenesis in strain 6Ac. Western blots also revealed the expression of fwd and mch, genes involved in methyl-group oxidation. Moreover, (13)C-labeling experiments suggested that the Methanosaeta strains might use the pathway as a methyl oxidation shunt during the aceticlastic metabolism. Because the mch mutants of Methanosarcina barkeri or M. acetivorans failed to grow on acetate, we suggest that Methanosaeta may use methyl-group oxidation pathway to generate reducing equivalents, possibly for biomass synthesis. An fpo operon, which encodes an electron transport complex for the reduction of CoM-CoB heterodisulfide, was found in the three genomes of the Methanosaeta strains. However, an incomplete protein complex lacking the FpoF subunit was predicted, as the gene for this protein was absent. Thus, F(420)H(2) was predicted not to serve as the electron donor. In addition, two gene clusters encoding the two types of heterodisulfide reductase (Hdr), hdrABC, and hdrED, respectively, were found in the three Methanosaeta genomes. Quantitative PCR determined that the expression of hdrED was about ten times higher than hdrABC, suggesting that hdrED plays a

  16. Genome-Wide Prediction of Metabolic Enzymes, Pathways, and Gene Clusters in Plants1[OPEN

    PubMed Central

    Zhang, Peifen; Kim, Taehyong; Banf, Michael; Chavali, Arvind K.; Nilo-Poyanco, Ricardo; Bernard, Thomas

    2017-01-01

    Plant metabolism underpins many traits of ecological and agronomic importance. Plants produce numerous compounds to cope with their environments but the biosynthetic pathways for most of these compounds have not yet been elucidated. To engineer and improve metabolic traits, we need comprehensive and accurate knowledge of the organization and regulation of plant metabolism at the genome scale. Here, we present a computational pipeline to identify metabolic enzymes, pathways, and gene clusters from a sequenced genome. Using this pipeline, we generated metabolic pathway databases for 22 species and identified metabolic gene clusters from 18 species. This unified resource can be used to conduct a wide array of comparative studies of plant metabolism. Using the resource, we discovered a widespread occurrence of metabolic gene clusters in plants: 11,969 clusters from 18 species. The prevalence of metabolic gene clusters offers an intriguing possibility of an untapped source for uncovering new metabolite biosynthesis pathways. For example, more than 1,700 clusters contain enzymes that could generate a specialized metabolite scaffold (signature enzymes) and enzymes that modify the scaffold (tailoring enzymes). In four species with sufficient gene expression data, we identified 43 highly coexpressed clusters that contain signature and tailoring enzymes, of which eight were characterized previously to be functional pathways. Finally, we identified patterns of genome organization that implicate local gene duplication and, to a lesser extent, single gene transposition as having played roles in the evolution of plant metabolic gene clusters. PMID:28228535

  17. Tissue Factor Pathway Inhibitor-1 Is a Valuable Marker for the Prediction of Deep Venous Thrombosis and Tumor Metastasis in Patients with Lung Cancer

    PubMed Central

    Yuan, Wufeng

    2017-01-01

    Activation of blood coagulation contributes to cancer progression. Tissue factor pathway inhibitor-1 (TFPI-1) is the main inhibitor of extrinsic coagulation pathway. The aim of this study is to assess the predicting significance of TFPI-1 for thrombotic complication and metastasis in lung cancer patients. Total of 188 non-small cell lung cancer (NSCLC) patients were included in this study. Plasma TFPI-1, D-dimer (D-D), antithrombin (AT), Fibrinogen (Fbg), and coagulating factor VIII activity (FVIII:C) were measured. In NSCLC patients, significantly decreased TFPI-1 and AT and increased D-D, Fbg, and FVIII:C levels were observed, and there was a significant correlation between TFPI-1 and other hemostatic parameters (P < 0.001, resp.). NSCLC patients with deep venous thrombosis (DVT) or metastasis had significantly lower TFPI-1 levels than those without DVT or metastasis (P < 0.01, resp.). Multivariate regression revealed that TFPI-1 acted as a predictor for DVT or tumor metastasis in NSCLC patients [OR: 4.15 or 3.28, P < 0.05, resp.]. The area under ROC curve of TFPI-1 was 0.905 (95% CI, 0.842~0.967) or 0.828 (95% CI, 0.742~0.915) for predicting DVT or metastasis (P < 0.001, resp.). The optimal point of TFPI-1 was 57.7 or 54.3 ng/mL for predicting DVT or metastasis, respectively. Combination of TFPI-1 and D-D measurements can improve the predicting power for DVT or metastasis in NSCLC patients. Our findings suggested that TFPI-1 was a valuable predictor of DVT and tumor metastasis in NSCLC patients. PMID:28246607

  18. Prediction of Candidate Drugs for Treating Pancreatic Cancer by Using a Combined Approach

    PubMed Central

    Dong, Xinran; Li, Ying; Yang, Bo; Tian, Weidong; Wang, Xiaoqin

    2016-01-01

    Pancreatic cancer is the leading cause of death from solid malignancies worldwide. Currently, gemcitabine is the only drug approved for treating pancreatic cancer. Developing new therapeutic drugs for this disease is, therefore, an urgent need. The C-Map project has provided a wealth of gene expression data that can be mined for repositioning drugs, a promising approach to new drug discovery. Typically, a drug is considered potentially useful for treating a disease if the drug-induced differential gene expression profile is negatively correlated with the differentially expressed genes in the target disease. However, many of the potentially useful drugs (PUDs) identified by gene expression profile correlation are likely false positives because, in C-Map, the cultured cell lines to which the drug is applied are not derived from diseased tissues. To solve this problem, we developed a combined approach for predicting candidate drugs for treating pancreatic cancer. We first identified PUDs for pancreatic cancer by using C-Map-based gene expression correlation analyses. We then applied an algorithm (Met-express) to predict key pancreatic cancer (KPC) enzymes involved in pancreatic cancer metabolism. Finally, we selected candidates from the PUDs by requiring that their targets be KPC enzymes or the substrates/products of KPC enzymes. Using this combined approach, we predicted seven candidate drugs for treating pancreatic cancer, three of which are supported by literature evidence, and three were experimentally validated to be inhibitory to pancreatic cancer celllines. PMID:26910401

  19. Distinct Pathways Regulated by RET and Estrogen Receptor in Luminal Breast Cancer Demonstrate the Biological Basis for Combination Therapy

    PubMed Central

    Spanheimer, Philip M.; Cyr, Anthony R.; Gillum, Matthew P.; Woodfield, George W.; Askeland, Ryan W.; Weigel, Ronald J.

    2014-01-01

    Objectives We investigated directed therapy based on TFAP2C-regulated pathways to inform new therapeutic approaches for treatment of luminal breast cancer. Background TFAP2C regulates the expression of genes characterizing the luminal phenotype including ESR1 and RET, but pathway cross talk and potential for distinct elements have not been characterized. Methods Activation of extracellular signal-regulated kinases (ERK) and AKT was assessed using phosphorylation-specific Western blot. Cell proliferation was measured with MTT [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] after siRNA (small interfering RNA) gene knockdown or drug treatment. Cell cycle, Ki-67, and cleaved caspase 3 were measured by fluorescence-activated cell sorting. Tumorigenesis was assessed in mice xenografts. Results Knockdown of TFAP2C or RET inhibited GDNF (glial cell line–derived neurotrophic factor)–mediated activation of ERK and AKT in MCF-7 cells. Similarly, sunitinib, a small-molecule inhibitor of RET, blocked GDNF-mediated activation of ERK and AKT. Inhibition of RET either by gene knockdown or by treatment with sunitinib or vandetanib reduced RET-dependent growth of luminal breast cancer cells. Interestingly, knockdown of TFAP2C, which controls both ER (estrogen receptor) and RET, demonstrated a greater effect on cell growth than either RET or ER alone. Parallel experiments using treatment with tamoxifen and sunitinib confirmed the increased effectiveness of dual inhibition of the ER and RET pathways in regulating cell growth. Whereas targeting the ER pathway altered cell proliferation, as measured by Ki-67 and S-phase, anti-RET primarily increased apoptosis, as demonstrated by cleaved caspase 3 and increased TUNEL (terminal deoxyneucleotidyl transferase dUTP nick end labeling) expression in xenografts. Conclusions ER and RET primarily function through distinct pathways regulating proliferation and cell survival, respectively. The findings inform a therapeutic

  20. Combination of Myostatin Pathway Interference and Dystrophin Rescue Enhances Tetanic and Specific Force in Dystrophic mdx Mice

    PubMed Central

    Dumonceaux, Julie; Marie, Solenne; Beley, Cyriaque; Trollet, Capucine; Vignaud, Alban; Ferry, Arnaud; Butler-Browne, Gillian; Garcia, Luis

    2010-01-01

    Duchenne muscular dystrophy is characterized by muscular atrophy, fibrosis, and fat accumulation. Several groups have demonstrated that in the mdx mouse, the exon-skipping strategy can restore a quasi-dystrophin in almost 100% of the muscle fibers. On the other hand, inhibition of the myostatin pathway in adult mice has been described to enhance muscle growth and improve muscle force. Our aim was to combine these two strategies to evaluate a possible additive effect. We have chosen to inhibit the myostatin pathway using the technique of RNA interference directed against the myostatin receptor AcvRIIb mRNA (sh-AcvRIIb). The restoration of a quasi-dystrophin was mediated by the vectorized U7 exon-skipping technique (U7-DYS). Adeno-associated vectors carrying either the sh-AcvrIIb construct alone, the U7-DYS construct alone, or a combination of both constructs were injected in the tibialis anterior (TA) muscle of dystrophic mdx mice. We show that even if each separate approach has some effects on muscle physiology, the combination of the dystrophin rescue and the downregulation of the myostatin receptor is required to massively improve both the tetanic force and the specific force. This study provides a novel pharmacogenetic strategy for treatment of certain neuromuscular diseases associated with muscle wasting. PMID:20104211

  1. Quantification of hurdles: predicting the combination of effects -- Interaction vs. non-interaction.

    PubMed

    Bidlas, Eva; Lambert, Ronald J W

    2008-11-30

    Combination of disparate as well as related antimicrobial effects constitutes the concept of hurdle technology. Quantification of combined effects, including claims of synergy, can be accomplished using surface response modelling, as is frequently done and reported. The Gamma hypothesis, however, states that the relative effects of different antimicrobial factors combine independently. Studies performed using time to detection have shown that the Gamma hypothesis is an adequate foundation for the analysis of multi-factor environmental stresses placed on microorganisms, including pH, weak acids and temperature. Data from the combined action of Na acetate and pH on Aeromonas hydrophila, Na acetate/pH , K sorbate/pH and combined Na acetate/K sorbate at pH 6.5, 6.0 and 5.5 on Escherichia coli and the combined action of Na acetate/pH and temperature on Enterobacter sakazakii were examined using nominal logistic modelling, response surface modelling (RS) and by using a Gamma model. The Gamma model can be used in a predictive manner unlike the RS models and the parameters of the RS models can be approximated from the fit of the Gamma model to the observed data. The expansion of the Gamma model explains the occurrence of the statistically significant cross terms of the RS polynomials. The emphasis within the literature of seeking interactions or synergies between environmental factors should be replaced with one emphasising the falsification of the Gamma approach. This can be done by examining the relative ratios of the gamma factors when in combination, but this also requires the use of appropriate functions to do this.

  2. Prediction of permeability of regular scaffolds for skeletal tissue engineering: a combined computational and experimental study.

    PubMed

    Truscello, S; Kerckhofs, G; Van Bael, S; Pyka, G; Schrooten, J; Van Oosterwyck, H

    2012-04-01

    Scaffold permeability is a key parameter combining geometrical features such as pore shape, size and interconnectivity, porosity and specific surface area. It can influence the success of bone tissue engineering scaffolds, by affecting oxygen and nutrient transport, cell seeding efficiency, in vitro three-dimensional (3D) cell culture and, ultimately, the amount of bone formation. An accurate and efficient prediction of scaffold permeability would be highly useful as part of a scaffold design process. The aim of this study was (i) to determine the accuracy of computational fluid dynamics (CFD) models for prediction of the permeability coefficient of three different regular Ti6Al4V scaffolds (each having a different porosity) by comparison with experimentally measured values and (ii) to verify the validity of the semi-empirical Kozeny equation to calculate the permeability analytically. To do so, five CFD geometrical models per scaffold porosity were built, based on different geometrical inputs: either based on the scaffold's computer-aided design (CAD) or derived from 3D microfocus X-ray computed tomography (micro-CT) data of the additive manufactured (AM) scaffolds. For the latter the influence of the spatial image resolution and the image analysis algorithm used to determine the scaffold's architectural features on the predicted permeability was analysed. CFD models based on high-resolution micro-CT images could predict the permeability coefficients of the studied scaffolds: depending on scaffold porosity and image analysis algorithm, relative differences between measured and predicted permeability values were between 2% and 27%. Finally, the analytical Kozeny equation was found to be valid. A linear correlation between the ratio Φ(3)/S(s)(2) and the permeability coefficient k was found for the predicted (by means of CFD) as well as measured values (relative difference of 16.4% between respective Kozeny coefficients), thus resulting in accurate and efficient

  3. Combining reverse genetics and nuclear magnetic resonance-based metabolomics unravels trypanosome-specific metabolic pathways.

    PubMed

    Bringaud, Frédéric; Biran, Marc; Millerioux, Yoann; Wargnies, Marion; Allmann, Stefan; Mazet, Muriel

    2015-06-01

    Numerous eukaryotes have developed specific metabolic traits that are not present in extensively studied model organisms. For instance, the procyclic insect form of Trypanosoma brucei, a parasite responsible for sleeping sickness in its mammalian-specific bloodstream form, metabolizes glucose into excreted succinate and acetate through pathways with unique features. Succinate is primarily produced from glucose-derived phosphoenolpyruvate in peroxisome-like organelles, also known as glycosomes, by a soluble NADH-dependent fumarate reductase only described in trypanosomes so far. Acetate is produced in the mitochondrion of the parasite from acetyl-CoA by a CoA-transferase, which forms an ATP-producing cycle with succinyl-CoA synthetase. The role of this cycle in ATP production was recently demonstrated in procyclic trypanosomes and has only been proposed so far for anaerobic organisms, in addition to trypanosomatids. We review how nuclear magnetic resonance spectrometry can be used to analyze the metabolic network perturbed by deletion (knockout) or downregulation (RNAi) of the candidate genes involved in these two particular metabolic pathways of procyclic trypanosomes. The role of succinate and acetate production in trypanosomes is discussed, as well as the connections between the succinate and acetate branches, which increase the metabolic flexibility probably required by the parasite to deal with environmental changes such as oxidative stress.

  4. Frequent disruption of the RB pathway in indolent follicular lymphoma suggests a new combination therapy

    PubMed Central

    Oricchio, Elisa; Ciriello, Giovanni; Jiang, Man; Boice, Michael H.; Schatz, Jonathan H.; Heguy, Adriana; Viale, Agnes; de Stanchina, Elisa; Teruya-Feldstein, Julie; Bouska, Alyssa; McKeithan, Tim; Sander, Chris; Tam, Wayne; Seshan, Venkatraman E.; Chan, Wing-Chung; Chaganti, R.S.K.

    2014-01-01

    Loss of cell cycle controls is a hallmark of cancer and has a well-established role in aggressive B cell malignancies. However, the role of such lesions in indolent follicular lymphoma (FL) is unclear and individual lesions have been observed with low frequency. By analyzing genomic data from two large cohorts of indolent FLs, we identify a pattern of mutually exclusive (P = 0.003) genomic lesions that impair the retinoblastoma (RB) pathway in nearly 50% of FLs. These alterations include homozygous and heterozygous deletions of the p16/CDKN2a/b (7%) and RB1 (12%) loci, and more frequent gains of chromosome 12 that include CDK4 (29%). These aberrations are associated with high-risk disease by the FL prognostic index (FLIPI), and studies in a murine FL model confirm their pathogenic role in indolent FL. Increased CDK4 kinase activity toward RB1 is readily measured in tumor samples and indicates an opportunity for CDK4 inhibition. We find that dual CDK4 and BCL2 inhibitor treatment is safe and effective against available models of FL. In summary, frequent RB pathway lesions in indolent, high-risk FLs indicate an untapped therapeutic opportunity. PMID:24913233

  5. Combined inhibition of p38 and Akt signaling pathways abrogates cyclosporine A-mediated pathogenesis of aggressive skin SCCs

    SciTech Connect

    Arumugam, Aadithya; Walsh, Stephanie B.; Xu, Jianmin; Afaq, Farrukh; Elmets, Craig A.; Athar, Mohammad

    2012-08-24

    Highlights: Black-Right-Pointing-Pointer p38 and Akt are the crucial molecular targets in the pathogenesis of SCCs in OTRs. Black-Right-Pointing-Pointer Combined inhibition of these targets diminished tumor growth by 90%. Black-Right-Pointing-Pointer Inhibition of these targets act through downregulating mTOR signaling pathway. -- Abstract: Non-melanoma skin cancers (NMSCs) are the most common neoplasm in organ transplant recipients (OTRs). These cancers are more invasive and metastatic as compared to those developed in normal cohorts. Previously, we have shown that immunosuppressive drug, cyclosporine A (CsA) directly alters tumor phenotype of cutaneous squamous cell carcinomas (SCCs) by activating TGF-{beta} and TAK1/TAB1 signaling pathways. Here, we identified novel molecular targets for the therapeutic intervention of these SCCs. We observed that combined blockade of Akt and p38 kinases-dependent signaling pathways in CsA-promoted human epidermoid carcinoma A431 xenograft tumors abrogated their growth by more than 90%. This diminution in tumor growth was accompanied by a significant decrease in proliferation and an increase in apoptosis. The residual tumors following the combined treatment with Akt inhibitor triciribine and p38 inhibitors SB-203580 showed significantly diminished expression of phosphorylated Akt and p38 and these tumors were less invasive and highly differentiated. Diminished tumor invasiveness was associated with the reduced epithelial-mesenchymal transition as ascertained by the enhanced E-cadherin and reduced vimentin and N-cadherin expression. Consistently, these tumors also manifested reduced MMP-2/9. The decreased p-Akt expression was accompanied by a significant reduction in p-mTOR. These data provide first important combinatorial pharmacological approach to block the pathogenesis of CsA-induced highly aggressive cutaneous neoplasm in OTRs.

  6. Combining Review Text Content and Reviewer-Item Rating Matrix to Predict Review Rating.

    PubMed

    Wang, Bingkun; Huang, Yongfeng; Li, Xing

    2016-01-01

    E-commerce develops rapidly. Learning and taking good advantage of the myriad reviews from online customers has become crucial to the success in this game, which calls for increasingly more accuracy in sentiment classification of these reviews. Therefore the finer-grained review rating prediction is preferred over the rough binary sentiment classification. There are mainly two types of method in current review rating prediction. One includes methods based on review text content which focus almost exclusively on textual content and seldom relate to those reviewers and items remarked in other relevant reviews. The other one contains methods based on collaborative filtering which extract information from previous records in the reviewer-item rating matrix, however, ignoring review textual content. Here we proposed a framework for review rating prediction which shows the effective combination of the two. Then we further proposed three specific methods under this framework. Experiments on two movie review datasets demonstrate that our review rating prediction framework has better performance than those previous methods.

  7. Combining mesocosm and field experiments to predict invasive plant performance: a hierarchical Bayesian approach.

    PubMed

    Wilson, Chris H; Caughlin, T Trevor; Civitello, David J; Flory, S Luke

    2015-04-01

    Invasive plant fecundity underlies propagule pressure and ultimately range expansion. Predicting fecundity across large spatial scales, from regions to landscapes, is critical for understanding invasion dynamics and optimizing management. However, to accurately predict fecundity and other demographic processes, improved models that scale individual plant responses to abiotic drivers across heterogeneous environments are needed. Here we combine two experimental data sets to predict fecundity of a widespread and problematic invasive grass over large spatial scales. First, we analyzed seed production as a function of plant biomass in a small-scale mesocosm experiment with manipulated light levels. Then, in a field introduction experiment, we tracked plant performance across 21 common garden sites that differed widely in available light and other factors. We jointly analyzed these data using a Bayesian hierarchical model (BHM) framework to predict fecundity as a function of light in the field. Our analysis reveals that the invasive species is likely to produce sufficient seed to overwhelm establishment resistance, even in deeply shaded environments, and is likely seed-limited across much of its range. Finally, we extend this framework to address the general problem of how to scale up plant demographic processes and analyze the factors that control plant distribution and abundance at large scales.

  8. Combining structure and sequence information allows automated prediction of substrate specificities within enzyme families.

    PubMed

    Röttig, Marc; Rausch, Christian; Kohlbacher, Oliver

    2010-01-08

    An important aspect of the functional annotation of enzymes is not only the type of reaction catalysed by an enzyme, but also the substrate specificity, which can vary widely within the same family. In many cases, prediction of family membership and even substrate specificity is possible from enzyme sequence alone, using a nearest neighbour classification rule. However, the combination of structural information and sequence information can improve the interpretability and accuracy of predictive models. The method presented here, Active Site Classification (ASC), automatically extracts the residues lining the active site from one representative three-dimensional structure and the corresponding residues from sequences of other members of the family. From a set of representatives with known substrate specificity, a Support Vector Machine (SVM) can then learn a model of substrate specificity. Applied to a sequence of unknown specificity, the SVM can then predict the most likely substrate. The models can also be analysed to reveal the underlying structural reasons determining substrate specificities and thus yield valuable insights into mechanisms of enzyme specificity. We illustrate the high prediction accuracy achieved on two benchmark data sets and the structural insights gained from ASC by a detailed analysis of the family of decarboxylating dehydrogenases. The ASC web service is available at http://asc.informatik.uni-tuebingen.de/.

  9. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction.

    PubMed

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H

    2017-01-09

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.

  10. Synergistic combination of clinical and imaging features predicts abnormal imaging patterns of pulmonary infections

    PubMed Central

    Bagci, Ulas; Jaster-Miller, Kirsten; Olivier, Kenneth N.; Yao, Jianhua; Mollura, Daniel J.

    2013-01-01

    We designed and tested a novel hybrid statistical model that accepts radiologic image features and clinical variables, and integrates this information in order to automatically predict abnormalities in chest computed-tomography (CT) scans and identify potentially important infectious disease biomarkers. In 200 patients, 160 with various pulmonary infections and 40 healthy controls, we extracted 34 clinical variables from laboratory tests and 25 textural features from CT images. From the CT scans, pleural effusion (PE), linear opacity (or thickening) (LT), tree-in-bud (TIB), pulmonary nodules, ground glass opacity (GGO), and consolidation abnormality patterns were analyzed and predicted through clinical, textural (imaging), or combined attributes. The presence and severity of each abnormality pattern was validated by visual analysis of the CT scans. The proposed biomarker identification system included two important steps: (i) a coarse identification of an abnormal imaging pattern by adaptively selected features (AmRMR), and (ii) a fine selection of the most important features from the previous step, and assigning them as biomarkers, depending on the prediction accuracy. Selected biomarkers were used to classify normal and abnormal patterns by using a boosted decision tree (BDT) classifier. For all abnormal imaging patterns, an average prediction accuracy of 76.15% was obtained. Experimental results demonstrated that our proposed biomarker identification approach is promising and may advance the data processing in clinical pulmonary infection research and diagnostic techniques. PMID:23930819

  11. Combining transcription factor binding affinities with open-chromatin data for accurate gene expression prediction

    PubMed Central

    Schmidt, Florian; Gasparoni, Nina; Gasparoni, Gilles; Gianmoena, Kathrin; Cadenas, Cristina; Polansky, Julia K.; Ebert, Peter; Nordström, Karl; Barann, Matthias; Sinha, Anupam; Fröhler, Sebastian; Xiong, Jieyi; Dehghani Amirabad, Azim; Behjati Ardakani, Fatemeh; Hutter, Barbara; Zipprich, Gideon; Felder, Bärbel; Eils, Jürgen; Brors, Benedikt; Chen, Wei; Hengstler, Jan G.; Hamann, Alf; Lengauer, Thomas; Rosenstiel, Philip; Walter, Jörn; Schulz, Marcel H.

    2017-01-01

    The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid costly TF ChIP-seq assays. Thus, it is important to develop computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices. TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq. Additionally, Histone-Marks (HMs) can be used to identify candidate TF binding sites. TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength. Using machine learning, we find low affinity binding sites to improve our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites. Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance. In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq data sets. Finally, these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively. PMID:27899623

  12. Propagation predictions and studies using a ray tracing program combined with a theoretical ionospheric model

    NASA Technical Reports Server (NTRS)

    Lee, M. K.; Nisbet, J. S.

    1975-01-01

    Radio wave propagation predictions are described in which modern comprehensive theoretical ionospheric models are coupled with ray-tracing programs. In the computer code described, a network of electron density and collision frequency parameters along a band about the great circle path is calculated by specifying the transmitter and receiver geographic coordinates, time, the day number, and the 2800-MHz solar flux. The ray paths are calculated on specifying the frequency, mode, range of elevation angles, and range of azimuth angles from the great circle direction. The current program uses a combination of the Penn State MKI E and F region models and the Mitra-Rowe D and E region model. Application of the technique to the prediction of satellite to ground propagation and calculation of oblique incidence propagation paths and absorption are described. The implications of the study to the development of the next generation of ionospheric models are discussed.

  13. Gaussian diffusion sphere model to predict deposition velocities under the combined effects of electrophoresis and thermophoresis

    NASA Astrophysics Data System (ADS)

    Kang, Soojin; Yook, Se-Jin; Lee, Kwan-Soo

    2014-03-01

    The Gaussian diffusion sphere model (GDSM) is proposed to predict the average deposition velocity of particles onto a flat plate exposed to parallel airflow after considering the combined effects of electrophoresis and thermophoresis. This model can account for convection, Brownian diffusion, gravitational settling, thermophoresis, and electrophoresis, and it provides fast calculation times and accurate predictions. Using the GDSM, the effects of the deposition surface size on the deposition velocity are analyzed. When the gravitational effect is dominant for a face-up surface or the attractive electrophoresis effect is dominant, the deposition velocity is estimated to be independent of the deposition surface size. Deposition under the influence of thermophoresis depends on the deposition surface size due to the formation of a thermal boundary layer. Deposition velocities for a 450-mm-long surface are studied under a temperature difference of 40 K and for electric field strengths ranging from 0 to 1000 V/cm.

  14. Sensitivity Analysis of the NPM-ALK Signalling Network Reveals Important Pathways for Anaplastic Large Cell Lymphoma Combination Therapy

    PubMed Central

    Buetti-Dinh, Antoine; O’Hare, Thomas

    2016-01-01

    A large subset of anaplastic large cell lymphoma (ALCL) patients harbour a somatic aberration in which anaplastic lymphoma kinase (ALK) is fused to nucleophosmin (NPM) resulting in a constitutively active signalling fusion protein, NPM-ALK. We computationally simulated the signalling network which mediates pathological cell survival and proliferation through NPM-ALK to identify therapeutically targetable nodes through which it may be possible to regain control of the tumourigenic process. The simulations reveal the predominant role of the VAV1-CDC42 (cell division control protein 42) pathway in NPM-ALK-driven cellular proliferation and of the Ras / mitogen-activated ERK kinase (MEK) / extracellular signal-regulated kinase (ERK) cascade in controlling cell survival. Our results also highlight the importance of a group of interleukins together with the Janus kinase 3 (JAK3) / signal transducer and activator of transcription 3 (STAT3) signalling in the development of NPM-ALK derived ALCL. Depending on the activity of JAK3 and STAT3, the system may also be sensitive to activation of protein tyrosine phosphatase-1 (SHP1), which has an inhibitory effect on cell survival and proliferation. The identification of signalling pathways active in tumourigenic processes is of fundamental importance for effective therapies. The prediction of alternative pathways that circumvent classical therapeutic targets opens the way to preventive approaches for countering the emergence of cancer resistance. PMID:27669408

  15. First-principles prediction of stable SiC cage structures and their synthesis pathways

    NASA Astrophysics Data System (ADS)

    Pochet, Pascal; Genovese, Luigi; Caliste, Damien; Rousseau, Ian; Goedecker, Stefan; Deutsch, Thierry

    2010-07-01

    In this paper we use density functional theory calculations to investigate the structure and the stability of different SiC cagelike clusters. In addition to the fullerene family and the mixed four and six membered ring family, we introduce a family based on reconstructed nanotube slices. We propose an alternative synthesis pathway starting from SiC nanotubes.

  16. Predictors of Abstinence from Heavy Drinking During Treatment in COMBINE and External Validation in PREDICT

    PubMed Central

    Gueorguieva, Ralitza; Wu, Ran; O'Connor, Patrick G; Weisner, Constance; Fucito, Lisa M.; Hoffmann, Sabine

    2015-01-01

    Background The goal of the current study was to use tree-based methods (Zhang and Singer, 2010) to identify predictors of abstinence from heavy drinking in COMBINE (Anton et al., 2006), the largest study of pharmacotherapy for alcoholism in the United States to date, and to validate these results in PREDICT (Mann et al., 2012), a parallel study conducted in Germany. Methods We compared a classification tree constructed according to purely statistical criteria to a tree constructed according to a combination of statistical criteria and clinical considerations for prediction of no heavy drinking during treatment in COMBINE. We considered over one-hundred baseline predictors. The tree approach was compared to logistic regression. The trees and a deterministic forest identified the most important predictors of no heavy drinking for direct testing in PREDICT. Results The tree built using both clinical and statistical considerations consisted of four splits based on consecutive days of abstinence (CDA) prior to randomization, age, family history of alcoholism (FHAlc) and confidence to resist drinking in response to withdrawal and urges. The tree based on statistical considerations with four splits also split on CDA and age but also on GGT level and drinking goal. Deterministic forest identified CDA, age and drinking goal as the most important predictors. Backward elimination logistic regression among the top 18 predictors identified in the deterministic forest analyses identified only age and CDA as significant main effects. Longer CDA and goal of complete abstinence were associated with better outcomes in both data sets. Conclusions The most reliable predictors of abstinence from heavy drinking were CDA and drinking goal. Trees provide binary decision rules and straightforward graphical representations for identification of subgroups based on response and may be easier to implement in clinical settings. PMID:25346505

  17. Construction of a protein-protein interaction network of Wilms' tumor and pathway prediction of molecular complexes.

    PubMed

    Teng, W J; Zhou, C; Liu, L J; Cao, X J; Zhuang, J; Liu, G X; Sun, C G

    2016-05-23

    Wilms' tumor (WT), or nephroblastoma, is the most common malignant renal cancer that affects the pediatric population. Great progress has been achieved in the treatment of WT, but it cannot be cured at present. Nonetheless, a protein-protein interaction network of WT should provide some new ideas and methods. The purpose of this study was to analyze the protein-protein interaction network of WT. We screened the confirmed disease-related genes using the Online Mendelian Inheritance in Man database, created a protein-protein interaction network based on biological function in the Cytoscape software, and detected molecular complexes and relevant pathways that may be included in the network. The results showed that the protein-protein interaction network of WT contains 654 nodes, 1544 edges, and 5 molecular complexes. Among them, complex 1 is predicted to be related to the Jak-STAT signaling pathway, regulation of hematopoiesis by cytokines, cytokine-cytokine receptor interaction, cytokine and inflammatory responses, and hematopoietic cell lineage pathways. Molecular complex 4 shows a correlation of WT with colorectal cancer and the ErbB signaling pathway. The proposed method can provide the bioinformatic foundation for further elucidation of the mechanisms of WT development.

  18. Taking the next step: combining incrementally valid indicators to improve recidivism prediction.

    PubMed

    Walters, Glenn D

    2011-06-01

    The possibility of combining indicators to improve recidivism prediction was evaluated in a sample of released federal prisoners randomly divided into a derivation subsample (n = 550) and a cross-validation subsample (n = 551). Five incrementally valid indicators were selected from five domains: demographic (age), historical (prior convictions), adjustment (prior incident reports), rating scale (Violation scale of the Lifestyle Criminality Screening Form), and self-report (General Criminal Thinking score from the Psychological Inventory of Criminal Thinking Styles). After converting scores on the five indicators to a common scale (z score), two combined scores were calculated: a simple summed score (unweighted summed score) and a score computed using beta weights from a Cox survival analysis of the derivation subsample (weighted summed score). Correlational and receiver operating characteristic analyses revealed that the unweighted and weighted summed scores produced equivalent results and that both improved significantly on the results of the five contributing indicators.

  19. A New Antileishmanial Preparation of Combined Solamargine and Solasonine Heals Cutaneous Leishmaniasis through Different Immunochemical Pathways

    PubMed Central

    McChesney, J. D.; Bastos, J. K.; Miranda, M. A.; Tiossi, R. F.; da Costa, J. de C.; Bentley, M. V.; Gaitan-Puch, S. E.

    2016-01-01

    Little has been done during the past 100 years to develop new antileishmanial drugs. Most infected individuals live in poor countries and have a low cash income to be attractive targets to pharmaceutical corporations. Two heterosidic steroids, solamargine and solasonine, initially identified as major components of the Brazilian plant Solanum lycocarpum, were tested for leishmanicidal activity. Both alkaloids killed intracellular and extracellular Leishmania mexicana parasites more efficiently than the reference drug sodium stibogluconate. A total of 10 μM each individual alkaloid significantly reduced parasite counts in infected macrophages and dendritic cells. In vivo treatment of C57BL/6 mice with a standardized topical preparation containing solamargine (45.1%) and solasonine (44.4%) gave significant reductions in lesion sizes and parasite counts recovered from lesions. Alkaloids present different immunochemical pathways in macrophages and dendritic cells. We conclude that this topical preparation is effective and a potential new and inexpensive treatment for cutaneous leishmaniasis. PMID:26883711

  20. Familial combined hyperlipidemia is associated with alterations in the cholesterol synthesis pathway

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Familial combined hyperlipidemia (FCH) is a common familial lipid disorder characterized by increases in plasma total cholesterol, triglyceride, and apolipoprotein B-100 levels. In light of prior metabolic and genetic research, our purpose was to ascertain whether FCH cases had significant abnormali...

  1. Detection of phytohormones in temperate forest fungi predicts consistent abscisic acid production and a common pathway for cytokinin biosynthesis.

    PubMed

    Morrison, Erin N; Knowles, Sarah; Hayward, Allison; Thorn, R Greg; Saville, Barry J; Emery, R J N

    2015-01-01

    The phytohormones, abscisic acid and cytokinin, once were thought to be present uniquely in plants, but increasing evidence suggests that these hormones are present in a wide variety of organisms. Few studies have examined fungi for the presence of these "plant" hormones or addressed whether their levels differ based on the nutrition mode of the fungus. This study examined 20 temperate forest fungi of differing nutritional modes (ectomycorrhizal, wood-rotting, saprotrophic). Abscisic acid and cytokinin were present in all fungi sampled; this indicated that the sampled fungi have the capacity to synthesize these two classes of phytohormones. Of the 27 cytokinins analyzed by HPLC-ESI MS/MS, seven were present in all fungi sampled. This suggested the existence of a common cytokinin metabolic pathway in fungi that does not vary among different nutritional modes. Predictions regarding the source of isopentenyl, cis-zeatin and methylthiol CK production stemming from the tRNA degradation pathway among fungi are discussed.

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

    PubMed

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

    2015-10-06

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

  3. Endocrine Disruptors: Data-based survey of in vivo tests, predictive models and the Adverse Outcome Pathway.

    PubMed

    Benigni, Romualdo; Battistelli, Chiara Laura; Bossa, Cecilia; Giuliani, Alessandro; Tcheremenskaia, Olga

    2017-02-20

    The protection from endocrine disruptors is a high regulatory priority. Key issues are the characterization of in vivo assays, and the identification of reference chemicals to validate alternative methods. In this exploration, publicly available databases for in vivo assays for endocrine disruption were collected and compared: Rodent Uterotrophic, Rodent Repeated Dose 28-day Oral Toxicity, 21-Day Fish, and Daphnia magna reproduction assays. Only the Uterotrophic and 21-Day Fish assays results correlated with each other. The in vivo assays data were viewed in relation to the Adverse Outcome Pathway, using as a probe 18 ToxCast in vitro assays for the ER pathway. These are the same data at the basis of the EPA agonist ToxERscore model, whose good predictivity was confirmed. The multivariate comparison of the in vitro/in vivo assays suggests that the interaction with receptors is a major determinant of in vivo results, and is the critical basis for building predictive computational models. In agreement with the above, this work also shows that it is possible to build predictive models for the Uterotrophic and 21-Day Fish assays using a limited selection of Toxcast assays.

  4. Predicting Global Fund grant disbursements for procurement of artemisinin-based combination therapies

    PubMed Central

    Cohen, Justin M; Singh, Inder; O'Brien, Megan E

    2008-01-01

    Background An accurate forecast of global demand is essential to stabilize the market for artemisinin-based combination therapy (ACT) and to ensure access to high-quality, life-saving medications at the lowest sustainable prices by avoiding underproduction and excessive overproduction, each of which can have negative consequences for the availability of affordable drugs. A robust forecast requires an understanding of the resources available to support procurement of these relatively expensive antimalarials, in particular from the Global Fund, at present the single largest source of ACT funding. Methods Predictive regression models estimating the timing and rate of disbursements from the Global Fund to recipient countries for each malaria grant were derived using a repeated split-sample procedure intended to avoid over-fitting. Predictions were compared against actual disbursements in a group of validation grants, and forecasts of ACT procurement extrapolated from disbursement predictions were evaluated against actual procurement in two sub-Saharan countries. Results Quarterly forecasts were correlated highly with actual smoothed disbursement rates (r = 0.987, p < 0.0001). Additionally, predicted ACT procurement, extrapolated from forecasted disbursements, was correlated strongly with actual ACT procurement supported by two grants from the Global Fund's first (r = 0.945, p < 0.0001) and fourth (r = 0.938, p < 0.0001) funding rounds. Conclusion This analysis derived predictive regression models that successfully forecasted disbursement patterning for individual Global Fund malaria grants. These results indicate the utility of this approach for demand forecasting of ACT and, potentially, for other commodities procured using funding from the Global Fund. Further validation using data from other countries in different regions and environments will be necessary to confirm its generalizability. PMID:18831742

  5. Combined Computational Approach Based on Density Functional Theory and Artificial Neural Networks for Predicting The Solubility Parameters of Fullerenes.

    PubMed

    Perea, J Darío; Langner, Stefan; Salvador, Michael; Kontos, Janos; Jarvas, Gabor; Winkler, Florian; Machui, Florian; Görling, Andreas; Dallos, Andras; Ameri, Tayebeh; Brabec, Christoph J

    2016-05-19

    The solubility of organic semiconductors in environmentally benign solvents is an important prerequisite for the widespread adoption of organic electronic appliances. Solubility can be determined by considering the cohesive forces in a liquid via Hansen solubility parameters (HSP). We report a numerical approach to determine the HSP of fullerenes using a mathematical tool based on artificial neural networks (ANN). ANN transforms the molecular surface charge density distribution (σ-profile) as determined by density functional theory (DFT) calculations within the framework of a continuum solvation model into solubility parameters. We validate our model with experimentally determined HSP of the fullerenes C60, PC61BM, bisPC61BM, ICMA, ICBA, and PC71BM and through comparison with previously reported molecular dynamics calculations. Most excitingly, the ANN is able to correctly predict the dispersive contributions to the solubility parameters of the fullerenes although no explicit information on the van der Waals forces is present in the σ-profile. The presented theoretical DFT calculation in combination with the ANN mathematical tool can be easily extended to other π-conjugated, electronic material classes and offers a fast and reliable toolbox for future pathways that may include the design of green ink formulations for solution-processed optoelectronic devices.

  6. Three-level prediction of protein function by combining profile-sequence search, profile-profile search, and domain co-occurrence networks.

    PubMed

    Wang, Zheng; Cao, Renzhi; Cheng, Jianlin

    2013-01-01

    Predicting protein function from sequence is useful for biochemical experiment design, mutagenesis analysis, protein engineering, protein design, biological pathway analysis, drug design, disease diagnosis, and genome annotation as a vast number of protein sequences with unknown function are routinely being generated by DNA, RNA and protein sequencing in the genomic era. However, despite significant progresses in the last several years, the accuracy of protein function prediction still needs to be improved in order to be used effectively in practice, particularly when little or no homology exists between a target protein and proteins with annotated function. Here, we developed a method that integrated profile-sequence alignment, profile-profile alignment, and Domain Co-Occurrence Networks (DCN) to predict protein function at different levels of complexity, ranging from obvious homology, to remote homology, to no homology. We tested the method blindingly in the 2011 Critical Assessment of Function Annotation (CAFA). Our experiments demonstrated that our three-level prediction method effectively increased the recall of function prediction while maintaining a reasonable precision. Particularly, our method can predict function terms defined by the Gene Ontology more accurately than three standard baseline methods in most situations, handle multi-domain proteins naturally, and make ab initio function prediction when no homology exists. These results show that our approach can combine complementary strengths of most widely used BLAST-based function prediction methods, rarely used in function prediction but more sensitive profile-profile comparison-based homology detection methods, and non-homology-based domain co-occurrence networks, to effectively extend the power of function prediction from high homology, to low homology, to no homology (ab initio cases).

  7. Three-Level Prediction of Protein Function by Combining Profile-Sequence Search, Profile-Profile Search, and Domain Co-Occurrence Networks

    PubMed Central

    2013-01-01

    Predicting protein function from sequence is useful for biochemical experiment design, mutagenesis analysis, protein engineering, protein design, biological pathway analysis, drug design, disease diagnosis, and genome annotation as a vast number of protein sequences with unknown function are routinely being generated by DNA, RNA and protein sequencing in the genomic era. However, despite significant progresses in the last several years, the accuracy of protein function prediction still needs to be improved in order to be used effectively in practice, particularly when little or no homology exists between a target protein and proteins with annotated function. Here, we developed a method that integrated profile-sequence alignment, profile-profile alignment, and Domain Co-Occurrence Networks (DCN) to predict protein function at different levels of complexity, ranging from obvious homology, to remote homology, to no homology. We tested the method blindingly in the 2011 Critical Assessment of Function Annotation (CAFA). Our experiments demonstrated that our three-level prediction method effectively increased the recall of function prediction while maintaining a reasonable precision. Particularly, our method can predict function terms defined by the Gene Ontology more accurately than three standard baseline methods in most situations, handle multi-domain proteins naturally, and make ab initio function prediction when no homology exists. These results show that our approach can combine complementary strengths of most widely used BLAST-based function prediction methods, rarely used in function prediction but more sensitive profile-profile comparison-based homology detection methods, and non-homology-based domain co-occurrence networks, to effectively extend the power of function prediction from high homology, to low homology, to no homology (ab initio cases). PMID:23514381

  8. Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma

    PubMed Central

    Paul, Rahul; Hawkins, Samuel H.; Balagurunathan, Yoganand; Schabath, Matthew B.; Gillies, Robert J.; Hall, Lawrence O.; Goldgof, Dmitry B.

    2016-01-01

    Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern. Deep feature extraction using pretrained convolutional neural networks (CNNs) has recently been successfully applied in some image domains. Here, we applied a pretrained CNN to extract deep features from 40 computed tomography images, with contrast, of non-small cell adenocarcinoma lung cancer, and combined deep features with traditional image features and trained classifiers to predict short- and long-term survivors. We experimented with several pretrained CNNs and several feature selection strategies. The best previously reported accuracy when using traditional quantitative features was 77.5% (area under the curve [AUC], 0.712), which was achieved by a decision tree classifier. The best reported accuracy from transfer learning and deep features was 77.5% (AUC, 0.713) using a decision tree classifier. When extracted deep neural network features were combined with traditional quantitative features, we obtained an accuracy of 90% (AUC, 0.935) with the 5 best post-rectified linear unit features extracted from a vgg-f pretrained CNN and the 5 best traditional features. The best results were achieved with the symmetric uncertainty feature ranking algorithm followed by a random forests classifier. PMID:28066809

  9. Deep Feature Transfer Learning in Combination with Traditional Features Predicts Survival Among Patients with Lung Adenocarcinoma.

    PubMed

    Paul, Rahul; Hawkins, Samuel H; Balagurunathan, Yoganand; Schabath, Matthew B; Gillies, Robert J; Hall, Lawrence O; Goldgof, Dmitry B

    2016-12-01

    Lung cancer is the most common cause of cancer-related deaths in the USA. It can be detected and diagnosed using computed tomography images. For an automated classifier, identifying predictive features from medical images is a key concern. Deep feature extraction using pretrained convolutional neural networks (CNNs) has recently been successfully applied in some image domains. Here, we applied a pretrained CNN to extract deep features from 40 computed tomography images, with contrast, of non-small cell adenocarcinoma lung cancer, and combined deep features with traditional image features and trained classifiers to predict short- and long-term survivors. We experimented with several pretrained CNNs and several feature selection strategies. The best previously reported accuracy when using traditional quantitative features was 77.5% (area under the curve [AUC], 0.712), which was achieved by a decision tree classifier. The best reported accuracy from transfer learning and deep features was 77.5% (AUC, 0.713) using a decision tree classifier. When extracted deep neural network features were combined with traditional quantitative features, we obtained an accuracy of 90% (AUC, 0.935) with the 5 best post-rectified linear unit features extracted from a vgg-f pretrained CNN and the 5 best traditional features. The best results were achieved with the symmetric uncertainty feature ranking algorithm followed by a random forests classifier.

  10. A Global Genomic and Genetic Strategy to Identify, Validate and Use Gene Signatures of Xenobiotic-Responsive Transcription Factors in Prediction of Pathway Activation in the Mouse Liver

    EPA Science Inventory

    Many drugs and environmentally-relevant chemicals activate xenobiotic-responsive transcription factors. Identification of target genes of these factors would be useful in predicting pathway activation in in vitro chemical screening as well as their involvement in disease states. ...

  11. Exploring the Combination of Dempster-Shafer Theory and Neural Network for Predicting Trust and Distrust.

    PubMed

    Wang, Xin; Wang, Ying; Sun, Hongbin

    2016-01-01

    In social media, trust and distrust among users are important factors in helping users make decisions, dissect information, and receive recommendations. However, the sparsity and imbalance of social relations bring great difficulties and challenges in predicting trust and distrust. Meanwhile, there are numerous inducing factors to determine trust and distrust relations. The relationship among inducing factors may be dependency, independence, and conflicting. Dempster-Shafer theory and neural network are effective and efficient strategies to deal with these difficulties and challenges. In this paper, we study trust and distrust prediction based on the combination of Dempster-Shafer theory and neural network. We firstly analyze the inducing factors about trust and distrust, namely, homophily, status theory, and emotion tendency. Then, we quantify inducing factors of trust and distrust, take these features as evidences, and construct evidence prototype as input nodes of multilayer neural network. Finally, we propose a framework of predicting trust and distrust which uses multilayer neural network to model the implementing process of Dempster-Shafer theory in different hidden layers, aiming to overcome the disadvantage of Dempster-Shafer theory without optimization method. Experimental results on a real-world dataset demonstrate the effectiveness of the proposed framework.

  12. Predicting protein ligand binding sites by combining evolutionary sequence conservation and 3D structure.

    PubMed

    Capra, John A; Laskowski, Roman A; Thornton, Janet M; Singh, Mona; Funkhouser, Thomas A

    2009-12-01

    Identifying a protein's functional sites is an important step towards characterizing its molecular function. Numerous structure- and sequence-based methods have been developed for this problem. Here we introduce ConCavity, a small molecule binding site prediction algorithm that integrates evolutionary sequence conservation estimates with structure-based methods for identifying protein surface cavities. In large-scale testing on a diverse set of single- and multi-chain protein structures, we show that ConCavity substantially outperforms existing methods for identifying both 3D ligand binding pockets and individual ligand binding residues. As part of our testing, we perform one of the first direct comparisons of conservation-based and structure-based methods. We find that the two approaches provide largely complementary information, which can be combined to improve upon either approach alone. We also demonstrate that ConCavity has state-of-the-art performance in predicting catalytic sites and drug binding pockets. Overall, the algorithms and analysis presented here significantly improve our ability to identify ligand binding sites and further advance our understanding of the relationship between evolutionary sequence conservation and structural and functional attributes of proteins. Data, source code, and prediction visualizations are available on the ConCavity web site (http://compbio.cs.princeton.edu/concavity/).

  13. Using networks to combine "big data" and traditional surveillance to improve influenza predictions.

    PubMed

    Davidson, Michael W; Haim, Dotan A; Radin, Jennifer M

    2015-01-29

    Seasonal influenza infects approximately 5-20% of the U.S. population every year, resulting in over 200,000 hospitalizations. The ability to more accurately assess infection levels and predict which regions have higher infection risk in future time periods can instruct targeted prevention and treatment efforts, especially during epidemics. Google Flu Trends (GFT) has generated significant hope that "big data" can be an effective tool for estimating disease burden and spread. The estimates generated by GFT come in real-time--two weeks earlier than traditional surveillance data collected by the U.S. Centers for Disease Control and Prevention (CDC). However, GFT had some infamous errors and is significantly less accurate at tracking laboratory-confirmed cases than syndromic influenza-like illness (ILI) cases. We construct an empirical network using CDC data and combine this with GFT to substantially improve its performance. This improved model predicts infections one week into the future as well as GFT predicts the present and does particularly well in regions that are most likely to facilitate influenza spread and during epidemics.

  14. Combined TMS and FMRI reveal dissociable cortical pathways for dynamic and static face perception.

    PubMed

    Pitcher, David; Duchaine, Bradley; Walsh, Vincent

    2014-09-08

    Faces contain structural information, for identifying individuals, as well as changeable information, which can convey emotion and direct attention. Neuroimaging studies reveal brain regions that exhibit preferential responses to invariant [1, 2] or changeable [3-5] facial aspects but the functional connections between these regions are unknown. We addressed this issue by causally disrupting two face-selective regions with thetaburst transcranial magnetic stimulation (TBS) and measuring the effects of this disruption in local and remote face-selective regions with functional magnetic resonance imaging (fMRI). Participants were scanned, over two sessions, while viewing dynamic or static faces and objects. During these sessions, TBS was delivered over the right occipital face area (rOFA) or right posterior superior temporal sulcus (rpSTS). Disruption of the rOFA reduced the neural response to both static and dynamic faces in the downstream face-selective region in the fusiform gyrus. In contrast, the response to dynamic and static faces was doubly dissociated in the rpSTS. Namely, disruption of the rOFA reduced the response to static but not dynamic faces, while disruption of the rpSTS itself reduced the response to dynamic but not static faces. These results suggest that dynamic and static facial aspects are processed via dissociable cortical pathways that begin in early visual cortex, a conclusion inconsistent with current models of face perception [6-9].

  15. Predictive monitoring of mobile patients by combining clinical observations with data from wearable sensors.

    PubMed

    Clifton, Lei; Clifton, David A; Pimentel, Marco A F; Watkinson, Peter J; Tarassenko, Lionel

    2014-05-01

    The majority of patients in the hospital are ambulatory and would benefit significantly from predictive and personalized monitoring systems. Such patients are well suited to having their physiological condition monitored using low-power, minimally intrusive wearable sensors. Despite data-collection systems now being manufactured commercially, allowing physiological data to be acquired from mobile patients, little work has been undertaken on the use of the resultant data in a principled manner for robust patient care, including predictive monitoring. Most current devices generate so many false-positive alerts that devices cannot be used for routine clinical practice. This paper explores principled machine learning approaches to interpreting large quantities of continuously acquired, multivariate physiological data, using wearable patient monitors, where the goal is to provide early warning of serious physiological determination, such that a degree of predictive care may be provided. We adopt a one-class support vector machine formulation, proposing a formulation for determining the free parameters of the model using partial area under the ROC curve, a method arising from the unique requirements of performing online analysis with data from patient-worn sensors. There are few clinical evaluations of machine learning techniques in the literature, so we present results from a study at the Oxford University Hospitals NHS Trust devised to investigate the large-scale clinical use of patient-worn sensors for predictive monitoring in a ward with a high incidence of patient mortality. We show that our system can combine routine manual observations made by clinical staff with the continuous data acquired from wearable sensors. Practical considerations and recommendations based on our experiences of this clinical study are discussed, in the context of a framework for personalized monitoring.

  16. Applying a Novel Combination of Techniques to Develop a Predictive Model for Diabetes Complications

    PubMed Central

    Sangi, Mohsen; Win, Khin Than; Shirvani, Farid; Namazi-Rad, Mohammad-Reza; Shukla, Nagesh

    2015-01-01

    Among the many related issues of diabetes management, its complications constitute the main part of the heavy burden of this disease. The aim of this paper is to develop a risk advisor model to predict the chances of diabetes complications according to the changes in risk factors. As the starting point, an inclusive list of (k) diabetes complications and (n) their correlated predisposing factors are derived from the existing endocrinology text books. A type of data meta-analysis has been done to extract and combine the numeric value of the relationships between these two. The whole n (risk factors) - k (complications) model was broken down into k different (n-1) relationships and these (n-1) dependencies were broken into n (1-1) models. Applying regression analysis (seven patterns) and artificial neural networks (ANN), we created models to show the (1-1) correspondence between factors and complications. Then all 1-1 models related to an individual complication were integrated using the naïve Bayes theorem. Finally, a Bayesian belief network was developed to show the influence of all risk factors and complications on each other. We assessed the predictive power of the 1-1 models by R2, F-ratio and adjusted R2 equations; sensitivity, specificity and positive predictive value were calculated to evaluate the final model using real patient data. The results suggest that the best fitted regression models outperform the predictive ability of an ANN model, as well as six other regression patterns for all 1-1 models. PMID:25902317

  17. EEG Error Prediction as a Solution for Combining the Advantages of Retrieval Practice and Errorless Learning

    PubMed Central

    Riley, Ellyn A.; McFarland, Dennis J.

    2017-01-01

    Given the frequency of naming errors in aphasia, a common aim of speech and language rehabilitation is the improvement of naming. Based on evidence of significant word recall improvements in patients with memory impairments, errorless learning methods have been successfully applied to naming therapy in aphasia; however, other evidence suggests that although errorless learning can lead to better performance during treatment sessions, retrieval practice may be the key to lasting improvements. Task performance may vary with brain state (e.g., level of arousal, degree of task focus), and changes in brain state can be detected using EEG. With the ultimate goal of designing a system that monitors patient brain state in real time during therapy, we sought to determine whether errors could be predicted using spectral features obtained from an analysis of EEG. Thus, this study aimed to investigate the use of individual EEG responses to predict error production in aphasia. Eight participants with aphasia each completed 900 object-naming trials across three sessions while EEG was recorded and response accuracy scored for each trial. Analysis of the EEG response for seven of the eight participants showed significant correlations between EEG features and response accuracy (correct vs. incorrect) and error correction (correct, self-corrected, incorrect). Furthermore, upon combining the training data for the first two sessions, the model generalized to predict accuracy for performance in the third session for seven participants when accuracy was used as a predictor, and for five participants when error correction category was used as a predictor. With such ability to predict errors during therapy, it may be possible to use this information to intervene with errorless learning strategies only when necessary, thereby allowing patients to benefit from both the high within-session success of errorless learning as well as the longer-term improvements associated with retrieval practice.

  18. Molecular regulation of angiogenesis and tumorigenesis by signal transduction pathways: evidence of predictable and reproducible patterns of synergy in diverse neoplasms.

    PubMed

    Arbiser, Jack L

    2004-04-01

    A large number of oncogenes, tumor suppressor genes, and signal transduction pathways have been described. Currently, a framework that allows prediction of tumor behavior based upon oncogenes, tumor suppressors, and signal transduction pathways is lacking. In 1869, Mendeleev published a periodic table of elements which allowed prediction of properties of elements based upon atomic weights that allowed prediction of chemical and physical properties of elements yet to be discovered. In this paper, I will discuss recurrent patterns of synergy found in the literature and our laboratory between tumor suppressor genes, oncogenes, and signaling pathways that allows one to predict the signaling pathway in a given tumor based upon the inactivation of a tumor suppressor gene. These patterns can be found in multiple different human neoplasms. Conversely, one can predict the inactivation of a tumor suppressor based upon the activation status of a signaling pathway. This knowledge can be used by a clinician or pathologist with access to immunohistochemistry to make predictions based upon simple technologies and determine the signaling pathways involved in a patient's tumor. These strategies may be useful in the design of prevention and treatment strategies for cancer.

  19. Combining multiple regression and principal component analysis for accurate predictions for column ozone in Peninsular Malaysia

    NASA Astrophysics Data System (ADS)

    Rajab, Jasim M.; MatJafri, M. Z.; Lim, H. S.

    2013-06-01

    This study encompasses columnar ozone modelling in the peninsular Malaysia. Data of eight atmospheric parameters [air surface temperature (AST), carbon monoxide (CO), methane (CH4), water vapour (H2Ovapour), skin surface temperature (SSKT), atmosphere temperature (AT), relative humidity (RH), and mean surface pressure (MSP)] data set, retrieved from NASA's Atmospheric Infrared Sounder (AIRS), for the entire period (2003-2008) was employed to develop models to predict the value of columnar ozone (O3) in study area. The combined method, which is based on using both multiple regressions combined with principal component analysis (PCA) modelling, was used to predict columnar ozone. This combined approach was utilized to improve the prediction accuracy of columnar ozone. Separate analysis was carried out for north east monsoon (NEM) and south west monsoon (SWM) seasons. The O3 was negatively correlated with CH4, H2Ovapour, RH, and MSP, whereas it was positively correlated with CO, AST, SSKT, and AT during both the NEM and SWM season periods. Multiple regression analysis was used to fit the columnar ozone data using the atmospheric parameter's variables as predictors. A variable selection method based on high loading of varimax rotated principal components was used to acquire subsets of the predictor variables to be comprised in the linear regression model of the atmospheric parameter's variables. It was found that the increase in columnar O3 value is associated with an increase in the values of AST, SSKT, AT, and CO and with a drop in the levels of CH4, H2Ovapour, RH, and MSP. The result of fitting the best models for the columnar O3 value using eight of the independent variables gave about the same values of the R (≈0.93) and R2 (≈0.86) for both the NEM and SWM seasons. The common variables that appeared in both regression equations were SSKT, CH4 and RH, and the principal precursor of the columnar O3 value in both the NEM and SWM seasons was SSKT.

  20. Combined biomarker testing for the prediction of left ventricular remodelling in ST-elevation myocardial infarction

    PubMed Central

    Reinstadler, Sebastian Johannes; Feistritzer, Hans-Josef; Reindl, Martin; Klug, Gert; Mayr, Agnes; Mair, Johannes; Jaschke, Werner; Metzler, Bernhard

    2016-01-01

    Objective The utility of different biomarkers for the prediction of left ventricular remodelling (LVR) following ST-elevation myocardial infarction (STEMI) has been evaluated in several studies. However, very few data exist on the prognostic value of combined biomarkers. The aim of this study was to comprehensively investigate the prognostic value for LVR of routinely available biomarkers measured after reperfused STEMI. Methods Serial measurements of N-terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity cardiac troponin T (hs-cTnT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH) and high-sensitivity C reactive protein (hs-CRP) were performed in 123 patients with STEMI treated with primary percutaneous coronary intervention in this prospective observational study. Patients underwent cardiac MRI at 2 (1–4) and 125 (121–146) days after infarction. An increase in end-diastolic volume of ≥20% was defined as LVR. Results LVR occurred in 16 (13%) patients. Peak concentrations of the following biomarkers showed significant areas under the curves (AUCs) for the prediction of LVR—NT-proBNP: 0.68 (95% CI 0.59 to 0.76, p=0.03), hs-cTnT: 0.75 (95% CI 0.66 to 0.82, p<0.01), AST: 0.72 (95% CI 0.63 to 0.79, p<0.01), ALT: 0.66 (95% CI 0.57 to 0.75, p=0.03), LDH: 0.78 (95% CI 0.70 to 0.85, p<0.01) and hs-CRP: 0.63 (95% CI 0.54 to 0.72, p=0.05). The combination of all biomarkers yielded a significant increase in AUC to 0.85 (95% CI 0.77 to 0.91) (all vs NT-proBNP: p=0.02, all vs hs-cTnT: p=0.02, all vs AST: p<0.01, all vs ALT: p<0.01, all vs hs-CRP: p<0.01 and all vs LDH: p=0.04). Conclusions In patients with reperfused STEMI, the combined assessment of peak NT-proBNP, hs-cTnT, AST, ALT, hs-CRP and LDH provide incremental prognostic information for the prediction of LVR when compared with single-biomarker measurement. PMID:27738517

  1. Simplified Protein Models: Predicting Folding Pathways and Structure Using Amino Acid Sequences

    NASA Astrophysics Data System (ADS)

    Adhikari, Aashish N.; Freed, Karl F.; Sosnick, Tobin R.

    2013-07-01

    We demonstrate the ability of simultaneously determining a protein’s folding pathway and structure using a properly formulated model without prior knowledge of the native structure. Our model employs a natural coordinate system for describing proteins and a search strategy inspired by the observation that real proteins fold in a sequential fashion by incrementally stabilizing nativelike substructures or “foldons.” Comparable folding pathways and structures are obtained for the twelve proteins recently studied using atomistic molecular dynamics simulations [K. Lindorff-Larsen, S. Piana, R. O. Dror, D. E. Shaw, Science 334, 517 (2011)], with our calculations running several orders of magnitude faster. We find that nativelike propensities in the unfolded state do not necessarily determine the order of structure formation, a departure from a major conclusion of the molecular dynamics study. Instead, our results support a more expansive view wherein intrinsic local structural propensities may be enhanced or overridden in the folding process by environmental context. The success of our search strategy validates it as an expedient mechanism for folding both in silico and in vivo.

  2. Stromal genes discriminate preinvasive from invasive disease, predict outcome, and highlight inflammatory pathways in digestive cancers

    PubMed Central

    Saadi, Amel; Shannon, Nicholas B.; Lao-Sirieix, Pierre; O’Donovan, Maria; Walker, Elaine; Clemons, Nicholas J.; Hardwick, James S.; Zhang, Chunsheng; Das, Madhumita; Save, Vicki; Novelli, Marco; Balkwill, Frances; Fitzgerald, Rebecca C.

    2010-01-01

    The stromal compartment is increasingly recognized to play a role in cancer. However, its role in the transition from preinvasive to invasive disease is unknown. Most gastrointestinal tumors have clearly defined premalignant stages, and Barrett’s esophagus (BE) is an ideal research model. Supervised clustering of gene expression profiles from microdissected stroma identified a gene signature that could distinguish between BE metaplasia, dysplasia, and esophageal adenocarcinoma (EAC). EAC patients overexpressing any of the five genes (TMEPAI, JMY, TSP1, FAPα, and BCL6) identified from this stromal signature had a significantly poorer outcome. Gene ontology analysis identified a strong inflammatory component in BE disease progression, and key pathways included cytokine–cytokine receptor interactions and TGF-β. Increased protein levels of inflammatory-related genes significantly up-regulated in EAC compared with preinvasive stages were confirmed in the stroma of independent samples, and in vitro assays confirmed functional relevance of these genes. Gene set enrichment analysis of external datasets demonstrated that the stromal signature was also relevant in the preinvasive to invasive transition of the stomach, colon, and pancreas. These data implicate inflammatory pathways in the genesis of gastrointestinal tract cancers, which can affect prognosis. PMID:20080664

  3. Pathways to School Readiness: Executive Functioning Predicts Academic and Social-Emotional Aspects of School Readiness

    ERIC Educational Resources Information Center

    Mann, Trisha D.; Hund, Alycia M.; Hesson-McInnis, Matthew S.; Roman, Zachary J.

    2017-01-01

    The current study specified the extent to which hot and cool aspects of executive functioning predicted academic and social-emotional indicators of school readiness. It was unique in focusing on positive aspects of social-emotional readiness, rather than problem behaviors. One hundred four 3-5-year-old children completed tasks measuring executive…

  4. A novel pathway for the biosynthesis of heme in Archaea: genome-based bioinformatic predictions and experimental evidence.

    PubMed

    Storbeck, Sonja; Rolfes, Sarah; Raux-Deery, Evelyne; Warren, Martin J; Jahn, Dieter; Layer, Gunhild

    2010-12-13

    Heme is an essential prosthetic group for many proteins involved in fundamental biological processes in all three domains of life. In Eukaryota and Bacteria heme is formed via a conserved and well-studied biosynthetic pathway. Surprisingly, in Archaea heme biosynthesis proceeds via an alternative route which is poorly understood. In order to formulate a working hypothesis for this novel pathway, we searched 59 completely sequenced archaeal genomes for the presence of gene clusters consisting of established heme biosynthetic genes and colocalized conserved candidate genes. Within the majority of archaeal genomes it was possible to identify such heme biosynthesis gene clusters. From this analysis we have been able to identify several novel heme biosynthesis genes that are restricted to archaea. Intriguingly, several of the encoded proteins display similarity to enzymes involved in heme d(1) biosynthesis. To initiate an experimental verification of our proposals two Methanosarcina barkeri proteins predicted to catalyze the initial steps of archaeal heme biosynthesis were recombinantly produced, purified, and their predicted enzymatic functions verified.

  5. How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology.

    PubMed

    Wittwehr, Clemens; Aladjov, Hristo; Ankley, Gerald; Byrne, Hugh J; de Knecht, Joop; Heinzle, Elmar; Klambauer, Günter; Landesmann, Brigitte; Luijten, Mirjam; MacKay, Cameron; Maxwell, Gavin; Meek, M E Bette; Paini, Alicia; Perkins, Edward; Sobanski, Tomasz; Villeneuve, Dan; Waters, Katrina M; Whelan, Maurice

    2017-02-01

    Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24-25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.

  6. How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

    PubMed Central

    Aladjov, Hristo; Ankley, Gerald; Byrne, Hugh J.; de Knecht, Joop; Heinzle, Elmar; Klambauer, Günter; Landesmann, Brigitte; Luijten, Mirjam; MacKay, Cameron; Maxwell, Gavin; Meek, M. E. (Bette); Paini, Alicia; Perkins, Edward; Sobanski, Tomasz; Villeneuve, Dan; Waters, Katrina M.; Whelan, Maurice

    2017-01-01

    Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework provides a systematic approach for organizing knowledge that may support such inference. Likewise, computational models of biological systems at various scales provide another means and platform to integrate current biological understanding to facilitate inference and extrapolation. We argue that the systematic organization of knowledge into AOP frameworks can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. This concept was explored as part of a workshop on AOP-Informed Predictive Modeling Approaches for Regulatory Toxicology held September 24–25, 2015. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development is described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment. PMID:27994170

  7. Predicting malignant transformation of esophageal squamous cell lesions by combined biomarkers in an endoscopic screening program

    PubMed Central

    Zhang, Hao; Li, Hao; Ma, Qing; Yang, Fang-Yan; Diao, Tao-Yu

    2016-01-01

    AIM To determine the association of p53, carcinoembryonic antigen (CEA) and CA19-9 protein expression with esophageal carcinogenesis. METHODS An iodine staining endoscopic screening program of esophageal lesions was carried out in the high-incidence area of Feicheng County, China. Seventy-seven patients with basal cell hyperplasia (BCH), 247 with low-grade dysplasia (LGD), 51 with high-grade dysplasia (HGD), 134 with invasive cancer, and 80 normal controls diagnosed by mucous membrane biopsy pathology were enrolled. Immunohistochemical detection of p53, CEA and CA19-9 proteins was performed. In the ROC curve analysis, the expression of a single biomarker and the expression of a combination of biomarkers were used to predict the risk of these four esophageal lesions. RESULTS The positive rates of p53 protein expression in invasive cancer, HGD, LGD, BCH and the normal control groups were 53.0%, 52.9%, 35.6%, 27.3% and 20.0%, respectively; the positive rates of CA19-9 protein expression were 44.0%, 33.3%, 16.5%, 9.2% and 6.2%, respectively; the positive rates of CEA protein expression were 74.6%, 60.8%, 23.3%, 23.7% and 16.2%, respectively. The positive rates of the combined expression of the three biomarkers were 84.3%, 76.5%, 47.6%, 42.9% and 27.5%, respectively. In the receiver operating characteristic curves of the combination of the three biomarkers, the specificity was 88.8% for the normal controls, and the sensitivity was 58.2% for invasive cancer, 25.5% for HGD, 11.2% for LGD, and 6.5% for BCH. CONCLUSION p53, CEA and CA19-9 protein expression was correlated with esophageal carcinogenesis, and testing for the combination of these biomarkers is useful for identifying high-risk patients with precancerous lesions. PMID:27818592

  8. Understanding and predicting suicidality using a combined genomic and clinical risk assessment approach

    PubMed Central

    Niculescu, A B; Levey, D F; Phalen, P L; Le-Niculescu, H; Dainton, H D; Jain, N; Belanger, E; James, A; George, S; Weber, H; Graham, D L; Schweitzer, R; Ladd, T B; Learman, R; Niculescu, E M; Vanipenta, N P; Khan, F N; Mullen, J; Shankar, G; Cook, S; Humbert, C; Ballew, A; Yard, M; Gelbart, T; Shekhar, A; Schork, N J; Kurian, S M; Sandusky, G E; Salomon, D R

    2015-01-01

    Worldwide, one person dies every 40 seconds by suicide, a potentially preventable tragedy. A limiting step in our ability to intervene is the lack of objective, reliable predictors. We have previously provided proof of principle for the use of blood gene expression biomarkers to predict future hospitalizations due to suicidality, in male bipolar disorder participants. We now generalize the discovery, prioritization, validation, and testing of such markers across major psychiatric disorders (bipolar disorder, major depressive disorder, schizoaffective disorder, and schizophrenia) in male participants, to understand commonalities and differences. We used a powerful within-participant discovery approach to identify genes that change in expression between no suicidal ideation and high suicidal ideation states (n=37 participants out of a cohort of 217 psychiatric participants followed longitudinally). We then used a convergent functional genomics (CFG) approach with existing prior evidence in the field to prioritize the candidate biomarkers identified in the discovery step. Next, we validated the top biomarkers from the prioritization step for relevance to suicidal behavior, in a demographically matched cohort of suicide completers from the coroner's office (n=26). The biomarkers for suicidal ideation only are enriched for genes involved in neuronal connectivity and schizophrenia, the biomarkers also validated for suicidal behavior are enriched for genes involved in neuronal activity and mood. The 76 biomarkers that survived Bonferroni correction after validation for suicidal behavior map to biological pathways involved in immune and inflammatory response, mTOR signaling and growth factor regulation. mTOR signaling is necessary for the effects of the rapid-acting antidepressant agent ketamine, providing a novel biological rationale for its possible use in treating acute suicidality. Similarly, MAOB, a target of antidepressant inhibitors, was one of the increased

  9. Combined crystal structure prediction and high-pressure crystallization in rational pharmaceutical polymorph screening

    NASA Astrophysics Data System (ADS)

    Neumann, M. A.; van de Streek, J.; Fabbiani, F. P. A.; Hidber, P.; Grassmann, O.

    2015-07-01

    Organic molecules, such as pharmaceuticals, agro-chemicals and pigments, frequently form several crystal polymorphs with different physicochemical properties. Finding polymorphs has long been a purely experimental game of trial-and-error. Here we utilize in silico polymorph screening in combination with rationally planned crystallization experiments to study the polymorphism of the pharmaceutical compound Dalcetrapib, with 10 torsional degrees of freedom one of the most flexible molecules ever studied computationally. The experimental crystal polymorphs are found at the bottom of the calculated lattice energy landscape, and two predicted structures are identified as candidates for a missing, thermodynamically more stable polymorph. Pressure-dependent stability calculations suggested high pressure as a means to bring these polymorphs into existence. Subsequently, one of them could indeed be crystallized in the 0.02 to 0.50 GPa pressure range and was found to be metastable at ambient pressure, effectively derisking the appearance of a more stable polymorph during late-stage development of Dalcetrapib.

  10. Model predictive control system and method for integrated gasification combined cycle power generation

    DOEpatents

    Kumar, Aditya; Shi, Ruijie; Kumar, Rajeeva; Dokucu, Mustafa

    2013-04-09

    Control system and method for controlling an integrated gasification combined cycle (IGCC) plant are provided. The system may include a controller coupled to a dynamic model of the plant to process a prediction of plant performance and determine a control strategy for the IGCC plant over a time horizon subject to plant constraints. The control strategy may include control functionality to meet a tracking objective and control functionality to meet an optimization objective. The control strategy may be configured to prioritize the tracking objective over the optimization objective based on a coordinate transformation, such as an orthogonal or quasi-orthogonal projection. A plurality of plant control knobs may be set in accordance with the control strategy to generate a sequence of coordinated multivariable control inputs to meet the tracking objective and the optimization objective subject to the prioritization resulting from the coordinate transformation.

  11. Genomic Prediction of Northern Corn Leaf Blight Resistance in Maize with Combined or Separated Training Sets for Heterotic Groups

    PubMed Central

    Technow, Frank; Bürger, Anna; Melchinger, Albrecht E.

    2013-01-01

    Northern corn leaf blight (NCLB), a severe fungal disease causing yield losses worldwide, is most effectively controlled by resistant varieties. Genomic prediction could greatly aid resistance breeding efforts. However, the development of accurate prediction models requires large training sets of genotyped and phenotyped individuals. Maize hybrid breeding is based on distinct heterotic groups that maximize heterosis (the dent and flint groups in Central Europe). The resulting allocation of resources to parallel breeding programs challenges the establishment of sufficiently sized training sets within groups. Therefore, using training sets combining both heterotic groups might be a possibility of increasing training set sizes and thereby prediction accuracies. The objectives of our study were to assess the prospect of genomic prediction of NCLB resistance in maize and the benefit of a training set that combines two heterotic groups. Our data comprised 100 dent and 97 flint lines, phenotyped for NCLB resistance per se and genotyped with high-density single-nucleotide polymorphism marker data. A genomic BLUP model was used to predict genotypic values. Prediction accuracies reached a maximum of 0.706 (dent) and 0.690 (flint), and there was a strong positive response to increases in training set size. The use of combined training sets led to significantly greater prediction accuracies for both heterotic groups. Our results encourage the application of genomic prediction in NCLB-resistance breeding programs and the use of combined training sets. PMID:23390596

  12. Multiple step-variable pathway hypothesis: a reason why predictions fail in atherosclerosis.

    PubMed

    Ferns, Gordon A A

    2008-12-01

    Cardiovascular risk factors are individually only modest predictors of events, and whilst more sophisticated algorithms appear to improve their prediction, a significant proportion of the population is miscategorised and therefore managed inappropriately. It is proposed that atherogenesis is a multi-step process, and that the critical transitions between steps requires 'bundles' of risk factors that may differ for each step. These bundles may not always contain a classical risk factor and may differ between individuals. This hypothesis would predict that the impact of specific risk factors is non-uniform during atherogenesis and therefore the efficacy of interventions will vary with stage. New therapeutic opportunities exist if the factors that promote progression between particular stages could be identified and targeted. The staging of disease using modalities such as imaging and functional assessment may be necessary to deliver the most effective treatment. Finally, risk assessment will invariably be inaccurate, even using complex algorithms.

  13. GIS-based prediction of stream chemistry using landscape composition, wet areas, and hydrological flow pathways

    NASA Astrophysics Data System (ADS)

    Tiwari, Tejshree; Lidman, Fredrik; Laudon, Hjalmar; Lidberg, William; Ågren, Anneli M.

    2017-01-01

    Landscape morphology exerts strong, scale-dependent controls on stream hydrology and biogeochemistry in heterogeneous catchments. We applied three descriptors of landscape structure at different spatial scales based on new geographic information system tools to predict variability in stream concentrations for a wide range of solutes (Al, Ba, Be, Ca, Fe, K, Mg, Na, S, Si, Sr, Sc, Co, Cr, Ni, Cu, As, Se, Rb, Y, Cd, Sb, Cs, La, Pb, Th, U, DOC, and Cl) using a linear regression analysis. Results showed that less reactive elements, which can be expected to behave more conservatively in the landscape (e.g., Na, K, Ca, Mg, Cl, and Si), generally were best predicted from the broader-scale description of landscape composition (areal coverage of peat, tills, and sorted sediments). These results highlight the importance of mineral weathering as a source of some elements, which was best captured by landscape-scale descriptors of catchment structure. By contrast, more nonconservative elements (e.g., DOC, Al, Cd, Cs, Co, Th, Y, and U), were best predicted by defining wet areas and/or flow path lengths of different patches in the landscape. This change in the predictive models reflect the importance of peat deposits, such as organic-rich riparian zones and mire ecosystems, which are favorable environments for biogeochemical reactions of more nonconservative elements. As such, using this understanding of landscape influences on stream chemistry can provide improved mitigation strategies and management plans that specifically target source areas, so as to minimize mobilization of undesired elements into streams.

  14. Nonlinear model predictive control using parameter varying BP-ARX combination model

    NASA Astrophysics Data System (ADS)

    Yang, J.-F.; Xiao, L.-F.; Qian, J.-X.; Li, H.

    2012-03-01

    A novel back-propagation AutoRegressive with eXternal input (BP-ARX) combination model is constructed for model predictive control (MPC) of MIMO nonlinear systems, whose steady-state relation between inputs and outputs can be obtained. The BP neural network represents the steady-state relation, and the ARX model represents the linear dynamic relation between inputs and outputs of the nonlinear systems. The BP-ARX model is a global model and is identified offline, while the parameters of the ARX model are rescaled online according to BP neural network and operating data. Sequential quadratic programming is employed to solve the quadratic objective function online, and a shift coefficient is defined to constrain the effect time of the recursive least-squares algorithm. Thus, a parameter varying nonlinear MPC (PVNMPC) algorithm that responds quickly to large changes in system set-points and shows good dynamic performance when system outputs approach set-points is proposed. Simulation results in a multivariable stirred tank and a multivariable pH neutralisation process illustrate the applicability of the proposed method and comparisons of the control effect between PVNMPC and multivariable recursive generalised predictive controller are also performed.

  15. Combining a weed traits database with a population dynamics model predicts shifts in weed communities

    PubMed Central

    Storkey, J; Holst, N; Bøjer, O Q; Bigongiali, F; Bocci, G; Colbach, N; Dorner, Z; Riemens, M M; Sartorato, I; Sønderskov, M; Verschwele, A

    2015-01-01

    A functional approach to predicting shifts in weed floras in response to management or environmental change requires the combination of data on weed traits with analytical frameworks that capture the filtering effect of selection pressures on traits. A weed traits database (WTDB) was designed, populated and analysed, initially using data for 19 common European weeds, to begin to consolidate trait data in a single repository. The initial choice of traits was driven by the requirements of empirical models of weed population dynamics to identify correlations between traits and model parameters. These relationships were used to build a generic model, operating at the level of functional traits, to simulate the impact of increasing herbicide and fertiliser use on virtual weeds along gradients of seed weight and maximum height. The model generated ‘fitness contours’ (defined as population growth rates) within this trait space in different scenarios, onto which two sets of weed species, defined as common or declining in the UK, were mapped. The effect of increasing inputs on the weed flora was successfully simulated; 77% of common species were predicted to have stable or increasing populations under high fertiliser and herbicide use, in contrast with only 29% of the species that have declined. Future development of the WTDB will aim to increase the number of species covered, incorporate a wider range of traits and analyse intraspecific variability under contrasting management and environments. PMID:26190870

  16. Combining a weed traits database with a population dynamics model predicts shifts in weed communities.

    PubMed

    Storkey, J; Holst, N; Bøjer, O Q; Bigongiali, F; Bocci, G; Colbach, N; Dorner, Z; Riemens, M M; Sartorato, I; Sønderskov, M; Verschwele, A

    2015-04-01

    A functional approach to predicting shifts in weed floras in response to management or environmental change requires the combination of data on weed traits with analytical frameworks that capture the filtering effect of selection pressures on traits. A weed traits database (WTDB) was designed, populated and analysed, initially using data for 19 common European weeds, to begin to consolidate trait data in a single repository. The initial choice of traits was driven by the requirements of empirical models of weed population dynamics to identify correlations between traits and model parameters. These relationships were used to build a generic model, operating at the level of functional traits, to simulate the impact of increasing herbicide and fertiliser use on virtual weeds along gradients of seed weight and maximum height. The model generated 'fitness contours' (defined as population growth rates) within this trait space in different scenarios, onto which two sets of weed species, defined as common or declining in the UK, were mapped. The effect of increasing inputs on the weed flora was successfully simulated; 77% of common species were predicted to have stable or increasing populations under high fertiliser and herbicide use, in contrast with only 29% of the species that have declined. Future development of the WTDB will aim to increase the number of species covered, incorporate a wider range of traits and analyse intraspecific variability under contrasting management and environments.

  17. Assessing and predicting protein interactions by combining manifold embedding with multiple information integration

    PubMed Central

    2012-01-01

    Background Protein-protein interactions (PPIs) play crucial roles in virtually every aspect of cellular function within an organism. Over the last decade, the development of novel high-throughput techniques has resulted in enormous amounts of data and provided valuable resources for studying protein interactions. However, these high-throughput protein interaction data are often associated with high false positive and false negative rates. It is therefore highly desirable to develop scalable methods to identify these errors from the computational perspective. Results We have developed a robust computational technique for assessing the reliability of interactions and predicting new interactions by combining manifold embedding with multiple information integration. Validation of the proposed method was performed with extensive experiments on densely-connected and sparse PPI networks of yeast respectively. Results demonstrate that the interactions ranked top by our method have high functional homogeneity and localization coherence. Conclusions Our proposed method achieves better performances than the existing methods no matter assessing or predicting protein interactions. Furthermore, our method is general enough to work over a variety of PPI networks irrespectively of densely-connected or sparse PPI network. Therefore, the proposed algorithm is a much more promising method to detect both false positive and false negative interactions in PPI networks. PMID:22595000

  18. Lenvatinib in combination with golvatinib overcomes hepatocyte growth factor pathway-induced resistance to vascular endothelial growth factor receptor inhibitor.

    PubMed

    Nakagawa, Takayuki; Matsushima, Tomohiro; Kawano, Satoshi; Nakazawa, Youya; Kato, Yu; Adachi, Yusuke; Abe, Takanori; Semba, Taro; Yokoi, Akira; Matsui, Junji; Tsuruoka, Akihiko; Funahashi, Yasuhiro

    2014-06-01

    Vascular endothelial growth factor receptor (VEGFR) inhibitors are approved for the treatment of several tumor types; however, some tumors show intrinsic resistance to VEGFR inhibitors, and some patients develop acquired resistance to these inhibitors. Therefore, a strategy to overcome VEGFR inhibitor resistance is urgently required. Recent reports suggest that activation of the hepatocyte growth factor (HGF) pathway through its cognate receptor, Met, contributes to VEGFR inhibitor resistance. Here, we explored the effect of the HGF/Met signaling pathway and its inhibitors on resistance to lenvatinib, a VEGFR inhibitor. In in vitro experiments, addition of VEGF plus HGF enhanced cell growth and tube formation of HUVECs when compared with stimulation by either factor alone. Lenvatinib potently inhibited the growth of HUVECs induced by VEGF alone, but cells induced by VEGF plus HGF showed lenvatinib resistance. This HGF-induced resistance was cancelled when the Met inhibitor, golvatinib, was added with lenvatinib. Conditioned medium from tumor cells producing high amounts of HGF also conferred resistance to inhibition by lenvatinib. In s.c. xenograft models based on various tumor cell lines with high HGF expression, treatment with lenvatinib alone showed weak antitumor effects, but treatment with lenvatinib plus golvatinib showed synergistic antitumor effects, accompanied by decreased tumor vessel density. These results suggest that HGF from tumor cells confers resistance to tumor endothelial cells against VEGFR inhibitors, and that combination therapy using VEGFR inhibitors with Met inhibitors may be effective for overcoming resistance to VEGFR inhibitors. Further evaluation in clinical trials is warranted.

  19. Combined Experimental and Computational Approach to Predict the Glass-Water Reaction

    SciTech Connect

    Pierce, Eric M.; Bacon, Diana H.

    2011-10-01

    The use of mineral and glass dissolution rates measured in laboratory experiments to predict the weathering of primary minerals and volcanic and nuclear waste glasses in field studies requires the construction of rate models that accurately describe the weathering process over geologic timescales. Additionally, the need to model the long-term behavior of nuclear waste glass for the purpose of estimating radionuclide release rates requires that rate models be validated with long-term experiments. Several long-term test methods have been developed to accelerate the glass-water reaction [drip test, vapor hydration test, product consistency test B, and pressurized unsaturated flow (PUF)], thereby reducing the duration required to evaluate long-term performance. Currently, the PUF test is the only method that mimics the unsaturated hydraulic properties expected in a subsurface disposal facility and simultaneously monitors the glass-water reaction. PUF tests are being conducted to accelerate the weathering of glass and validate the model parameters being used to predict long-term glass behavior. A one-dimensional reactive chemical transport simulation of glass dissolution and secondary phase formation during a 1.5-year-long PUF experiment was conducted with the Subsurface Transport Over Reactive Multiphases (STORM) code. Results show that parameterization of the computer model by combining direct bench scale laboratory measurements and thermodynamic data provides an integrated approach to predicting glass behavior over the length of the experiment. Over the 1.5-year-long test duration, the rate decreased from 0.2 to 0.01 g/(m2 day) based on B release for low-activity waste glass LAWA44. The observed decrease is approximately two orders of magnitude higher than the decrease observed under static conditions with the SON68 glass (estimated to be a decrease by four orders of magnitude) and suggests that the gel-layer properties are less protective under these dynamic

  20. Combined Experimental and Computational Approach to Predict the Glass-Water Reaction

    SciTech Connect

    Pierce, Eric M; Bacon, Diana

    2011-01-01

    The use of mineral and glass dissolution rates measured in laboratory experiments to predict the weathering of primary minerals and volcanic and nuclear waste glasses in field studies requires the construction of rate models that accurately describe the weathering process over geologic time-scales. Additionally, the need to model the long-term behavior of nuclear waste glass for the purpose of estimating radionuclide release rates requires that rate models are validated with long-term experiments. Several long-term test methods have been developed to accelerate the glass-water reaction [drip test, vapor hydration test, product consistency test-B, and pressurized unsaturated flow (PUF)], thereby reducing the duration required to evaluate long-term performance. Currently, the PUF test is the only method that mimics the unsaturated hydraulic properties expected in a subsurface disposal facility and simultaneously monitors the glass-water reaction. PUF tests are being conducted to accelerate the weathering of glass and validate the model parameters being used to predict long-term glass behavior. A one-dimensional reactive chemical transport simulation of glass dissolution and secondary phase formation during a 1.5-year long PUF experiment was conducted with the subsurface transport over reactive multi-phases code. Results show that parameterization of the computer model by combining direct bench-scale laboratory measurements and thermodynamic data provides an integrated approach to predicting glass behavior over the length of the experiment. Over the 1.5-year long test duration, the rate decreased from 0.2 to 0.01 g/(m2 d) base on B release. The observed decrease is approximately two orders of magnitude higher than the decrease observed under static conditions with the SON68 glass (estimated to be a decrease by 4 orders of magnitude) and suggest the gel-layer properties are less protective under these dynamic conditions.

  1. Combining meteorological ensemble prediction, data assimilation and hydrological multimodel to reduce and untangle sources of uncertainty

    NASA Astrophysics Data System (ADS)

    Thiboult, Antoine; Anctil, François; Boucher, Marie-Amélie

    2015-04-01

    Hydrological ensemble prediction systems offer the possibility to dynamically assess forecast uncertainty. An ensemble may be issued wherever the uncertainty is situated along the meteorological chain. We commonly identify three main sources of uncertainty: meteorological forcing, hydrological initial conditions, and structural and parameter uncertainty. To address these uncertainties, different techniques have been developed. Meteorological ensemble prediction systems gained in popularity among researchers and operational forecasters as it allows to account for forcing uncertainties. Many data assimilation techniques have been applied to hydrology to reinitialize model states in order to issue more accurate and sharper predictive density functions. At last, multimodel simulation allows to get away from the quest of single best parameter and structure pitfall. The knowledge about these individual techniques is getting extensive and many individual applications can be found in the literature. Even though they proved to improve upon traditional forecasting, they frequently fail to issue fully reliable hydrological forecast as all sources of uncertainty are not tackled. Therefore, an improvement can be obtained in combining them, as it provides a more comprehensive handling of errors. Moreover, using these techniques separately or in combination allows to issue more reliable forecasts but also to identify explicitly the amount of total uncertainty that each technique accounts for. At the end, these sources of error can be characterized in terms of magnitude and lead time influence. As these techniques are frequently used alone, they are usually tuned to perform individually. To reach optimal performance, they should be set jointly. Among them, the data assimilation technique offers a large flexibility in its setting and therefore requires a proper setting considering the other ensemble techniques used. This question is also raised for the hydrological model selection

  2. Insulin combined with Chinese medicine improves glycemic outcome through multiple pathways in patients with type 2 diabetes mellitus

    PubMed Central

    Zhang, Xinxia; Liu, Ya; Xiong, Daqian; Xie, Chunguang

    2015-01-01

    Introduction/Aims Insufficient insulin secretion or inefficient insulin response are responsible for the clinical outcome of type 2 diabetes mellitus. Administration of insulin alone is prone to cause secondary effects, resulting in an unsatisfactory outcome. Shen-Qi-Formula (SQF), a well-known Chinese medicinal formula, has been used for diabetic treatment for a long time. The present study was designed to investigate whether SQF in combination with insulin improved the clinical outcome of type 2 diabetes mellitus, and what mechanisms were possibly involved in the treatment. Materials and Methods A total of 219 patients were included in the study. Of these, 110 patients were treated with insulin monotherapy, and 109 with the combination therapy of SQF and insulin. Before and after 12-week treatment, the fasting blood glucose, postprandial blood glucose, β-cell function, insulin resistance and blood lipids were measured. Results The 12 weeks of SQF treatment in combination with insulin significantly decreased the fasting and postprandial blood glucose levels. Insulin secretion was not increased after the treatment, but β-cell function and insulin resistance were obviously improved. Furthermore, 12 weeks of treatment with SQF and insulin improved the levels of glucagon-like peptide-1, oxidative stress, blood lipids, coagulation function and bodyweight. Conclusion The results from our study showed that the combination therapy of SQF and insulin significantly improved the clinical outcome of type 2 diabetes mellitus compared with insulin monotherapy. The mechanism of improvement was possibly involved in the multiple pathways. PMID:26543546

  3. Methodological issues in current practice may lead to bias in the development of biomarker combinations for predicting acute kidney injury

    PubMed Central

    Meisner, Allison; Kerr, Kathleen F.; Thiessen-Philbrook, Heather; Coca, Steven G.; Parikh, Chirag R.

    2015-01-01

    Individual biomarkers of renal injury are only modestly predictive of acute kidney injury (AKI). Using multiple biomarkers has the potential to improve predictive capacity. In this systematic review, statistical methods of articles developing biomarker combinations to predict acute kidney injury were assessed. We identified and described three potential sources of bias (resubstitution bias, model selection bias and bias due to center differences) that may compromise the development of biomarker combinations. Fifteen studies reported developing kidney injury biomarker combinations for the prediction of AKI after cardiac surgery (8 articles), in the intensive care unit (4 articles) or other settings (3 articles). All studies were susceptible to at least one source of bias and did not account for or acknowledge the bias. Inadequate reporting often hindered our assessment of the articles. We then evaluated, when possible (7 articles), the performance of published biomarker combinations in the TRIBE-AKI cardiac surgery cohort. Predictive performance was markedly attenuated in six out of seven cases. Thus, deficiencies in analysis and reporting are avoidable and care should be taken to provide accurate estimates of risk prediction model performance. Hence, rigorous design, analysis and reporting of biomarker combination studies are essential to realizing the promise of biomarkers in clinical practice. PMID:26398494

  4. Interpreting Hemoglobin A1C in Combination With Conventional Risk Factors for Prediction of Cardiovascular Risk

    PubMed Central

    Jarmul, Jamie A.; Pignone, Michael; Pletcher, Mark J.

    2015-01-01

    Background Hemoglobin A1C (HbA1C) is associated with increased risk of cardiovascular events, but its use for prediction of cardiovascular disease (CVD) events in combination with conventional risk factors has not been well defined. Methods and Results To understand the effect of HbA1C on CVD risk in the context of other CVD risk factors, we analyzed HbA1C and other CVD risk factor measurements in 2000 individuals aged 40-79 years old without pre-existing diabetes or cardiovascular disease from the 2011-2012 NHANES survey. The resulting regression model was used to predict the HbA1C distribution based on individual patient characteristics. We then calculated post-test 10-year atherosclerotic cardiovascular disease (ASCVD) risk incorporating the actual versus predicted HbA1C, according to established methods, for a set of example scenarios. Age, gender, race/ethnicity and traditional cardiovascular risk factors were significant predictors of HbA1C in our model, with the expected HbA1C distribution being significantly higher in non-Hispanic black, non-Hispanic Asian and Hispanic individuals than non-Hispanic white/other individuals. Incorporating the expected HbA1C distribution into pretest ASCVD risk has a modest effect on post-test ASCVD risk. In the patient examples we assessed, having an HbA1C < 5.7% reduced post-test risk by 0.4%-2.0% points, whereas having an HbA1C ≥ 6.5% increased post-test risk by 1.0%-2.5% points, depending on the scenario. The post-test risk increase from having an HbA1C ≥ 6.5 % tends to approximate the risk increase from being five years older in age. Conclusions HbA1C has modest effects on predicted ASCVD risk when considered in the context of conventional risk factors. PMID:26349840

  5. How Adverse Outcome Pathways Can Aid the Development and Use of Computational Prediction Models for Regulatory Toxicology

    SciTech Connect

    Wittwehr, Clemens; Aladjov, Hristo; Ankley, Gerald; Byrne, Hugh J.; de Knecht, Joop; Heinzle, Elmar; Klambauer, Günter; Landesmann, Brigitte; Luijten, Mirjam; MacKay, Cameron; Maxwell, Gavin; Meek, M. E.; Paini, Alicia; Perkins, Edward; Sobanski, Tomasz; Villeneuve, Dan; Waters, Katrina M.; Whelan, Maurice

    2016-12-19

    Efforts are underway to transform regulatory toxicology and chemical safety assessment from a largely empirical science based on direct observation of apical toxicity outcomes in whole organism toxicity tests to a predictive one in which outcomes and risk are inferred from accumulated mechanistic understanding. The adverse outcome pathway (AOP) framework has emerged as a systematic approach for organizing knowledge that supports such inference. We argue that this systematic organization of knowledge can inform and help direct the design and development of computational prediction models that can further enhance the utility of mechanistic and in silico data for chemical safety assessment. Examples of AOP-informed model development and its application to the assessment of chemicals for skin sensitization and multiple modes of endocrine disruption are provided. The role of problem formulation, not only as a critical phase of risk assessment, but also as guide for both AOP and complementary model development described. Finally, a proposal for actively engaging the modeling community in AOP-informed computational model development is made. The contents serve as a vision for how AOPs can be leveraged to facilitate development of computational prediction models needed to support the next generation of chemical safety assessment.

  6. Kynurenine pathway metabolomics predicts and provides mechanistic insight into multiple sclerosis progression

    PubMed Central

    Lim, Chai K.; Bilgin, Ayse; Lovejoy, David B.; Tan, Vanessa; Bustamante, Sonia; Taylor, Bruce V.; Bessede, Alban; Brew, Bruce J.; Guillemin, Gilles J.

    2017-01-01

    Activation of the kynurenine pathway (KP) of tryptophan metabolism results from chronic inflammation and is known to exacerbate progression of neurodegenerative disease. To gain insights into the links between inflammation, the KP and multiple sclerosis (MS) pathogenesis, we investigated the KP metabolomics profile of MS patients. Most significantly, we found aberrant levels of two key KP metabolites, kynurenic acid (KA) and quinolinic acid (QA). The balance between these metabolites is important as it determines overall excitotoxic activity at the N-methyl-D-Aspartate (NMDA) receptor. We also identified that serum KP metabolic signatures in patients can discriminate clinical MS subtypes with high sensitivity and specificity. A C5.0 Decision Tree classification model discriminated the clinical subtypes of MS with a sensitivity of 91%. After validation in another independent cohort, sensitivity was maintained at 85%. Collectively, our studies suggest that abnormalities in the KP may be associated with the switch from early-mild stage MS to debilitating progressive forms of MS and that analysis of KP metabolites in MS patient serum may have application as MS disease biomarkers. PMID:28155867

  7. Pharmacogenomics of Methotrexate Membrane Transport Pathway: Can Clinical Response to Methotrexate in Rheumatoid Arthritis Be Predicted?

    PubMed Central

    Lima, Aurea; Bernardes, Miguel; Azevedo, Rita; Medeiros, Rui; Seabra, Vitor

    2015-01-01

    Background: Methotrexate (MTX) is widely used for rheumatoid arthritis (RA) treatment. Single nucleotide polymorphisms (SNPs) could be used as predictors of patients’ therapeutic outcome variability. Therefore, this study aims to evaluate the influence of SNPs in genes encoding for MTX membrane transport proteins in order to predict clinical response to MTX. Methods: Clinicopathological data from 233 RA patients treated with MTX were collected, clinical response defined, and patients genotyped for 23 SNPs. Genotype and haplotype analyses were performed using multivariate methods and a genetic risk index (GRI) for non-response was created. Results: Increased risk for non-response was associated to SLC22A11 rs11231809 T carriers; ABCC1 rs246240 G carriers; ABCC1 rs3784864 G carriers; CGG haplotype for ABCC1 rs35592, rs2074087 and rs3784864; and CGG haplotype for ABCC1 rs35592, rs246240 and rs3784864. GRI demonstrated that patients with Index 3 were 16-fold more likely to be non-responders than those with Index 1. Conclusions: This study revealed that SLC22A11 and ABCC1 may be important to identify those patients who will not benefit from MTX treatment, highlighting the relevance in translating these results to clinical practice. However, further validation by independent studies is needed to develop the field of personalized medicine to predict clinical response to MTX treatment. PMID:26086825

  8. Parameterization of phosphine ligands reveals mechanistic pathways and predicts reaction outcomes

    NASA Astrophysics Data System (ADS)

    Niemeyer, Zachary L.; Milo, Anat; Hickey, David P.; Sigman, Matthew S.

    2016-06-01

    The mechanistic foundation behind the identity of a phosphine ligand that best promotes a desired reaction outcome is often non-intuitive, and thus has been addressed in numerous experimental and theoretical studies. In this work, multivariate correlations of reaction outcomes using 38 different phosphine ligands were combined with classic potentiometric analyses to study a Suzuki reaction, for which the site selectivity of oxidative addition is highly dependent on the nature of the phosphine. These studies shed light on the generality of hypotheses regarding the structural influence of different classes of phosphine ligands on the reaction mechanism(s), and deliver a methodology that should prove useful in future studies of phosphine ligands.

  9. Parasite identification, succession and infection pathways in perch fry (Perca fluviatilis): new insights through a combined morphological and genetic approach.

    PubMed

    Behrmann-Godel, Jasminca

    2013-04-01

    Identification of parasite species is particularly challenging in larval and juvenile hosts, and this hampers the understanding of parasite acquisition in early life. The work described here employs a new combination of methods to identify parasite species and study parasite succession in fry of perch (Perca fluviatilis) from Lake Constance, Germany. Classical morphological diagnostics are combined with sequence comparisons between parasite life-stages collected from various hosts within the same ecosystem. In perch fry at different stages of development, 13 different parasite species were found. Incomplete morphological identifications of cestodes of the order Proteocephalidea, and trematodes of the family Diplostomatidae were complemented with sequences of mitochondrial DNA (cytochrome oxidase 1) and/or nuclear (28 s rDNA) genes. Sequences were compared to published data and used to link the parasites in perch to stages from molluscs, arthropods and more easily identifiable developmental stages from other fishes collected in Lake Constance, which both aided parasite identification and clarified transmission pathways. There were distinct changes in parasite community composition and abundance associated with perch fry age and habitat shifts. Some parasites became more abundant in older fish, whereas the composition of parasite communities was more strongly affected by the ontogenetic shifts from the pelagic to the littoral zone.

  10. The combination of transcriptomics and informatics identifies pathways targeted by miR-204 during neurogenesis and axon guidance

    PubMed Central

    Conte, Ivan; Merella, Stefania; Garcia-Manteiga, Jose Manuel; Migliore, Chiara; Lazarevic, Dejan; Carrella, Sabrina; Marco-Ferreres, Raquel; Avellino, Raffaella; Davidson, Nathan Paul; Emmett, Warren; Sanges, Remo; Bockett, Nicholas; Van Heel, David; Meroni, Germana; Bovolenta, Paola; Stupka, Elia; Banfi, Sandro

    2014-01-01

    Vertebrate organogenesis is critically sensitive to gene dosage and even subtle variations in the expression levels of key genes may result in a variety of tissue anomalies. MicroRNAs (miRNAs) are fundamental regulators of gene expression and their role in vertebrate tissue patterning is just beginning to be elucidated. To gain further insight into this issue, we analysed the transcriptomic consequences of manipulating the expression of miR-204 in the Medaka fish model system. We used RNA-Seq and an innovative bioinformatics approach, which combines conventional differential expression analysis with the behavior expected by miR-204 targets after its overexpression and knockdown. With this approach combined with a correlative analysis of the putative targets, we identified a wider set of miR-204 target genes belonging to different pathways. Together, these approaches confirmed that miR-204 has a key role in eye development and further highlighted its putative function in neural differentiation processes, including axon guidance as supported by in vivo functional studies. Together, our results demonstrate the advantage of integrating next-generation sequencing and bioinformatics approaches to investigate miRNA biology and provide new important information on the role of miRNAs in the control of axon guidance and more broadly in nervous system development. PMID:24895435

  11. Finding Reaction Pathways of Type A + B → X: Toward Systematic Prediction of Reaction Mechanisms.

    PubMed

    Maeda, Satoshi; Morokuma, Keiji

    2011-08-09

    In these five decades, many useful tools have been developed for exploring quantum chemical potential energy surfaces. The success in theoretical studies of chemical reaction mechanisms has been greatly supported by these tools. However, systematic prediction of reaction mechanisms starting only from given reactants and catalysts is still very difficult. Toward this goal, we describe the artificial force induced reaction (AFIR) method for automatically finding reaction paths of type A + B → X (+ Y). By imposing an artificial force to given reactants and catalysts, the method can find the reactive sites very efficiently. Further pressing by the artificial force provides approximate transition states and product structures, which can be easily reoptimized to the corresponding true ones. This procedure can be executed very efficiently just by minimizing a single function called the AFIR function. All important reaction paths can be found by repeating this cycle starting from many initial orientations. We also discuss perspectives of automated reaction path search methods toward the above goal.

  12. Prediction of effective RNA interference targets and pathway-related genes in lepidopteran insects by RNA sequencing analysis.

    PubMed

    Guan, Ruo-Bing; Li, Hai-Chao; Miao, Xue-Xia

    2017-01-06

    When using RNA interference (RNAi) to study gene functions in Lepidoptera insects, we discovered that some genes could not be suppressed; instead, their expression levels could be up-regulated by double-stranded RNA (dsRNA). To predict which genes could be easily silenced, we treated the Asian corn borer (Ostrinia furnacalis) with dsGFP (green fluorescent protein) and dsMLP (muscle lim protein). A transcriptome sequence analysis was conducted using the cDNAs 6 h after treatment with dsRNA. The results indicated that 160 genes were up-regulated and 44 genes were down-regulated by the two dsRNAs. Then, 50 co-up-regulated, 25 co-down-regulated and 43 unaffected genes were selected to determine their RNAi responses. All the 25 down-regulated genes were knocked down by their corresponding dsRNA. However, several of the up-regulated and unaffected genes were up-regulated when treated with their corresponding dsRNAs instead of being knocked down. The genes up-regulated by the dsGFP treatment may be involved in insect immune responses or the RNAi pathway. When the immune-related genes were excluded, only seven genes were induced by dsGFP, including ago-2 and dicer-2. These results not only provide a reference for efficient RNAi target predications, but also provide some potential RNAi pathway-related genes for further study.

  13. Combined analysis of eIF4E and 4E-binding protein expression predicts breast cancer survival and estimates eIF4E activity.

    PubMed

    Coleman, L J; Peter, M B; Teall, T J; Brannan, R A; Hanby, A M; Honarpisheh, H; Shaaban, A M; Smith, L; Speirs, V; Verghese, E T; McElwaine, J N; Hughes, T A

    2009-05-05

    Increased eukaryotic translation initiation factor 4E (eIF4E) expression occurs in many cancers, and makes fundamental contributions to carcinogenesis by stimulating the expression of cancer-related genes at post-transcriptional levels. This key role is highlighted by the facts that eIF4E levels can predict prognosis, and that eIF4E is an established therapeutic target. However, eIF4E activity is a complex function of expression levels and phosphorylation statuses of eIF4E and eIF4E-binding proteins (4E-BPs). Our hypothesis was that the combined analyses of these pathway components would allow insights into eIF4E activity and its influence on cancer. We have determined expression levels of eIF4E, 4E-BP1, 4E-BP2 and phosphorylated 4E-BP1 within 424 breast tumours, and have carried out analyses to combine these and relate the product to patient survival, in order to estimate eIF4E activity. We show that this analysis gives greater prognostic insights than that of eIF4E alone. We show that eIF4E and 4E-BP expression are positively associated, and that 4E-BP2 has a stronger influence on cancer behaviour than 4E-BP1. Finally, we examine eIF4E, estimated eIF4E activity, and phosphorylated 4E-BP1 as potential predictive biomarkers for eIF4E-targeted therapies, and show that each determines selection of different patient groups. We conclude that eIF4E's influence on cancer survival is modulated substantially by 4E-BPs, and that combined pathway analyses can estimate functional eIF4E.

  14. Combining Structural Modeling with Ensemble Machine Learning to Accurately Predict Protein Fold Stability and Binding Affinity Effects upon Mutation

    PubMed Central

    Garcia Lopez, Sebastian; Kim, Philip M.

    2014-01-01

    Advances in sequencing have led to a rapid accumulation of mutations, some of which are associated with diseases. However, to draw mechanistic conclusions, a biochemical understanding of these mutations is necessary. For coding mutations, accurate prediction of significant changes in either the stability of proteins or their affinity to their binding partners is required. Traditional methods have used semi-empirical force fields, while newer methods employ machine learning of sequence and structural features. Here, we show how combining both of these approaches leads to a marked boost in accuracy. We introduce ELASPIC, a novel ensemble machine learning approach that is able to predict stability effects upon mutation in both, domain cores and domain-domain interfaces. We combine semi-empirical energy terms, sequence conservation, and a wide variety of molecular details with a Stochastic Gradient Boosting of Decision Trees (SGB-DT) algorithm. The accuracy of our predictions surpasses existing methods by a considerable margin, achieving correlation coefficients of 0.77 for stability, and 0.75 for affinity predictions. Notably, we integrated homology modeling to enable proteome-wide prediction and show that accurate prediction on modeled structures is possible. Lastly, ELASPIC showed significant differences between various types of disease-associated mutations, as well as between disease and common neutral mutations. Unlike pure sequence-based prediction methods that try to predict phenotypic effects of mutations, our predictions unravel the molecular details governing the protein instability, and help us better understand the molecular causes of diseases. PMID:25243403

  15. Pilot Clinical Trial of Hedgehog Pathway Inhibitor GDC-0449 (Vismodegib) in Combination with Gemcitabine in Patients with Metastatic Pancreatic Adenocarcinoma

    PubMed Central

    Abel, Ethan V.; Griffith, Kent A.; Greenson, Joel K.; Takebe, Naoko; Khan, Gazala N.; Blau, John L.; Craig, Ronald; Balis, Ulysses G.; Zalupski, Mark M.; Simeone, Diane M.

    2014-01-01

    Background The hedgehog (HH) signaling pathway is a key regulator in tumorigenesis of pancreatic adenocarcinoma (PDA) and is up-regulated in PDA cancer stem cells (CSCs). GDC-0449 is an oral small-molecule inhibitor of HH pathway. This study assessed the effect of GDC-0449-mediated HH inhibition in paired biopsies, followed by combined treatment with gemcitabine, in patients with metastatic PDA. Methods Twenty-five patients were enrolled of which 23 underwent core biopsies at baseline and following 3 weeks of GDC-0449. On day 29, 23 patients started weekly gemcitabine while continuing GDC-0449. We evaluated GLI1 and PTCH1 inhibition, change in CSCs, Ki-67, fibrosis, and assessed tumor response, survival and toxicity. Results On pre-treatment biopsy, 75% of patients had elevated sonic hedgehog (SHH) expression. On post-treatment biopsy, GLI1 and PTCH1 decreased in 95.6% and 82.6% of 23 patients, fibrosis decreased in 45.4% of 22 and Ki-67 in 52.9% of 17 evaluable patients. No significant changes were detected in CSCs pre- and post-biopsy. The median progression-free and overall survival for all treated patients was 2.8 and 5.3 months. The response and disease control rate was 21.7% and 65.2%. No significant correlation was noted between CSCs, fibrosis, SHH, Ki-67, GLI1, PTCH1 (baseline values, or relative change on post-treatment biopsy) and survival. Grade >3 adverse events were noted in 56% of patients. Conclusion We show that GDC-0449 for 3 weeks leads to down-modulation of GLI1 and PTCH1, without significant changes in CSCs compared to baseline. GDC-0449 and gemcitabine was not superior to gemcitabine alone in the treatment of metastatic pancreatic cancer. PMID:25278454

  16. Combined fluxomics and transcriptomics analysis of glucose catabolism via a partially cyclic pentose phosphate pathway in Gluconobacter oxydans 621H.

    PubMed

    Hanke, Tanja; Nöh, Katharina; Noack, Stephan; Polen, Tino; Bringer, Stephanie; Sahm, Hermann; Wiechert, Wolfgang; Bott, Michael

    2013-04-01

    In this study, the distribution and regulation of periplasmic and cytoplasmic carbon fluxes in Gluconobacter oxydans 621H with glucose were studied by (13)C-based metabolic flux analysis ((13)C-MFA) in combination with transcriptomics and enzyme assays. For (13)C-MFA, cells were cultivated with specifically (13)C-labeled glucose, and intracellular metabolites were analyzed for their labeling pattern by liquid chromatography-mass spectrometry (LC-MS). In growth phase I, 90% of the glucose was oxidized periplasmically to gluconate and partially further oxidized to 2-ketogluconate. Of the glucose taken up by the cells, 9% was phosphorylated to glucose 6-phosphate, whereas 91% was oxidized by cytoplasmic glucose dehydrogenase to gluconate. Additional gluconate was taken up into the cells by transport. Of the cytoplasmic gluconate, 70% was oxidized to 5-ketogluconate and 30% was phosphorylated to 6-phosphogluconate. In growth phase II, 87% of gluconate was oxidized to 2-ketogluconate in the periplasm and 13% was taken up by the cells and almost completely converted to 6-phosphogluconate. Since G. oxydans lacks phosphofructokinase, glucose 6-phosphate can be metabolized only via the oxidative pentose phosphate pathway (PPP) or the Entner-Doudoroff pathway (EDP). (13)C-MFA showed that 6-phosphogluconate is catabolized primarily via the oxidative PPP in both phases I and II (62% and 93%) and demonstrated a cyclic carbon flux through the oxidative PPP. The transcriptome comparison revealed an increased expression of PPP genes in growth phase II, which was supported by enzyme activity measurements and correlated with the increased PPP flux in phase II. Moreover, genes possibly related to a general stress response displayed increased expression in growth phase II.

  17. Combined Fluxomics and Transcriptomics Analysis of Glucose Catabolism via a Partially Cyclic Pentose Phosphate Pathway in Gluconobacter oxydans 621H

    PubMed Central

    Hanke, Tanja; Noack, Stephan; Polen, Tino; Bringer, Stephanie; Sahm, Hermann; Wiechert, Wolfgang

    2013-01-01

    In this study, the distribution and regulation of periplasmic and cytoplasmic carbon fluxes in Gluconobacter oxydans 621H with glucose were studied by 13C-based metabolic flux analysis (13C-MFA) in combination with transcriptomics and enzyme assays. For 13C-MFA, cells were cultivated with specifically 13C-labeled glucose, and intracellular metabolites were analyzed for their labeling pattern by liquid chromatography-mass spectrometry (LC-MS). In growth phase I, 90% of the glucose was oxidized periplasmically to gluconate and partially further oxidized to 2-ketogluconate. Of the glucose taken up by the cells, 9% was phosphorylated to glucose 6-phosphate, whereas 91% was oxidized by cytoplasmic glucose dehydrogenase to gluconate. Additional gluconate was taken up into the cells by transport. Of the cytoplasmic gluconate, 70% was oxidized to 5-ketogluconate and 30% was phosphorylated to 6-phosphogluconate. In growth phase II, 87% of gluconate was oxidized to 2-ketogluconate in the periplasm and 13% was taken up by the cells and almost completely converted to 6-phosphogluconate. Since G. oxydans lacks phosphofructokinase, glucose 6-phosphate can be metabolized only via the oxidative pentose phosphate pathway (PPP) or the Entner-Doudoroff pathway (EDP). 13C-MFA showed that 6-phosphogluconate is catabolized primarily via the oxidative PPP in both phases I and II (62% and 93%) and demonstrated a cyclic carbon flux through the oxidative PPP. The transcriptome comparison revealed an increased expression of PPP genes in growth phase II, which was supported by enzyme activity measurements and correlated with the increased PPP flux in phase II. Moreover, genes possibly related to a general stress response displayed increased expression in growth phase II. PMID:23377928

  18. Mechanism of Chemoprevention against Colon Cancer Cells Using Combined Gelam Honey and Ginger Extract via mTOR and Wnt/β-catenin Pathways.

    PubMed

    Wee, Lee Heng; Morad, Noor Azian; Aan, Goon Jo; Makpol, Suzana; Wan Ngah, Wan Zurinah; Mohd Yusof, Yasmin Anum

    2015-01-01

    The PI3K-Akt-mTOR, Wnt/β-catenin and apoptosis signaling pathways have been shown to be involved in genesis of colorectal cancer (CRC). The aim of this study was to elucidate whether combination of Gelam honey and ginger might have chemopreventive properties in HT29 colon cancer cells by modulating the mTOR, Wnt/β-catenin and apoptosis signaling pathways. Treatment with Gelam honey and ginger reduced the viability of the HT29 cells dose dependently with IC50 values of 88 mg/ml and 2.15 mg/ml respectively, their while the combined treatment of 2 mg/ml of ginger with 31 mg/ml of Gelam honey inhibited growth of most HT29 cells. Gelam honey, ginger and combination induced apoptosis in a dose dependent manner with the combined treatment exhibiting the highest apoptosis rate. The combined treatment downregulated the gene expressions of Akt, mTOR, Raptor, Rictor, β-catenin, Gsk3β, Tcf4 and cyclin D1 while cytochrome C and caspase 3 genes were shown to be upregulated. In conclusion, the combination of Gelam honey and ginger may serve as a potential therapy in the treatment of colorectal cancer through inhibiton of mTOR, Wnt/β catenin signaling pathways and induction of apoptosis pathway.

  19. The utility and predictive value of combinations of low penetrance genes for screening and risk prediction of colorectal cancer.

    PubMed

    Hawken, Steven J; Greenwood, Celia M T; Hudson, Thomas J; Kustra, Rafal; McLaughlin, John; Yang, Quanhe; Zanke, Brent W; Little, Julian

    2010-07-01

    Despite the fact that colorectal cancer (CRC) is a highly treatable form of cancer if detected early, a very low proportion of the eligible population undergoes screening for this form of cancer. Integrating a genomic screening profile as a component of existing screening programs for CRC could potentially improve the effectiveness of population screening by allowing the assignment of individuals to different types and intensities of screening and also by potentially increasing the uptake of existing screening programs. We evaluated the utility and predictive value of genomic profiling as applied to CRC, and as a potential component of a population-based cancer screening program. We generated simulated data representing a typical North American population including a variety of genetic profiles, with a range of relative risks and prevalences for individual risk genes. We then used these data to estimate parameters characterizing the predictive value of a logistic regression model built on genetic markers for CRC. Meta-analyses of genetic associations with CRC were used in building science to inform the simulation work, and to select genetic variants to include in logistic regression model-building using data from the ARCTIC study in Ontario, which included 1,200 CRC cases and a similar number of cancer-free population-based controls. Our simulations demonstrate that for reasonable assumptions involving modest relative risks for individual genetic variants, that substantial predictive power can be achieved when risk variants are common (e.g., prevalence > 20%) and data for enough risk variants are available (e.g., approximately 140-160). Pilot work in population data shows modest, but statistically significant predictive utility for a small collection of risk variants, smaller in effect than age and gender alone in predicting an individual's CRC risk. Further genotyping and many more samples will be required, and indeed the discovery of many more risk loci

  20. A Hybrid Chalcone Combining the Trimethoxyphenyl and Isatinyl Groups Targets Multiple Oncogenic Proteins and Pathways in Hepatocellular Carcinoma Cells

    PubMed Central

    Cao, Lili; Zhang, Lijun; Zhao, Xiang; Zhang, Ye

    2016-01-01

    Small molecule inhibitors that can simultaneously inhibit multiple oncogenic proteins in essential pathways are promising therapeutic chemicals for hepatocellular carcinoma (HCC). To combine the anticancer effects of combretastatins, chalcones and isatins, we synthesized a novel hybrid molecule 3’,4’,5’-trimethoxy-5-chloro-isatinylchalcone (3MCIC). 3MCIC inhibited proliferation of cultured HepG2 cells, causing rounding-up of the cells and massive vacuole accumulation in the cytoplasm. Paxillin and focal adhesion plaques were downregulated by 3MCIC. Surprisingly, unlike the microtubule (MT)-targeting agent CA-4 that inhibits tubulin polymerization, 3MCIC stabilized tubulin polymers both in living cells and in cell lysates. 3MCIC treatment reduced cyclin B1, CDK1, p-CDK1/2, and Rb, but increased p53 and p21. Moreover, 3MCIC caused GSK3β degradation by promoting GSK3β-Ser9 phosphorylation. Nevertheless, 3MCIC inhibited the Wnt/β-catenin pathway by downregulating β-catenin, c-Myc, cyclin D1 and E2F1. 3MCIC treatment not only activated the caspase-3-dependent apoptotic pathway, but also caused massive autophagy evidenced by rapid and drastic changes of LC3 and p62. 3MCIC also promoted cleavage and maturation of the lysosomal protease cathepsin D. Using ligand-affinity chromatography (LAC), target proteins captured onto the Sephacryl S1000-C12-3MCIC resins were isolated and analyzed by mass spectrometry (MS). Some of the LAC-MS identified targets, i.e., septin-2, vimentin, pan-cytokeratin, nucleolin, EF1α1/2, EBP1 (PA2G4), cyclin B1 and GSK3β, were further detected by Western blotting. Moreover, both septin-2 and HIF-1α decreased drastically in 3MCIC-treated HepG2 cells. Our data suggest that 3MCIC is a promising anticancer lead compound with novel targeting mechanisms, and also demonstrate the efficiency of LAC-MS based target identification in anticancer drug development. PMID:27525972

  1. Utilization of a combined EEG/NIRS system to predict driver drowsiness

    PubMed Central

    Nguyen, Thien; Ahn, Sangtae; Jang, Hyojung; Jun, Sung Chan; Kim, Jae Gwan

    2017-01-01

    The large number of automobile accidents due to driver drowsiness is a critical concern of many countries. To solve this problem, numerous methods of countermeasure have been proposed. However, the results were unsatisfactory due to inadequate accuracy of drowsiness detection. In this study, we introduce a new approach, a combination of EEG and NIRS, to detect driver drowsiness. EEG, EOG, ECG and NIRS signals have been measured during a simulated driving task, in which subjects underwent both awake and drowsy states. The blinking rate, eye closure, heart rate, alpha and beta band power were used to identify subject’s condition. Statistical tests were performed on EEG and NIRS signals to find the most informative parameters. Fisher’s linear discriminant analysis method was employed to classify awake and drowsy states. Time series analysis was used to predict drowsiness. The oxy-hemoglobin concentration change and the beta band power in the frontal lobe were found to differ the most between the two states. In addition, these two parameters correspond well to an awake to drowsy state transition. A sharp increase of the oxy-hemoglobin concentration change, together with a dramatic decrease of the beta band power, happened several seconds before the first eye closure. PMID:28266633

  2. Combined crystal structure prediction and high-pressure crystallization in rational pharmaceutical polymorph screening

    PubMed Central

    Neumann, M. A.; van de Streek, J.; Fabbiani, F. P. A.; Hidber, P.; Grassmann, O.

    2015-01-01

    Organic molecules, such as pharmaceuticals, agro-chemicals and pigments, frequently form several crystal polymorphs with different physicochemical properties. Finding polymorphs has long been a purely experimental game of trial-and-error. Here we utilize in silico polymorph screening in combination with rationally planned crystallization experiments to study the polymorphism of the pharmaceutical compound Dalcetrapib, with 10 torsional degrees of freedom one of the most flexible molecules ever studied computationally. The experimental crystal polymorphs are found at the bottom of the calculated lattice energy landscape, and two predicted structures are identified as candidates for a missing, thermodynamically more stable polymorph. Pressure-dependent stability calculations suggested high pressure as a means to bring these polymorphs into existence. Subsequently, one of them could indeed be crystallized in the 0.02 to 0.50 GPa pressure range and was found to be metastable at ambient pressure, effectively derisking the appearance of a more stable polymorph during late-stage development of Dalcetrapib. PMID:26198974

  3. Prediction of nitroxide hyperfine coupling constants in solution from combined nanosecond scale simulations and quantum computations

    NASA Astrophysics Data System (ADS)

    Houriez, Céline; Ferré, Nicolas; Masella, Michel; Siri, Didier

    2008-06-01

    We present a combined theoretical approach based on analyzing molecular dynamics trajectories (at the nanosecond scale) generated by use of classical polarizable force fields and on quantum calculations to compute averaged hyperfine coupling constants. That method is used to estimate the constant of a prototypical nitroxide: the dimethylnitroxide. The molecule is embedded during the simulations in a cubic box containing about 500 water molecules and the molecular dynamics is generated using periodic conditions. Once the trajectories are achieved, the nitroxide and its first hydration shell molecules are extracted, and the coupling constants are computed by considering the latter aggregates by means of quantum computations. However, all the water molecules of the bulk are also accounted for during those computations by means of the electrostatic potential fitted method. Our results exhibit that in order to predict accurate and reliable coupling constants, one needs to describe carefully the out-of-plane motion of the nitroxide nitrogen and to sample trajectories with a time interval of 400 fs at least to generate an uncorrelated large set of nitroxide structures. Compared to Car-Parrinello molecular dynamics techniques, our approach can be used readily to compute hyperfine coupling constants of large systems, such as nitroxides of great size interacting with macromolecules such as proteins or polymers.

  4. Towards predictive resistance models for agrochemicals by combining chemical and protein similarity via proteochemometric modelling.

    PubMed

    van Westen, Gerard J P; Bender, Andreas; Overington, John P

    2014-10-01

    Resistance to pesticides is an increasing problem in agriculture. Despite practices such as phased use and cycling of 'orthogonally resistant' agents, resistance remains a major risk to national and global food security. To combat this problem, there is a need for both new approaches for pesticide design, as well as for novel chemical entities themselves. As summarized in this opinion article, a technique termed 'proteochemometric modelling' (PCM), from the field of chemoinformatics, could aid in the quantification and prediction of resistance that acts via point mutations in the target proteins of an agent. The technique combines information from both the chemical and biological domain to generate bioactivity models across large numbers of ligands as well as protein targets. PCM has previously been validated in prospective, experimental work in the medicinal chemistry area, and it draws on the growing amount of bioactivity information available in the public domain. Here, two potential applications of proteochemometric modelling to agrochemical data are described, based on previously published examples from the medicinal chemistry literature.

  5. Chapter 8. Methods for in silico prediction of microbial polyketide and nonribosomal peptide biosynthetic pathways from DNA sequence data.

    PubMed

    Bachmann, Brian O; Ravel, Jacques

    2009-01-01

    Fore-knowledge of the secondary metabolic potential of cultivated and previously uncultivated microorganisms can potentially facilitate the process of natural product discovery. By combining sequence-based knowledge with biochemical precedent, translated gene sequence data can be used to rapidly derive structural elements encoded by secondary metabolic gene clusters from microorganisms. These structural elements provide an estimate of the secondary metabolic potential of a given organism and a starting point for identification of potential lead compounds in isolation/structure elucidation campaigns. The accuracy of these predictions for a given translated gene sequence depends on the biochemistry of the metabolite class, similarity to known metabolite gene clusters, and depth of knowledge concerning its biosynthetic machinery. This chapter introduces methods for prediction of structural elements for two well-studied classes: modular polyketides and nonribosomally encoded peptides. A bioinformatics tool is presented for rapid preliminary analysis of these modular systems, and prototypical methods for converting these analyses into substructural elements are described.

  6. Activities of multiple cancer-related pathways are associated with BRAF mutation and predict the resistance to BRAF/MEK inhibitors in melanoma cells

    PubMed Central

    Liu, Dingxie; Liu, Xuan; Xing, Mingzhao

    2014-01-01

    Drug resistance is a major obstacle in the targeted therapy of melanoma using BRAF/MEK inhibitors. This study was to identify BRAF V600E-associated oncogenic pathways that predict resistance of BRAF-mutated melanoma to BRAF/MEK inhibitors. We took in silico approaches to analyze the activities of 24 cancer-related pathways in melanoma cells and identify those whose activation was associated with BRAF V600E and used the support vector machine (SVM) algorithm to predict the resistance of BRAF-mutated melanoma cells to BRAF/MEK inhibitors. We then experimentally confirmed the in silico findings. In a microarray gene expression dataset of 63 melanoma cell lines, we found that activation of multiple oncogenic pathways preferentially occurred in BRAF-mutated melanoma cells. This finding was reproduced in 5 additional independent melanoma datasets. Further analysis of 46 melanoma cell lines that harbored BRAF mutation showed that 7 pathways, including TNFα, EGFR, IFNα, hypoxia, IFNγ, STAT3, and MYC, were significantly differently expressed in AZD6244-resistant compared with responsive melanoma cells. A SVM classifier built on this 7-pathway activation pattern correctly predicted the response of 10 BRAF-mutated melanoma cell lines to the MEK inhibitor AZD6244 in our experiments. We experimentally showed that TNFα, EGFR, IFNα, and IFNγ pathway activities were also upregulated in melanoma cell A375 compared with its sub-line DRO, while DRO was much more sensitive to AZD6244 than A375. In conclusion, we have identified specific oncogenic pathways preferentially activated in BRAF-mutated melanoma cells and a pathway pattern that predicts resistance of BRAF-mutated melanoma to BRAF/MEK inhibitors, providing novel clinical implications for melanoma therapy. PMID:24200969

  7. Combined approaches using adverse outcome pathways and big data to find potential diseases associated with humidifier disinfectant.

    PubMed

    Leem, Jong-Han; Chung, Kyu Hyuck

    2016-01-01

    According to previous survey, about two million of people were expected to suffer from toxic effects due to humidifier disinfectant (HD), regardless of healing or not. Extremely small group are recognized as HDs' victims. Up to now, previous research tried to focus on interstitial fibrosis on terminal bronchiole because it is specific finding, compared with other diseases. To figure out overall effects from HDs, we recommend adverse outcome pathways (AOPs) as new approach. Reactive oxygen species (ROS) generation, decreased T-cell and pro-inflammatory cytokine release from macrophage could be key events between the exposure to HDs and diseases. ROS generation, decreased cell and pro-inflammatory cytokine release from macrophage could be cause of interstitial fibrosis, pneumonia and many other diseases such as asthma, allergic rhinitis, allergic dermatitis, fetal death, premature baby, autoimmune disease, hepatic toxicity, renal toxicity, cancer, and so on. We predict potential disease candidate by AOPs. We can validate the real risk of the adverse outcome by epidemiologic and toxicologic study using big data such as National Health Insurance data and AOPs knowledge base. Application of these kinds of new methods can find the potential disease list from the exposure to HD.

  8. Combined approaches using adverse outcome pathways and big data to find potential diseases associated with humidifier disinfectant

    PubMed Central

    2017-01-01

    According to previous survey, about two million of people were expected to suffer from toxic effects due to humidifier disinfectant (HD), regardless of healing or not. Extremely small group are recognized as HDs’ victims. Up to now, previous research tried to focus on interstitial fibrosis on terminal bronchiole because it is specific finding, compared with other diseases. To figure out overall effects from HDs, we recommend adverse outcome pathways (AOPs) as new approach. Reactive oxygen species (ROS) generation, decreased T-cell and pro-inflammatory cytokine release from macrophage could be key events between the exposure to HDs and diseases. ROS generation, decreased cell and pro-inflammatory cytokine release from macrophage could be cause of interstitial fibrosis, pneumonia and many other diseases such as asthma, allergic rhinitis, allergic dermatitis, fetal death, premature baby, autoimmune disease, hepatic toxicity, renal toxicity, cancer, and so on. We predict potential disease candidate by AOPs. We can validate the real risk of the adverse outcome by epidemiologic and toxicologic study using big data such as National Health Insurance data and AOPs knowledge base. Application of these kinds of new methods can find the potential disease list from the exposure to HD. PMID:28111421

  9. The utility and predictive value of combinations of low penetrance genes for screening and risk prediction of colorectal cancer

    PubMed Central

    Hawken, Steven J.; Greenwood, Celia M. T.; Hudson, Thomas J.; Kustra, Rafal; McLaughlin, John; Yang, Quanhe; Zanke, Brent W.

    2010-01-01

    Despite the fact that colorectal cancer (CRC) is a highly treatable form of cancer if detected early, a very low proportion of the eligible population undergoes screening for this form of cancer. Integrating a genomic screening profile as a component of existing screening programs for CRC could potentially improve the effectiveness of population screening by allowing the assignment of individuals to different types and intensities of screening and also by potentially increasing the uptake of existing screening programs. We evaluated the utility and predictive value of genomic profiling as applied to CRC, and as a potential component of a population-based cancer screening program. We generated simulated data representing a typical North American population including a variety of genetic profiles, with a range of relative risks and prevalences for individual risk genes. We then used these data to estimate parameters characterizing the predictive value of a logistic regression model built on genetic markers for CRC. Meta-analyses of genetic associations with CRC were used in building science to inform the simulation work, and to select genetic variants to include in logistic regression model-building using data from the ARCTIC study in Ontario, which included 1,200 CRC cases and a similar number of cancer-free population-based controls. Our simulations demonstrate that for reasonable assumptions involving modest relative risks for individual genetic variants, that substantial predictive power can be achieved when risk variants are common (e.g., prevalence > 20%) and data for enough risk variants are available (e.g., ~140–160). Pilot work in population data shows modest, but statistically significant predictive utility for a small collection of risk variants, smaller in effect than age and gender alone in predicting an individual’s CRC risk. Further genotyping and many more samples will be required, and indeed the discovery of many more risk loci associated with

  10. Improving model predictions for RNA interference activities that use support vector machine regression by combining and filtering features

    PubMed Central

    Peek, Andrew S

    2007-01-01

    Background RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathways. To dissect the factors that result in effective siRNA sequences a regression kernel Support Vector Machine (SVM) approach was used to quantitatively model RNA interference activities. Results Eight overall feature mapping methods were compared in their abilities to build SVM regression models that predict published siRNA activities. The primary factors in predictive SVM models are position specific nucleotide compositions. The secondary factors are position independent sequence motifs (N-grams) and guide strand to passenger strand sequence thermodynamics. Finally, the factors that are least contributory but are still predictive of efficacy are measures of intramolecular guide strand secondary structure and target strand secondary structure. Of these, the site of the 5' most base of the guide strand is the most informative. Conclusion The capacity of specific feature mapping methods and their ability to build predictive models of RNAi activity suggests a relative biological importance of these features. Some feature mapping methods are more informative in building predictive models and overall t-test filtering provides a method to remove some noisy features or make comparisons among datasets. Together, these features can yield predictive SVM regression models with increased predictive accuracy between predicted and observed activities both within datasets by cross validation, and between independently collected RNAi activity datasets. Feature filtering to remove features should be approached carefully in that it is possible to reduce feature set size without substantially reducing predictive models, but the features retained in the candidate models become increasingly distinct. Software to perform feature prediction and SVM training and testing on nucleic acid sequences can be found at

  11. Combining metagenomics with metaproteomics and stable isotope probing reveals metabolic pathways used by a naturally occurring marine methylotroph.

    PubMed

    Grob, Carolina; Taubert, Martin; Howat, Alexandra M; Burns, Oliver J; Dixon, Joanna L; Richnow, Hans H; Jehmlich, Nico; von Bergen, Martin; Chen, Yin; Murrell, J Colin

    2015-10-01

    A variety of culture-independent techniques have been developed that can be used in conjunction with culture-dependent physiological and metabolic studies of key microbial organisms in order to better understand how the activity of natural populations influences and regulates all major biogeochemical cycles. In this study, we combined deoxyribonucleic acid-stable isotope probing (DNA-SIP) with metagenomics and metaproteomics to characterize an uncultivated marine methylotroph that actively incorporated carbon from (13) C-labeled methanol into biomass. By metagenomic sequencing of the heavy DNA, we retrieved virtually the whole genome of this bacterium and determined its metabolic potential. Through protein-stable isotope probing, the RuMP cycle was established as the main carbon assimilation pathway, and the classical methanol dehydrogenase-encoding gene mxaF, as well as three out of four identified xoxF homologues were found to be expressed. This proof-of-concept study is the first in which the culture-independent techniques of DNA-SIP and protein-SIP have been used to characterize the metabolism of a naturally occurring Methylophaga-like bacterium in the marine environment (i.e. Methylophaga thiooxydans L4) and thus provides a powerful approach to access the genome and proteome of uncultivated microbes involved in key processes in the environment.

  12. A grading system combining architectural features and mitotic count predicts recurrence in stage I lung adenocarcinoma.

    PubMed

    Kadota, Kyuichi; Suzuki, Kei; Kachala, Stefan S; Zabor, Emily C; Sima, Camelia S; Moreira, Andre L; Yoshizawa, Akihiko; Riely, Gregory J; Rusch, Valerie W; Adusumilli, Prasad S; Travis, William D

    2012-08-01

    The International Association for the Study of Lung Cancer (IASLC)/American Thoracic Society (ATS)/European Respiratory Society (ERS) has recently proposed a new lung adenocarcinoma classification. We investigated whether nuclear features can stratify prognostic subsets. Slides of 485 stage I lung adenocarcinoma patients were reviewed. We evaluated nuclear diameter, nuclear atypia, nuclear/cytoplasmic ratio, chromatin pattern, prominence of nucleoli, intranuclear inclusions, mitotic count/10 high-power fields (HPFs) or 2.4 mm(2), and atypical mitoses. Tumors were classified into histologic subtypes according to the IASLC/ATS/ERS classification and grouped by architectural grade into low (adenocarcinoma in situ, minimally invasive adenocarcinoma, or lepidic predominant), intermediate (papillary or acinar), and high (micropapillary or solid). Log-rank tests and Cox regression models evaluated the ability of clinicopathologic factors to predict recurrence-free probability. In univariate analyses, nuclear diameter (P=0.007), nuclear atypia (P=0.006), mitotic count (P<0.001), and atypical mitoses (P<0.001) were significant predictors of recurrence. The recurrence-free probability of patients with high mitotic count (≥5/10 HPF: n=175) was the lowest (5-year recurrence-free probability=73%), followed by intermediate (2-4/10 HPF: n=106, 80%), and low (0-1/10 HPF: n=204, 91%, P<0.001). Combined architectural/mitotic grading system stratified patient outcomes (P<0.001): low grade (low architectural grade with any mitotic count and intermediate architectural grade with low mitotic count: n=201, 5-year recurrence-free probability=92%), intermediate grade (intermediate architectural grade with intermediate-high mitotic counts: n=206, 78%), and high grade (high architectural grade with any mitotic count: n=78, 68%). The advantage of adding mitotic count to architectural grade is in stratifying patients with intermediate architectural grade into two prognostically

  13. Electronic Nose Based on Independent Component Analysis Combined with Partial Least Squares and Artificial Neural Networks for Wine Prediction

    PubMed Central

    Aguilera, Teodoro; Lozano, Jesús; Paredes, José A.; Álvarez, Fernando J.; Suárez, José I.

    2012-01-01

    The aim of this work is to propose an alternative way for wine classification and prediction based on an electronic nose (e-nose) combined with Independent Component Analysis (ICA) as a dimensionality reduction technique, Partial Least Squares (PLS) to predict sensorial descriptors and Artificial Neural Networks (ANNs) for classification purpose. A total of 26 wines from different regions, varieties and elaboration processes have been analyzed with an e-nose and tasted by a sensory panel. Successful results have been obtained in most cases for prediction and classification. PMID:22969387

  14. Generation of computationally predicted Adverse Outcome Pathway networks through integration of publicly available in vivo, in vitro, phenotype, and biological pathway data.

    EPA Science Inventory

    The Adverse Outcome Pathway (AOP) framework is becoming a widely used tool for organizing and summarizing the mechanistic information connecting molecular perturbations by environmental stressors with adverse ecological and human health outcomes. However, the conventional process...

  15. Development of computationally predicted Adverse Outcome Pathway (AOP) networks through data mining and integration of publicly available in vivo, in vitro, phenotype, and biological pathway data

    EPA Science Inventory

    The Adverse Outcome Pathway (AOP) framework is increasingly being adopted as a tool for organizing and summarizing the mechanistic information connecting molecular perturbations by environmental stressors with adverse outcomes relevant for ecological and human health outcomes. Ho...

  16. Predicting Treatment Seekers Readiness to Change their Drinking Behavior in the COMBINE Study

    PubMed Central

    DiClemente, Carlo C.; Doyle, Suzanne R.; Donovan, Dennis

    2010-01-01

    Background Initial motivation and readiness to change are complex constructs and have been important but inconsistent predictors of treatment attendance and drinking outcomes in studies of alcoholism treatment. Motivation can be described in multiple ways as simply the accumulation of consequences that push change, a shift in intentions, or engagement in various tasks that are part of a larger process of change. Method Using baseline data from participants in the COMBINE Study, this study re-evaluated the psychometric properties of a 24-item, measure of motivation derived from the URICA that yielded four subscales representing attitudes and experiences related to tasks of stages of Precontemplation, Contemplation, Action, and Maintenance Striving as well as a second order factor score representing a multidimensional view of readiness to change drinking. A variety of hypothesized predictors of readiness and the stage subscales were examined using multiple regression analyses in order to better understand the nature of this measure of motivation. Results Findings supported the basic subscale structure and the overall motivational readiness score derived from this measure. Readiness to change drinking behavior was predicted by baseline measures of perceived stress, drinking severity, psychiatric co-morbidity, self-efficacy, craving, and with positive treatment outcome expectancies. However, absolute values were small indicating that readiness for change is not explained simply by demographic, drinking severity, treatment, change process, or contextual variables. Conclusion This measure demonstrated good psychometric properties and results supported the independence as well as convergent and divergent validity of the measured constructs. Predictors of overall readiness and subscale scores indicate that a variety of personal and contextual factors contribute to treatment seekers motivation to change in an understandable but complex manner. PMID:19320633

  17. Simultaneous learning and filtering without delusions: a Bayes-optimal combination of Predictive Inference and Adaptive Filtering.

    PubMed

    Kneissler, Jan; Drugowitsch, Jan; Friston, Karl; Butz, Martin V

    2015-01-01

    Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares.

  18. TP53 hotspot mutations are predictive of survival in primary central nervous system lymphoma patients treated with combination chemotherapy.

    PubMed

    Munch-Petersen, Helga D; Asmar, Fazila; Dimopoulos, Konstantinos; Areškevičiūtė, Aušrinė; Brown, Peter; Girkov, Mia Seremet; Pedersen, Anja; Sjö, Lene D; Heegaard, Steffen; Broholm, Helle; Kristensen, Lasse S; Ralfkiaer, Elisabeth; Grønbæk, Kirsten

    2016-04-22

    Primary central nervous system lymphoma (PCNSL) is an aggressive variant of diffuse large B-cell lymphoma (DLBCL) confined to the CNS. TP53 mutations (MUT-TP53) were investigated in the context of MIR34A/B/C- and DAPK promoter methylation status, and associated with clinical outcomes in PCNSL patients. In a total of 107 PCNSL patients clinical data were recorded, histopathology reassessed, and genetic and epigenetic aberrations of the p53-miR34-DAPK network studied. TP53 mutational status (exon 5-8), with structural classification of single nucleotide variations according to the IARC-TP53-Database, methylation status of MIR34A/B/C and DAPK, and p53-protein expression were assessed. The 57/107 (53.2 %) patients that were treated with combination chemotherapy +/- rituximab (CCT-treated) had a significantly better median overall survival (OS) (31.3 months) than patients treated with other regimens (high-dose methotrexate/whole brain radiation therapy, 6.0 months, or no therapy, 0.83 months), P < 0.0001. TP53 mutations were identified in 32/86 (37.2 %), among which 12 patients had hotspot/direct DNA contact mutations. CCT-treated patients with PCNSL harboring a hotspot/direct DNA contact MUT-TP53 (n = 9) had a significantly worse OS and progression free survival (PFS) compared to patients with non-hotspot/non-direct DNA contact MUT-TP53 or wild-type TP53 (median PFS 4.6 versus 18.2 or 45.7 months), P = 0.041 and P = 0.00076, respectively. Multivariate Cox regression analysis confirmed that hotspot/direct DNA contact MUT-TP53 was predictive of poor outcome in CCT-treated PCNSL patients, P = 0.012 and P = 0.008; HR: 1.86 and 1.95, for OS and PFS, respectively. MIR34A, MIR34B/C, and DAPK promoter methylation were detected in 53/93 (57.0 %), 80/84 (95.2 %), and 70/75 (93.3 %) of the PCNSL patients with no influence on survival. Combined MUT-TP53 and MIR34A methylation was associated with poor PFS (median 6.4 versus 38.0 months), P = 0

  19. Predicting locations of rare aquatic species’ habitat with a combination of species-specific and assemblage-based models

    USGS Publications Warehouse

    McKenna, James E.; Carlson, Douglas M.; Payne-Wynne, Molly L.

    2013-01-01

    Aim: Rare aquatic species are a substantial component of biodiversity, and their conservation is a major objective of many management plans. However, they are difficult to assess, and their optimal habitats are often poorly known. Methods to effectively predict the likely locations of suitable rare aquatic species habitats are needed. We combine two modelling approaches to predict occurrence and general abundance of several rare fish species. Location: Allegheny watershed of western New York State (USA) Methods: Our method used two empirical neural network modelling approaches (species specific and assemblage based) to predict stream-by-stream occurrence and general abundance of rare darters, based on broad-scale habitat conditions. Species-specific models were developed for longhead darter (Percina macrocephala), spotted darter (Etheostoma maculatum) and variegate darter (Etheostoma variatum) in the Allegheny drainage. An additional model predicted the type of rare darter-containing assemblage expected in each stream reach. Predictions from both models were then combined inclusively and exclusively and compared with additional independent data. Results Example rare darter predictions demonstrate the method's effectiveness. Models performed well (R2 ≥ 0.79), identified where suitable darter habitat was most likely to occur, and predictions matched well to those of collection sites. Additional independent data showed that the most conservative (exclusive) model slightly underestimated the distributions of these rare darters or predictions were displaced by one stream reach, suggesting that new darter habitat types were detected in the later collections. Main conclusions Broad-scale habitat variables can be used to effectively identify rare species' habitats. Combining species-specific and assemblage-based models enhances our ability to make use of the sparse data on rare species and to identify habitat units most likely and least likely to support those species

  20. Ultra Short-term Prediction of Pole Coordinates via Combination of Empirical Mode Decomposition and Neural Networks

    NASA Astrophysics Data System (ADS)

    Lei, Yu; Zhao, Danning; Cai, Hongbing

    2016-12-01

    It was shown in the previous study that the increase of pole coordinates prediction error for about 100 days in the future is mostly caused by irregular short period oscillations. In this paper, the ultra short-term prediction of pole coordinates is studied for 10 days in the future by means of combination of empirical mode decomposition (EMD) and neural networks (NN), denoted EMD-NN. In the algorithm, EMD is employed as a low pass filter for eliminating high frequency signals from observed pole coordinates data. Then the annual and Chandler wobbles are removed a priori from pole coordinates data with high frequency signals eliminated. Finally, the radial basis function (RBF) networks are used to model and predict the residuals. The prediction performance of the EMD-NN approach is compared with that of the NN-only solution and the prediction methods and techniques involved in the Earth orientation parameters prediction comparison campaign (EOP PCC). The results show that the prediction accuracy of the EMD-NN algorithm is better than that of the NN-only solution and is also comparable with that of the other existing prediction method and techniques.

  1. ARIA 2016: Care pathways implementing emerging technologies for predictive medicine in rhinitis and asthma across the life cycle.

    PubMed

    Bousquet, J; Hellings, P W; Agache, I; Bedbrook, A; Bachert, C; Bergmann, K C; Bewick, M; Bindslev-Jensen, C; Bosnic-Anticevitch, S; Bucca, C; Caimmi, D P; Camargos, P A M; Canonica, G W; Casale, T; Chavannes, N H; Cruz, A A; De Carlo, G; Dahl, R; Demoly, P; Devillier, P; Fonseca, J; Fokkens, W J; Guldemond, N A; Haahtela, T; Illario, M; Just, J; Keil, T; Klimek, L; Kuna, P; Larenas-Linnemann, D; Morais-Almeida, M; Mullol, J; Murray, R; Naclerio, R; O'Hehir, R E; Papadopoulos, N G; Pawankar, R; Potter, P; Ryan, D; Samolinski, B; Schunemann, H J; Sheikh, A; Simons, F E R; Stellato, C; Todo-Bom, A; Tomazic, P V; Valiulis, A; Valovirta, E; Ventura, M T; Wickman, M; Young, I; Yorgancioglu, A; Zuberbier, T; Aberer, W; Akdis, C A; Akdis, M; Annesi-Maesano, I; Ankri, J; Ansotegui, I J; Anto, J M; Arnavielhe, S; Asarnoj, A; Arshad, H; Avolio, F; Baiardini, I; Barbara, C; Barbagallo, M; Bateman, E D; Beghé, B; Bel, E H; Bennoor, K S; Benson, M; Białoszewski, A Z; Bieber, T; Bjermer, L; Blain, H; Blasi, F; Boner, A L; Bonini, M; Bonini, S; Bosse, I; Bouchard, J; Boulet, L P; Bourret, R; Bousquet, P J; Braido, F; Briggs, A H; Brightling, C E; Brozek, J; Buhl, R; Bunu, C; Burte, E; Bush, A; Caballero-Fonseca, F; Calderon, M A; Camuzat, T; Cardona, V; Carreiro-Martins, P; Carriazo, A M; Carlsen, K H; Carr, W; Cepeda Sarabia, A M; Cesari, M; Chatzi, L; Chiron, R; Chivato, T; Chkhartishvili, E; Chuchalin, A G; Chung, K F; Ciprandi, G; de Sousa, J Correia; Cox, L; Crooks, G; Custovic, A; Dahlen, S E; Darsow, U; Dedeu, T; Deleanu, D; Denburg, J A; De Vries, G; Didier, A; Dinh-Xuan, A T; Dokic, D; Douagui, H; Dray, G; Dubakiene, R; Durham, S R; Du Toit, G; Dykewicz, M S; Eklund, P; El-Gamal, Y; Ellers, E; Emuzyte, R; Farrell, J; Fink Wagner, A; Fiocchi, A; Fletcher, M; Forastiere, F; Gaga, M; Gamkrelidze, A; Gemicioğlu, B; Gereda, J E; van Wick, R Gerth; González Diaz, S; Grisle, I; Grouse, L; Gutter, Z; Guzmán, M A; Hellquist-Dahl, B; Heinrich, J; Horak, F; Hourihane, J O' B; Humbert, M; Hyland, M; Iaccarino, G; Jares, E J; Jeandel, C; Johnston, S L; Joos, G; Jonquet, O; Jung, K S; Jutel, M; Kaidashev, I; Khaitov, M; Kalayci, O; Kalyoncu, A F; Kardas, P; Keith, P K; Kerkhof, M; Kerstjens, H A M; Khaltaev, N; Kogevinas, M; Kolek, V; Koppelman, G H; Kowalski, M L; Kuitunen, M; Kull, I; Kvedariene, V; Lambrecht, B; Lau, S; Laune, D; Le, L T T; Lieberman, P; Lipworth, B; Li, J; Lodrup Carlsen, K C; Louis, R; Lupinek, C; MacNee, W; Magar, Y; Magnan, A; Mahboub, B; Maier, D; Majer, I; Malva, J; Manning, P; De Manuel Keenoy, E; Marshall, G D; Masjedi, M R; Mathieu-Dupas, E; Maurer, M; Mavale-Manuel, S; Melén, E; Melo-Gomes, E; Meltzer, E O; Mercier, J; Merk, H; Miculinic, N; Mihaltan, F; Milenkovic, B; Millot-Keurinck, J; Mohammad, Y; Momas, I; Mösges, R; Muraro, A; Namazova-Baranova, L; Nadif, R; Neffen, H; Nekam, K; Nieto, A; Niggemann, B; Nogueira-Silva, L; Nogues, M; Nyembue, T D; Ohta, K; Okamoto, Y; Okubo, K; Olive-Elias, M; Ouedraogo, S; Paggiaro, P; Pali-Schöll, I; Palkonen, S; Panzner, P; Papi, A; Park, H S; Passalacqua, G; Pedersen, S; Pereira, A M; Pfaar, O; Picard, R; Pigearias, B; Pin, I; Plavec, D; Pohl, W; Popov, T A; Portejoie, F; Postma, D; Poulsen, L K; Price, D; Rabe, K F; Raciborski, F; Roberts, G; Robalo-Cordeiro, C; Rodenas, F; Rodriguez-Mañas, L; Rolland, C; Roman Rodriguez, M; Romano, A; Rosado-Pinto, J; Rosario, N; Rottem, M; Sanchez-Borges, M; Sastre-Dominguez, J; Scadding, G K; Scichilone, N; Schmid-Grendelmeier, P; Serrano, E; Shields, M; Siroux, V; Sisul, J C; Skrindo, I; Smit, H A; Solé, D; Sooronbaev, T; Spranger, O; Stelmach, R; Sterk, P J; Strandberg, T; Sunyer, J; Thijs, C; Triggiani, M; Valenta, R; Valero, A; van Eerd, M; van Ganse, E; van Hague, M; Vandenplas, O; Varona, L L; Vellas, B; Vezzani, G; Vazankari, T; Viegi, G; Vontetsianos, T; Wagenmann, M; Walker, S; Wang, D Y; Wahn, U; Werfel, T; Whalley, B; Williams, D M; Williams, S; Wilson, N; Wright, J; Yawn, B P; Yiallouros, P K; Yusuf, O M; Zaidi, A; Zar, H J; Zernotti, M E; Zhang, L; Zhong, N; Zidarn, M

    2016-01-01

    The Allergic Rhinitis and its Impact on Asthma (ARIA) initiative commenced during a World Health Organization workshop in 1999. The initial goals were (1) to propose a new allergic rhinitis classification, (2) to promote the concept of multi-morbidity in asthma and rhinitis and (3) to develop guidelines with all stakeholders that could be used globally for all countries and populations. ARIA-disseminated and implemented in over 70 countries globally-is now focusing on the implementation of emerging technologies for individualized and predictive medicine. MASK [MACVIA (Contre les Maladies Chroniques pour un Vieillissement Actif)-ARIA Sentinel NetworK] uses mobile technology to develop care pathways for the management of rhinitis and asthma by a multi-disciplinary group and by patients themselves. An app (Android and iOS) is available in 20 countries and 15 languages. It uses a visual analogue scale to assess symptom control and work productivity as well as a clinical decision support system. It is associated with an inter-operable tablet for physicians and other health care professionals. The scaling up strategy uses the recommendations of the European Innovation Partnership on Active and Healthy Ageing. The aim of the novel ARIA approach is to provide an active and healthy life to rhinitis sufferers, whatever their age, sex or socio-economic status, in order to reduce health and social inequalities incurred by the disease.

  2. Influence of anabolic combinations of an androgen plus an estrogen on biochemical pathways in bovine uterine endometrium and ovary.

    PubMed

    Becker, C; Riedmaier, I; Reiter, M; Tichopad, A; Groot, M J; Stolker, A A M; Pfaffl, M W; Nielen, M F W; Meyer, H H D

    2011-07-01

    The application of anabolic steroids in food producing animals is forbidden in the EU since 1988, but the abuse of such drugs is a potential problem. The existing test systems are based on known compounds and can be eluded by newly emerging substances. The examination of physiological effects of anabolic hormones on different tissues to indirectly detect misuse might overcome this problem. Two studies were conducted with post-pubertal 24-months old Nguni heifers and pre-pubertal female 2-4 weeks old Holstein Friesian calves, respectively. The animals of the accordant treatment groups were administered combinations of estrogenic and androgenic compounds. The measurement of the gene expression pattern was undertaken with RT-qPCR. Target genes of different functional groups (receptors, angiogenesis, steroid synthesis, proliferation, apoptosis, nutrient metabolism and others) have been quantified. Several biochemical pathways were shown to be influenced by anabolic treatment. Both studies identified significant regulations in steroid and growth factor receptors (AR, ERβ, LHR, FSHR, Flt-1, PR, IGF-1R, Alk-6), angiogenic and tissue remodeling factors (VEGFs, FGFs, BMPs, ANGPT-2, MMPs, TIMP-2, CTSB), steroid synthesis (S5A1, HSD17, CYP19A1), proliferation (TNFα, IGF-1, IGFBPs, p53, c-fos; CEBPD, c-kit), apoptosis (CASP3, FasL, p53) and others (C7, INHA, STAR). Several genes were regulated to opposite directions in post-pubertal compared to pre-pubertal animals. PCA for Nguni heifers demonstrated a distinct separation between the control and the treatment group. In conclusion, anabolics modify hormone sensitivity and steroid synthesis, and they induce proliferative effects in the whole reproductive tract (uterus and ovary) as well as anti-angiogenic effects in the ovary. However, the extent will depend on the developmental stage of the animals.

  3. Efficacy of unfractionated heparin, low molecular weight heparin and both combined for releasing total and free tissue factor pathway inhibitor.

    PubMed

    Altman, R; Scazziota, A; Rouvier, J

    1998-01-01

    Unfractionated heparin (UFH) exerts its anticoagulant properties by increasing the inactivation of thrombin and activated factor X by antithrombin III. Apart from this main action release of tissue factor pathway inhibitor (TFPI) from endothelial cells could also be important for the antithrombotic activity of heparins. Four different heparin preparations were injected subcutaneously into 5 healthy volunteers 1 week apart: (1) UFH 2,500 IU fix dose (FixUFH), (2) 1 mg/kg body weight of low molecular weight heparin (LMWH), (3) the combined LMWH-adjusted dose plus UFH 2,500 IU fix dose (ComHep) and (4) UFH 2,500 IU/10 kg body weight (UFHvar). Plasma samples were drawn before and 1, 2, 4, 6, 12 and 24 h afterwards. FixUFH did not affect the concentration of total and free TFPI. Total TFPI increased in the 1st hour after LMWH injection from 74 to 124 ng/ml (p < 0.01), after ComHep from 82 to 144 ng/ml (p < 0.01), and after UFHvar from 91 to 113 ng/ml (p < 0.05). All observed elevations were significant at the peak value (+/- 2 h, p < 0.01 compared with baselines). The increase of free TFPI produced by UFHvar (74.5 and 70.5 ng/ml) was significantly higher than with LMWH (42.8 and 38.0 ng/ml) at 2 and 4 h (p < 0.001 and p < 0.01, respectively). UFHvar and ComHep but not LMWH produced a statistically significant increase of free TFPI compared with FixUFH at 2, 4 and 6 h (p < 0. 01). We concluded that at comparable therapeutic doses, subcutaneous UFHvar released more free TFPI than LMWH and ComHep. A synergism between LMWH and low dose of UFH was found in 4-, 6- and 12-hour blood samples.

  4. New IVIVE method for the prediction of total human clearance and relative elimination pathway contributions from in vitro hepatocyte and microsome data.

    PubMed

    Riede, Julia; Poller, Birk; Umehara, Ken-ichi; Huwyler, Jörg; Camenisch, Gian

    2016-04-30

    Total human clearance is a key determinant for the pharmacokinetic behavior of drug candidates. Our group recently introduced the Extended Clearance Model (ECM) as an accurate in vitro-in vivo extrapolation (IVIVE) method for the prediction of hepatic clearance. Yet, knowledge about relative elimination pathway contributions is needed in order to predict the total human clearance of drug candidates. In the present work, a training set of 18 drug compounds was used to describe the affiliations between in vitro sinusoidal uptake clearance and the fractional contributions of hepatic (metabolic and biliary) or renal clearance to overall in vivo elimination. By means of these quantitative relationships and using a validation set of 10 diverse drug molecules covering different (sub)classes of the Extended Clearance Concept Classification System (ECCCS), the relative contributions of elimination pathways were calculated and demonstrated to well correlate with human reference data. Likewise, ECM- and pathway-based predictions of total clearances from both data sets demonstrated a strong correlation with the observed clinical values with 26 out of 28 compounds within a three-fold deviation. Hence, total human clearance and relative contributions of elimination pathways were successfully predicted by the presented method using solely hepatocyte and microsome in vitro data.

  5. Predictive Value of National Football League Scouting Combine on Future Performance of Running Backs and Wide Receivers.

    PubMed

    Teramoto, Masaru; Cross, Chad L; Willick, Stuart E

    2016-05-01

    The National Football League (NFL) Scouting Combine is held each year before the NFL Draft to measure athletic abilities and football skills of college football players. Although the NFL Scouting Combine can provide the NFL teams with an opportunity to evaluate college players for the upcoming NFL Draft, its value for predicting future success of players has been questioned. This study examined whether the NFL Combine measures can predict future performance of running backs (RBs) and wide receivers (WRs) in the NFL. We analyzed the 2000-09 Combine data of RBs (N = 276) and WRs (N = 447) and their on-field performance for the first 3 years after the draft and over their entire careers in the NFL, using correlation and regression analyses, along with a principal component analysis (PCA). The results of the analyses showed that, after accounting for the number of games played, draft position, height (HT), and weight (WT), the time on 10-yard dash was the most important predictor of rushing yards per attempt of the first 3 years (p = 0.002) and of the careers (p < 0.001) in RBs. For WRs, vertical jump was found to be significantly associated with receiving yards per reception of the first 3 years (p = 0.001) and of the careers (p = 0.004) in the NFL, after adjusting for the covariates above. Furthermore, HT was most important in predicting future performance of WRs. The analyses also revealed that the 8 athletic drills in the Combine seemed to have construct validity. It seems that the NFL Scouting Combine has some value for predicting future performance of RBs and WRs in the NFL.

  6. Future daily PM10 concentrations prediction by combining regression models and feedforward backpropagation models with principle component analysis (PCA)

    NASA Astrophysics Data System (ADS)

    Ul-Saufie, Ahmad Zia; Yahaya, Ahmad Shukri; Ramli, Nor Azam; Rosaida, Norrimi; Hamid, Hazrul Abdul

    2013-10-01

    Future PM10 concentration prediction is very important because it can help local authorities to enact preventative measures to reduce the impact of air pollution. The aims of this study are to improve prediction of Multiple Linear Regression (MLR) and Feedforward backpropagation (FFBP) by combining them with principle component analysis for predicting future (next day, next two-day and next three-day) PM10 concentration in Negeri Sembilan, Malaysia. Annual hourly observations for PM10 in Negeri Sembilan, Malaysia from January 2003 to December 2010 were selected for predicting PM10 concentration level. Eighty percent of the monitoring records were used for training and twenty percent were used for validation of the models. Three accuracy measures - Prediction Accuracy (PA), Coefficient of Determination (R2) and Index of Agreement (IA), as well as two error measures - Normalized Absolute Error (NAE) and Root Mean Square Error (RMSE) were used to evaluate the performance of the models. Results show that PCA models combined with MLR and PCA with FFBP improved MLR and FFBP models for all three days in advance of predicting PM10 concentration, with reduced errors by as much as 18.1% (PCA-MLR) and 17.68% (PCA-FFBP) for next day, 19.2% (PCA-MLR) and 22.1% (PCA-FFBP) for next two-day and 18.7% (PCA-MLR) and 22.79% (PCA-FFBP) for next three-day predictions. Including PCA improved the accuracy of the models by as much as by 12.9% (PCA-MLR) and 13.3% (PCA-FFBP) for next day, 32.3% (PCA-MLR) and 14.7% (PCA-FFBP) for next two-day and 46.1% (PCA-MLR) and 19.3% (PCA-FFBP) for next three-day predictions.

  7. Prediction of individual combined benefit and harm for patients with atrial fibrillation considering warfarin therapy: a study protocol

    PubMed Central

    Li, Guowei; Holbrook, Anne; Delate, Thomas; Witt, Daniel M; Levine, Mitchell AH; Thabane, Lehana

    2015-01-01

    Introduction Clinical prediction rules have been validated and widely used in patients with atrial fibrillation (AF) to predict stroke and major bleeding. However, these prediction rules were not developed in the same population, and do not provide the key information that patients and prescribers need at the time anticoagulants are being considered—what is the individual patient-specific risk of both benefit (decreased stroke) and harm (increased major bleeding). In this study, our primary objective is to develop and validate a prediction model for patients’ individual combined benefit and harm outcomes (stroke, major bleeding and neither event) with and without warfarin therapy. Our secondary outcome is all-cause mortality. Methods and analysis We will use data from the Kaiser Permanente Colorado (KPCO) anticoagulation management databases and electronic medical records. Patients with a primary or secondary diagnosis during an ambulatory KPCO medical office visit, emergency department visit, or inpatient stay between 1 January 2005 and 31 December 2012 with no AF diagnosis in the previous 180 days will be included. Patients’ demographic characteristics, laboratory data, comorbidities, warfarin medication data and concurrent use of medication will be used to construct the prediction model. For primary outcomes (stroke with no major bleeding, and major bleeding with no stroke), we will perform polytomous logistic regression to develop a prediction model for patients’ individual combined benefit and harm outcomes, taking neither event group as the reference group. As regards death, we will use Cox proportional hazards regression analysis to build a prediction model for all-cause mortality. Ethics and dissemination This study has been approved by the KPCO Institutional Review Board and the Hamilton Integrated Research Ethics Board. Results from this study will be published in a peer-reviewed journal electronically and in print. The prediction models may aid

  8. The combination of the functionalities of feedback circuits is determinant for the attractors’ number and size in pathway-like Boolean networks

    PubMed Central

    Azpeitia, Eugenio; Muñoz, Stalin; González-Tokman, Daniel; Martínez-Sánchez, Mariana Esther; Weinstein, Nathan; Naldi, Aurélien; Álvarez-Buylla, Elena R.; Rosenblueth, David A.; Mendoza, Luis

    2017-01-01

    Molecular regulation was initially assumed to follow both a unidirectional and a hierarchical organization forming pathways. Regulatory processes, however, form highly interlinked networks with non-hierarchical and non-unidirectional structures that contain statistically overrepresented circuits or motifs. Here, we analyze the behavior of pathways containing non-unidirectional (i.e. bidirectional) and non-hierarchical interactions that create motifs. In comparison with unidirectional and hierarchical pathways, our pathways have a high diversity of behaviors, characterized by the size and number of attractors. Motifs have been studied individually showing that feedback circuit motifs regulate the number and size of attractors. It is less clear what happens in molecular networks that usually contain multiple feedbacks. Here, we find that the way feedback circuits couple to each other (i.e., the combination of the functionalities of feedback circuits) regulate both the number and size of the attractors. We show that the different expected results of epistasis analysis (a method to infer regulatory interactions) are produced by many non-hierarchical and non-unidirectional structures. Thus, these structures cannot be correctly inferred by epistasis analysis. Finally, we show that the combinations of functionalities, combined with other network properties, allow for a better characterization of regulatory structures. PMID:28186191

  9. The combination of the functionalities of feedback circuits is determinant for the attractors' number and size in pathway-like Boolean networks.

    PubMed

    Azpeitia, Eugenio; Muñoz, Stalin; González-Tokman, Daniel; Martínez-Sánchez, Mariana Esther; Weinstein, Nathan; Naldi, Aurélien; Álvarez-Buylla, Elena R; Rosenblueth, David A; Mendoza, Luis

    2017-02-10

    Molecular regulation was initially assumed to follow both a unidirectional and a hierarchical organization forming pathways. Regulatory processes, however, form highly interlinked networks with non-hierarchical and non-unidirectional structures that contain statistically overrepresented circuits or motifs. Here, we analyze the behavior of pathways containing non-unidirectional (i.e. bidirectional) and non-hierarchical interactions that create motifs. In comparison with unidirectional and hierarchical pathways, our pathways have a high diversity of behaviors, characterized by the size and number of attractors. Motifs have been studied individually showing that feedback circuit motifs regulate the number and size of attractors. It is less clear what happens in molecular networks that usually contain multiple feedbacks. Here, we find that the way feedback circuits couple to each other (i.e., the combination of the functionalities of feedback circuits) regulate both the number and size of the attractors. We show that the different expected results of epistasis analysis (a method to infer regulatory interactions) are produced by many non-hierarchical and non-unidirectional structures. Thus, these structures cannot be correctly inferred by epistasis analysis. Finally, we show that the combinations of functionalities, combined with other network properties, allow for a better characterization of regulatory structures.

  10. The combination of the functionalities of feedback circuits is determinant for the attractors’ number and size in pathway-like Boolean networks

    NASA Astrophysics Data System (ADS)

    Azpeitia, Eugenio; Muñoz, Stalin; González-Tokman, Daniel; Martínez-Sánchez, Mariana Esther; Weinstein, Nathan; Naldi, Aurélien; Álvarez-Buylla, Elena R.; Rosenblueth, David A.; Mendoza, Luis

    2017-02-01

    Molecular regulation was initially assumed to follow both a unidirectional and a hierarchical organization forming pathways. Regulatory processes, however, form highly interlinked networks with non-hierarchical and non-unidirectional structures that contain statistically overrepresented circuits or motifs. Here, we analyze the behavior of pathways containing non-unidirectional (i.e. bidirectional) and non-hierarchical interactions that create motifs. In comparison with unidirectional and hierarchical pathways, our pathways have a high diversity of behaviors, characterized by the size and number of attractors. Motifs have been studied individually showing that feedback circuit motifs regulate the number and size of attractors. It is less clear what happens in molecular networks that usually contain multiple feedbacks. Here, we find that the way feedback circuits couple to each other (i.e., the combination of the functionalities of feedback circuits) regulate both the number and size of the attractors. We show that the different expected results of epistasis analysis (a method to infer regulatory interactions) are produced by many non-hierarchical and non-unidirectional structures. Thus, these structures cannot be correctly inferred by epistasis analysis. Finally, we show that the combinations of functionalities, combined with other network properties, allow for a better characterization of regulatory structures.

  11. Energetic-environmental-economic assessment of the biogas system with three utilization pathways: Combined heat and power, biomethane and fuel cell.

    PubMed

    Wu, Bin; Zhang, Xiangping; Shang, Dawei; Bao, Di; Zhang, Suojiang; Zheng, Tao

    2016-08-01

    A typical biogas system with three utilization pathways, i.e., biogas upgrading, biogas combined heat and power (CHP), biogas solid oxide fuel cells (SOFCs) were designed. It was assessed from the viewpoint of energy, environment and economy by using energy efficiency, green degree and net present value index respectively. The assessment considered the trade-off relationships among these indexes, which is more comprehensive than previous systematic evaluation work only included single or two of the pathway(s) by using one or two of the index(es). Assessment results indicated that biogas upgrading pathway has the highest systematic energy efficiency (46.5%) and shortest payback period (8.9year) with the green degree production is the lowest (9.29gd/day). While for biogas SOFC pathway, although the green degree production is the highest (21.77gd/day), the payback period is longer (14.5year) and the energy efficiency is 13.6% lower than the biogas upgrading pathway.

  12. The prediction of human skin responses by using the combined in vitro fluorescein leakage/Alamar Blue (resazurin) assay.

    PubMed

    Clothier, Richard; Starzec, Gemma; Pradel, Lionel; Baxter, Victoria; Jones, Melanie; Cox, Helen; Noble, Linda

    2002-01-01

    A range of cosmetics formulations with human patch-test data were supplied in a coded form, for the examination of the use of a combined in vitro permeability barrier assay and cell viability assay to generate, and then test, a prediction model for assessing potential human skin patch-test results. The target cells employed were of the Madin Darby canine kidney cell line, which establish tight junctions and adherens junctions able to restrict the permeability of sodium fluorescein across the barrier of the confluent cell layer. The prediction model for interpretation of the in vitro assay results included initial effects and the recovery profile over 72 hours. A set of the hand-wash, surfactant-based formulations were tested to generate the prediction model, and then six others were evaluated. The model system was then also evaluated with powder laundry detergents and hand moisturisers: their effects were predicted by the in vitro test system. The model was under-predictive for two of the ten hand-wash products. It was over-predictive for the moisturisers, (two out of six) and eight out of ten laundry powders. However, the in vivo human patch test data were variable, and 19 of the 26 predictions were correct or within 0.5 on the 0-4.0 scale used for the in vivo scores, i.e. within the same variable range reported for the repeat-test hand-wash in vivo data.

  13. Improving the prediction of arsenic contents in agricultural soils by combining the reflectance spectroscopy of soils and rice plants

    NASA Astrophysics Data System (ADS)

    Shi, Tiezhu; Wang, Junjie; Chen, Yiyun; Wu, Guofeng

    2016-10-01

    Visible and near-infrared reflectance spectroscopy provides a beneficial tool for investigating soil heavy metal contamination. This study aimed to investigate mechanisms of soil arsenic prediction using laboratory based soil and leaf spectra, compare the prediction of arsenic content using soil spectra with that using rice plant spectra, and determine whether the combination of both could improve the prediction of soil arsenic content. A total of 100 samples were collected and the reflectance spectra of soils and rice plants were measured using a FieldSpec3 portable spectroradiometer (350-2500 nm). After eliminating spectral outliers, the reflectance spectra were divided into calibration (n = 62) and validation (n = 32) data sets using the Kennard-Stone algorithm. Genetic algorithm (GA) was used to select useful spectral variables for soil arsenic prediction. Thereafter, the GA-selected spectral variables of the soil and leaf spectra were individually and jointly employed to calibrate the partial least squares regression (PLSR) models using the calibration data set. The regression models were validated and compared using independent validation data set. Furthermore, the correlation coefficients of soil arsenic against soil organic matter, leaf arsenic and leaf chlorophyll were calculated, and the important wavelengths for PLSR modeling were extracted. Results showed that arsenic prediction using the leaf spectra (coefficient of determination in validation, Rv2 = 0.54; root mean square error in validation, RMSEv = 12.99 mg kg-1; and residual prediction deviation in validation, RPDv = 1.35) was slightly better than using the soil spectra (Rv2 = 0.42, RMSEv = 13.35 mg kg-1, and RPDv = 1.31). However, results also showed that the combinational use of soil and leaf spectra resulted in higher arsenic prediction (Rv2 = 0.63, RMSEv = 11.94 mg kg-1, RPDv = 1.47) compared with either soil or leaf spectra alone. Soil spectral bands near 480, 600, 670, 810, 1980, 2050 and

  14. cAMP-PKA-CaMKII signaling pathway is involved in aggravated cardiotoxicity during Fuzi and Beimu Combination Treatment of Experimental Pulmonary Hypertension

    PubMed Central

    Zhuang, Pengwei; Huang, Yingying; Lu, Zhiqiang; Yang, Zhen; Xu, Liman; Sun, Fengjiao; Zhang, Yanjun; Duan, Jinao

    2016-01-01

    Aconiti Lateralis Radix Praeparata (Fuzi) and Fritillariae Thunbergii bulbus (Beimu) have been widely used clinically to treat cardiopulmonary related diseases in China. However, according to the classic rules of traditional Chinese medicine, Fuzi and Beimu should be prohibited to use as a combination for their incompatibility. Therefore, it is critical to elucidate the paradox on the use of Fuzi and Beimu combination therapy. Monocrotaline-induced pulmonary hypertension rats were treated with either Fuzi, Beimu, or their combination at different stages of PH. We demonstrated that at the early stage of PH, Fuzi and Beimu combination significantly improved lung function and reduced pulmonary histopathology. However, as the disease progressed, when Fuzi and Beimu combination were used at the late stage of PH, right ventricular chamber dilation was histologically apparent and myocardial apoptosis was significantly increased compared with each drug alone. Western-blotting results indicated that the main chemical ingredient of Beimu could down-regulate the protein phosphorylation levels of Akt and PDE4D, whereas the combination of Fuzi and Beimu could up-regulate PKA and CaMKII signaling pathways. Therefore, we concluded that Fuzi and Beimu combination potentially aggravated the heart injury due to the inhibition of PDK1/Akt/PDE4D axis and subsequent synergistic activation of βAR-Gs-PKA/CaMKII signaling pathway. PMID:27739450

  15. Combining algorithms to predict bacterial protein sub-cellular location: Parallel versus concurrent implementations.

    PubMed

    Taylor, Paul D; Attwood, Teresa K; Flower, Darren R

    2006-12-06

    We describe a novel and potentially important tool for candidate subunit vaccine selection through in silico reverse-vaccinology. A set of Bayesian networks able to make individual predictions for specific subcellular locations is implemented in three pipelines with different architectures: a parallel implementation with a confidence level-based decision engine and two serial implementations with a hierarchical decision structure, one initially rooted by prediction between membrane types and another rooted by soluble versus membrane prediction. The parallel pipeline outperformed the serial pipeline, but took twice as long to execute. The soluble-rooted serial pipeline outperformed the membrane-rooted predictor. Assessment using genomic test sets was more equivocal, as many more predictions are made by the parallel pipeline, yet the serial pipeline identifies 22 more of the 74 proteins of known location.

  16. Clathrate Structure Determination by Combining Crystal Structure Prediction with Computational and Experimental (129) Xe NMR Spectroscopy.

    PubMed

    Selent, Marcin; Nyman, Jonas; Roukala, Juho; Ilczyszyn, Marek; Oilunkaniemi, Raija; Bygrave, Peter J; Laitinen, Risto; Jokisaari, Jukka; Day, Graeme M; Lantto, Perttu

    2017-01-23

    An approach is presented for the structure determination of clathrates using NMR spectroscopy of enclathrated xenon to select from a set of predicted crystal structures. Crystal structure prediction methods have been used to generate an ensemble of putative structures of o- and m-fluorophenol, whose previously unknown clathrate structures have been studied by (129) Xe NMR spectroscopy. The high sensitivity of the (129) Xe chemical shift tensor to the chemical environment and shape of the crystalline cavity makes it ideal as a probe for porous materials. The experimental powder NMR spectra can be used to directly confirm or reject hypothetical crystal structures generated by computational prediction, whose chemical shift tensors have been simulated using density functional theory. For each fluorophenol isomer one predicted crystal structure was found, whose measured and computed chemical shift tensors agree within experimental and computational error margins and these are thus proposed as the true fluorophenol xenon clathrate structures.

  17. Consensus Data Mining (CDM) Protein Secondary Structure Prediction Server: combining GOR V and Fragment Database Mining (FDM).

    PubMed

    Cheng, Haitao; Sen, Taner Z; Jernigan, Robert L; Kloczkowski, Andrzej

    2007-10-01

    One of the challenges in protein secondary structure prediction is to overcome the cross-validated 80% prediction accuracy barrier. Here, we propose a novel approach to surpass this barrier. Instead of using a single algorithm that relies on a limited data set for training, we combine two complementary methods having different strengths: Fragment Database Mining (FDM) and GOR V. FDM harnesses the availability of the known protein structures in the Protein Data Bank and provides highly accurate secondary structure predictions when sequentially similar structural fragments are identified. In contrast, the GOR V algorithm is based on information theory, Bayesian statistics, and PSI-BLAST multiple sequence alignments to predict the secondary structure of residues inside a sliding window along a protein chain. A combination of these two different methods benefits from the large number of structures in the PDB and significantly improves the secondary structure prediction accuracy, resulting in Q3 ranging from 67.5 to 93.2%, depending on the availability of highly similar fragments in the Protein Data Bank.

  18. Can We Predict Individual Combined Benefit and Harm of Therapy? Warfarin Therapy for Atrial Fibrillation as a Test Case

    PubMed Central

    Li, Guowei; Thabane, Lehana; Delate, Thomas; Witt, Daniel M.; Levine, Mitchell A. H.; Cheng, Ji; Holbrook, Anne

    2016-01-01

    Objectives To construct and validate a prediction model for individual combined benefit and harm outcomes (stroke with no major bleeding, major bleeding with no stroke, neither event, or both) in patients with atrial fibrillation (AF) with and without warfarin therapy. Methods Using the Kaiser Permanente Colorado databases, we included patients newly diagnosed with AF between January 1, 2005 and December 31, 2012 for model construction and validation. The primary outcome was a prediction model of composite of stroke or major bleeding using polytomous logistic regression (PLR) modelling. The secondary outcome was a prediction model of all-cause mortality using the Cox regression modelling. Results We included 9074 patients with 4537 and 4537 warfarin users and non-users, respectively. In the derivation cohort (n = 4632), there were 136 strokes (2.94%), 280 major bleedings (6.04%) and 1194 deaths (25.78%) occurred. In the prediction models, warfarin use was not significantly associated with risk of stroke, but increased the risk of major bleeding and decreased the risk of death. Both the PLR and Cox models were robust, internally and externally validated, and with acceptable model performances. Conclusions In this study, we introduce a new methodology for predicting individual combined benefit and harm outcomes associated with warfarin therapy for patients with AF. Should this approach be validated in other patient populations, it has potential advantages over existing risk stratification approaches as a patient-physician aid for shared decision-making PMID:27513986

  19. Kinetic network study of the diversity and temperature dependence of Trp-Cage folding pathways: combining transition path theory with stochastic simulations.

    PubMed

    Zheng, Weihua; Gallicchio, Emilio; Deng, Nanjie; Andrec, Michael; Levy, Ronald M

    2011-02-17

    We present a new approach to study a multitude of folding pathways and different folding mechanisms for the 20-residue mini-protein Trp-Cage using the combined power of replica exchange molecular dynamics (REMD) simulations for conformational sampling, transition path theory (TPT) for constructing folding pathways, and stochastic simulations for sampling the pathways in a high dimensional structure space. REMD simulations of Trp-Cage with 16 replicas at temperatures between 270 and 566 K are carried out with an all-atom force field (OPLSAA) and an implicit solvent model (AGBNP). The conformations sampled from all temperatures are collected. They form a discretized state space that can be used to model the folding process. The equilibrium population for each state at a target temperature can be calculated using the weighted-histogram-analysis method (WHAM). By connecting states with similar structures and creating edges satisfying detailed balance conditions, we construct a kinetic network that preserves the equilibrium population distribution of the state space. After defining the folded and unfolded macrostates, committor probabilities (P(fold)) are calculated by solving a set of linear equations for each node in the network and pathways are extracted together with their fluxes using the TPT algorithm. By clustering the pathways into folding "tubes", a more physically meaningful picture of the diversity of folding routes emerges. Stochastic simulations are carried out on the network, and a procedure is developed to project sampled trajectories onto the folding tubes. The fluxes through the folding tubes calculated from the stochastic trajectories are in good agreement with the corresponding values obtained from the TPT analysis. The temperature dependence of the ensemble of Trp-Cage folding pathways is investigated. Above the folding temperature, a large number of diverse folding pathways with comparable fluxes flood the energy landscape. At low temperature

  20. Combined Detection of Serum IL-10, IL-17, and CXCL10 Predicts Acute Rejection Following Adult Liver Transplantation

    PubMed Central

    Kim, Nayoung; Yoon, Young-In; Yoo, Hyun Ju; Tak, Eunyoung; Ahn, Chul-Soo; Song, Gi-Won; Lee, Sung-Gyu; Hwang, Shin

    2016-01-01

    Discovery of non-invasive diagnostic and predictive biomarkers for acute rejection in liver transplant patients would help to ensure the preservation of liver function in the graft, eventually contributing to improved graft and patient survival. We evaluated selected cytokines and chemokines in the sera from liver transplant patients as potential biomarkers for acute rejection, and found that the combined detection of IL-10, IL-17, and CXCL10 at 1-2 weeks post-operation could predict acute rejection following adult liver transplantation with 97% specificity and 94% sensitivity. PMID:27498551

  1. Assessing the impact of land use change on hydrology by ensemble modelling (LUCHEM) II: Ensemble combinations and predictions

    USGS Publications Warehouse

    Viney, N.R.; Bormann, H.; Breuer, L.; Bronstert, A.; Croke, B.F.W.; Frede, H.; Graff, T.; Hubrechts, L.; Huisman, J.A.; Jakeman, A.J.; Kite, G.W.; Lanini, J.; Leavesley, G.; Lettenmaier, D.P.; Lindstrom, G.; Seibert, J.; Sivapalan, M.; Willems, P.

    2009-01-01

    This paper reports on a project to compare predictions from a range of catchment models applied to a mesoscale river basin in central Germany and to assess various ensemble predictions of catchment streamflow. The models encompass a large range in inherent complexity and input requirements. In approximate order of decreasing complexity, they are DHSVM, MIKE-SHE, TOPLATS, WASIM-ETH, SWAT, PRMS, SLURP, HBV, LASCAM and IHACRES. The models are calibrated twice using different sets of input data. The two predictions from each model are then combined by simple averaging to produce a single-model ensemble. The 10 resulting single-model ensembles are combined in various ways to produce multi-model ensemble predictions. Both the single-model ensembles and the multi-model ensembles are shown to give predictions that are generally superior to those of their respective constituent models, both during a 7-year calibration period and a 9-year validation period. This occurs despite a considerable disparity in performance of the individual models. Even the weakest of models is shown to contribute useful information to the ensembles they are part of. The best model combination methods are a trimmed mean (constructed using the central four or six predictions each day) and a weighted mean ensemble (with weights calculated from calibration performance) that places relatively large weights on the better performing models. Conditional ensembles, in which separate model weights are used in different system states (e.g. summer and winter, high and low flows) generally yield little improvement over the weighted mean ensemble. However a conditional ensemble that discriminates between rising and receding flows shows moderate improvement. An analysis of ensemble predictions shows that the best ensembles are not necessarily those containing the best individual models. Conversely, it appears that some models that predict well individually do not necessarily combine well with other models in

  2. Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD.

    PubMed

    Galatzer-Levy, I R; Ma, S; Statnikov, A; Yehuda, R; Shalev, A Y

    2017-03-21

    To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the use of data analytic tools that are ill equipped for modeling the complex interactions between biological and environmental factors that underlay post-traumatic psychopathology. Further, using symptom-based diagnostic status as the group outcome overlooks the inherent heterogeneity of PTSD, potentially contributing to failures to replicate. To examine the potential yield of novel analytic tools, we reanalyzed data from a large longitudinal study of individuals identified following trauma in the general emergency room (ER) that failed to find a linear association between cortisol response to traumatic events and subsequent PTSD. First, latent growth mixture modeling empirically identified trajectories of post-traumatic symptoms, which then were used as the study outcome. Next, support vector machines with feature selection identified sets of features with stable predictive accuracy and built robust classifiers of trajectory membership (area under the receiver operator characteristic curve (AUC)=0.82 (95% confidence interval (CI)=0.80-0.85)) that combined clinical, neuroendocrine, psychophysiological and demographic information. Finally, graph induction algorithms revealed a unique path from childhood trauma via lower cortisol during ER admission, to non-remitting PTSD. Traditional general linear modeling methods then confirmed the newly revealed association, thereby delineating a specific target population for early endocrine interventions. Advanced computational approaches offer innovative ways for uncovering clinically significant, non-shared biological signals in heterogeneous samples.

  3. Short-term prediction of UT1-UTC by combination of the grey model and neural networks

    NASA Astrophysics Data System (ADS)

    Lei, Yu; Guo, Min; Hu, Dan-dan; Cai, Hong-bing; Zhao, Dan-ning; Hu, Zhao-peng; Gao, Yu-ping

    2017-01-01

    UT1-UTC predictions especially short-term predictions are essential in various fields linked to reference systems such as space navigation and precise orbit determinations of artificial Earth satellites. In this paper, an integrated model combining the grey model GM(1, 1) and neural networks (NN) are proposed for predicting UT1-UTC. In this approach, the effects of the Solid Earth tides and ocean tides together with leap seconds are first removed from observed UT1-UTC data to derive UT1R-TAI. Next the derived UT1R-TAI time-series are de-trended using the GM(1, 1) and then residuals are obtained. Then the residuals are used to train a network. The subsequently predicted residuals are added to the GM(1, 1) to obtain the UT1R-TAI predictions. Finally, the predicted UT1R-TAI are corrected for the tides together with leap seconds to obtain UT1-UTC predictions. The daily values of UT1-UTC between January 7, 2010 and August 6, 2016 from the International Earth Rotation and Reference Systems Service (IERS) 08 C04 series are used for modeling and validation of the proposed model. The results of the predictions up to 30 days in the future are analyzed and compared with those by the GM(1, 1)-only model and combination of the least-squares (LS) extrapolation of the harmonic model including the linear part, annual and semi-annual oscillations and NN. It is found that the proposed model outperforms the other two solutions. In addition, the predictions are compared with those from the Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC) lasting from October 1, 2005 to February 28, 2008. The results show that the prediction accuracy is inferior to that of those methods taking into account atmospheric angular momentum (AAM), i.e., Kalman filter and adaptive transform from AAM to LODR, but noticeably better that of the other existing methods and techniques, e.g., autoregressive filtering and least-squares collocation.

  4. USING VISUAL PLUMES PREDICTIONS TO MODULATE COMBINED SEWER OVERFLOW (CSO) RATES

    EPA Science Inventory

    High concentrations of pathogens and toxic residues in creeks and rivers can pose risks to human health and ecological systems. Combined Sewer Overflows (CSOs) discharging into these watercourses often contribute significantly to elevating pollutant concentrations during wet weat...

  5. Reducing uncertainty in predictions in ungauged basins by combining hydrologic indices regionalization and multiobjective optimization

    NASA Astrophysics Data System (ADS)

    Zhang, Zhenxing; Wagener, Thorsten; Reed, Patrick; Bhushan, Rashi

    2008-12-01

    Approaches to predictions in ungauged basins have so far mainly focused on a priori parameter estimates from physical watershed characteristics or on the regionalization of model parameters. Recent studies suggest that the regionalization of hydrologic indices (e.g., streamflow characteristics) provides an additional way to extrapolate information about the expected watershed response to ungauged locations for use in continuous watershed modeling. This study contributes a novel multiobjective framework for identifying behavioral parameter ensembles for ungauged basins using suites of regionalized hydrologic indices. The new formulation enables the use of multiobjective optimization algorithms for the identification of model ensembles for predictions in ungauged basins for the first time. Application of the new formulation to 30 watersheds located in England and Wales and comparison of the results with a Monte Carlo approach demonstrate that the new formulation will significantly advance our ability to reduce the uncertainty of predictions in ungauged basins.

  6. One hybrid model combining singular spectrum analysis and LS + ARMA for polar motion prediction

    NASA Astrophysics Data System (ADS)

    Shen, Yi; Guo, Jinyun; Liu, Xin; Wei, Xiaobei; Li, Wudong

    2017-01-01

    Accurate real-time polar motion parameters play an important role in satellite navigation and positioning and spacecraft tracking. To meet the needs for real-time and high-accuracy polar motion prediction, a hybrid model that integrated singular spectrum analysis (SSA), least-squares (LS) extrapolation and an autoregressive moving average (ARMA) model was proposed. SSA was applied to separate the trend, the annual and the Chandler components from a given polar motion time series. LS extrapolation models were constructed for the separated trend, annual and Chandler components. An ARMA model was established for a synthetic sequence that contained the remaining SSA component and the residual series of LS fitting. In applying this hybrid model, multiple sets of polar motion predictions with lead times of 360 days were made based on an IERS 08 C04 series. The results showed that the proposed method could effectively predict the polar motion parameters.

  7. FINDSITE: a combined evolution/structure-based approach to protein function prediction

    PubMed Central

    Brylinski, Michal

    2009-01-01

    A key challenge of the post-genomic era is the identification of the function(s) of all the molecules in a given organism. Here, we review the status of sequence and structure-based approaches to protein function inference and ligand screening that can provide functional insights for a significant fraction of the ∼50% of ORFs of unassigned function in an average proteome. We then describe FINDSITE, a recently developed algorithm for ligand binding site prediction, ligand screening and molecular function prediction, which is based on binding site conservation across evolutionary distant proteins identified by threading. Importantly, FINDSITE gives comparable results when high-resolution experimental structures as well as predicted protein models are used. PMID:19324930

  8. Numerical Predictions of Wind Turbine Power and Aerodynamic Loads for the NREL Phase II and IV Combined Experiment Rotor

    NASA Technical Reports Server (NTRS)

    Duque, Earl P. N.; Johnson, Wayne; vanDam, C. P.; Chao, David D.; Cortes, Regina; Yee, Karen

    1999-01-01

    Accurate, reliable and robust numerical predictions of wind turbine rotor power remain a challenge to the wind energy industry. The literature reports various methods that compare predictions to experiments. The methods vary from Blade Element Momentum Theory (BEM), Vortex Lattice (VL), to variants of Reynolds-averaged Navier-Stokes (RaNS). The BEM and VL methods consistently show discrepancies in predicting rotor power at higher wind speeds mainly due to inadequacies with inboard stall and stall delay models. The RaNS methodologies show promise in predicting blade stall. However, inaccurate rotor vortex wake convection, boundary layer turbulence modeling and grid resolution has limited their accuracy. In addition, the inherently unsteady stalled flow conditions become computationally expensive for even the best endowed research labs. Although numerical power predictions have been compared to experiment. The availability of good wind turbine data sufficient for code validation experimental data that has been extracted from the IEA Annex XIV download site for the NREL Combined Experiment phase II and phase IV rotor. In addition, the comparisons will show data that has been further reduced into steady wind and zero yaw conditions suitable for comparisons to "steady wind" rotor power predictions. In summary, the paper will present and discuss the capabilities and limitations of the three numerical methods and make available a database of experimental data suitable to help other numerical methods practitioners validate their own work.

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

    PubMed Central

    Das, Shiva K.; Chen, Shifeng; Deasy, Joseph O.; Zhou, Sumin; Yin, Fang-Fang; Marks, Lawrence B.

    2008-01-01

    The fusion of predictions from disparate models has been used in several fields to obtain a more realistic and robust estimate of the “ground truth” by allowing the models to reinforce each other when consensus exists, or, conversely, negate each other when there is no consensus. Fusion has been shown to be most effective when the models have some complementary strengths arising from different approaches. In this work, we fuse the results from four common but methodologically different nonlinear multivariate models (Decision Trees, Neural Networks, Support Vector Machines, Self-Organizing Maps) that were trained to predict radiation-induced pneumonitis risk on a database of 219 lung cancer patients treated with radiotherapy (34 with Grade 2+ postradiotherapy pneumonitis). Each model independently incorporated a small number of features from the available set of dose and nondose patient variables to predict pneumonitis; no two models had all features in common. Fusion was achieved by simple averaging of the predictions for each patient from all four models. Since a model’s prediction for a patient can be dependent on the patient training set used to build the model, the average of several different predictions from each model was used in the fusion (predictions were made by repeatedly testing each patient with a model built from different cross-validation training sets that excluded the patient being tested). The area under the receiver operating characteristics curve for the fused cross-validated results was 0.79, with lower variance than the individual component models. From the fusion, five features were extracted as the consensus among all four models in predicting radiation pneumonitis. Arranged in order of importance, the features are (1) chemotherapy; (2) equivalent uniform dose (EUD) for exponent a=1.2 to 3; (3) EUD for a=0.5 to 1.2, lung volume receiving >20–30 Gy; (4) female sex; and (5) squamous cell histology. To facilitate ease of interpretation

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

    SciTech Connect

    Das, Shiva K.; Chen Shifeng; Deasy, Joseph O.; Zhou Sumin; Yin Fangfang; Marks, Lawrence B.

    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 ease of interpretation and

  11. Tissue factor pathway inhibitor for prediction of placenta-mediated adverse pregnancy outcomes in high-risk women: AngioPred study

    PubMed Central

    Di Bartolomeo, Aurélie; Chauleur, Céline; Gris, Jean-Christophe; Chapelle, Céline; Noblot, Edouard; Laporte, Silvy

    2017-01-01

    Objective The study aimed to evaluate if the rate of tissue factor pathway inhibitor during pregnancy and following delivery could be a predictive factor for placenta-mediated adverse pregnancy outcomes in high-risk women. Methods This was a prospective multicentre cohort study of 200 patients at a high risk of occurrence or recurrence of placenta-mediated adverse pregnancy outcomes conducted between June 2008 and October 2010. Measurements of tissue factor pathway inhibitor resistance (normalized ratio) and tissue factor pathway inhibitor activity were performed for the last 72 patients at 20, 24, 28, 32, and 36 weeks of gestation and during the postpartum period. Results Overall, 15 patients presented a placenta-mediated adverse pregnancy outcome. There was no difference in normalized tissue factor pathway inhibitor ratios between patients with and without placenta-mediated adverse pregnancy outcomes during pregnancy and in the post-partum period. Patients with placenta-mediated adverse pregnancy outcomes had tissue factor pathway inhibitor activity rates that were significantly higher than those in patients without at as early as 24 weeks of gestation. The same results were observed following delivery. Conclusion Among high-risk women, the tissue factor pathway inhibitor activity of patients with gestational vascular complications is higher than that in other patients. Hence, these markers could augment a screening strategy that includes an analysis of angiogenic factors as well as clinical and ultrasound imaging with Doppler measurement of the uterine arteries. PMID:28328938

  12. Strain analysis for the prediction of the preferential nucleation sites of stacked quantum dots by combination of FEM and APT

    PubMed Central

    2013-01-01

    The finite elements method (FEM) is a useful tool for the analysis of the strain state of semiconductor heterostructures. It has been used for the prediction of the nucleation sites of stacked quantum dots (QDs), but often using either simulated data of the atom positions or two-dimensional experimental data, in such a way that it is difficult to assess the validity of the predictions. In this work, we assess the validity of the FEM method for the prediction of stacked QD nucleation sites using three-dimensional experimental data obtained by atom probe tomography (APT). This also allows us to compare the simulation results with the one obtained experimentally. Our analysis demonstrates that FEM and APT constitute a good combination to resolve strain–stress problems of epitaxial semiconductor structures. PMID:24308663

  13. Novel Parameter Predicting Grade 2 Rectal Bleeding After Iodine-125 Prostate Brachytherapy Combined With External Beam Radiation Therapy

    SciTech Connect

    Shiraishi, Yutaka; Hanada, Takashi; Ohashi, Toshio; Yorozu, Atsunori; Toya, Kazuhito; Saito, Shiro; Shigematsu, Naoyuki

    2013-09-01

    Purpose: To propose a novel parameter predicting rectal bleeding on the basis of generalized equivalent uniform doses (gEUD) after {sup 125}I prostate brachytherapy combined with external beam radiation therapy and to assess the predictive value of this parameter. Methods and Materials: To account for differences among radiation treatment modalities and fractionation schedules, rectal dose–volume histograms (DVHs) of 369 patients with localized prostate cancer undergoing combined therapy retrieved from corresponding treatment planning systems were converted to equivalent dose-based DVHs. The gEUDs for the rectum were calculated from these converted DVHs. The total gEUD (gEUD{sub sum}) was determined by a summation of the brachytherapy and external-beam radiation therapy components. Results: Thirty-eight patients (10.3%) developed grade 2+ rectal bleeding. The grade 2+ rectal bleeding rate increased as the gEUD{sub sum} increased: 2.0% (2 of 102 patients) for <70 Gy, 10.3% (15 of 145 patients) for 70-80 Gy, 15.8% (12 of 76 patients) for 80-90 Gy, and 19.6% (9 of 46 patients) for >90 Gy (P=.002). Multivariate analysis identified age (P=.024) and gEUD{sub sum} (P=.000) as risk factors for grade 2+ rectal bleeding. Conclusions: Our results demonstrate gEUD to be a potential predictive factor for grade 2+ late rectal bleeding after combined therapy for prostate cancer.

  14. Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

    ERIC Educational Resources Information Center

    Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

    2009-01-01

    In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

  15. Predictive Factors for Radiation Pneumonitis in Hodgkin Lymphoma Patients Receiving Combined-Modality Therapy

    SciTech Connect

    Fox, Amy M.; Dosoretz, Arie P.; Mauch, Peter M.; Chen, Yu-Hui; Fisher, David C.; LaCasce, Ann S.; Freedman, Arnold S.; Silver, Barbara; Ng, Andrea K.

    2012-05-01

    Purpose: This study sought to quantify the risk of radiation pneumonitis (RP) in Hodgkin lymphoma (HL) patients receiving mediastinal radiation therapy (RT) and to identify predictive factors for RP. Methods and Materials: We identified 75 patients with newly diagnosed HL treated with mediastinal RT and 17 patients with relapsed/refractory HL treated with mediastinal RT before or after transplant. Lung dose-volumetric parameters including mean lung dose and percentage of lungs receiving 20 Gy were calculated. Factors associated with RP were explored by use of the Fisher exact test. Results: RP developed in 7 patients (10%) who received mediastinal RT as part of initial therapy (Radiation Therapy Oncology Group Grade 1 in 6 cases). A mean lung dose of 13.5 Gy or greater (p = 0.04) and percentage of lungs receiving 20 Gy of 33.5% or greater (p = 0.009) significantly predicted for RP. RP developed in 6 patients (35%) with relapsed/refractory HL treated with peri-transplant mediastinal RT (Grade 3 in 4 cases). Pre-transplant mediastinal RT, compared with post-transplant mediastinal RT, significantly predicted for Grade 3 RP (57% vs. 0%, p = 0.015). Conclusions: We identified threshold lung metrics predicting for RP in HL patients receiving mediastinal RT as part of initial therapy, with the majority of cases being of mild severity. The risk of RP is significantly higher with peri-transplant mediastinal RT, especially among those who receive pre-transplant RT.

  16. Combining ARS Process-Based Water and Wind Erosion Prediction Technologies

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Erosion process research in the United States has long been separated by location, experimental data collection, and prediction technologies. Erosion experiment stations were established in the l930’s throughout the country, however most examined erosion by water while a few in the Plains states we...

  17. Combining quantitative trait loci analysis with physiological models to predict genotype-specific transpiration rates.

    PubMed

    Reuning, Gretchen A; Bauerle, William L; Mullen, Jack L; McKay, John K

    2015-04-01

    Transpiration is controlled by evaporative demand and stomatal conductance (gs ), and there can be substantial genetic variation in gs . A key parameter in empirical models of transpiration is minimum stomatal conductance (g0 ), a trait that can be measured and has a large effect on gs and transpiration. In Arabidopsis thaliana, g0 exhibits both environmental and genetic variation, and quantitative trait loci (QTL) have been mapped. We used this information to create a genetically parameterized empirical model to predict transpiration of genotypes. For the parental lines, this worked well. However, in a recombinant inbred population, the predictions proved less accurate. When based only upon their genotype at a single g0 QTL, genotypes were less distinct than our model predicted. Follow-up experiments indicated that both genotype by environment interaction and a polygenic inheritance complicate the application of genetic effects into physiological models. The use of ecophysiological or 'crop' models for predicting transpiration of novel genetic lines will benefit from incorporating further knowledge of the genetic control and degree of independence of core traits/parameters underlying gs variation.

  18. Predictive Value of Combining the Ankle-Brachial Index and SYNTAX Score for the Prediction of Outcome After Percutaneous Coronary Intervention (from the SHINANO Registry).

    PubMed

    Ueki, Yasushi; Miura, Takashi; Miyashita, Yusuke; Motoki, Hirohiko; Shimada, Kentaro; Kobayashi, Masanori; Nakajima, Hiroyuki; Kimura, Hikaru; Akanuma, Hiroshi; Mawatari, Eiichiro; Sato, Toshio; Hotta, Shoji; Kamiyoshi, Yuichi; Maruyama, Takuya; Watanabe, Noboru; Eisawa, Takayuki; Aso, Shinichi; Uchikawa, Shinichiro; Hashizume, Naoto; Sekimura, Noriyuki; Morita, Takehiro; Ebisawa, Soichiro; Izawa, Atsushi; Koyama, Jun; Ikeda, Uichi

    2016-01-15

    The Synergy Between PCI With TAXUS and Cardiac Surgery (SYNTAX) score is effective in predicting clinical outcome after percutaneous coronary intervention (PCI). However, its prediction ability is low because it reflects only the coronary characterization. We assessed the predictive value of combining the ankle-brachial index (ABI) and SYNTAX score to predict clinical outcomes after PCI. The ABI-SYNTAX score was calculated for 1,197 patients recruited from the Shinshu Prospective Multi-center Analysis for Elderly Patients with Coronary Artery Disease Undergoing Percutaneous Coronary Intervention (SHINANO) registry, a prospective, observational, multicenter cohort study in Japan. The primary end points were major adverse cardiovascular and cerebrovascular events (MACE; all-cause death, myocardial infarction, and stroke) in the first year after PCI. The ABI-SYNTAX score was calculated by categorizing and summing up the ABI and SYNTAX scores. ABI ≤ 0.49 was defined as 4, 0.5 to 0.69 as 3, 0.7 to 0.89 as 2, 0.9 to 1.09 as 1, and 1.1 to 1.5 as 0; an SYNTAX score ≤ 22 was defined as 0, 23 to 32 as 1, and ≥ 33 as 2. Patients were divided into low (0), moderate (1 to 2), and high (3 to 6) groups. The MACE rate was significantly higher in the high ABI-SYNTAX score group than in the lower 2 groups (low: 4.6% vs moderate: 7.0% vs high: 13.9%, p = 0.002). Multivariate regression analysis found that ABI-SYNTAX score independently predicted MACE (hazards ratio 1.25, 95% confidence interval 1.02 to 1.52, p = 0.029). The respective C-statistic for the ABI-SYNTAX and SYNTAX score for 1-year MACE was 0.60 and 0.55, respectively. In conclusion, combining the ABI and SYNTAX scores improved the prediction of 1-year adverse ischemic events compared with the SYNTAX score alone.

  19. Brain targeting by intranasal drug delivery (INDD): a combined effect of trans-neural and para-neuronal pathway.

    PubMed

    Mustafa, Gulam; Alrohaimi, Abdulmohsen H; Bhatnagar, Aseem; Baboota, Sanjula; Ali, Javed; Ahuja, Alka

    2016-01-01

    The effectiveness of intranasal drug delivery for brain targeting has emerged as a hope of remedy for various CNS disorders. The nose to brain absorption of therapeutic molecules claims two effective pathways, which include trans-neuronal for immediate action and para-neuronal for delayed action. To evaluate the contribution of both the pathways in absorption of therapeutic molecules and nanocarriers, lidocaine, a nerve-blocking agent, was used to impair the action potential of olfactory nerve. An anti-Parkinson drug ropinirole was covalently complexes with (99m)Tc in presence of SnCl2 using in-house developed reduction technology. The radiolabeled formulations were administered intranasally in lidocaine challenged rabbit and rat. The qualitative and quantitative outcomes of neural and non-neural pathways were estimated using gamma scintigraphy and UHPLC-MS/MS, respectively. The results showed a significant (p ≤ 0.005) increase in radioactivity counts and drug concentration in the brain of rabbit and rat compared to the animal groups challenged with lidocaine. This concludes the significant contribution (p ≤ 0.005) of trans-neuronal and para-neuronal pathway in nose to brain drug delivery. Therefore, results proved that it is an art of a formulator scientist to make the drug carriers to exploit the choice of absorption pathway for their instant and extent of action.

  20. Predictive values of serum VEGF and CRP levels combined with contrast enhanced MRI in hepatocellular carcinoma patients after TACE

    PubMed Central

    Li, Zhi; Xue, Tong-Qing; Chen, Xiao-Yu

    2016-01-01

    This study explored the predictive value of serum vascular endothelial growth factor (VEGF) and C-reactive protein (CRP) levels combined with enhanced magnetic resonance imaging (MRI) in hepatocellular carcinoma (HCC) patients after transcatheter arterial chemoembolization (TACE). One hundred and seventeen patients who received TACE from June 2010 to December 2012 in our hospital were included in this study. Serum VEGF and CRP levels before and after TACE were determined by ELISA and single immunodiffusion method for analyzing the association of serum levels with pathological features. Enhanced MRI was utilized before and after TACE to measure tumor size and ADC value in enhanced region and non-enhanced region. MRI data were combined with serum VEGF and CRP levels to analyze the predictive value in efficacy and prognosis for HCC patients after TACE. The serum VEGF and CRP levels after TACE were increased, but can return to normal levels in a certain time. VEGF and CRP levels were not statistically associated with tumor location, tumor staining or presence of membrane (all P > 0.05), but closely correlated with combined portal vein tumor thrombus, combined arteriovenous fistula and distant metastasis (all P < 0.05). Low levels of serum VEGF and CRP, small tumor size and low ADC value before treatment indicated a better prognosis. The sensitivity and specificity of serum VEGF and CRP levels, tumor size and ADC value were respectively 92.31% and 88.46%, 93.85% and 90.38%, 81.54% and 78.85% as well as 47.69% and 84.62%. Serum VEGF and CRP levels, tumor size and ADC value could predict the efficacy of TACE for HCC patients. Serum VEGF and CRP levels combined with enhanced MRI may serve as markers for efficacy and prognosis evaluation in HCC patients after TACE. PMID:27822426

  1. Human and server docking prediction for CAPRI round 30-35 using LZerD with combined scoring functions.

    PubMed

    Peterson, Lenna X; Kim, Hyungrae; Esquivel-Rodriguez, Juan; Roy, Amitava; Han, Xusi; Shin, Woong-Hee; Zhang, Jian; Terashi, Genki; Lee, Matt; Kihara, Daisuke

    2017-03-01

    We report the performance of protein-protein docking predictions by our group for recent rounds of the Critical Assessment of Prediction of Interactions (CAPRI), a community-wide assessment of state-of-the-art docking methods. Our prediction procedure uses a protein-protein docking program named LZerD developed in our group. LZerD represents a protein surface with 3D Zernike descriptors (3DZD), which are based on a mathematical series expansion of a 3D function. The appropriate soft representation of protein surface with 3DZD makes the method more tolerant to conformational change of proteins upon docking, which adds an advantage for unbound docking. Docking was guided by interface residue prediction performed with BindML and cons-PPISP as well as literature information when available. The generated docking models were ranked by a combination of scoring functions, including PRESCO, which evaluates the native-likeness of residues' spatial environments in structure models. First, we discuss the overall performance of our group in the CAPRI prediction rounds and investigate the reasons for unsuccessful cases. Then, we examine the performance of several knowledge-based scoring functions and their combinations for ranking docking models. It was found that the quality of a pool of docking models generated by LZerD, that is whether or not the pool includes near-native models, can be predicted by the correlation of multiple scores. Although the current analysis used docking models generated by LZerD, findings on scoring functions are expected to be universally applicable to other docking methods. Proteins 2017; 85:513-527. © 2016 Wiley Periodicals, Inc.

  2. Predictive Potential of Flux Balance Analysis of Saccharomyces cerevisiae Using as Optimization Function Combinations of Cell Compartmental Objectives

    PubMed Central

    García Sánchez, Carlos Eduardo; Vargas García, César Augusto; Torres Sáez, Rodrigo Gonzalo

    2012-01-01

    Background The main objective of flux balance analysis (FBA) is to obtain quantitative predictions of metabolic fluxes of an organism, and it is necessary to use an appropriate objective function to guarantee a good estimation of those fluxes. Methodology In this study, the predictive performance of FBA was evaluated, using objective functions arising from the linear combination of different cellular objectives. This approach is most suitable for eukaryotic cells, owing to their multiplicity of cellular compartments. For this reason, Saccharomyces cerevisiae was used as model organism, and its metabolic network was represented using the genome-scale metabolic model iMM904. As the objective was to evaluate the predictive performance from the FBA using the kind of objective function previously described, substrate uptake and oxygen consumption were the only input data used for the FBA. Experimental information about microbial growth and exchange of metabolites with the environment was used to assess the quality of the predictions. Conclusions The quality of the predictions obtained with the FBA depends greatly on the knowledge of the oxygen uptake rate. For the most of studied classifications, the best predictions were obtained with “maximization of growth”, and with some combinations that include this objective. However, in the case of exponential growth with unknown oxygen exchange flux, the objective function “maximization of growth, plus minimization of NADH production in cytosol, plus minimization of NAD(P)H consumption in mitochondrion” gave much more accurate estimations of fluxes than the obtained with any other objective function explored in this study. PMID:22912775

  3. Reducing the Scatter in Reliability Predictions for Ceramic Components Using a Combined MCMC and BOOTSTRAP Approach

    SciTech Connect

    Roudi, S.; Riesch-Oppermann, H.

    2004-11-16

    Reliability of ceramic components is usually obtained in terms of failure probability from Finite Element stress analysis and subsequent numerical integration of the stress field. Due to scatter in the material parameters that enter the numerical integration, the uncertainty in the resulting failure probabilities depends strongly on the quality and abundance of the underlying data base. Material parameters that enter the calculation are obtained at different levels of, e.g. temperature or loading rate. It would be helpful to have a framework which allows efficient allocation of specimens to different types of experiments with respect to minimizing the resulting scatter in the failure probability predictions.For the prediction of confidence intervals for the failure probability, we use bootstrap resampling methods based on observed samples for material strength measurements. For the description of temperature dependent material behavior we have implemented two methods for regression neural networks, namely D. J. C. MacKay's Gaussian approximation method and also Markov Chain Monte Carlo Method of R. M. Neal.This procedure allows also a 'pooling' of the data -- in our case we obtain one large room temperature sample from various samples at different temperatures -- leading to reduced prediction uncertainty. Using the regression network, we can generate a relation between the strength data base and the corresponding reliability prediction uncertainty. In order to investigate the influence of different data bases, we use a parametric bootstrap approach to generate artificial samples from the original data. Imposing weights to different samples, a procedure is obtained which detects the relevance of specific samples to the uncertainties in the final failure probability prediction.Thus strategies can be proposed for efficient allocation of available specimens to selected experimental conditions.

  4. Loxapine and Cyproheptadine Combined Limit Clozapine Rebound Psychosis and May Also Predict Clozapine Response

    PubMed Central

    McCarthy, Richard H.

    2016-01-01

    Clozapine has been consistently shown to be superior to other antipsychotics in the treatment of psychosis. However, clozapine usage has been limited due to required routine blood monitoring and the potential for life threatening side effects. We report a case of a 66-year-old female patient, who developed clozapine-induced agranulocytosis after 10 weeks of clozapine treatment and was subsequently successfully treated with a combination of loxapine and cyproheptadine. The combination is thought to mimic the pharmacological profile of clozapine, rendering it as a possible alternative to traditional clozapine treatment. PMID:27433365

  5. Loxapine and Cyproheptadine Combined Limit Clozapine Rebound Psychosis and May Also Predict Clozapine Response.

    PubMed

    Aboueid, Lila; McCarthy, Richard H

    2016-01-01

    Clozapine has been consistently shown to be superior to other antipsychotics in the treatment of psychosis. However, clozapine usage has been limited due to required routine blood monitoring and the potential for life threatening side effects. We report a case of a 66-year-old female patient, who developed clozapine-induced agranulocytosis after 10 weeks of clozapine treatment and was subsequently successfully treated with a combination of loxapine and cyproheptadine. The combination is thought to mimic the pharmacological profile of clozapine, rendering it as a possible alternative to traditional clozapine treatment.

  6. Prediction of span loading of straight-wing/propeller combinations up to stall. [propeller slipstreams and wing loading

    NASA Technical Reports Server (NTRS)

    Mcveigh, M. A.; Gray, L.; Kisielowski, E.

    1975-01-01

    A method is presented for calculating the spanwise lift distribution on straight-wing/propeller combinations. The method combines a modified form of the Prandtl wing theory with a realistic representation of the propeller slipstream distribution. The slipstream analysis permits calculations of the nonuniform axial and rotational slipstream velocity field of propeller/nacelle combinations. This nonuniform field was then used to calculate the wing lift distribution by means of the modified Prandtl wing theory. The theory was developed for any number of nonoverlapping propellers, on a wing with partial or full-span flaps, and is applicable throughout an aspect ratio range from 2.0 and higher. A computer program was used to calculate slipstream characteristics and wing span load distributions for a number of configurations for which experimental data are available, and favorable comparisons are demonstrated between the theoretical predictions and the existing data.

  7. The novel combination of dual mTOR inhibitor AZD2014 and pan-PIM inhibitor AZD1208 inhibits growth in acute myeloid leukemia via HSF pathway suppression.

    PubMed

    Harada, Masako; Benito, Juliana; Yamamoto, Shinichi; Kaur, Surinder; Arslan, Dirim; Ramirez, Santiago; Jacamo, Rodrigo; Platanias, Leonidas; Matsushita, Hiromichi; Fujimura, Tsutomu; Kazuno, Saiko; Kojima, Kensuke; Tabe, Yoko; Konopleva, Marina

    2015-11-10

    Mammalian target of rapamycin (mTOR) signaling is a critical pathway in the biology of acute myeloid leukemia (AML). Proviral integration site for moloney murine leukemia virus (PIM) serine/threonine kinase signaling takes part in various pathways exerting tumorigenic properties. We hypothesized that the combination of a PIM kinase inhibitor with an mTOR inhibitor might have complementary growth-inhibitory effects against AML. The simultaneous inhibition of the PIM kinase by pan-PIM inhibitor AZD1208 and of mTOR by selective mTORC1/2 dual inhibitor AZD2014 exerted anticancer properties in AML cell lines and in cells derived from primary AML samples with or without supportive stromal cell co-culture, leading to suppressed proliferation and increased apoptosis. The combination of AZD1208 and AZD2014 rapidly activated AMPKα, a negative regulator of translation machinery through mTORC1/2 signaling in AML cells; profoundly inhibited AKT and 4EBP1 activation; and suppressed polysome formation. Inhibition of both mTOR and PIM counteracted induction of heat-shock family proteins, uncovering the master negative regulation of heat shock factor 1 (HSF1), the dominant transcription factor controlling cellular stress responses. The novel combination of the dual mTOR inhibitor and pan-PIM inhibitor synergistically inhibited AML growth by effectively reducing protein synthesis through heat shock factor pathway suppression.

  8. Positive teacher and peer relations combine to predict primary school students' academic skill development.

    PubMed

    Kiuru, Noona; Aunola, Kaisa; Lerkkanen, Marja-Kristiina; Pakarinen, Eija; Poskiparta, Elisa; Ahonen, Timo; Poikkeus, Anna-Maija; Nurmi, Jari-Erik

    2015-04-01

    This study examined cross-lagged associations between positive teacher and peer relations and academic skill development. Reading and math skills were tested among 625 students in kindergarten and Grade 4. Teacher reports of positive affect toward each student and classmate reports of peer acceptance were gathered in Grades 1-3. The results showed, first, that positive teacher affect toward the student and peer acceptance were reciprocally associated: Positive teacher affect predicted higher peer acceptance, and higher peer acceptance predicted a higher level of positive teacher affect. Second, the effect of positive teacher affect on academic skill development was partly mediated via peer acceptance, while the effect of early academic skills on peer acceptance was partly mediated via positive teacher affect. The results suggest that a warm and supportive teacher can increase a student's peer acceptance, which, in turn, is positively associated with learning outcomes.

  9. Predicting protein N-glycosylation by combining functional domain and secretion information.

    PubMed

    Li, Sujun; Liu, Boshu; Cai, Yudong; Li, Yixue

    2007-08-01

    Protein N-glycosylation plays an important role in protein function. Yet, at present, few computational methods are available for the prediction of this protein modification. This prompted our development of a support vector machine (SVM)-based method for this task, as well as a partial least squares (PLS) regression based prediction method for comparison. A functional domain feature space was used to create SVM and PLS models, which achieved accuracies of 83.91% and 79.89%, respectively, as evaluated by a leave-one-out cross-validation. Subsequently, SVM and PLS models were developed based on functional domain and protein secretion information, which yielded accuracies of 89.13% and 86%, respectively. This analysis demonstrates that the protein functional domain and secretion information are both efficient predictors of N-glycosylation.

  10. Combining experimental and computational studies to understand and predict reactivities of relevance to homogeneous catalysis.

    PubMed

    Tsang, Althea S-K; Sanhueza, Italo A; Schoenebeck, Franziska

    2014-12-08

    This article showcases three major uses of computational chemistry in reactivity studies: the application after, in combination with, and before experiment. Following a brief introduction of suitable computational tools, challenges and opportunities in the implementation of computational chemistry in reactivity studies are discussed, exemplified with selected case studies from our and other laboratories.

  11. GI-POP: a combinational annotation and genomic island prediction pipeline for ongoing microbial genome projects.

    PubMed

    Lee, Chi-Ching; Chen, Yi-Ping Phoebe; Yao, Tzu-Jung; Ma, Cheng-Yu; Lo, Wei-Cheng; Lyu, Ping-Chiang; Tang, Chuan Yi

    2013-04-10

    Sequencing of microbial genomes is important because of microbial-carrying antibiotic and pathogenetic activities. However, even with the help of new assembling software, finishing a whole genome is a time-consuming task. In most bacteria, pathogenetic or antibiotic genes are carried in genomic islands. Therefore, a quick genomic island (GI) prediction method is useful for ongoing sequencing genomes. In this work, we built a Web server called GI-POP (http://gipop.life.nthu.edu.tw) which integrates a sequence assembling tool, a functional annotation pipeline, and a high-performance GI predicting module, in a support vector machine (SVM)-based method called genomic island genomic profile scanning (GI-GPS). The draft genomes of the ongoing genome projects in contigs or scaffolds can be submitted to our Web server, and it provides the functional annotation and highly probable GI-predicting results. GI-POP is a comprehensive annotation Web server designed for ongoing genome project analysis. Researchers can perform annotation and obtain pre-analytic information include possible GIs, coding/non-coding sequences and functional analysis from their draft genomes. This pre-analytic system can provide useful information for finishing a genome sequencing project.

  12. Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level

    NASA Astrophysics Data System (ADS)

    He, Fei; Han, Ye; Gong, Jianting; Song, Jiazhi; Wang, Han; Li, Yanwen

    2017-03-01

    Small interfering RNAs (siRNAs) may induce to targeted gene knockdown, and the gene silencing effectiveness relies on the efficacy of the siRNA. Therefore, the task of this paper is to construct an effective siRNA prediction method. In our work, we try to describe siRNA from both quantitative and qualitative aspects. For quantitative analyses, we form four groups of effective features, including nucleotide frequencies, thermodynamic stability profile, thermodynamic of siRNA-mRNA interaction, and mRNA related features, as a new mixed representation, in which thermodynamic of siRNA-mRNA interaction is introduced to siRNA efficacy prediction for the first time to our best knowledge. And then an F-score based feature selection is employed to investigate the contribution of each feature and remove the weak relevant features. Meanwhile, we encode the siRNA sequence and existed empirical design rules as a qualitative siRNA representation. These two kinds of siRNA representations are combined to predict siRNA efficacy by supported Vector Regression (SVR) at score level. The experimental results indicate that our method may select the features with powerful discriminative ability and make the two kinds of siRNA representations work at full capacity. The prediction results also demonstrate that our method can outperform other popular siRNA efficacy prediction algorithms.

  13. Predicting siRNA efficacy based on multiple selective siRNA representations and their combination at score level

    PubMed Central

    He, Fei; Han, Ye; Gong, Jianting; Song, Jiazhi; Wang, Han; Li, Yanwen

    2017-01-01

    Small interfering RNAs (siRNAs) may induce to targeted gene knockdown, and the gene silencing effectiveness relies on the efficacy of the siRNA. Therefore, the task of this paper is to construct an effective siRNA prediction method. In our work, we try to describe siRNA from both quantitative and qualitative aspects. For quantitative analyses, we form four groups of effective features, including nucleotide frequencies, thermodynamic stability profile, thermodynamic of siRNA-mRNA interaction, and mRNA related features, as a new mixed representation, in which thermodynamic of siRNA-mRNA interaction is introduced to siRNA efficacy prediction for the first time to our best knowledge. And then an F-score based feature selection is employed to investigate the contribution of each feature and remove the weak relevant features. Meanwhile, we encode the siRNA sequence and existed empirical design rules as a qualitative siRNA representation. These two kinds of siRNA representations are combined to predict siRNA efficacy by supported Vector Regression (SVR) at score level. The experimental results indicate that our method may select the features with powerful discriminative ability and make the two kinds of siRNA representations work at full capacity. The prediction results also demonstrate that our method can outperform other popular siRNA efficacy prediction algorithms. PMID:28317874

  14. MicroRNA signatures predict dysregulated vitamin D receptor and calcium pathways status in limb girdle muscle dystrophies (LGMD) 2A/2B.

    PubMed

    Aguennouz, M; Lo Giudice, C; Licata, N; Rodolico, C; Musumeci, O; Fanin, M; Migliorato, A; Ragusa, M; Macaione, V; Di Giorgio, R M; Angelini, C; Toscano, A

    2016-08-01

    miRNA expression profile and predicted pathways involved in selected limb-girdle muscular dystrophy (LGMD)2A/2B patients were investigated. A total of 187 miRNAs were dysregulated in all patients, with six miRNAs showing opposite regulation in LGMD2A versus LGMD2B patients. Silico analysis evidence: (1) a cluster of the dysregulated miRNAs resulted primarily involved in inflammation and calcium metabolism, and (2) two genes predicted as controlled by calcium-assigned miRNAs (Vitamin D Receptor gene and Guanine Nucleotide Binding protein beta polypeptide 1gene) showed an evident upregulation in LGMD2B patients, in accordance with miRNA levels. Our data support alterations in calcium pathway status in LGMD 2A/B, suggesting myofibre calcium imbalance as a potential therapeutic target. Copyright © 2016 John Wiley & Sons, Ltd.

  15. An antagonist treatment in combination with tracer experiments revealed isocitrate pathway dominant to oxalate biosynthesis in Rumex obtusifolius L

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Oxalate accumulates in leaves of certain plants such as Rumex species (Polygonaceae). Oxalate plays important roles in defense to predator, detoxification of metallic ions, and in hydroxyl peroxide formation upon wounding/senescence. However, biosynthetic pathways of soluble oxalate are largely unkn...

  16. Predictive error detection in pianists: a combined ERP and motion capture study

    PubMed Central

    Maidhof, Clemens; Pitkäniemi, Anni; Tervaniemi, Mari

    2013-01-01

    Performing a piece of music involves the interplay of several cognitive and motor processes and requires extensive training to achieve a high skill level. However, even professional musicians commit errors occasionally. Previous event-related potential (ERP) studies have investigated the neurophysiological correlates of pitch errors during piano performance, and reported pre-error negativity already occurring approximately 70–100 ms before the error had been committed and audible. It was assumed that this pre-error negativity reflects predictive control processes that compare predicted consequences with actual consequences of one's own actions. However, in previous investigations, correct and incorrect pitch events were confounded by their different tempi. In addition, no data about the underlying movements were available. In the present study, we exploratively recorded the ERPs and 3D movement data of pianists' fingers simultaneously while they performed fingering exercises from memory. Results showed a pre-error negativity for incorrect keystrokes when both correct and incorrect keystrokes were performed with comparable tempi. Interestingly, even correct notes immediately preceding erroneous keystrokes elicited a very similar negativity. In addition, we explored the possibility of computing ERPs time-locked to a kinematic landmark in the finger motion trajectories defined by when a finger makes initial contact with the key surface, that is, at the onset of tactile feedback. Results suggest that incorrect notes elicited a small difference after the onset of tactile feedback, whereas correct notes preceding incorrect ones elicited negativity before the onset of tactile feedback. The results tentatively suggest that tactile feedback plays an important role in error-monitoring during piano performance, because the comparison between predicted and actual sensory (tactile) feedback may provide the information necessary for the detection of an upcoming error. PMID

  17. Combining climatic and soil properties better predicts covers of Brazilian biomes.

    PubMed

    Arruda, Daniel M; Fernandes-Filho, Elpídio I; Solar, Ricardo R C; Schaefer, Carlos E G R

    2017-04-01

    Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km(2) for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

  18. Combining climatic and soil properties better predicts covers of Brazilian biomes

    NASA Astrophysics Data System (ADS)

    Arruda, Daniel M.; Fernandes-Filho, Elpídio I.; Solar, Ricardo R. C.; Schaefer, Carlos E. G. R.

    2017-04-01

    Several techniques have been used to model the area covered by biomes or species. However, most models allow little freedom of choice of response variables and are conditioned to the use of climate predictors. This major restriction of the models has generated distributions of low accuracy or inconsistent with the actual cover. Our objective was to characterize the environmental space of the most representative biomes of Brazil and predict their cover, using climate and soil-related predictors. As sample units, we used 500 cells of 100 km2 for ten biomes, derived from the official vegetation map of Brazil (IBGE 2004). With a total of 38 (climatic and soil-related) predictors, an a priori model was run with the random forest classifier. Each biome was calibrated with 75% of the samples. The final model was based on four climate and six soil-related predictors, the most important variables for the a priori model, without collinearity. The model reached a kappa value of 0.82, generating a highly consistent prediction with the actual cover of the country. We showed here that the richness of biomes should not be underestimated, and that in spite of the complex relationship, highly accurate modeling based on climatic and soil-related predictors is possible. These predictors are complementary, for covering different parts of the multidimensional niche. Thus, a single biome can cover a wide range of climatic space, versus a narrow range of soil types, so that its prediction is best adjusted by soil-related variables, or vice versa.

  19. RSLpred: an integrative system for predicting subcellular localization of rice proteins combining compositional and evolutionary information.

    PubMed

    Kaundal, Rakesh; Raghava, Gajendra P S

    2009-05-01

    The attainment of complete map-based sequence for rice (Oryza sativa) is clearly a major milestone for the research community. Identifying the localization of encoded proteins is the key to understanding their functional characteristics and facilitating their purification. Our proposed method, RSLpred, is an effort in this direction for genome-scale subcellular prediction of encoded rice proteins. First, the support vector machine (SVM)-based modules have been developed using traditional amino acid-, dipeptide- (i+1) and four parts-amino acid composition and achieved an overall accuracy of 81.43, 80.88 and 81.10%, respectively. Secondly, a similarity search-based module has been developed using position-specific iterated-basic local alignment search tool and achieved 68.35% accuracy. Another module developed using evolutionary information of a protein sequence extracted from position-specific scoring matrix achieved an accuracy of 87.10%. In this study, a large number of modules have been developed using various encoding schemes like higher-order dipeptide composition, N- and C-terminal, splitted amino acid composition and the hybrid information. In order to benchmark RSLpred, it was tested on an independent set of rice proteins where it outperformed widely used prediction methods such as TargetP, Wolf-PSORT, PA-SUB, Plant-Ploc and ESLpred. To assist the plant research community, an online web tool 'RSLpred' has been developed for subcellular prediction of query rice proteins, which is freely accessible at http://www.imtech.res.in/raghava/rslpred.

  20. MET Gene Amplification and MET Receptor Activation Are Not Sufficient to Predict Efficacy of Combined MET and EGFR Inhibitors in EGFR TKI-Resistant NSCLC Cells

    PubMed Central

    Presutti, Dario; Santini, Simonetta; Cardinali, Beatrice; Papoff, Giuliana; Lalli, Cristiana; Samperna, Simone; Fustaino, Valentina; Giannini, Giuseppe; Ruberti, Giovina

    2015-01-01

    Epidermal growth factor receptor (EGFR), member of the human epidermal growth factor receptor (HER) family, plays a critical role in regulating multiple cellular processes including proliferation, differentiation, cell migration and cell survival. Deregulation of the EGFR signaling has been found to be associated with the development of a variety of human malignancies including lung, breast, and ovarian cancers, making inhibition of EGFR the most promising molecular targeted therapy developed in the past decade against cancer. Human non small cell lung cancers (NSCLC) with activating mutations in the EGFR gene frequently experience significant tumor regression when treated with EGFR tyrosine kinase inhibitors (TKIs), although acquired resistance invariably develops. Resistance to TKI treatments has been associated to secondary mutations in the EGFR gene or to activation of additional bypass signaling pathways including the ones mediated by receptor tyrosine kinases, Fas receptor and NF-kB. In more than 30–40% of cases, however, the mechanisms underpinning drug-resistance are still unknown. The establishment of cellular and mouse models can facilitate the unveiling of mechanisms leading to drug-resistance and the development or validation of novel therapeutic strategies aimed at overcoming resistance and enhancing outcomes in NSCLC patients. Here we describe the establishment and characterization of EGFR TKI-resistant NSCLC cell lines and a pilot study on the effects of a combined MET and EGFR inhibitors treatment. The characterization of the erlotinib-resistant cell lines confirmed the association of EGFR TKI resistance with loss of EGFR gene amplification and/or AXL overexpression and/or MET gene amplification and MET receptor activation. These cellular models can be instrumental to further investigate the signaling pathways associated to EGFR TKI-resistance. Finally the drugs combination pilot study shows that MET gene amplification and MET receptor activation

  1. Quantitative analysis of tin alloy combined with artificial neural network prediction

    SciTech Connect

    Oh, Seong Y.; Yueh, Fang-Yu; Singh, Jagdish P.

    2010-05-01

    Laser-induced breakdown spectroscopy was applied to quantitative analysis of three impurities in Sn alloy. The impurities analysis was based on the internal standard method using the Sn I 333.062-nm line as the reference line to achieve the best reproducible results. Minor-element concentrations (Ag, Cu, Pb) in the alloy were comparatively evaluated by artificial neural networks (ANNs) and calibration curves. ANN was found to effectively predict elemental concentrations with a trend of nonlinear growth due to self-absorption. The limits of detection for Ag, Cu, and Pb in Sn alloy were determined to be 29, 197, and 213 ppm, respectively.

  2. Previous hospital admissions and disease severity predict the use of antipsychotic combination treatment in patients with schizophrenia

    PubMed Central

    2011-01-01

    Background Although not recommended in treatment guidelines, previous studies have shown a frequent use of more than one antipsychotic agent among patients with schizophrenia. The main aims of the present study were to explore the antipsychotic treatment regimen among patients with schizophrenia in a catchment area-based sample and to investigate clinical characteristics associated with antipsychotic combination treatment. Methods The study included 329 patients diagnosed with schizophrenia using antipsychotic medication. Patients were recruited from all psychiatric hospitals in Oslo. Diagnoses were obtained by use of the Structured Clinical Interview for DSM-IV Axis I disorders (SCID-I). Additionally, Global Assessment of Functioning (GAF), Positive and Negative Syndrome Scale (PANSS) and number of hospitalisations and pharmacological treatment were assessed. Results Multiple hospital admissions, low GAF scores and high PANSS scores, were significantly associated with the prescription of combination treatment with two or more antipsychotics. The use of combination treatment increased significantly from the second hospital admission. Combination therapy was not significantly associated with age or gender. Regression models confirmed that an increasing number of hospital admission was the strongest predictor of the use of two or more antipsychotics. Conclusions Previous hospital admissions and disease severity measured by high PANSS scores and low GAF scores, predict the use of antipsychotic combination treatment in patients with schizophrenia. Future studies should further explore the use of antipsychotic drug treatment in clinical practice and partly based on such data establish more robust treatment guidelines for patients with persistently high symptom load. PMID:21812996

  3. Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors

    NASA Astrophysics Data System (ADS)

    Tan, Wen; Mei, Hu; Chao, Li; Liu, Tengfei; Pan, Xianchao; Shu, Mao; Yang, Li

    2013-12-01

    P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter. The over expression of P-gp leads to the development of multidrug resistance (MDR), which is a major obstacle to effective treatment of cancer. Thus, designing effective P-gp inhibitors has an extremely important role in the overcoming MDR. In this paper, both ligand-based quantitative structure-activity relationship (QSAR) and receptor-based molecular docking are used to predict P-gp inhibitors. The results show that each method achieves good prediction performance. According to the results of tenfold cross-validation, an optimal linear SVM model with only three descriptors is established on 857 training samples, of which the overall accuracy (Acc), sensitivity, specificity, and Matthews correlation coefficient are 0.840, 0.873, 0.813, and 0.683, respectively. The SVM model is further validated by 418 test samples with the overall Acc of 0.868. Based on a homology model of human P-gp established, Surflex-dock is also performed to give binding free energy-based evaluations with the overall accuracies of 0.823 for the test set. Furthermore, a consensus evaluation is also performed by using these two methods. Both QSAR and molecular docking studies indicate that molecular volume, hydrophobicity and aromaticity are three dominant factors influencing the inhibitory activities.

  4. Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors.

    PubMed

    Tan, Wen; Mei, Hu; Chao, Li; Liu, Tengfei; Pan, Xianchao; Shu, Mao; Yang, Li

    2013-12-01

    P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter. The over expression of P-gp leads to the development of multidrug resistance (MDR), which is a major obstacle to effective treatment of cancer. Thus, designing effective P-gp inhibitors has an extremely important role in the overcoming MDR. In this paper, both ligand-based quantitative structure-activity relationship (QSAR) and receptor-based molecular docking are used to predict P-gp inhibitors. The results show that each method achieves good prediction performance. According to the results of tenfold cross-validation, an optimal linear SVM model with only three descriptors is established on 857 training samples, of which the overall accuracy (Acc), sensitivity, specificity, and Matthews correlation coefficient are 0.840, 0.873, 0.813, and 0.683, respectively. The SVM model is further validated by 418 test samples with the overall Acc of 0.868. Based on a homology model of human P-gp established, Surflex-dock is also performed to give binding free energy-based evaluations with the overall accuracies of 0.823 for the test set. Furthermore, a consensus evaluation is also performed by using these two methods. Both QSAR and molecular docking studies indicate that molecular volume, hydrophobicity and aromaticity are three dominant factors influencing the inhibitory activities.

  5. A Novel Method for Functional Annotation Prediction Based on Combination of Classification Methods

    PubMed Central

    Jung, Jaehee; Lee, Heung Ki

    2014-01-01

    Automated protein function prediction defines the designation of functions of unknown protein functions by using computational methods. This technique is useful to automatically assign gene functional annotations for undefined sequences in next generation genome analysis (NGS). NGS is a popular research method since high-throughput technologies such as DNA sequencing and microarrays have created large sets of genes. These huge sequences have greatly increased the need for analysis. Previous research has been based on the similarities of sequences as this is strongly related to the functional homology. However, this study aimed to designate protein functions by automatically predicting the function of the genome by utilizing InterPro (IPR), which can represent the properties of the protein family and groups of the protein function. Moreover, we used gene ontology (GO), which is the controlled vocabulary used to comprehensively describe the protein function. To define the relationship between IPR and GO terms, three pattern recognition techniques have been employed under different conditions, such as feature selection and weighted value, instead of a binary one. PMID:25133242

  6. Combining monolithic zirconia crowns, digital impressioning, and regenerative cement for a predictable restorative alternative to PFM.

    PubMed

    Griffin, Jack D

    2013-03-01

    Advances in indirect esthetic materials in recent years have provided the dental profession higher levels of strength and esthetics than ever before with products like lithium disilicate and zirconium oxide. Providing excellent fit and versatile performance, and because there is no porcelain to delaminate, chip, or fracture, monolithic zirconia crowns have the potential to outperform other layered restorations such as porcelain-fused-to-metal (PFM). This review of monolithic zirconia highlights a clinical case in which all-zirconia restorations were combined with CAD/CAM technology for a successful esthetic restorative outcome.

  7. Evolutionary Covariance Combined with Molecular Dynamics Predicts a Framework for Allostery in the MutS DNA Mismatch Repair Protein

    PubMed Central

    2017-01-01

    Mismatch repair (MMR) is an essential, evolutionarily conserved pathway that maintains genome stability by correcting base-pairing errors in DNA. Here we examine the sequence and structure of MutS MMR protein to decipher the amino acid framework underlying its two key activities—recognizing mismatches in DNA and using ATP to initiate repair. Statistical coupling analysis (SCA) identified a network (sector) of coevolved amino acids in the MutS protein family. The potential functional significance of this SCA sector was assessed by performing molecular dynamics (MD) simulations for alanine mutants of the top 5% of 160 residues in the distribution, and control nonsector residues. The effects on three independent metrics were monitored: (i) MutS domain conformational dynamics, (ii) hydrogen bonding between MutS and DNA/ATP, and (iii) relative ATP binding free energy. Each measure revealed that sector residues contribute more substantively to MutS structure–function than nonsector residues. Notably, sector mutations disrupted MutS contacts with DNA and/or ATP from a distance via contiguous pathways and correlated motions, supporting the idea that SCA can identify amino acid networks underlying allosteric communication. The combined SCA/MD approach yielded novel, experimentally testable hypotheses for unknown roles of many residues distributed across MutS, including some implicated in Lynch cancer syndrome. PMID:28135092

  8. Combining Hi-C data with phylogenetic correlation to predict the target genes of distal regulatory elements in human genome.

    PubMed

    Lu, Yulan; Zhou, Yuanpeng; Tian, Weidong

    2013-12-01

    Defining the target genes of distal regulatory elements (DREs), such as enhancer, repressors and insulators, is a challenging task. The recently developed Hi-C technology is designed to capture chromosome conformation structure by high-throughput sequencing, and can be potentially used to determine the target genes of DREs. However, Hi-C data are noisy, making it difficult to directly use Hi-C data to identify DRE-target gene relationships. In this study, we show that DREs-gene pairs that are confirmed by Hi-C data are strongly phylogenetic correlated, and have thus developed a method that combines Hi-C read counts with phylogenetic correlation to predict long-range DRE-target gene relationships. Analysis of predicted DRE-target gene pairs shows that genes regulated by large number of DREs tend to have essential functions, and genes regulated by the same DREs tend to be functionally related and co-expressed. In addition, we show with a couple of examples that the predicted target genes of DREs can help explain the causal roles of disease-associated single-nucleotide polymorphisms located in the DREs. As such, these predictions will be of importance not only for our understanding of the function of DREs but also for elucidating the causal roles of disease-associated noncoding single-nucleotide polymorphisms.

  9. Assimilation of Combined Microwave and Lightning Measurement in a Mesoscale Weather Prediction Model

    NASA Technical Reports Server (NTRS)

    Chang, Dong-Eon; Weinman, James A.; Busalacchi, Antonio J. (Technical Monitor)

    2000-01-01

    Intermittent measurements of precipitation and integrated water vapor (IWV) distributions were retrieved from the Special Sensor Microwave/Imager (SSM/I) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) radiometers. Lightning generates very low frequency (VLF) radio noise pulses called sferics. Those pulses propagate over large distances so that they can be continuously monitored with a sparse network of ground based radio receivers. Sferics data, tuned with intermittent spaceborne microwave radiometer data, were used to generate estimated rainfall that was assimilated into a mesoscale weather prediction model. Both continuous latent heating adjustment and a variational technique are applied as assimilation procedures to evaluate the impact of lightning observations on the forecast of an intense winter squall line over the Gulf of Mexico. Sensitivities to the assimilation of additional measurements such as IWV and sea surface temperature (SST), and measurement errors will also be discussed.

  10. Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes

    PubMed Central

    Gerstung, Moritz; Pellagatti, Andrea; Malcovati, Luca; Giagounidis, Aristoteles; Porta, Matteo G Della; Jädersten, Martin; Dolatshad, Hamid; Verma, Amit; Cross, Nicholas C. P.; Vyas, Paresh; Killick, Sally; Hellström-Lindberg, Eva; Cazzola, Mario; Papaemmanuil, Elli; Campbell, Peter J.; Boultwood, Jacqueline

    2015-01-01

    Cancer is a genetic disease, but two patients rarely have identical genotypes. Similarly, patients differ in their clinicopathological parameters, but how genotypic and phenotypic heterogeneity are interconnected is not well understood. Here we build statistical models to disentangle the effect of 12 recurrently mutated genes and 4 cytogenetic alterations on gene expression, diagnostic clinical variables and outcome in 124 patients with myelodysplastic syndromes. Overall, one or more genetic lesions correlate with expression levels of ~20% of all genes, explaining 20–65% of observed expression variability. Differential expression patterns vary between mutations and reflect the underlying biology, such as aberrant polycomb repression for ASXL1 and EZH2 mutations or perturbed gene dosage for copy-number changes. In predicting survival, genomic, transcriptomic and diagnostic clinical variables all have utility, with the largest contribution from the transcriptome. Similar observations are made on the TCGA acute myeloid leukaemia cohort, confirming the general trends reported here. PMID:25574665

  11. Combined expression patterns of QTL-linked candidate genes best predict thermotolerance in Drosophila melanogaster.

    PubMed

    Norry, Fabian M; Larsen, Peter F; Liu, Yongjie; Loeschcke, Volker

    2009-11-01

    Knockdown resistance to high temperature (KRHT) is a thermal adaptation trait in Drosophila melanogaster. Here we used quantitative real-time PCR (qRT-PCR) to test for possible associations between KRHT and the expression of candidate genes within quantitative trait loci (QTL) in eight recombinant inbred lines (RIL). hsp60 and hsc70-3 map within an X-linked QTL, while CG10383, catsup, ddc, trap1, and cyp6a13 are linked in a KRHT-QTL on chromosome 2. hsc70-3 expression increased by heat-hardening. Principal Components analysis revealed that catsup, ddc and trap1 were either co-expressed or combined in their expression levels. This composite expression variable (e-PC1) was positively associated to KRHT in non-hardened RIL. In heat-hardened flies, hsp60 was negatively related to hsc70-3 on e-PC2, with effects on KRHT. These results are consistent with the notion that QTL can be shaped by expression variation in combined candidate loci. We found composite variables of gene expression (e-PCs) that best correlated to KRHT. Network effects with other untested linked loci are apparent because, in spite of their associations with KRHT phenotypes, e-PCs were sometimes uncorrelated with their QTL genotype.

  12. Predictive value of combined clinically diagnosed bruxism and occlusal features for TMJ pain.

    PubMed

    Manfredini, Daniele; Peretta, Redento; Guarda-Nardini, Luca; Ferronato, Giuseppe

    2010-04-01

    Several works showed a decreased role for occlusion in the etiology of temporomandibular disorders (TMD). Nonetheless, it may be hypothesized that occlusion acts as a modulator through which bruxism activities may cause damage to the stomatognathic structures. To test this hypothesis, a logistic regression model was created with the inclusion of clinically diagnosed bruxism and eight occlusal features as potential predictors for temporomandibular joint (TMJ) pain in a sample of 276 consecutive TMD patients. The final logit showed that the percentage of the total log likelihood for TMJ pain explained by the significant factors was small and amounted to 13.2%, with unacceptable levels of sensitivity (16.4%). The parameters overbite > or = 4 mm combined with clinically diagnosed bruxism [OR (odds ratio) 4.62], overjet > or = 5 mm (OR 2.83), and asymmetrical molar relationship combined with clinically diagnosed bruxism (OR 2.77) were those with the highest odds for disease, even though none of those values was significant with respect to confidence intervals. Thus, the hypothesis under evaluation has to be rejected. It is possible that future studies with a higher discriminatory power for the different bruxism activities might be indicated to get deeper into the analysis of the potential mechanisms through which occlusion may play a role, even if small, in the etiology of the different TMD.

  13. 2D dynamic studies combined with the surface curvature analysis to predict Arias Intensity amplification

    NASA Astrophysics Data System (ADS)

    Torgoev, Almaz; Havenith, Hans-Balder

    2016-07-01

    A 2D elasto-dynamic modelling of the pure topographic seismic response is performed for six models with a total length of around 23.0 km. These models are reconstructed from the real topographic settings of the landslide-prone slopes situated in the Mailuu-Suu River Valley, Southern Kyrgyzstan. The main studied parameter is the Arias Intensity (Ia, m/sec), which is applied in the GIS-based Newmark method to regionally map the seismically-induced landslide susceptibility. This method maps the Ia values via empirical attenuation laws and our studies investigate a potential to include topographic input into them. Numerical studies analyse several signals with varying shape and changing central frequency values. All tests demonstrate that the spectral amplification patterns directly affect the amplification of the Ia values. These results let to link the 2D distribution of the topographically amplified Ia values with the parameter called as smoothed curvature. The amplification values for the low-frequency signals are better correlated with the curvature smoothed over larger spatial extent, while those values for the high-frequency signals are more linked to the curvature with smaller smoothing extent. The best predictions are provided by the curvature smoothed over the extent calculated according to Geli's law. The sample equations predicting the Ia amplification based on the smoothed curvature are presented for the sinusoid-shape input signals. These laws cannot be directly implemented in the regional Newmark method, as 3D amplification of the Ia values addresses more problem complexities which are not studied here. Nevertheless, our 2D results prepare the theoretical framework which can potentially be applied to the 3D domain and, therefore, represent a robust basis for these future research targets.

  14. Prediction of nucleosome DNA formation potential and nucleosome positioning using increment of diversity combined with quadratic discriminant analysis.

    PubMed

    Zhao, Xiujuan; Pei, Zhiyong; Liu, Jia; Qin, Sheng; Cai, Lu

    2010-11-01

    In this work, a novel method was developed to distinguish nucleosome DNA and linker DNA based on increment of diversity combined with quadratic discriminant analysis (IDQD), using k-mer frequency of nucleotides in genome. When used to predict DNA potential for forming nucleosomes, the model achieved a high accuracy of 94.94%, 77.60%, and 86.81%, respectively, for Saccharomyces cerevisiae, Homo sapiens, and Drosophila melanogaster. The area under the receiver operator characteristics curve of our classifier was 0.982 for S. cerevisiae. Our results indicate that DNA sequence preference is critical for nucleosome formation potential and is likely conserved across eukaryotes. The model successfully identified nucleosome-enriched or nucleosome-depleted regions in S. cerevisiae genome, suggesting nucleosome positioning depends on DNA sequence preference. Thus, IDQD classifier is useful for predicting nucleosome positioning.

  15. ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches.

    PubMed

    Wang, Shuangquan; Sun, Huiyong; Liu, Hui; Li, Dan; Li, Youyong; Hou, Tingjun

    2016-08-01

    Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse data set. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naive Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification models showed improved predictive capability over any single pharmacophore hypothesis, suggesting that the broad binding polyspecificity of hERG can only be well characterized by multiple pharmacophores. The best SVM model achieved the prediction accuracies of 84.7% for the training set and 82.1% for the external test set. Notably, the accuracies for the hERG blockers and nonblockers in the test set reached 83.6% and 78.2%, respectively. Analysis of significant pharmacophores helps to understand the multimechanisms of action of hERG blockers. We believe that the combination of pharmacophore modeling and SVM is a powerful strategy to develop reliable theoretical models for the prediction of potential hERG liability.

  16. Predicting the oral uptake efficiency of chemicals in mammals: Combining the hydrophilic and lipophilic range

    SciTech Connect

    O'Connor, Isabel A.; Huijbregts, Mark A.J.; Ragas, Ad M.J.; Hendriks, A. Jan

    2013-01-01

    Environmental risk assessment requires models for estimating the bioaccumulation of untested compounds. So far, bioaccumulation models have focused on lipophilic compounds, and only a few have included hydrophilic compounds. Our aim was to extend an existing bioaccumulation model to estimate the oral uptake efficiency of pollutants in mammals for compounds over a wide K{sub ow} range with an emphasis on hydrophilic compounds, i.e. compounds in the lower K{sub ow} range. Usually, most models use octanol as a single surrogate for the membrane and thus neglect the bilayer structure of the membrane. However, compounds with polar groups can have different affinities for the different membrane regions. Therefore, an existing bioaccumulation model was extended by dividing the diffusion resistance through the membrane into an outer and inner membrane resistance, where the solvents octanol and heptane were used as surrogates for these membrane regions, respectively. The model was calibrated with uptake efficiencies of environmental pollutants measured in different mammals during feeding studies combined with human oral uptake efficiencies of pharmaceuticals. The new model estimated the uptake efficiency of neutral (RMSE = 14.6) and dissociating (RMSE = 19.5) compounds with logK{sub ow} ranging from − 10 to + 8. The inclusion of the K{sub hw} improved uptake estimation for 33% of the hydrophilic compounds (logK{sub ow} < 0) (r{sup 2} = 0.51, RMSE = 22.8) compared with the model based on K{sub ow} only (r{sup 2} = 0.05, RMSE = 34.9), while hydrophobic compounds (logK{sub ow} > 0) were estimated equally by both model versions with RMSE = 15.2 (K{sub ow} and K{sub hw}) and RMSE = 15.7 (K{sub ow} only). The model can be used to estimate the oral uptake efficiency for both hydrophilic and hydrophobic compounds. -- Highlights: ► A mechanistic model was developed to estimate oral uptake efficiency. ► Model covers wide logK{sub ow} range (- 10 to + 8) and several mammalian

  17. Simvastatin in combination with bergamottin potentiates TNF-induced apoptosis through modulation of NF-κB signalling pathway in human chronic myelogenous leukaemia.

    PubMed

    Kim, Sung-Moo; Lee, Eun-Jung; Lee, Jong Hyun; Yang, Woong Mo; Nam, Dongwoo; Lee, Jun-Hee; Lee, Seok-Geun; Um, Jae-Young; Shim, Bum Sang; Ahn, Kwang Seok

    2016-10-01

    Context Simvastatin (SV) and bergamottin (BGM) are known to exhibit diverse anti-cancer and anti-inflammatory activities. Objective Very little is known about the potential efficacy of combination of these two agents to potentiate TNF-induced apoptosis in human chronic myelogenous leukaemia (CML). Materials and methods In the present study, we investigated whether SV combined with BGM mediates its effect through suppression of NF-κB-signalling pathway. Results We found that the combination treatment enhanced cytotoxicity and potentiated the apoptosis induced by TNF as indicated by intracellular esterase activity, Annexin V staining and caspase activation. This effect of co-treatment correlated with down-regulation of various gene products that mediate cell proliferation (cyclin D1), cell survival (cIAP-1, Bcl-2, Bcl-xL and Survivin), invasion (MMP-9) and angiogenesis (VEGF); all known to be regulated by NF-κB. SV combined with BGM also produced TNF-induced cell-cycle arrest in S-phase and this arrest correlated with a concomitant increase in the levels of cyclin-dependent inhibitor p21 and p27. The combination therapy inhibited TNF-induced NF-κB activation, IκBα degradation and p65 translocation to the nucleus as compared with the treatment with individual agents alone. Besides, SV combined with BGM did not significantly potentiate apoptotic effect induced by TNF in p65(-)(/)(-) cells, as compared with wild-type fibroblasts. Discussion and conclusion Our results provide novel insight into the role of SV and BGM in potentially preventing and treating cancer through modulation of NF-κB signalling pathway and its regulated gene products.

  18. Conceptual Knowledge Discovery in Databases for Drug Combinations Predictions in Malignant Melanoma

    PubMed Central

    Regan, Kelly; Raje, Satyajeet; Saravanamuthu, Cartik; Payne, Philip R.O.

    2016-01-01

    The worldwide incidence of melanoma is rising faster than any other cancer, and prognosis for patients with metastatic disease is poor. Current targeted therapies are limited in their durability and/or effect size in certain patient populations due to acquired mechanisms of resistance. Thus, the development of synergistic combinatorial treatment regimens holds great promise to improve patient outcomes. We have previously shown that a model for in-silico knowledge discovery, Translational Ontology-anchored Knowledge Discovery Engine (TOKEn), is able to generate valid relationships between bimolecular and clinical phenotypes. In this study, we have aggregated observational and canonical knowledge consisting of melanoma-related biomolecular entities and targeted therapeutics in a computationally tractable model. We demonstrate here that the explicit linkage of therapeutic modalities with biomolecular underpinnings of melanoma utilizing the TOKEn pipeline yield a set of informed relationships that have the potential to generate combination therapy strategies. PMID:26262134

  19. Online Community Use Predicts Abstinence in Combined Internet/Phone Intervention for Smoking Cessation

    PubMed Central

    Papandonatos, George D.; Erar, Bahar; Stanton, Cassandra A.; Graham, Amanda L.

    2016-01-01

    Objective To estimate the causal effects of online community use on 30-day point prevalence abstinence at 3 months among smokers randomized to combined Internet+Phone intervention for smoking cessation. Method Participants were N=399 adult smokers in the Internet+Phone arm of The iQUITT Study, a randomized trial of Internet and proactive telephone counseling for smoking cessation. All participants accessed a web-based smoking-cessation program with an established online community and received telephone counseling. Automated tracking metrics of passive (e.g., reading posts, viewing profiles) and active (e.g., writing posts, sending messages) community use were extracted at 3 months. Self-selected community use defines the groups of interest: None, Passive, and Both (passive+active). Inverse probability of treatment weighting corrected for baseline imbalances on demographic, smoking, and psychosocial variables. Propensity weights estimated via generalized boosted models were used to calculate Average Treatment Effects (ATE) and Average Treatment effects on the Treated (ATT). Results Patterns of community use were: None=145 (36.3%), Passive=82 (20.6%), and Both=172 (43.1%). ATE-weighted abstinence rates were: None=12.2% (95% CI=6.7–17.7); Passive=25.2% (95% CI=15.1–35.2); Both=35.5% (95% CI=28.1–42.9). ATT-weighted abstinence rates indicated even greater benefits of passive community use by non-users. Conclusions More than one third of participants who received telephone counseling and used the community both passively and actively achieved abstinence. Participation in an established online community as part of a combined Internet+phone intervention has the potential to promote short-term abstinence. Results also demonstrated that information and support that originate in the community can serve as a resource for all users. PMID:27100127

  20. Combined discrete particle and continuum model predicting solid-state fermentation in a drum fermentor.

    PubMed

    Schutyser, M A I; Briels, W J; Boom, R M; Rinzema, A

    2004-05-20

    The development of mathematical models facilitates industrial (large-scale) application of solid-state fermentation (SSF). In this study, a two-phase model of a drum fermentor is developed that consists of a discrete particle model (solid phase) and a continuum model (gas phase). The continuum model describes the distribution of air in the bed injected via an aeration pipe. The discrete particle model describes the solid phase. In previous work, mixing during SSF was predicted with the discrete particle model, although mixing simulations were not carried out in the current work. Heat and mass transfer between the two phases and biomass growth were implemented in the two-phase model. Validation experiments were conducted in a 28-dm3 drum fermentor. In this fermentor, sufficient aeration was provided to control the temperatures near the optimum value for growth during the first 45-50 hours. Several simulations were also conducted for different fermentor scales. Forced aeration via a single pipe in the drum fermentors did not provide homogeneous cooling in the substrate bed. Due to large temperature gradients, biomass yield decreased severely with increasing size of the fermentor. Improvement of air distribution would be required to avoid the need for frequent mixing events, during which growth is hampered. From these results, it was concluded that the two-phase model developed is a powerful tool to investigate design and scale-up of aerated (mixed) SSF fermentors.

  1. Establishment of an in silico phototoxicity prediction method by combining descriptors related to photo-absorption and photo-reaction.

    PubMed

    Haranosono, Yu; Kurata, Masaaki; Sakaki, Hideyuki

    2014-08-01

    One of the mechanisms of phototoxicity is photo-reaction, such as reactive oxygen species (ROS) generation following photo-absorption. We focused on ROS generation and photo-absorption as key-steps, because these key-steps are able to be described by photochemical properties, and these properties are dependent on chemical structure. Photo-reactivity of a compound is described by HOMO-LUMO Gap (HLG), generally. Herein, we showed that HLG can be used as a descriptor of the generation of reactive oxygen species. Moreover, the maximum-conjugated π electron number (PENMC), which we found as a descriptor of photo-absorption, could also predict in vitro phototoxicity. Each descriptor could predict in vitro phototoxicity with 70.0% concordance, but there was un-predicted area found (gray zone). Interestingly, some compounds in each gray zone were not common, indicating that the combination of two descriptors could improve prediction potential. We reset the cut-off lines to define positive zone, negative zone and gray zone for each descriptor. Thereby we overlapped HLG and PENMC in a graph, and divided the total area to nine zones with cut-off lines of each descriptor. The rules to prediction were decided to achieve the best concordance, and the concordances were improved up to 82.8% for self-validation, 81.6% for cross-validation. We found common properties among false positive or negative compounds, photo-reactive structure and photo-allergenic, respectively. In addition, our method could be adapted to compounds rich in structural diversity using only chemical structure without any statistical analysis and complicated calculation.

  2. Improved sea level anomaly prediction through combination of data relationship analysis and genetic programming in Singapore Regional Waters

    NASA Astrophysics Data System (ADS)

    Kurniawan, Alamsyah; Ooi, Seng Keat; Babovic, Vladan

    2014-11-01

    With recent advances in measurement and information technology, there is an abundance of data available for analysis and modelling of hydrodynamic systems. Spatial and temporal data coverage, better quality and reliability of data modelling and data driven techniques have resulted in more favourable acceptance by the hydrodynamic community. The data mining tools and techniques are being applied in variety of hydro-informatics applications ranging from data mining for pattern discovery to data driven models and numerical model error correction. The present study explores the feasibility of applying mutual information theory by evaluating the amount of information contained in observed and prediction errors of non-tidal barotropic numerical modelling (i.e. assuming that the hydrodynamic model, available at this point, is best representation of the physics in the domain of interest) by relating them to variables that reflect the state at which the predictions are made such as input data, state variables and model output. In addition, the present study explores the possibility of employing ‘genetic programming' (GP) as an offline data driven modelling tool to capture the sea level anomaly (SLA) dynamics and then using them for updating the numerical model prediction in real time applications. These results suggest that combination of data relationship analysis and GP models helps to improve the forecasting ability by providing information of significant predicative parameters. It is found that GP based SLA prediction error forecast model can provide significant improvement when applied as data assimilation schemes for updating the SLA prediction obtained from primary hydrodynamic models.

  3. Combining regression analysis and air quality modelling to predict benzene concentration levels

    NASA Astrophysics Data System (ADS)

    Vlachokostas, Ch.; Achillas, Ch.; Chourdakis, E.; Moussiopoulos, N.

    2011-05-01

    State of the art epidemiological research has found consistent associations between traffic-related air pollution and various outcomes, such as respiratory symptoms and premature mortality. However, many urban areas are characterised by the absence of the necessary monitoring infrastructure, especially for benzene (C 6H 6), which is a known human carcinogen. The use of environmental statistics combined with air quality modelling can be of vital importance in order to assess air quality levels of traffic-related pollutants in an urban area in the case where there are no available measurements. This paper aims at developing and presenting a reliable approach, in order to forecast C 6H 6 levels in urban environments, demonstrated for Thessaloniki, Greece. Multiple stepwise regression analysis is used and a strong statistical relationship is detected between C 6H 6 and CO. The adopted regression model is validated in order to depict its applicability and representativeness. The presented results demonstrate that the adopted approach is capable of capturing C 6H 6 concentration trends and should be considered as complementary to air quality monitoring.

  4. Prediction of denosumab effects on bone remodeling: A combined pharmacokinetics and finite element modeling.

    PubMed

    Hambli, Ridha; Boughattas, Mohamed Hafedh; Daniel, Jean-Luc; Kourta, Azeddine

    2016-07-01

    Denosumab is a fully human monoclonal antibody that inhibits receptor activator of nuclearfactor-kappa B ligand (RANKL). This key mediator of osteoclast activities has been shown to inhibit osteoclast differentiation and hence, to increase bone mineral density (BMD) in treated patients. In the current study, we develop a computer model to simulate the effects of denosumab treatments (dose and duration) on the proximal femur bone remodeling process quantified by the variation in proximal femur BMD. The simulation model is based on a coupled pharmacokinetics model of denosumab with a pharmacodynamics model consisting of a mechanobiological finite element remodeling model which describes the activities of osteoclasts and osteoblasts. The mechanical behavior of bone is described by taking into account the bone material fatigue damage accumulation and mineralization. A coupled strain-damage stimulus function is proposed which controls the level of bone cell autocrine and paracrine factors. The cellular behavior is based on Komarova et al.׳s (2003) dynamic law which describes the autocrine and paracrine interactions between osteoblasts and osteoclasts and computes cell population dynamics and changes in bone mass at a discrete site of bone remodeling. Therefore, when an external mechanical stress is applied, bone formation and resorption is governed by cell dynamics rather than by adaptive elasticity approaches. The proposed finite element model was implemented in the finite element code Abaqus (UMAT routine). In order to perform a preliminary validation, in vivo human proximal femurs were selected and scanned at two different time intervals (at baseline and at a 36-month interval). Then, a 3D FE model was generated and the denosumab-remodeling algorithm was applied to the scans at t0 simulating daily walking activities for a duration of 36 months. The predicted results (density variation) were compared to existing published ones performed on a human cohort (FREEDOM).

  5. Inhibition of NF-κB Pathway and Modulation of MAPK Signaling Pathways in Glioblastoma and Implications for Lovastatin and Tumor Necrosis Factor-Related Apoptosis Inducing Ligand (TRAIL) Combination Therapy

    PubMed Central

    Deng, Yi; Wang, Cheng Dong; Su, Xian Wei; Zhou, Jing Ye; Chan, Tat Ming; Hu, Xiang; Poon, Wai Sang

    2017-01-01

    Glioblastoma is a common malignant brain tumor and it is refractory to therapy because it usually contains a mixture of cell types. The tumor necrosis factor-related apoptosis inducing ligand (TRAIL) has been shown to induce apoptosis in a range of tumor cell types. Previously, we found that two human glioblastoma cell lines are resistant to TRAIL, while lovastatin sensitizes these glioblastoma cells to TRAIL-induced cell death. In this study, we investigated the mechanisms underlying the TRAIL-induced apoptosis in human glioblastoma cell lines by lovastatin. Furthermore, we have confirmed the anti-tumor effect of combination therapy with lovastatin and TRAIL in the subcutaneous brain tumor model. We showed that lovastatin significantly up-regulated the expression of death receptor 5 (DR5) in glioblastoma cell lines as well as in tumor-bearing mice with peri-tumoral administration of lovastatin. Further study in glioblastoma cell lines suggested that lovastatin treatment could inhibit NF-κB and Erk/MAPK pathways but activates JNK pathway. These results suggest that lovastatin sensitizes TRAIL-induced apoptosis by up-regulation of DR5 level via NF-κB inactivation, but also directly induces apoptosis by dysregulation of MAPK pathway. Our in vivo study showed that local peri-tumoral co-injection of lovastatin and TRAIL substantially reduced tumor growth compared with single injection of lovastatin or TRAIL in subcutaneous nude mice model. This study suggests that combined treatment of lovastatin and TRAIL is a promising therapeutic strategy to TRAIL-resistant glioblastoma. PMID:28135339

  6. A Pathway Approach to Predicting Thyroid Hormone Disrupting Activity of Chemicals Using in vitro, ex vivo and in vivo Assays

    EPA Science Inventory

    The potential for commercial and industrial chemicals that may be released into the environment to have endocrine disrupting activity is of concern for human health and wildlife. Most initial endocrine disruptor research has focused on estrogen- or androgen-mediated pathways. In ...

  7. Predicting Species Cover of Marine Macrophyte and Invertebrate Species Combining Hyperspectral Remote Sensing, Machine Learning and Regression Techniques

    PubMed Central

    Kotta, Jonne; Kutser, Tiit; Teeveer, Karolin; Vahtmäe, Ele; Pärnoja, Merli

    2013-01-01

    In order to understand biotic patterns and their changes in nature there is an obvious need for high-quality seamless measurements of such patterns. If remote sensing methods have been applied with reasonable success in terrestrial environment, their use in aquatic ecosystems still remained challenging. In the present study we combined hyperspectral remote sensing and boosted regression tree modelling (BTR), an ensemble method for statistical techniques and machine learning, in order to test their applicability in predicting macrophyte and invertebrate species cover in the optically complex seawater of the Baltic Sea. The BRT technique combined with remote sensing and traditional spatial modelling succeeded in identifying, constructing and testing functionality of abiotic environmental predictors on the coverage of benthic macrophyte and invertebrate species. Our models easily predicted a large quantity of macrophyte and invertebrate species cover and recaptured multitude of interactions between environment and biota indicating a strong potential of the method in the modelling of aquatic species in the large variety of ecosystems. PMID:23755113

  8. Prediction of canine and premolar size using the widths of various permanent teeth combinations: A cross-sectional study

    PubMed Central

    Vanjari, Kalasandhya; Nuvvula, Sivakumar; Kamatham, Rekhalakshmi

    2015-01-01

    Aims: To suggest the best predictor/s for determining the mesio-distal widths (MDWs) of canines (C) and premolars (Ps), and propose regression equation/s for hitherto unreported population. Methods: Impressions of maxillary and mandibular arches were made for 201 children (100 boys and 101 girls; age range: 11–15 years) who met the inclusion criteria and poured with dental stone. The maximum MDWs of all the permanent teeth were measured using digital vernier caliper. Thirty-three possible combinations (patterns) of permanent maxillary and mandibular first molars, central and lateral incisors were framed and correlated with MDWs of C and Ps using Pearson correlation test. Results: There were significant correlations between the considered patterns and MDWs of C and Ps, with difference noted between girls (range of r: 0.34–0.66) and boys (range of r: 0.28–0.77). Simple linear and multiple regression equations for boys, girls, and combined sample were determined to predict MDW of C and Ps in both the arches. Conclusions: The accuracy of prediction improved considerably with the inclusion of as many teeth as possible in the regression equations. The newly proposed equations based on the erupted teeth may be considered clinically useful for space analysis in the considered population. PMID:26604576

  9. Combination of IAP antagonist and IFNγ activates novel caspase-10- and RIPK1-dependent cell death pathways.

    PubMed

    Tanzer, Maria C; Khan, Nufail; Rickard, James A; Etemadi, Nima; Lalaoui, Najoua; Spall, Sukhdeep Kaur; Hildebrand, Joanne M; Segal, David; Miasari, Maria; Chau, Diep; Wong, WendyWei-Lynn; McKinlay, Mark; Chunduru, Srinivas K; Benetatos, Christopher A; Condon, Stephen M; Vince, James E; Herold, Marco J; Silke, John

    2017-03-01

    Peptido-mimetic inhibitor of apoptosis protein (IAP) antagonists (Smac mimetics (SMs)) can kill tumour cells by depleting endogenous IAPs and thereby inducing tumour necrosis factor (TNF) production. We found that interferon-γ (IFNγ) synergises with SMs to kill cancer cells independently of TNF- and other cell death receptor signalling pathways. Surprisingly, CRISPR/Cas9 HT29 cells doubly deficient for caspase-8 and the necroptotic pathway mediators RIPK3 or MLKL were still sensitive to IFNγ/SM-induced killing. Triple CRISPR/Cas9-knockout HT29 cells lacking caspase-10 in addition to caspase-8 and RIPK3 or MLKL were resistant to IFNγ/SM killing. Caspase-8 and RIPK1 deficiency was, however, sufficient to protect cells from IFNγ/SM-induced cell death, implying a role for RIPK1 in the activation of caspase-10. These data show that RIPK1 and caspase-10 mediate cell death in HT29 cells when caspase-8-mediated apoptosis and necroptosis are blocked and help to clarify how SMs operate as chemotherapeutic agents.

  10. A Nanoparticle-Based Combination Chemotherapy Delivery System for Enhanced Tumor Killing by Dynamic Rewiring of Signaling Pathways

    PubMed Central

    Morton, Stephen W.; Lee, Michael J.; Deng, Zhou J.; Dreaden, Erik C.; Siouve, Elise; Shopsowitz, Kevin E.; Shah, Nisarg J.; Yaffe, Michael B.; Hammond, Paula T.

    2014-01-01

    Exposure to the EGFR (epidermal growth factor receptor) inhibitor erlotinib promotes the dynamic rewiring of apoptotic pathways, which sensitizes cells within a specific period to subsequent exposure to the DNA-damaging agent doxorubicin. A critical challenge for translating this therapeutic network rewiring into clinical practice is the design of optimal drug delivery systems. We report the generation of a nanoparticle delivery vehicle that contained more than one therapeutic agent and produced a controlled sequence of drug release. Liposomes, representing the first clinically approved nanomedicine systems, are well-characterized, simple, and versatile platforms for the manufacture of functional and tunable drug carriers. Using the hydrophobic and hydrophilic compartments of liposomes, we effectively incorporated both hydrophobic (erlotinib) and hydrophilic (doxorubicin) small molecules, through which we achieved the desired time sequence of drug release. We also coated the liposomes with folate to facilitate targeting to cancer cells. When compared to the time-staggered application of individual drugs, staggered release from tumor-targeted single liposomal particles enhanced dynamic rewiring of apoptotic signaling pathways, resulting in improved tumor cell killing in culture and tumor shrinkage in animal models. PMID:24825919

  11. mzGroupAnalyzer-Predicting Pathways and Novel Chemical Structures from Untargeted High-Throughput Metabolomics Data

    PubMed Central

    Wang, Lei; Engelmeier, Doris; Lyon, David; Weckwerth, Wolfram

    2014-01-01

    The metabolome is a highly dynamic entity and the final readout of the genotype x environment x phenotype (GxExP) relationship of an organism. Monitoring metabolite dynamics over time thus theoretically encrypts the whole range of possible chemical and biochemical transformations of small molecules involved in metabolism. The bottleneck is, however, the sheer number of unidentified structures in these samples. This represents the next challenge for metabolomics technology and is comparable with genome sequencing 30 years ago. At the same time it is impossible to handle the amount of data involved in a metabolomics analysis manually. Algorithms are therefore imperative to allow for automated m/z feature extraction and subsequent structure or pathway assignment. Here we provide an automated pathway inference strategy comprising measurements of metabolome time series using LC- MS with high resolution and high mass accuracy. An algorithm was developed, called mzGroupAnalyzer, to automatically explore the metabolome for the detection of metabolite transformations caused by biochemical or chemical modifications. Pathways are extracted directly from the data and putative novel structures can be identified. The detected m/z features can be mapped on a van Krevelen diagram according to their H/C and O/C ratios for pattern recognition and to visualize oxidative processes and biochemical transformations. This method was applied to Arabidopsis thaliana treated simultaneously with cold and high light. Due to a protective antioxidant response the plants turn from green to purple color via the accumulation of flavonoid structures. The detection of potential biochemical pathways resulted in 15 putatively new compounds involved in the flavonoid-pathway. These compounds were further validated by product ion spectra from the same data. The mzGroupAnalyzer is implemented in the graphical user interface (GUI) of the metabolomics toolbox COVAIN (Sun & Weckwerth, 2012, Metabolomics 8: 81

  12. COMBINED P16 AND HUMAN PAPILLOMAVIRUS TESTING PREDICTS HEAD AND NECK CANCER SURVIVAL

    PubMed Central

    Salazar, C. R.; Anayannis, N.; Smith, R. V.; Wang, Y.; Haigentz, M.; Garg, M.; Schiff, B. A.; Kawachi, N.; Elman, J.; Belbin, T. J.; Prystowsky, M. B.; Burk, R. D.; Schlecht, N. F.

    2014-01-01

    While its prognostic significance remains unclear, p16INK4a protein expression is increasingly being used as a surrogate marker for oncogenic human papillomavirus (HPV) infection in head and neck squamous cell carcinomas (HNSCC). To evaluate the prognostic utility of p16 expression in HNSCC, we prospectively collected 163 primary tumor specimens from histologically confirmed HNSCC patients who were followed for up to 9.4 years. Formalin fixed tumor specimens were tested for p16 protein expression by immunohistochemistry. HPV type-16 DNA and RNA was detected by MY09/11-PCR and E6/E7 RT-PCR on matched frozen tissue, respectively. P16 protein expression was detected more often in oropharyngeal tumors (53%) as compared with laryngeal (24%), hypopharyngeal (8%), or oral cavity tumors (4%; P<0.0001). With respect to prognosis, p16-positive oropharyngeal tumors exhibited significantly better overall survival than p16-negative tumors (log-rank test p=0.04), whereas no survival benefit was observed for non-oropharyngeal tumors. However, when both p16 and HPV DNA test results were considered, concordantly positive non-oropharyngeal tumors had significantly better disease-specific survival than concordantly negative non-oropharyngeal tumors after controlling for sex, nodal stage, tumor size, tumor subsite, primary tumor site number, smoking, and drinking (adjusted hazard ratio [HR]=0.04, 0.01–0.54). Compared with concordantly negative non-oropharyngeal HNSCC, p16(+)/HPV16(-) non-oropharyngeal HNSCC (n=13, 7%) demonstrated no significant improvement in disease-specific survival when HPV16 was detected by RNA (adjusted HR=0.83, 0.22–3.17). Our findings show that p16 immunohistochemistry alone has potential as a prognostic test for oropharyngeal cancer survival, but combined p16/HPV testing is necessary to identify HPV-associated non-oropharyngeal HNSCC with better prognosis. PMID:24706381

  13. Combining PubMed knowledge and EHR data to develop a weighted bayesian network for pancreatic cancer prediction.

    PubMed

    Zhao, Di; Weng, Chunhua

    2011-10-01

    In this paper, we propose a novel method that combines PubMed knowledge and Electronic Health Records to develop a weighted Bayesian Network Inference (BNI) model for pancreatic cancer prediction. We selected 20 common risk factors associated with pancreatic cancer and used PubMed knowledge to weigh the risk factors. A keyword-based algorithm was developed to extract and classify PubMed abstracts into three categories that represented positive, negative, or neutral associations between each risk factor and pancreatic cancer. Then we designed a weighted BNI model by adding the normalized weights into a conventional BNI model. We used this model to extract the EHR values for patients with or without pancreatic cancer, which then enabled us to calculate the prior probabilities for the 20 risk factors in the BNI. The software iDiagnosis was designed to use this weighted BNI model for predicting pancreatic cancer. In an evaluation using a case-control dataset, the weighted BNI model significantly outperformed the conventional BNI and two other classifiers (k-Nearest Neighbor and Support Vector Machine). We conclude that the weighted BNI using PubMed knowledge and EHR data shows remarkable accuracy improvement over existing representative methods for pancreatic cancer prediction.

  14. Sugar and acid content of Citrus prediction modeling using FT-IR fingerprinting in combination with multivariate statistical analysis.

    PubMed

    Song, Seung Yeob; Lee, Young Koung; Kim, In-Jung

    2016-01-01

    A high-throughput screening system for Citrus lines were established with higher sugar and acid contents using Fourier transform infrared (FT-IR) spectroscopy in combination with multivariate analysis. FT-IR spectra confirmed typical spectral differences between the frequency regions of 950-1100 cm(-1), 1300-1500 cm(-1), and 1500-1700 cm(-1). Principal component analysis (PCA) and subsequent partial least square-discriminant analysis (PLS-DA) were able to discriminate five Citrus lines into three separate clusters corresponding to their taxonomic relationships. The quantitative predictive modeling of sugar and acid contents from Citrus fruits was established using partial least square regression algorithms from FT-IR spectra. The regression coefficients (R(2)) between predicted values and estimated sugar and acid content values were 0.99. These results demonstrate that by using FT-IR spectra and applying quantitative prediction modeling to Citrus sugar and acid contents, excellent Citrus lines can be early detected with greater accuracy.

  15. Prediction of Pharmacokinetic Parameters Using a Genetic Algorithm Combined with an Artificial Neural Network for a Series of Alkaloid Drugs

    PubMed Central

    Zandkarimi, Majid; Shafiei, Mohammad; Hadizadeh, Farzin; Darbandi, Mohammad Ali; Tabrizian, Kaveh

    2014-01-01

    An important goal for drug development within the pharmaceutical industry is the application of simple methods to determine human pharmacokinetic parameters. Effective computing tools are able to increase scientists’ ability to make precise selections of chemical compounds in accordance with desired pharmacokinetic and safety profiles. This work presents a method for making predictions of the clearance, plasma protein binding, and volume of distribution for alkaloid drugs. The tools used in this method were genetic algorithms (GAs) combined with artificial neural networks (ANNs) and these were applied to select the most relevant molecular descriptors and to develop quantitative structure-pharmacokinetic relationship (QSPkR) models. Results showed that three-dimensional structural descriptors had more influence on QSPkR models. The models developed in this study were able to predict systemic clearance, volume of distribution, and plasma protein binding with normalized root mean square error (NRMSE) values of 0.151, 0.263, and 0.423, respectively. These results demonstrate an acceptable level of efficiency of the developed models for the prediction of pharmacokinetic parameters. PMID:24634842

  16. Prediction of Thermostability from Amino Acid Attributes by Combination of Clustering with Attribute Weighting: A New Vista in Engineering Enzymes

    PubMed Central

    Ebrahimi, Mansour; Lakizadeh, Amir; Agha-Golzadeh, Parisa; Ebrahimie, Esmaeil; Ebrahimi, Mahdi

    2011-01-01

    The engineering of thermostable enzymes is receiving increased attention. The paper, detergent, and biofuel industries, in particular, seek to use environmentally friendly enzymes instead of toxic chlorine chemicals. Enzymes typically function at temperatures below 60°C and denature if exposed to higher temperatures. In contrast, a small portion of enzymes can withstand higher temperatures as a result of various structural adaptations. Understanding the protein attributes that are involved in this adaptation is the first step toward engineering thermostable enzymes. We employed various supervised and unsupervised machine learning algorithms as well as attribute weighting approaches to find amino acid composition attributes that contribute to enzyme thermostability. Specifically, we compared two groups of enzymes: mesostable and thermostable enzymes. Furthermore, a combination of attribute weighting with supervised and unsupervised clustering algorithms was used for prediction and modelling of protein thermostability from amino acid composition properties. Mining a large number of protein sequences (2090) through a variety of machine learning algorithms, which were based on the analysis of more than 800 amino acid attributes, increased the accuracy of this study. Moreover, these models were successful in predicting thermostability from the primary structure of proteins. The results showed that expectation maximization clustering in combination with uncertainly and correlation attribute weighting algorithms can effectively (100%) classify thermostable and mesostable proteins. Seventy per cent of the weighting methods selected Gln content and frequency of hydrophilic residues as the most important protein attributes. On the dipeptide level, the frequency of Asn-Glu was the key factor in distinguishing mesostable from thermostable enzymes. This study demonstrates the feasibility of predicting thermostability irrespective of sequence similarity and will serve as a

  17. Combining KINEROS with SM-hsB for Flash Flood Predictions in Small to Medium Sized Watersheds

    NASA Astrophysics Data System (ADS)

    Broxton, P. D.; Troch, P. A.; Schaffner, M.; Unkrich, C.; Goodrich, D. C.

    2011-12-01

    Currently, there are a number of operational tools available to address and forecast flash floods, though many of these tools have shortcomings such as inadequate spatial and temporal resolutions and missing formulations for snow and snowmelt. In this research, we improve upon existing flash flood predictions by designing and implementing a high resolution model system that can estimate both the timing and magnitude of flash floods, while simultaneously considering the importance of overland flow/channel routing, variable infiltration, subsurface flow, and snowmelt. We have combined the KINematic runoff and EROSion (KINEROS) model with the Soil Moisture-HillSlope Bousinesq (SM-hsB) model, a continuous subsurface flow model developed at the University of Arizona that includes physical descriptions of hydrological processes at pedon, hillslope and catchment scales, including an energy balance snowmelt formulation. The combined model system, which is tested in humid and semi-arid watersheds in Delaware County, New York, and near Tucson, Arizona, is spatially distributed and has a temporal resolution of five minutes during events. It uses readily available data sources for past and present forcings, including Stage III radar data (5 minute, 1 degree by 1 km), for calibration and real-time operation. We believe that this system will make a meaningful contribution to the flash-flood forecasting community because it has very high spatial and temporal resolutions, and its ability to run in continuous mode allows for improved consistency of flash flood predictions. It especially improves flash flood predictions in humid environments where lateral subsurface flow significantly contributes to streamflow and in situations in which snow is involved.

  18. Combined In-Plane and Through-the-Thickness Analysis for Failure Prediction of Bolted Composite Joints

    NASA Technical Reports Server (NTRS)

    Kradinov, V.; Madenci, E.; Ambur, D. R.

    2004-01-01

    Although two-dimensional methods provide accurate predictions of contact stresses and bolt load distribution in bolted composite joints with multiple bolts, they fail to capture the effect of thickness on the strength prediction. Typically, the plies close to the interface of laminates are expected to be the most highly loaded, due to bolt deformation, and they are usually the first to fail. This study presents an analysis method to account for the variation of stresses in the thickness direction by augmenting a two-dimensional analysis with a one-dimensional through the thickness analysis. The two-dimensional in-plane solution method based on the combined complex potential and variational formulation satisfies the equilibrium equations exactly, and satisfies the boundary conditions and constraints by minimizing the total potential. Under general loading conditions, this method addresses multiple bolt configurations without requiring symmetry conditions while accounting for the contact phenomenon and the interaction among the bolts explicitly. The through-the-thickness analysis is based on the model utilizing a beam on an elastic foundation. The bolt, represented as a short beam while accounting for bending and shear deformations, rests on springs, where the spring coefficients represent the resistance of the composite laminate to bolt deformation. The combined in-plane and through-the-thickness analysis produces the bolt/hole displacement in the thickness direction, as well as the stress state in each ply. The initial ply failure predicted by applying the average stress criterion is followed by a simple progressive failure. Application of the model is demonstrated by considering single- and double-lap joints of metal plates bolted to composite laminates.

  19. A Novel Combination of Calprotectin and CXCL12 for Predicting Malignancy in Patients with Exudative Pleural Effusion.

    PubMed

    Luo, Jian; Wang, Maoyun; Li, Chuntao; Liang, Binmiao; Liu, Dan; Shi, Chaoli; Jiang, Faming; Wang, Ting; Li, Peijun; Liang, Zongan

    2015-11-01

    Pleural effusion (PE) remains a significant challenge and public health problem, which needs novel noninvasive biomarkers for the precise diagnosis. The aim of this study was to further determine the clinical efficacy and diagnostic accuracy of a novel combination of calprotectin and CXCL12 for predicting malignancy in patients with exudative PE.Calprotectin and CXCL12 concentrations were measured in 95 individuals of exudative PE, with 39 malignant PE (MPE) and 56 benign PE (BPE). The accuracy of calprotectin and CXCL12 levels for discriminating MPE from BPE or tuberculous PE were evaluated using receiver-operating characteristic (ROC) curves. Univariate and multivariate logistic regression analyses were performed to test the association between calprotectin and CXCL12 levels and MPE.Calprotectin and CXCL12 levels of patients with MPE were significantly lower than that of BPE and tuberculous PE (P < 0.05). The area under the curve (AUC) of calprotectin and CXCL12 was 0.683 and 0.641 in MPE and BPE, and a combination of calprotectin ≤500.19 ng/mL and CXCL12 ≤6.11 ng/mL rendered a sensitivity and specificity of 48.72% and 78.57%, respectively. While in MPE and tuberculous PE, the AUC of calprotectin and CXCL12 was 0.696 and 0.690, and a combination of calprotectin ≤421.73 ng/mL and CXCL12 ≤3.71 ng/mL presented a sensitivity and specificity of 25.64% and 95.45%, respectively. Multivariate logistic regression demonstrated that both calprotectin and CXCL12 were independent predictors of MPE.Calprotectin and CXCL12 in pleural fluid are informative diagnostic biomarkers for predicting patients with MPE.

  20. Combination of OipA, BabA, and SabA as candidate biomarkers for predicting Helicobacter pylori-related gastric cancer

    PubMed Central

    Su, Yu-Lin; Huang, Hsiang-Ling; Huang, Bo-Shih; Chen, Po-Chung; Chen, Chien-Sheng; Wang, Hong-Long; Lin, Pin-Hsin; Chieh, Meng-Shu; Wu, Jiunn-Jong; Yang, Jyh-Chin; Chow, Lu-Ping

    2016-01-01

    Helicobacter pylori (H. pylori ) infection is a major cause of chronic gastritis and is highly related to duodenal ulcer (DU) and gastric cancer (GC). To identify H. pylori-related GC biomarkers with high seropositivity in GC patients, differences in levels of protein expression between H. pylori from GC and DU patients were analyzed by isobaric tag for relative and absolute quantitation (iTRAQ). In total, 99 proteins showed increased expression (>1.5-fold) in GC patients compared to DU patients, and 40 of these proteins were categorized by KEGG pathway. The four human disease-related adhesin identified, AlpA, OipA, BabA, and SabA, were potential GC-related antigens, with a higher seropositivity in GC patients (n = 76) than in non-GC patients (n = 100). Discrimination between GC and non-GC patients was improved using multiple antigens, with an odds ratio of 9.16 (95% CI, 2.99–28.07; p < 0.0001) for three antigens recognized. The optimized combination of OipA, BabA, and SabA gave a 77.3% correct prediction rate. A GC-related protein microarray was further developed using these antigens. The combination of OipA, BabA, and SabA showed significant improvement in the diagnostic accuracy and the protein microarray containing above antigens should provide a rapid and convenient diagnosis of H. pylori-associated GC. PMID:27819260

  1. Integrative Pathway Analysis of Metabolic Signature in Bladder Cancer: A Linkage to The Cancer Genome Atlas Project and Prediction of Survival

    PubMed Central

    von Rundstedt, Friedrich-Carl; Rajapakshe, Kimal; Ma, Jing; Arnold, James M.; Gohlke, Jie; Putluri, Vasanta; Krishnapuram, Rashmi; Piyarathna, D. Badrajee; Lotan, Yair; Gödde, Daniel; Roth, Stephan; Störkel, Stephan; Levitt, Jonathan M.; Michailidis, George; Sreekumar, Arun; Lerner, Seth P.; Coarfa, Cristian; Putluri, Nagireddy

    2016-01-01

    Purpose We used targeted mass spectrometry to study the metabolic fingerprint of urothelial cancer and determine whether the biochemical pathway analysis gene signature would have a predictive value in independent cohorts of patients with bladder cancer. Materials and Methods Pathologically evaluated, bladder derived tissues, including benign adjacent tissue from 14 patients and bladder cancer from 46, were analyzed by liquid chromatography based targeted mass spectrometry. Differential metabolites associated with tumor samples in comparison to benign tissue were identified by adjusting the p values for multiple testing at a false discovery rate threshold of 15%. Enrichment of pathways and processes associated with the metabolic signature were determined using the GO (Gene Ontology) Database and MSigDB (Molecular Signature Database). Integration of metabolite alterations with transcriptome data from TCGA (The Cancer Genome Atlas) was done to identify the molecular signature of 30 metabolic genes. Available outcome data from TCGA portal were used to determine the association with survival. Results We identified 145 metabolites, of which analysis revealed 31 differential metabolites when comparing benign and tumor tissue samples. Using the KEGG (Kyoto Encyclopedia of Genes and Genomes) Database we identified a total of 174 genes that correlated with the altered metabolic pathways involved. By integrating these genes with the transcriptomic data from the corresponding TCGA data set we identified a metabolic signature consisting of 30 genes. The signature was significant in its prediction of survival in 95 patients with a low signature score vs 282 with a high signature score (p = 0.0458). Conclusions Targeted mass spectrometry of bladder cancer is highly sensitive for detecting metabolic alterations. Applying transcriptome data allows for integration into larger data sets and identification of relevant metabolic pathways in bladder cancer progression. PMID:26802582

  2. Computational approaches for protein function prediction: a combined strategy from multiple sequence alignment to molecular docking-based virtual screening.

    PubMed

    Pierri, Ciro Leonardo; Parisi, Giovanni; Porcelli, Vito

    2010-09-01

    The functional characterization of proteins represents a daily challenge for biochemical, medical and computational sciences. Although finally proved on the bench, the function of a protein can be successfully predicted by computational approaches that drive the further experimental assays. Current methods for comparative modeling allow the construction of accurate 3D models for proteins of unknown structure, provided that a crystal structure of a homologous protein is available. Binding regions can be proposed by using binding site predictors, data inferred from homologous crystal structures, and data provided from a careful interpretation of the multiple sequence alignment of the investigated protein and its homologs. Once the location of a binding site has been proposed, chemical ligands that have a high likelihood of binding can be identified by using ligand docking and structure-based virtual screening of chemical libraries. Most docking algorithms allow building a list sorted by energy of the lowest energy docking configuration for each ligand of the library. In this review the state-of-the-art of computational approaches in 3D protein comparative modeling and in the study of protein-ligand interactions is provided. Furthermore a possible combined/concerted multistep strategy for protein function prediction, based on multiple sequence alignment, comparative modeling, binding region prediction, and structure-based virtual screening of chemical libraries, is described by using suitable examples. As practical examples, Abl-kinase molecular modeling studies, HPV-E6 protein multiple sequence alignment analysis, and some other model docking-based characterization reports are briefly described to highlight the importance of computational approaches in protein function prediction.

  3. Subpixel mapping on remote sensing imagery using a prediction model combining wavelet transform and radial basis function neural network

    NASA Astrophysics Data System (ADS)

    Dai, Xiaoyan; Guo, Zhongyang; Zhang, Liquan; Xu, Wencheng

    2009-12-01

    Soft classification methods can be used for mixed-pixel classification on remote sensing imagery by estimating different land cover class fractions of every pixel. However, the spatial distribution and location of these class components within the pixel remain unknown. To map land cover at subpixel scale and increase the spatial resolution of land cover classification maps, in this paper, a prediction model combining wavelet transform and Radial Basis Functions (RBF) neural network, abbreviated as Wavelet-RBFNN, is constructed by predicting high-frequency wavelet coefficients from low-frequency coefficients at the same resolution with RBF network and taking wavelet coefficients at coarser resolution as training samples. According to different land cover class fraction images obtained from mixed-pixel classification, based on the assumption of neighborhood dependence of wavelet coefficients, subpixel mapping on remote sensing imagery can be accomplished through two steps, i.e., prediction of land cover class compositions within subpixels and hard classification. The experimental results obtained with artificial images, QuickBird image and Landsat 7 ETM+ image indicate that the subpixel mapping method proposed in this paper can successfully produce super-resolution land cover classification maps from remote sensing imagery, outperforming cubic B-spline and Kriging interpolation method in visual effect and prediction accuracy. The Wavelet-RBFNN model can also be applied to simulate higher spatial resolution image, and automatically identify and locate land cover targets at the subpixel scales, when the cost and availability of high resolution imagery prohibit its use in many areas of work.

  4. Targeting cancer cell metabolism: The combination of metformin and 2-Deoxyglucose regulates apoptosis in ovarian cancer cells via p38 MAPK/JNK signaling pathway

    PubMed Central

    Zhu, Jie; Zheng, Ya; Zhang, Haiyan; Sun, Hong

    2016-01-01

    Targeting cancer cell metabolism is a new promising strategy to fight cancer. Metformin, a first-line treatment for type 2 diabetes mellitus, exerts anti-cancer and anti-proliferative action. 2-deoxyglucose (2-DG), a glucose analog, works as a competitive inhibitor of glycolysis. In this study, we show for the first time that metformin in combination with 2-DG inhibited growth, migration, invasion and induced cell cycle arrest of ovarian cancer cells in vitro. Moreover, metformin and 2-DG could efficiently induce apoptosis in ovarian cancer cells, which was achieved by activating p38 MAPK and JNK pathways. Our study reinforces the growing interest of metabolic interference in cancer therapy and highlights the potential use of the combination of metformin and 2-DG as an anti-tumor treatment in ovarian cancer. PMID:27904682

  5. Epoxide pathways improve model predictions of isoprene markers and reveal key role of acidity in aerosol formation

    EPA Science Inventory

    Isoprene significantly contributes to organic aerosol in the southeastern United States where biogenic hydrocarbons mix with anthropogenic emissions. In this work, the Community Multiscale Air Quality model is updated to predict isoprene aerosol from epoxides produced under both ...

  6. Evaluation and intercomparison of meteorological predictions by five MM5-PBL parameterizations in combination with three land-surface models

    NASA Astrophysics Data System (ADS)

    Han, Zhiwei; Ueda, Hiromasa; An, Junling

    In this study, MM5 predictions with five PBL parameterizations in combination with three land-surface models (LSMs) are intercompared and evaluated by using a wide variety of observations derived from WMO routine surface weather stations, TRACE-P aircraft experiments, intense radiosonde soundings and satellite measurements. Six scenarios with various PBL schemes and LSMs are designed to investigate the similarities and differences in model predictions. For near-surface variables, all scenarios yield good correlation between prediction and observation for 2 m-temperature (T2) and 2 m-water vapor mixing ratio (Q2), and relatively poor ones for wind fields. On average, T2 was consistently underpredicted by all scenarios, whereas Q2 was overpredicted by five of the six scenarios. It is found that the application of Noah land-surface model instead of the five-layer soil model is able to enhance the prediction accuracy of Q2. For 10 m-wind speed, the GSE scenario (Gayno-Seaman scheme with the five-layer soil model) produces somewhat smaller correlation, but better consistency in magnitude than those of the other scenarios. Model predictions are more consistent for upper air as a result of using FDDA reanalysis nudging and the reducing influence of underlying surface with altitude. All scenarios show the tendencies to underpredict temperature and to overpredict wind speed at altitudes <1 km and to underpredict wind speed at altitudes >3 km. The correlations for water vapor mixing ratio are much smaller at altitudes >3 km than that in the boundary layer. The differences in predicted PBL height among scenarios are large. GSE scenario performs best for phase (correlation), whereas PCX scenario (Pleim-Chang scheme with Pleim-Xiu LSM) produces the best statistics for magnitude of PBL height. Diurnal variation of PBL height over the western Pacific region during the study period is characterized by the typical day and night cycling superimposed by occasional expansion

  7. Connectivity of thalamo-cortical pathway in rat brain: combined diffusion spectrum imaging and functional MRI at 11.7 T.

    PubMed

    Kim, Young Beom; Kalthoff, Daniel; Po, Chrystelle; Wiedermann, Dirk; Hoehn, Mathias

    2012-07-01

    Fiber tracking in combination with functional MRI has recently attracted strong interest, as it may help to elucidate the structural basis for functional connectivities and may be selective in the determination of the fiber bundles responsible for a particular circuit. Diffusion spectrum imaging provides a more complex analysis of fiber circuits than the commonly used diffusion tensor imaging approach, also allowing the discrimination of crossing fibers in the brain. For the understanding of pathophysiological alterations during brain lesion and recovery, such studies need to be extended to small-animal models. In this article, we present the first study combining functional MRI with high-resolution diffusion spectrum imaging in vivo. We have chosen the well-characterized electrical forepaw stimulation paradigm in the rat to examine the thalamo-cortical pathway. Using the functionally activated areas in both thalamus and somatosensory cortex as seed and target regions for fiber tracking, we are able to characterize the fibers responsible for this stimulation pathway. Moreover, we show that the selection of the thalamic nucleus and primary somatosensory cortex on the basis of anatomical description results in a larger fiber bundle, probably encompassing connectivities between the thalamus and other areas of the somatosensory cortex, such as the hindpaw and large barrel field cortex.

  8. Computer-aided detection of lung cancer: combining pulmonary nodule detection systems with a tumor risk prediction model

    NASA Astrophysics Data System (ADS)

    Setio, Arnaud A. A.; Jacobs, Colin; Ciompi, Francesco; van Riel, Sarah J.; Winkler Wille, Mathilde M.; Dirksen, Asger; van Rikxoort, Eva M.; van Ginneken, Bram

    2015-03-01

    Computer-Aided Detection (CAD) has been shown to be a promising tool for automatic detection of pulmonary nodules from computed tomography (CT) images. However, the vast majority of detected nodules are benign and do not require any treatment. For effective implementation of lung cancer screening programs, accurate identification of malignant nodules is the key. We investigate strategies to improve the performance of a CAD system in detecting nodules with a high probability of being cancers. Two strategies were proposed: (1) combining CAD detections with a recently published lung cancer risk prediction model and (2) the combination of multiple CAD systems. First, CAD systems were used to detect the nodules. Each CAD system produces markers with a certain degree of suspicion. Next, the malignancy probability was automatically computed for each marker, given nodule characteristics measured by the CAD system. Last, CAD degree of suspicion and malignancy probability were combined using the product rule. We evaluated the method using 62 nodules which were proven to be malignant cancers, from 180 scans of the Danish Lung Cancer Screening Trial. The malignant nodules were considered as positive samples, while all other findings were considered negative. Using a product rule, the best proposed system achieved an improvement in sensitivity, compared to the best individual CAD system, from 41.9% to 72.6% at 2 false positives (FPs)/scan and from 56.5% to 88.7% at 8 FPs/scan. Our experiment shows that combining a nodule malignancy probability with multiple CAD systems can increase the performance of computerized detection of lung cancer.

  9. A copper-induced quinone degradation pathway provides protection against combined copper/quinone stress in Lactococcus lactis IL1403.

    PubMed

    Mancini, Stefano; Abicht, Helge K; Gonskikh, Yulia; Solioz, Marc

    2015-02-01

    Quinones are ubiquitous in the environment. They occur naturally but are also in widespread use in human and industrial activities. Quinones alone are relatively benign to bacteria, but in combination with copper, they become toxic by a mechanism that leads to intracellular thiol depletion. Here, it was shown that the yahCD-yaiAB operon of Lactococcus lactis IL1403 provides resistance to combined copper/quinone stress. The operon is under the control of CopR, which also regulates expression of the copRZA copper resistance operon as well as other L. lactis genes. Expression of the yahCD-yaiAB operon is induced by copper but not by quinones. Two of the proteins encoded by the operon appear to play key roles in alleviating quinone/copper stress: YaiB is a flavoprotein that converts p-benzoquinones to less toxic hydroquinones, using reduced nicotinamide adenine dinucleotide phosphate (NADPH) as reductant; YaiA is a hydroquinone dioxygenase that converts hydroquinone putatively to 4-hydroxymuconic semialdehyde in an oxygen-consuming reaction. Hydroquinone and methylhydroquinone are both substrates of YaiA. Deletion of yaiB causes increased sensitivity of L. lactis to quinones and complete growth arrest under combined quinone and copper stress. Copper induction of the yahCD-yaiAB operon offers protection to copper/quinone toxicity and could provide a growth advantage to L. lactis in some environments.

  10. Combined brain and anterior visual pathways' MRIs assist in early identification of neuromyelitis optica spectrum disorder at onset of optic neuritis.

    PubMed

    Buch, D; Savatovsky, J; Gout, O; Vignal, C; Deschamps, R

    2017-03-01

    Acute optic neuritis (ON) is the initial presentation in half of neuromyelitis optica spectrum disorder (NMO-SD) cases. Our objective was to evaluate accuracy of combined MRIs of the anterior visual pathways and of the brain to correctly identify NMO-SD among patients with acute ON. We performed a retrospective study on patients with acute ON in NMO-SD (16 episodes) and first-event non-NMO-SD (32 episodes). All MRIs included exams of the brain and anterior visual pathways using T2-weighted and post-gadolinium T1-weighted coronal thin slices. Images were reviewed by a neuroradiologist who was blinded to the final diagnosis. There were no multiple sclerosis (MS)-like lesions with dissemination in space (DIS) with NMO-SD (0 vs. 53%, p < 0.01). Non-NMO-SD ON usually spared the chiasma (3 vs. 44%, p < 0.01) and the optic tracts (0 vs. 19%, p < 0.01). Optic nerve lesions were longer [median (range) 26 mm (14-64) vs. 13 mm [8-36], p < 0.01] and the number of segments involved higher (3 [1-8] vs. 1 [1-4], p < 0.01) in NMO-SD. Bilateral optic nerve involvement, or involvement of ≥3 segments, or involvement of the chiasma, or optic tracts in the absence of MS-like lesions with DIS were suggestive of NMO-SD with a sensitivity of 69% (CI 95% 41-89) and a specificity of 97% (CI 95% 84-99) (p < 0.01). Combining brain and anterior visual pathways' MRIs seems efficient for detecting acute ON patients who are at high risk for NMO-SD.

  11. A combined approach to predict spatial temperature evolution and its consequences during FIB processing of soft matter.

    PubMed

    Schmied, Roland; Fröch, Johannes E; Orthacker, Angelina; Hobisch, Josephine; Trimmel, Gregor; Plank, Harald

    2014-04-07

    Accessing local temperatures and their evolution during focused ion beam (FIB) processing is of particular importance in the context of polymers or biomaterials as they tend to undergo severe chemical and morphological damage due to the high temperatures arising. In this study we present a combination of ion trajectory simulations and thermal spike model based calculations, which allows predicting local temperatures, lateral distributions and evolution during FIB patterning. Simulations and calculations have been done without any approximation or correction factors and lead to results in very good agreement with experiments on polymers taking into account their thermal behaviour. Finally, the model is applied to different scanning strategies which demonstrate how classically applied patterning strategies lead to massive temperature increases which can be the knock out criteria for low melting materials.

  12. A method for predicting the outcomes of combined pharmacologic and deep brain stimulation therapy for Parkinson's disease.

    PubMed

    Shamir, Reuben R; Dolbert, Trygve; Noecker, Angela M; Frankemolle, Anneke M; Walter, Benjamin L; McIntyre, Cameron C

    2014-01-01

    Deep brain stimulation (DBS) is an established therapy for the management of advanced Parkinson's disease (PD). However, the coupled adjustment of pharmacologic therapy and stimulation parameter settings is a time-consuming process and treatment outcomes are not always optimal. In this study, we develop a linear function that relates the DBS parameters, the levodopa dosage, and patient-specific preoperative clinical data with the actual treatment motor outcomes. To this end, we incorporate image-based patient-specific computer models of the volume of tissue activated by DBS in a multilinear regression analysis (6 PD patients; 60 follow up visits). The resulting predictor function was highly correlated with the actual motor outcomes (r = 0.76; p < 0.05). These results demonstrate that the outcomes of a combined pharmacologic-DBS therapy can be predicted and may facilitate patient-specific treatment optimization for maximal benefits and minimal adverse effects.

  13. RVMAB: Using the Relevance Vector Machine Model Combined with Average Blocks to Predict the Interactions of Proteins from Protein Sequences.

    PubMed

    An, Ji-Yong; You, Zhu-Hong; Meng, Fan-Rong; Xu, Shu-Juan; Wang, Yin

    2016-05-18

    Protein-Protein Interactions (PPIs) play essential roles in most cellular processes. Knowledge of PPIs is becoming increasingly more important, which has prompted the development of technologies that are capable of discovering large-scale PPIs. Although many high-throughput biological technologies have been proposed to detect PPIs, there are unavoidable shortcomings, including cost, time intensity, and inherently high false positive and false negative rates. For the sake of these reasons, in silico methods are attracting much attention due to their good performances in predicting PPIs. In this paper, we propose a novel computational method known as RVM-AB that combines the Relevance Vector Machine (RVM) model and Average Blocks (AB) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the AB feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We performed five-fold cross-validation experiments on yeast and Helicobacter pylori datasets, and achieved very high accuracies of 92.98% and 95.58% respectively, which is significantly better than previous works. In addition, we also obtained good prediction accuracies of 88.31%, 89.46%, 91.08%, 91.55%, and 94.81% on other five independent datasets C. elegans, M. musculus, H. sapiens, H. pylori, and E. coli for cross-species prediction. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the yeast dataset. The experimental results demonstrate that our RVM-AB method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool. To facilitate extensive studies for future proteomics research, we developed a freely

  14. Heterogenous graft rejection pathways in class I major histocompatibility complex-disparate combinations and their differential susceptibility to immunomodulation induced by intravenous presensitization with relevant alloantigens

    PubMed Central

    1991-01-01

    The present study investigates the heterogeneity of graft rejection pathways in class I major histocompatibility complex (MHC)-disparate combinations and the susceptibility of each pathway to immunomodulation induced by intravenous presensitization with alloantigens. Depletion of CD8+ T cells was induced by repeated administration of anti-CD8 monoclonal antibody. CD8+ T cell-depleted mice failed to generate anti- allo class I MHC cytotoxic T cell (CTL) responses but exhibited anti- allo class I MHC T cell responses, such as mixed lymphocyte reaction (MLR)/IL-2 production, that were induced by CD4+ T cells. In contrast, donor-specific intravenous presensitization (DSP), as a model of donor- specific transfusion, induced almost complete elimination of CD4+ and CD8+ T cell-mediated MLR/IL-2 production, whereas this regimen did not affect the generation of CTL responses induced by DSP-resistant elements (CD8+ CTL precursors and CD4+ CTL helpers). Prolongation of skin graft survival was not induced by either of the above two regimens alone, but by the combination of these. Prolonged graft survival was obtained irrespective of whether the administration of anti-CD8 antibody capable of eliminating CTL was started before or after DSP. The combination of DSP with injection of anti-CD4 antibody also effectively prolonged graft survival. However, this was the case only when the injection of antibody was started before DSP, because such antibody administration was capable of inhibiting the generation of CTL responses by eliminating DSP-resistant CD4+ CTL helpers. These results indicate that (a) the graft rejection in class I-disparate combinations is induced by CD8+ CTL-involved and -independent pathways that are resistant and susceptible to DSP, respectively; (b) DSP contributes to, but is not sufficient for, the prolongation of graft survival; and (c) the suppression of graft rejection requires an additional treatment for reducing DSP-resistant CTL responses. The results are

  15. Usefulness of combined white blood cell count and plasma glucose for predicting in-hospital outcomes after acute myocardial infarction.

    PubMed

    Ishihara, Masaharu; Kojima, Sunao; Sakamoto, Tomohiro; Asada, Yujiro; Kimura, Kazuo; Miyazaki, Shunichi; Yamagishi, Masakazu; Tei, Chuwa; Hiraoka, Hisatoyo; Sonoda, Masahiro; Tsuchihashi, Kazufumi; Shinoyama, Nobuo; Honda, Takashi; Ogata, Yasuhiro; Ogawa, Hisao

    2006-06-01

    Admission white blood cell (WBC) count and plasma glucose (PG) have been associated with adverse outcomes after acute myocardial infarction (AMI). This study investigated the joint effect of WBC count and PG on predicting in-hospital outcomes in patients with AMI. WBC count and PG were measured at the time of hospital admission in 3,665 patients with AMI. Patients were stratified into tertiles (low, medium, and high) based on WBC count and PG. Patients with a high WBC count had a 2.0-fold increase in in-hospital mortality compared with those with a low WBC count. Patients with a high PG level had a 2.7-fold increase in mortality compared with those with a low PG level. When a combination of different strata for each variable was analyzed, a stepwise increase in mortality was seen. There was a considerable number of patients with a high WBC count and low PG level or with a low WBC count and high PG level. These patients had an intermediate risk, whereas those with a high WBC count and high PG level had the highest risk, i.e., 4.8-fold increase in mortality, compared with those with a low WBC count and low PG level. Multivariate analysis was performed to assess the predictor for in-hospital mortality using WBC count and PG level as continuous variables and showed that WBC count and PG level were independently associated with in-hospital mortality. These findings suggested that a simple combination of WBC count and PG level might provide further information for predicting outcomes in patients with AMI.

  16. Use of the Charlson Combined Comorbidity Index To Predict Postradiotherapy Quality of Life for Prostate Cancer Patients

    SciTech Connect

    Wahlgren, Thomas; Levitt, Seymour; Kowalski, Jan; Nilsson, Sten; Brandberg, Yvonne

    2011-11-15

    Purpose: To determine the impact of pretreatment comorbidity on late health-related quality of life (HRQoL) scores after patients have undergone combined radiotherapy for prostate cancer, including high-dose rate brachytherapy boost and hormonal deprivation therapy. Methods and Materials: Results from the European Organization for Research and Treatment of Cancer QLQ-C30 questionnaire survey of 158 patients 5 years or more after completion of therapy were used from consecutively accrued subjects treated with curative radiotherapy at our institution, with no signs of disease at the time of questionnaire completion. HRQoL scores were compared with the Charlson combined comorbidity index (CCI), using analysis of covariance and multivariate regression models together with pretreatment factors including tumor stage, tumor grade, pretreatment prostate-specific antigen level, neoadjuvant hormonal treatment, diabetes status, cardiovascular status, and age and Charlson score as separate variables or the composite CCI. Results: An inverse correlation between the two HRQoL domains, long-term global health (QL) and physical function (PF) scores, and the CCI score was observed, indicating an impact of comorbidity in these function areas. Selected pretreatment factors poorly explained the variation in functional HRQoL in the multivariate models; however, a statistically significant impact was found for the CCI (with QL and PF scores) and the presence of diabetes (with QL and emotional function). Cognitive function and social function were not statistically significantly predicted by any of the pretreatment factors. Conclusions: The CCI proved to be valid in this context, but it seems useful mainly in predicting long-term QL and PF scores. Of the other variables investigated, diabetes had more impact than cardiovascular morbidity on HRQoL outcomes in prostate cancer.

  17. Denoising techniques combined to Monte Carlo simulations for the prediction of high-resolution portal images in radiotherapy treatment verification

    NASA Astrophysics Data System (ADS)

    Lazaro, D.; Barat, E.; Le Loirec, C.; Dautremer, T.; Montagu, T.; Guérin, L.; Batalla, A.

    2013-05-01

    This work investigates the possibility of combining Monte Carlo (MC) simulations to a denoising algorithm for the accurate prediction of images acquired using amorphous silicon (a-Si) electronic portal imaging devices (EPIDs). An accurate MC model of the Siemens OptiVue1000 EPID was first developed using the penelope code, integrating a non-uniform backscatter modelling. Two already existing denoising algorithms were then applied on simulated portal images, namely the iterative reduction of noise (IRON) method and the locally adaptive Savitzky-Golay (LASG) method. A third denoising method, based on a nonparametric Bayesian framework and called DPGLM (for Dirichlet process generalized linear model) was also developed. Performances of the IRON, LASG and DPGLM methods, in terms of smoothing capabilities and computation time, were compared for portal images computed for different values of the RMS pixel noise (up to 10%) in three different configurations, a heterogeneous phantom irradiated by a non-conformal 15 × 15 cm2 field, a conformal beam from a pelvis treatment plan, and an IMRT beam from a prostate treatment plan. For all configurations, DPGLM outperforms both IRON and LASG by providing better smoothing performances and demonstrating a better robustness with respect to noise. Additionally, no parameter tuning is required by DPGLM, which makes the denoising step very generic and easy to handle for any portal image. Concerning the computation time, the denoising of 1024 × 1024 images takes about 1 h 30 min, 2 h and 5 min using DPGLM, IRON, and LASG, respectively. This paper shows the feasibility to predict within a few hours and with the same resolution as real images accurate portal images, combining MC simulations with the DPGLM denoising algorithm.

  18. MicroRNA target site polymorphisms in the VHL-HIF1α pathway predict renal cell carcinoma risk

    PubMed Central

    Wei, Hua; Ke, Hung-Lung; Lin, Jie; Shete, Sanjay; Wood, Christopher G.; Hildebrandt, Michelle A.T.

    2012-01-01

    Renal cell carcinoma (RCC) accounts for ~4% of all human malignancies and is the 9th leading cause of male cancer death in the United States. The purpose of this study was to determine the effect of variation within microRNA (miRNA) binding sites of genes in the VHL-HIF1α pathway on RCC risk. We identified 429 miRNA binding site single nucleotide polymorphisms (SNPs) in 102 pathway genes and assessed 53 tagging-SNPs for 31 of these genes for risk in a case-control study consisting of 894 RCC cases and 1,516 controls. Results showed that five SNPs were significantly associated with RCC risk. The most significant finding was rs743409 in MAPK1. Under the additive model, the variant was associated with a 10% risk reduction (OR: 0.90, 95% CI, 0.77-0.98). Other significant findings were for SNPs in CDCP1, TFRC, and DEC1. Cumulative effects analysis showed that subjects carrying four or five unfavorable genotypes had a 2.14-fold increase in risk (95% CI, 1.03-4.43, P = 0.04) than those with no unfavorable genotypes. Potential higher-order gene-gene interactions were identified and categorized subjects into different risk groups. The OR of the high-risk group defined by two SNPs: CDCP1:rs6773576 (GG) and DEC1:rs10982724 (GG) was 4.46-times higher than that of low-risk reference group (95% CI, 1.31-15.08). Overall, our study provides the first evidence supporting a connection between miRNA binding site SNPs within the VHL-HIF1α pathway and RCC risk. These novel genetic risk factors might help identify individuals at high risk to enable detection of tumors at an early, curable stage. PMID:22517515

  19. Combination of Vessel-Targeting Agents and Fractionated Radiation Therapy: The Role of the SDF-1/CXCR4 Pathway

    SciTech Connect

    Chen, Fang-Hsin; Fu, Sheng-Yung; Yang, Ying-Chieh; Wang, Chun-Chieh; Chiang, Chi-Shiun; Hong, Ji-Hong

    2013-07-15

    Purpose: To investigate vascular responses during fractionated radiation therapy (F-RT) and the effects of targeting pericytes or bone marrow-derived cells (BMDCs) on the efficacy of F-RT. Methods and Materials: Murine prostate TRAMP-C1 tumors were grown in control mice or mice transplanted with green fluorescent protein-tagged bone marrow (GFP-BM), and irradiated with 60 Gy in 15 fractions. Mice were also treated with gefitinib (an epidermal growth factor receptor inhibitor) or AMD3100 (a CXCR4 antagonist) to examine the effects of combination treatment. The responses of tumor vasculatures to these treatments and changes of tumor microenvironment were assessed. Results: After F-RT, the tumor microvascular density (MVD) was reduced; however, the surviving vessels were dilated, incorporated with GFP-positive cells, tightly adhered to pericytes, and well perfused with Hoechst 33342, suggesting a more mature structure formed primarily via vasculogenesis. Although the gefitinib+F-RT combination affected the vascular structure by dissociating pericytes from the vascular wall, it did not further delay tumor growth. These tumors had higher MVD and better vascular perfusion function, leading to less hypoxia and tumor necrosis. By contrast, the AMD3100+F-RT combination significantly enhanced tumor growth delay more than F-RT alone, and these tumors had lower MVD and poorer vascular perfusion function, resulting in increased hypoxia. These tumor vessels were rarely covered by pericytes and free of GFP-positive cells. Conclusions: Vasculogenesis is a major mechanism for tumor vessel survival during F-RT. Complex interactions occur between vessel-targeting agents and F-RT, and a synergistic effect may not always exist. To enhance F-RT, using CXCR4 inhibitor to block BM cell influx and the vasculogenesis process is a better strategy than targeting pericytes by epidermal growth factor receptor inhibitor.

  20. Pathway Evidence of How Musical Perception Predicts Word-Level Reading Ability in Children with Reading Difficulties

    PubMed Central

    Cogo-Moreira, Hugo; Brandão de Ávila, Clara Regina; Ploubidis, George B.; de Jesus Mari, Jair

    2013-01-01

    Objective To investigate whether specific domains of musical perception (temporal and melodic domains) predict the word-level reading skills of eight- to ten-year-old children (n = 235) with reading difficulties, normal quotient of intelligence, and no previous exposure to music education classes. Method A general-specific solution of the Montreal Battery of Evaluation of Amusia (MBEA), which underlies a musical perception construct and is constituted by three latent factors (the general, temporal, and the melodic domain), was regressed on word-level reading skills (rate of correct isolated words/non-words read per minute). Results General and melodic latent domains predicted word-level reading skills. PMID:24358358

  1. Toward a structure determination method for biomineral-associated protein using combined solid- state NMR and computational structure prediction.

    PubMed

    Masica, David L; Ash, Jason T; Ndao, Moise; Drobny, Gary P; Gray, Jeffrey J

    2010-12-08

    Protein-biomineral interactions are paramount to materials production in biology, including the mineral phase of hard tissue. Unfortunately, the structure of biomineral-associated proteins cannot be determined by X-ray crystallography or solution nuclear magnetic resonance (NMR). Here we report a method for determining the structure of biomineral-associated proteins. The method combines solid-state NMR (ssNMR) and ssNMR-biased computational structure prediction. In addition, the algorithm is able to identify lattice geometries most compatible with ssNMR constraints, representing a quantitative, novel method for investigating crystal-face binding specificity. We use this method to determine most of the structure of human salivary statherin interacting with the mineral phase of tooth enamel. Computation and experiment converge on an ensemble of related structures and identify preferential binding at three crystal surfaces. The work represents a significant advance toward determining structure of biomineral-adsorbed protein using experimentally biased structure prediction. This method is generally applicable to proteins that can be chemically synthesized.

  2. Early prediction of lung cancer based on the combination of trace element analysis in urine and an Adaboost algorithm.

    PubMed

    Tan, Chao; Chen, Hui; Xia, Chengyun

    2009-04-05

    Early detection of cancer is the key to effective treatment and long-term survival. Lung cancer is one of the most frequently occurring cancers and its early detection is particularly of interest. This work investigates the feasibility of a combination of Adaboost (ensemble from machining learning) using decision stumps as weak classifier and trace element analysis for predicting early lung cancer. A dataset involving the determination of 9 trace elements of 122 urine samples is used for illustration. Kennard and Stone (KS) algorithm coupled with an alternate re-sampling was used to realize sample set partitioning. The whole dataset was split into equally sized training and test set, which were then reversed to yield a second operating case, we called them case A and case B, respectively. The prediction results based on the Adaboost were compared with those from Fisher discriminant analysis (FDA). On the test set, the final Adaboost classifiers achieved a sensitivity of 100% for both cases, a specificity of 93.8%, 95.7%, and an overall accuracy of 95.1%, 96.7%, for case A and case B, respectively. In either case, Adaboost always achieves better performance than FDA; also, it is less sensitive to the composition of the training set compared to FDA and easy to control over-fitting. It seems that Adaboost is superior to FDA in the present task, indicating that integrating Adaboost and trace element analysis of urine can serve as a useful tool for diagnosing early lung cancer in clinical practice.

  3. The SOA formation model combined with semiempirical quantum chemistry for predicting UV-Vis absorption of secondary organic aerosols.

    PubMed

    Zhong, Min; Jang, Myoseon; Oliferenko, Alexander; Pillai, Girinath G; Katritzky, Alan R

    2012-07-07

    A new model for predicting the UV-visible absorption spectra of secondary organic aerosols (SOA) has been developed. The model consists of two primary parts: a SOA formation model and a semiempirical quantum chemistry method. The mass of SOA is predicted using the PHRCSOA (Partitioning Heterogeneous Reaction Consortium Secondary Organic Aerosol) model developed by Cao and Jang [Environ. Sci. Technol., 2010, 44, 727]. The chemical composition is estimated using a combination of the kinetic model (MCM) and the PHRCSOA model. The absorption spectrum is obtained by taking the sum of the spectrum of each SOA product calculated using a semiempirical NDDO (Neglect of Diatomic Differential Overlap)-based method. SOA was generated from the photochemical reaction of toluene or α-pinene at different NO(x) levels (low NO(x): 24-26 ppm, middle NO(x): 49 ppb, high NO(x): 104-105 ppb) using a 2 m(3) indoor Teflon film chamber. The model simulation reasonably agrees with the measured absorption spectra of α-pinene SOA but underestimates toluene SOA under high and middle NO(x) conditions. The absorption spectrum of toluene SOA is moderately enhanced with increasing NO(x) concentrations, while that of α-pinene SOA is not affected. Both measured and calculated UV-visible spectra show that the light absorption of toluene SOA is much stronger than that of α-pinene SOA.

  4. Combining the ASA Physical Classification System and Continuous Intraoperative Surgical Apgar Score Measurement in Predicting Postoperative Risk.

    PubMed

    Jering, Monika Zdenka; Marolen, Khensani N; Shotwell, Matthew S; Denton, Jason N; Sandberg, Warren S; Ehrenfeld, Jesse Menachem

    2015-11-01

    The surgical Apgar score predicts major 30-day postoperative complications using data assessed at the end of surgery. We hypothesized that evaluating the surgical Apgar score continuously during surgery may identify patients at high risk for postoperative complications. We retrospectively identified general, vascular, and general oncology patients at Vanderbilt University Medical Center. Logistic regression methods were used to construct a series of predictive models in order to continuously estimate the risk of major postoperative complications, and to alert care providers during surgery should the risk exceed a given threshold. Area under the receiver operating characteristic curve (AUROC) was used to evaluate the discriminative ability of a model utilizing a continuously measured surgical Apgar score relative to models that use only preoperative clinical factors or continuously monitored individual constituents of the surgical Apgar score (i.e. heart rate, blood pressure, and blood loss). AUROC estimates were validated internally using a bootstrap method. 4,728 patients were included. Combining the ASA PS classification with continuously measured surgical Apgar score demonstrated improved discriminative ability (AUROC 0.80) in the pooled cohort compared to ASA (0.73) and the surgical Apgar score alone (0.74). To optimize the tradeoff between inadequate and excessive alerting with future real-time notifications, we recommend a threshold probability of 0.24. Continuous assessment of the surgical Apgar score is predictive for major postoperative complications. In the future, real-time notifications might allow for detection and mitigation of changes in a patient's accumulating risk of complications during a surgical procedure.

  5. IL17a and IL21 combined with surgical status predict the outcome of ovarian cancer patients.

    PubMed

    Chen, Yu-Li; Chou, Cheng-Yang; Chang, Ming-Cheng; Lin, Han-Wei; Huang, Ching-Ting; Hsieh, Shu-Feng; Chen, Chi-An; Cheng, Wen-Fang

    2015-10-01

    Aside from tumor cells, ovarian cancer-related ascites contains the immune components. The aim of this study was to evaluate whether a combination of clinical and immunological parameters can predict survival in patients with ovarian cancer. Ascites specimens and medical records from 144 ovarian cancer patients at our hospital were used as the derivation group to select target clinical and immunological factors to generate a risk-scoring system to predict patient survival. Eighty-two cases from another hospital were used as the validation group to evaluate this system. The surgical status and expression levels of interleukin 17a (IL17a) and IL21 in ascites were selected for the risk-scoring system in the derivation group. The areas under the receiver operating characteristic (AUROC) curves of the overall score for disease-free survival (DFS) of the ovarian cancer patients were 0.84 in the derivation group, 0.85 in the validation group, and 0.84 for all the patients. The AUROC curves of the overall score for overall survival (OS) of cases were 0.78 in the derivation group, 0.76 in the validation group, and 0.76 for all the studied patients. Good correlations between overall risk score and survival of the ovarian cancer patients were demonstrated by sub-grouping all participants into four groups (P for trend <0.001 for DFS and OS). Therefore, acombination of clinical and immunological parameters can provide a practical scoring system to predict the survival of patients with ovarian carcinoma. IL17a and IL21 can potentially be used as prognostic and therapeutic biomarkers.

  6. Combining cow and bull reference populations to increase accuracy of genomic prediction and genome-wide association studies.

    PubMed

    Calus, M P L; de Haas, Y; Veerkamp, R F

    2013-10-01

    Genomic selection holds the promise to be particularly beneficial for traits that are difficult or expensive to measure, such that access to phenotypes on large daughter groups of bulls is limited. Instead, cow reference populations can be generated, potentially supplemented with existing information from the same or (highly) correlated traits available on bull reference populations. The objective of this study, therefore, was to develop a model to perform genomic predictions and genome-wide association studies based on a combined cow and bull reference data set, with the accuracy of the phenotypes differing between the cow and bull genomic selection reference populations. The developed bivariate Bayesian stochastic search variable selection model allowed for an unbalanced design by imputing residuals in the residual updating scheme for all missing records. The performance of this model is demonstrated on a real data example, where the analyzed trait, being milk fat or protein yield, was either measured only on a cow or a bull reference population, or recorded on both. Our results were that the developed bivariate Bayesian stochastic search variable selection model was able to analyze 2 traits, even though animals had measurements on only 1 of 2 traits. The Bayesian stochastic search variable selection model yielded consistently higher accuracy for fat yield compared with a model without variable selection, both for the univariate and bivariate analyses, whereas the accuracy of both models was very similar for protein yield. The bivariate model identified several additional quantitative trait loci peaks compared with the single-trait models on either trait. In addition, the bivariate models showed a marginal increase in accuracy of genomic predictions for the cow traits (0.01-0.05), although a greater increase in accuracy is expected as the size of the bull population increases. Our results emphasize that the chosen value of priors in Bayesian genomic prediction

  7. Pathway Pattern-based prediction of active drug components and gene targets from H1N1 influenza's treatment with maxingshigan-yinqiaosan formula.

    PubMed

    Dai, Wen; Chen, Jianxin; Lu, Peng; Gao, Yibo; Chen, Lin; Liu, Xi; Song, Jianglong; Xu, Haiyu; Chen, Di; Yang, Yiping; Yang, Hongjun; Huang, Luqi

    2013-03-01

    Traditional Chinese Medicine (TCM) remedies are composed of different chemical compounds. To understand the underlying pharmacological basis, we need to explore the active components, which function systematically against multiple gene targets to exert efficacy. Predicting active component-gene target interactions could help us decipher the mechanism of action of TCM. Here, we introduce a Pathway Pattern-based method to prioritize the 153 candidate compounds and 7895 associated genes using the extracted Pathway Pattern, which is made up of groups of pathways. The gene prioritization result is compared to previous literature findings to demonstrate the top ranked genes' roles in the pathogenesis of H1N1 influenza. Further, molecular docking is utilized to validate compounds' effects through docking compounds into drug targets of oseltamivir. After setting thresholds, 16 active components, 29 gene targets and 162 active component-gene target interactions are finally identified to elucidate the pharmacology of maxingshigan-yinqiaosan formula. This novel strategy is expected to serve as a springboard for the efforts to standardize and modernize TCM.

  8. Prostate cancer cell malignancy via modulation of HIF-1α pathway with isoflurane and propofol alone and in combination

    PubMed Central

    Huang, H; Benzonana, L L; Zhao, H; Watts, H R; Perry, N J S; Bevan, C; Brown, R; Ma, D

    2014-01-01

    Background: Surgery is considered to be the first line treatment for solid tumours. Recently, retrospective studies reported that general anaesthesia was associated with worse long-term cancer-free survival when compared with regional anaesthesia. This has important clinical implications; however, the mechanisms underlying those observations remain unclear. We aim to investigate the effect of anaesthetics isoflurane and propofol on prostate cancer malignancy. Methods: Prostate cancer (PC3) cell line was exposed to commonly used anaesthetic isoflurane and propofol. Malignant potential was assessed through evaluation of expression level of hypoxia-inducible factor-1α (HIF-1α) and its downstream effectors, cell proliferation and migration as well as development of chemoresistance. Results: We demonstrated that isoflurane, at a clinically relevant concentration induced upregulation of HIF-1α and its downstream effectors in PC3 cell line. Consequently, cancer cell characteristics associated with malignancy were enhanced, with an increase of proliferation and migration, as well as development of chemoresistance. Inhibition of HIF-1α neosynthesis through upper pathway blocking by a PI-3K-Akt inhibitor or HIF-1α siRNA abolished isoflurane-induced effects. In contrast, the intravenous anaesthetic propofol inhibited HIF-1α activation induced by hypoxia or CoCl2. Propofol also prevented isoflurane-induced HIF-1α activation, and partially reduced cancer cell malignant activities. Conclusions: Our findings suggest that modulation of HIF-1α activity by anaesthetics may affect cancer recurrence following surgery. If our data were to be extrapolated to the clinical setting, isoflurane but not propofol should be avoided for use in cancer surgery. Further work involving in vivo models and clinical trials is urgently needed to determine the optimal anaesthetic regimen for cancer patients. PMID:25072260

  9. HIV Clinical Pathway: A New Approach to Combine Guidelines and Sustainability of Anti-Retroviral Treatment in Italy.

    PubMed

    Croce, Davide; Lazzarin, Adriano; Rizzardini, Giuliano; Gianotti, Nicola; Scolari, Francesca; Foglia, Emanuela; Garagiola, Elisabetta; Ricci, Elena; Bini, Teresa; Quirino, Tiziana; Viganò, Paolo; Re, Tiziana; D'Arminio Monforte, Antonella; Bonfanti, Paolo

    2016-01-01

    The present article describes the case study of a "real world" HIV practice within the debate concerning the strategic role of Clinical Governance (CG) tools in the management of a National Healthcare System's sustainability. The study aimed at assessing the impact of a Clinical Pathway (CP) implementation, required by the Regional Healthcare Service, in terms of effectiveness (virological and immunological conditions) and efficiency (economic resources absorption), from the budget holder perspective. Data derived from a multi-centre cohort of patients treated in 6 Hospitals that provided care to approximately 42% of the total HIV+ patients, in Lombardy Region, Italy. Two phases were compared: Pre-CP (2009-2010) vs. Post-CP implementation (2011-2012). All HIV infected adults, observed in the participating hospitals during the study periods, were enrolled and stratified into the 3 categories defined by the Regional CP: first-line, switch for toxicity/other, and switch for failure. The study population was composed of 1,284 patients (Pre-CP phase) and 1,135 patients (Post-CP phase). The results showed that the same level of virological and immunological effectiveness was guaranteed to HIV+ patients: 81.2% of Pre-CP phase population and 83.2% of Post-CP phase population had undetectable HIV-RNA (defined as <50 copies/mL) at 12-month follow up. CD4+ cell counts increased by 28 ± 4 cells/mm3 in Pre-CP Phase and 39 ± 5 cells/mm3 in Post-CP Phase. From an economic point of view, the CP implementation led to a substantial advantage: the mean total costs related to the management of the HIV disease (ART, hospital admission and laboratory tests) decreased (-8.60%) in the Post-CP phase (p-value < 0.0001). Results confirmed that the CP provided appropriateness and quality of care, with a cost reduction for the budget holder.

  10. A Technology Pathway for Airbreathing, Combined-Cycle, Horizontal Space Launch Through SR-71 Based Trajectory Modeling

    NASA Technical Reports Server (NTRS)

    Kloesel, Kurt J.; Ratnayake, Nalin A.; Clark, Casie M.

    2011-01-01

    Access to space is in the early stages of commercialization. Private enterprises, mainly under direct or indirect subsidy by the government, have been making headway into the LEO launch systems infrastructure, of small-weight-class payloads of approximately 1000 lbs. These moderate gains have emboldened the launch industry and they are poised to move into the middle-weight class (roughly 5000 lbs). These commercially successful systems are based on relatively straightforward LOX-RP, two-stage, bi-propellant rocket technology developed by the government 40 years ago, accompanied by many technology improvements. In this paper we examine a known generic LOX-RP system with the focus on the booster stage (1st stage). The booster stage is then compared to modeled Rocket-Based and Turbine-Based Combined Cycle booster stages. The air-breathing propulsion stages are based on/or extrapolated from known performance parameters of ground tested RBCC (the Marquardt Ejector Ramjet) and TBCC (the SR-71/J-58 engine) data. Validated engine models using GECAT and SCCREAM are coupled with trajectory optimization and analysis in POST-II to explore viable launch scenarios using hypothetical aerospaceplane platform obeying the aerodynamic model of the SR-71. Finally, and assessment is made of the requisite research technology advances necessary for successful commercial and government adoption of combined-cycle engine systems for space access.

  11. Everolimus Combined With Gefitinib in Patients with Metastatic Castration-Resistant Prostate Cancer: Phase I/II Results and Signaling Pathway Implications

    PubMed Central

    Rathkopf, Dana E.; Larson, Steven M.; Anand, Aseem; Morris, Michael J.; Slovin, Susan F.; Shaffer, David R.; Heller, Glenn; Carver, Brett; Rosen, Neal; Scher, Howard I.

    2015-01-01

    Background The effects of mammalian target of rapamycin (mTOR) inhibition are limited by feedback reactivation of receptor tyrosine kinase signaling in PTEN-null tumors, thus we tested the combination of mTOR inhibition (everolimus) and EGFR inhibition (gefitinib) in castration-resistant prostate cancer (CRPC). Methods In phase I, 12 patients (10 CRPC, 2 glioblastoma) received daily gefitinib (250 mg) with weekly everolimus (30, 50, or 70 mg). In phase II, 27 CRPC patients received gefitinib with everolimus 70 mg. Results Phase I revealed no pharmacokinetic interactions and no dose-limiting toxicities. In phase II, 18 of 27 (67%) patients discontinued treatment before the 12-week evaluation due to progression as evidenced by prostate-specific antigen (PSA) levels (n=6) or imaging (n=5), or grade ≥2 toxicity (n=7). Thirteen of the total 37 (35%) CRPC patients exhibited a rapidly rising PSA after starting treatment which declined upon discontinuation. Fluorodeoxyglucose positron emission tomography at 24 to 72 hours after starting treatment showed a decrease in standardized uptake value consistent with mTOR inhibition in 27 of 33 (82%) evaluable patients; there was a corresponding rise in PSA in 20 of these 27 patients (74%). Conclusions The combination of gefitinib and everolimus did not result in significant antitumor activity. The induction of PSA in tumors treated with mTOR inhibitors was consistent with preclinical data that PI3K pathway signaling feedback inhibits the androgen receptor (AR). This clinical evidence of relief of feedback inhibition promoting enhanced AR activity supports future studies combining PI3K pathway inhibitors and second-generation AR inhibitors in CRPC. PMID:26178426

  12. Prediction of CL-20 chemical degradation pathways, theoretical and experimental evidence for dependence on competing modes of reaction

    SciTech Connect

    Qasim, Mohammad M.; Fredrickson, Herbert L.; Honea, P.; Furey, John; Leszczynski, Jerzy; Okovytyy, S.; Szecsody, Jim E.; Kholod, Y.

    2005-10-01

    Highest occupied and lowest unoccupied molecular orbital energies, formation energies, bond lengths and FTIR spectra all suggest competing CL-20 degradation mechanisms. This second of two studies investigates recalcitrant, toxic, aromatic CL-20 intermediates that absorb from 370 to 430 nm. Our earlier study (Struct. Chem., 15, 2004) revealed that these intermediates were formed at high OH- concentrations via the chemically preferred pathway of breaking the C-C bond between the two cyclopentanes, thereby eliminating nitro groups, forming conjugated π bonds, and resulting in a pyrazine three-ring aromatic intermediate. In attempting to find and make dominant a more benign CL-20 transformation pathway, this current research validates hydroxylation results from both studies and examines CL-20 transformations via photo-induced free radical reactions. This article discusses CL-20 competing modes of degradation revealed through: computational calculation; UV/VIS and SF spectroscopy following alkaline hydrolysis; and photochemical irradiation to degrade CL-20 and its byproducts at their respective wavelengths of maximum absorption.

  13. Programmed cell death protein-1/programmed cell death ligand-1 pathway inhibition and predictive biomarkers: understanding transforming growth factor-beta role

    PubMed Central

    González-Cao, María; Viteri, Santiago; Karachaliou, Niki; Altavilla, Giuseppe; Rosell, Rafael

    2015-01-01

    A deeper understanding of the key role of the immune system in regulating tumor growth and progression has led to the development of a number of immunotherapies, including cancer vaccines and immune checkpoint inhibitors. Immune checkpoint inhibitors target molecular pathways involved in immunosuppression, such as cytotoxic T-lymphocyte-associated antigen-4 (CTLA-4) and programmed cell death protein-1 (PD-1)/programmed cell death ligand-1 (PD-L1) pathway, with the goal to enhance the host’s own immune anticancer response. In phase I–III trials, anti-PD-1/PD-L1 antibodies have demonstrated to be effective treatment strategies by inducing significant durable tumor responses, with manageable toxicities, in patients with various malignancies, including those traditionally considered non-immunogenic, such as non-small cell lung cancer (NSCLC). Identification of predictive biomarkers to select patients for immune therapies is currently being investigated to improve their therapeutic efficacy. Transforming growth factor-β (TGF-β), a pleiotropic cytokine with immunosuppressive effects on multiple cell types of the innate and adaptive immune system, has emerged as one of the potential key factors modulating response to immune checkpoint inhibitors. However, due to the complexity of the anti-cancer immune response, the predictive value of many other factors related to cancer cells or tumor microenvironment needs to be further explored. PMID:26798582

  14. Prediction of Mild Cognitive Impairment Conversion Using a Combination of Independent Component Analysis and the Cox Model

    PubMed Central

    Liu, Ke; Chen, Kewei; Yao, Li; Guo, Xiaojuan

    2017-01-01

    Mild cognitive impairment (MCI) represents a transitional stage from normal aging to Alzheimer’s disease (AD) and corresponds to a higher risk of developing AD. Thus, it is necessary to explore and predict the onset of AD in MCI stage. In this study, we propose a combination of independent component analysis (ICA) and the multivariate Cox proportional hazards regression model to investigate promising risk factors associated with MCI conversion among 126 MCI converters and 108 MCI non-converters from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Using structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) data, we extracted brain networks from AD and normal control groups via ICA and then constructed Cox models that included network-based neuroimaging factors for the MCI group. We carried out five separate Cox analyses and the two-modality neuroimaging Cox model identified three significant network-based risk factors with higher prediction performance (accuracy = 73.50%) than those in either single-modality model (accuracy = 68.80%). Additionally, the results of the comprehensive Cox model, including significant neuroimaging factors and clinical variables, demonstrated that MCI individuals with reduced gray matter volume in a temporal lobe-related network of structural MRI [hazard ratio (HR) = 8.29E-05 (95% confidence interval (CI), 5.10E- 07 ~ 0.013)], low glucose metabolism in the posterior default mode network based on FDG-PET [HR = 0.066 (95% CI, 4.63E-03 ~ 0.928)], positive apolipoprotein E ε4-status [HR = 1. 988 (95% CI, 1.531 ~ 2.581)], increased Alzheimer’s Disease Assessment Scale-Cognitive Subscale scores [HR = 1.100 (95% CI, 1.059 ~ 1.144)] and Sum of Boxes of Clinical Dementia Rating scores [HR = 1.622 (95% CI, 1.364 ~ 1.930)] were more likely to convert to AD within 36 months after baselines. These significant risk factors in such comprehensive Cox model had the best prediction

  15. Field application of a combined pig and poultry market chain and risk pathway analysis within the Pacific Islands region as a tool for targeted disease surveillance and biosecurity.

    PubMed

    Brioudes, Aurélie; Gummow, Bruce

    2016-07-01

    Limited resources are one of the major constraints in effective disease monitoring and control in developing countries. This paper examines the pig and poultry market chains of four targeted Pacific Island countries and territories (PICTs): Fiji, Papua New Guinea, Solomon Islands and Vanuatu and combines them with a risk pathway analysis to identify the highest risk areas (risk hotspots) and risky practices and behaviours (risk factors) of animal disease introduction and/or spread, using highly pathogenic avian influenza (HPAI) and foot-and-mouth disease (FMD) as model diseases because of their importance in the region. The results show that combining a market chain analysis with risk pathways is a practical way of communicating risk to animal health officials and improving biosecurity. It provides a participatory approach that helps officials to better understand the trading regulations in place in their country and to better evaluate their role as part of the control system. Common risk patterns were found to play a role in all four PICTs. Legal trade pathways rely essentially on preventive measures put in place in the exporting countries while no or only limited control measures are undertaken by the importing countries. Legal importations of animals and animal products are done mainly by commercial farms which then supply local smallholders. Targeting surveillance on these potential hotspots would limit the risk of introduction and spread of animal diseases within the pig and poultry industry and better rationalize use of skilled manpower. Swill feeding is identified as a common practice in the region that represents a recognized risk factor for dissemination of pathogens to susceptible species. Illegal introduction of animals and animal products is suspected, but appears restricted to small holder farms in remote areas, limiting the risk of spread of transboundary animal diseases along the market chain. Introduction of undeclared goods hidden within a legal

  16. Analysis and Prediction of Pathways in HeLa Cells by Integrating Biological Levels of Organization with Systems-Biology Approaches

    PubMed Central

    Higareda-Almaraz, Juan Carlos; Valtierra-Gutiérrez, Ilse A.; Hernandez-Ortiz, Magdalena; Contreras, Sandra; Hernandez, Erika; Encarnacion, Sergio

    2013-01-01

    It has recently begun to be considered that cancer is a systemic disease and that it must be studied at every level of complexity using many of the currently available approaches, including high-throughput technologies and bioinformatics. To achieve such understanding in cervical cancer, we collected information on gene, protein and phosphoprotein expression of the HeLa cell line and performed a comprehensive analysis of the different signaling pathways, transcription networks and metabolic events in which they participate. A total expression analysis by RNA-Seq of the HeLa cell line showed that 19,974 genes were transcribed. Of these, 3,360 were over-expressed, and 2,129 under-expressed when compared to the NHEK cell line. A protein-protein interaction network was derived from the over-expressed genes and used to identify central elements and, together with the analysis of over-represented transcription factor motifs, to predict active signaling and regulatory pathways. This was further validated by Metal-Oxide Affinity Chromatography (MOAC) and Tandem Mass Spectrometry (MS/MS) assays which retrieved phosphorylated proteins. The 14-3-3 family members emerge as important regulators in carcinogenesis and as possible clinical targets. We observed that the different over- and under-regulated pathways in cervical cancer could be interrelated through elements that participate in crosstalks, therefore belong to what we term “meta-pathways”. Additionally, we highlighted the relations of each one of the differentially represented pathways to one or more of the ten hallmarks of cancer. These features could be maintained in many other types of cancer, regardless of mutations or genomic rearrangements, and favor their robustness, adaptations and the evasion of tissue control. Probably, this could explain why cancer cells are not eliminated by selective pressure and why therapy trials directed against molecular targets are not as effective as expected. PMID:23785426

  17. HIV Clinical Pathway: A New Approach to Combine Guidelines and Sustainability of Anti-Retroviral Treatment in Italy

    PubMed Central

    Croce, Davide; Lazzarin, Adriano; Rizzardini, Giuliano; Gianotti, Nicola; Scolari, Francesca; Foglia, Emanuela; Garagiola, Elisabetta; Ricci, Elena; Bini, Teresa; Quirino, Tiziana; Viganò, Paolo; Re, Tiziana; D’Arminio Monforte, Antonella; Bonfanti, Paolo

    2016-01-01

    The present article describes the case study of a “real world” HIV practice within the debate concerning the strategic role of Clinical Governance (CG) tools in the management of a National Healthcare System’s sustainability. The study aimed at assessing the impact of a Clinical Pathway (CP) implementation, required by the Regional Healthcare Service, in terms of effectiveness (virological and immunological conditions) and efficiency (economic resources absorption), from the budget holder perspective. Data derived from a multi-centre cohort of patients treated in 6 Hospitals that provided care to approximately 42% of the total HIV+ patients, in Lombardy Region, Italy. Two phases were compared: Pre-CP (2009–2010) vs. Post-CP implementation (2011–2012). All HIV infected adults, observed in the participating hospitals during the study periods, were enrolled and stratified into the 3 categories defined by the Regional CP: first-line, switch for toxicity/other, and switch for failure. The study population was composed of 1,284 patients (Pre-CP phase) and 1,135 patients (Post-CP phase). The results showed that the same level of virological and immunological effectiveness was guaranteed to HIV+ patients: 81.2% of Pre-CP phase population and 83.2% of Post-CP phase population had undetectable HIV-RNA (defined as <50 copies/mL) at 12-month follow up. CD4+ cell counts increased by 28 ± 4 cells/mm3 in Pre-CP Phase and 39 ± 5 cells/mm3 in Post-CP Phase. From an economic point of view, the CP implementation led to a substantial advantage: the mean total costs related to the management of the HIV disease (ART, hospital admission and laboratory tests) decreased (-8.60%) in the Post-CP phase (p-value < 0.0001). Results confirmed that the CP provided appropriateness and quality of care, with a cost reduction for the budget holder. PMID:28030621

  18. Genetic Variants in the PIWI-piRNA Pathway Gene DCP1A Predict Melanoma Disease-specific Survival

    PubMed Central

    Zhang, Weikang; Liu, Hongliang; Yin, Jieyun; Wu, Wenting; Zhu, Dakai; Amos, Christopher I.; Fang, Shenying; Lee, Jeffrey E.; Li, Yi; Han, Jiali; Wei, Qingyi

    2017-01-01

    The Piwi-piRNA pathway is important for germ cell maintenance, genome integrity, DNA methylation and retrotransposon control and thus may be involved in cancer development. In the present study, we comprehensively analyzed prognostic roles of 3,116 common SNPs in PIWI-piRNA pathway genes in melanoma disease-specific survival. A published genome-wide association study (GWAS) by The University of Texas M.D. Anderson Cancer Center was used to identify associated SNPs, which were later validated by another GWAS from the Harvard Nurses’ Health Study and Health Professionals Follow-up Study. After multiple testing correction, we found that there were 27 common SNPs in two genes (PIWIL4 and DCP1A) with false discovery rate < 0.2 in the discovery dataset. Three tagSNPs (i.e., rs7933369 and rs508485 in PIWIL4; rs11551405 in DCP1A) were replicated. The rs11551405 A allele, located at the 3’ UTR microRNA binding site of DCP1A, was associated with an increased risk of melanoma disease-specific death in both discovery dataset [adjusted Hazards ratio (HR) = 1.66, 95% confidence interval (CI) = 1.21–2.27, P =1.50×10−3] and validation dataset (HR = 1.55, 95% CI = 1.03–2.34, P = 0.038), compared with the C allele, and their meta-analysis showed an HR of 1.62 (95% CI,1.26–2.08, P =1.55×10−4). Using RNA-seq data from the 1000 Genomes Project, we found that DCP1A mRNA expression levels increased significantly with the A allele number of rs11551405. Additional large, prospective studies are needed to validate these findings. PMID:27578485

  19. The combination of blueberry juice and probiotics reduces apoptosis of alcoholic fatty liver of mice by affecting SIRT1 pathway

    PubMed Central

    Zhu, Juanjuan; Ren, Tingting; Zhou, Mingyu; Cheng, Mingliang

    2016-01-01

    Purpose To explore the effects of the combination of blueberry juice and probiotics on the apoptosis of alcoholic fatty liver disease (AFLD). Methods Healthy C57BL/6J mice were used in the control group (CG). AFLD mice models were established with Lieber–DeCarli ethanol diet and evenly assigned to six groups with different treatments: MG (model), SI (SIRT1 [sirtuin type 1] small interfering RNA [siRNA]), BJ (blueberry juice), BJSI (blueberry juice and SIRT1 siRNA), BJP (blueberry juice and probiotics), and BJPSI (blueberry juice, probiotics, and SIRT1 siRNA). Hepatic tissue was observed using hematoxylin and eosin (HE) and Oil Red O (ORO) staining. Biochemical indexes of the blood serum were analyzed. The levels of SIRT1, caspase-3, forkhead box protein O1 (FOXO1), FasL (tumor necrosis factor ligand superfamily member 6), BAX, and Bcl-2 were measured by reverse transcription-polymerase chain reaction and Western blotting. Results HE and ORO staining showed that the hepatocytes were heavily destroyed with large lipid droplets in MG and SI groups, while the severity was reduced in the CG, BJ, and BJP groups (P<0.05). The levels of superoxide dismutase (SOD), reduced glutathione (GSH), and high-density lipoprotein-cholesterol (HDL-C) were increased in BJ and BJP groups when compared with the model group (P<0.05). In contrast, the levels of aspartate aminotransferase (AST) and alanine aminotransferase (ALT), total triglycerides (TGs), total cholesterol, low-density lipoprotein-cholesterol (LDL-C), and malondialdehyde (MDA) were lower in BJ and BJP groups than in the model group (P<0.05). The level of SIRT1 was increased, while the levels of FOXO1, phosphorylated FOXO1, acetylated FOXO1, FasL, caspase-3, BAX, and Bcl-2 were decreased in CG, BJ, and BJP groups (P<0.05). Meanwhile, SIRT1 silence resulted in increase of the levels of FOXO1, phosphorylated FOXO1, acetylated FOXO1, FasL, caspase-3, BAX, and Bcl-2. Conclusion The combination of blueberry juice and

  20. Ultraperformance liquid chromatography-mass spectrometry based comprehensive metabolomics combined with pattern recognition and network analysis methods for characterization of metabolites and metabolic pathways from biological data sets.

    PubMed

    Zhang, Ai-hua; Sun, Hui; Han, Ying; Yan, Guang-li; Yuan, Ye; Song, Gao-chen; Yuan, Xiao-xia; Xie, Ning; Wang, Xi-jun

    2013-08-06

    Metabolomics is the study of metabolic changes in biological systems and provides the small molecule fingerprints related to the disease. Extracting biomedical information from large metabolomics data sets by multivariate data analysis is of considerable complexity. Therefore, more efficient and optimizing metabolomics data processing technologies are needed to improve mass spectrometry applications in biomarker discovery. Here, we report the findings of urine metabolomic investigation of hepatitis C virus (HCV) patients by high-throughput ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) coupled with pattern recognition methods (principal component analysis, partial least-squares, and OPLS-DA) and network pharmacology. A total of 20 urinary differential metabolites (13 upregulated and 7 downregulated) were identified and contributed to HCV progress, involve several key metabolic pathways such as taurine and hypotaurine metabolism, glycine, serine and threonine metabolism, histidine metabolism, arginine and proline metabolism, and so forth. Metabolites identified through metabolic profiling may facilitate the development of more accurate marker algorithms to better monitor disease progression. Network analysis validated close contact between these metabolites and implied the importance of the metabolic pathways. Mapping altered metabolites to KEGG pathways identified alterations in a variety of biological processes mediated through complex networks. These findings may be promising to yield a valuable and noninvasive tool that insights into the pathophysiology of HCV and to advance the early diagnosis and monitor the progression of disease. Overall, this investigation illustrates the power of the UPLC-MS platform combined with the pattern recognition and network analysis methods that can engender new insights into HCV pathobiology.

  1. Costunolide and dehydrocostuslactone combination treatment inhibit breast cancer by inducing cell cycle arrest and apoptosis through c-Myc/p53 and AKT/14-3-3 pathway.

    PubMed

    Peng, Zhangxiao; Wang, Yan; Fan, Jianhui; Lin, Xuejing; Liu, Chunying; Xu, Yang; Ji, Weidan; Yan, Chao; Su, Changqing

    2017-01-24

    Our previous studies demonstrated that volatile oil from saussurea lappa root (VOSL), rich in two natural sesquiterpene lactones, costunolide (Cos) and dehydrocostuslactone (Dehy), exerts better anti-breast cancer efficacy and lower side effects than Cos or Dehy alone in vivo, however, their anti-cancer molecular mechanisms were still unknown. In this study, we investigated the underlying mechanisms of Cos and Dehy combination treatment (CD) on breast cancer cells through proteomics technology coupled with Western blot validation. Ingenuity Pathways Analysis (IPA) results based on the differentially expressed proteins revealed that both VOSL and CD affect the 14-3-3-mediated signaling, c-Myc mediated apoptosis signaling and protein kinase A (PKA) signaling. Western blot coupled with cell cycle and apoptosis analysis validated the results of proteomics analysis. Cell cycle arrest and apoptosis were induced in a dose-dependent manner, and the expressions of p53 and p-14-3-3 were significantly up-regulated, whereas the expressions of c-Myc, p-AKT, p-BID were significantly down-regulated, furthermore, the ratio of BAX/BCL-2 were significantly increased in breast cancer cells after CD and VOSL treatment. The findings indicated that VOSL and CD could induce breast cancer cell cycle arrest and apoptosis through c-Myc/p53 and AKT/14-3-3 signaling pathways and may be novel effective candidates for breast cancer treatment.

  2. Costunolide and dehydrocostuslactone combination treatment inhibit breast cancer by inducing cell cycle arrest and apoptosis through c-Myc/p53 and AKT/14-3-3 pathway

    PubMed Central

    Peng, Zhangxiao; Wang, Yan; Fan, Jianhui; Lin, Xuejing; Liu, Chunying; Xu, Yang; Ji, Weidan; Yan, Chao; Su, Changqing

    2017-01-01

    Our previous studies demonstrated that volatile oil from saussurea lappa root (VOSL), rich in two natural sesquiterpene lactones, costunolide (Cos) and dehydrocostuslactone (Dehy), exerts better anti-breast cancer efficacy and lower side effects than Cos or Dehy alone in vivo, however, their anti-cancer molecular mechanisms were still unknown. In this study, we investigated the underlying mechanisms of Cos and Dehy combination treatment (CD) on breast cancer cells through proteomics technology coupled with Western blot validation. Ingenuity Pathways Analysis (IPA) results based on the differentially expressed proteins revealed that both VOSL and CD affect the 14-3-3-mediated signaling, c-Myc mediated apoptosis signaling and protein kinase A (PKA) signaling. Western blot coupled with cell cycle and apoptosis analysis validated the results of proteomics analysis. Cell cycle arrest and apoptosis were induced in a dose-dependent manner, and the expressions of p53 and p-14-3-3 were significantly up-regulated, whereas the expressions of c-Myc, p-AKT, p-BID were significantly down-regulated, furthermore, the ratio of BAX/BCL-2 were significantly increased in breast cancer cells after CD and VOSL treatment. The findings indicated that VOSL and CD could induce breast cancer cell cycle arrest and apoptosis through c-Myc/p53 and AKT/14-3-3 signaling pathways and may be novel effective candidates for breast cancer treatment. PMID:28117370

  3. Dispositional Pathways to Trust: Self-Esteem and Agreeableness Interact to Predict Trust and Negative Emotional Disclosure.

    PubMed

    McCarthy, Megan H; Wood, Joanne V; Holmes, John G

    2017-03-30

    Expressing our innermost thoughts and feelings is critical to the development of intimacy (Reis & Shaver, 1988), but also risks negative evaluation and rejection. Past research suggests that people with high self-esteem are more expressive and self-disclosing because they trust that others care for them and will not reject them (Gaucher et al., 2012). However, feeling good about oneself may not always be enough; disclosure may also depend on how we feel about other people. Drawing on the principles of risk regulation theory (Murray et al., 2006), we propose that agreeableness-a trait that refers to the positivity of interpersonal motivations and behaviors-is a key determinant of trust in a partner's caring and responsiveness, and may work in conjunction with self-esteem to predict disclosure. We examined this possibility by exploring how both self-esteem and agreeableness predict a particularly risky and intimate form of self-disclosure, the disclosure of emotional distress. In 6 studies using correlational, partner-report, and experimental methods, we demonstrate that self-esteem and agreeableness interact to predict disclosure: People who are high in both self-esteem and agreeableness show higher emotional disclosure. We also found evidence that trust mediates this effect. People high in self-esteem and agreeableness are most self-revealing, it seems, because they are especially trusting of their partners' caring. Self-esteem and agreeableness were particularly important for the disclosure of vulnerable emotions (i.e., sadness; Study 5) and disclosures that were especially risky (Study 6). These findings illustrate how dispositional variables can work together to explain behavior in close relationships. (PsycINFO Database Record

  4. Combined overexpression of HIVEP3 and SOX9 predicts unfavorable biochemical recurrence-free survival in patients with prostate cancer

    PubMed Central

    Qin, Guo-qiang; He, Hui-chan; Han, Zhao-dong; Liang, Yu-xiang; Yang, Sheng-bang; Huang, Ya-qiang; Zhou, Liang; Fu, Hao; Li, Jie-xian; Jiang, Fu-neng; Zhong, Wei-de

    2014-01-01

    Background To clarify the involvement of HIVEP3 and SOX9 coexpression in prostate cancer (PCa). Methods A small interfering RNA was used to knockdown SOX9 expression in a PCa cell line and to analyze the effects of SOX9 inhibition on the expression of HIVEP3 in vitro. Then, HIVEP3 and SOX9 expression patterns in the human PCa tissues were detected using quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis and immunohistochemistry. Results We found that the downregulation of SOX9 could inhibit the expression of HIVEP3 in the PCa cells in vitro. In addition, both HIVEP3 and SOX9 messenger RNA expression levels in the PCa tissues were significantly higher than those in the noncancerous prostate tissues (P=0.006 and P<0.001, respectively). Moreover, the immunohistochemical staining scores of HIVEP3 in the PCa tissues with PSA failure were significantly higher than those without (P=0.042); the increased SOX9 protein expression was more frequently found in the PCa tissues with a high Gleason score (P=0.045) and a high clinical stage (P=0.012). The tumors showing the HIVEP3-high/SOX9-high expression more frequently had PSA failure (P=0.024). When the patients with an HIVEP3 overexpression combined with the SOX9 overexpression, this group had a worse biochemical recurrence-free survival (P<0.001). Furthermore, the multivariate analysis showed that the HIVEP3/SOX9 coexpression was an independent predictor of an unfavorable biochemical recurrence-free survival. Conclusion Our data offer the convincing evidence for the first time that a combined analysis of HIVEP3 and SOX9 may help to predict the tumor progression and prognosis of PCa patients. In particular, the overexpression of HIVEP3 in PCa might partly explain the poor prognosis of patients with an upregulation of SOX9. PMID:24493929

  5. Muscle Biopsy Findings in Combination With Myositis‐Specific Autoantibodies Aid Prediction of Outcomes in Juvenile Dermatomyositis

    PubMed Central

    Deakin, Claire T.; Yasin, Shireena A.; Simou, Stefania; Arnold, Katie A.; Tansley, Sarah L.; Betteridge, Zoe E.; McHugh, Neil J.; Varsani, Hemlata; Holton, Janice L.; Jacques, Thomas S.; Pilkington, Clarissa A.; Nistala, Kiran; Armon, Kate; Ellis‐Gage, Joe; Roper, Holly; Briggs, Vanja; Watts, Joanna; McCann, Liza; Roberts, Ian; Baildam, Eileen; Hanna, Louise; Lloyd, Olivia; Wadeson, Susan; Riley, Phil; McGovern, Ann; Ryder, Clive; Scott, Janis; Thomas, Beverley; Southwood, Taunton; Al‐Abadi, Eslam; Wyatt, Sue; Jackson, Gillian; Amin, Tania; Wood, Mark; VanRooyen, Vanessa; Burton, Deborah; Davidson, Joyce; Gardner‐Medwin, Janet; Martin, Neil; Ferguson, Sue; Waxman, Liz; Browne, Michael; Friswell, Mark; Swift, Alison; Jandial, Sharmila; Stevenson, Vicky; Wade, Debbie; Sen, Ethan; Smith, Eve; Qiao, Lisa; Watson, Stuart; Duong, Claire; Venning, Helen; Satyapal, Rangaraj; Stretton, Elizabeth; Jordan, Mary; Mosley, Ellen; Frost, Anna; Crate, Lindsay; Warrier, Kishore; Stafford, Stefanie; Hasson, Nathan; Maillard, Sue; Halkon, Elizabeth; Brown, Virginia; Juggins, Audrey; Smith, Sally; Lunt, Sian; Enayat, Elli; Kassoumeri, Laura; Beard, Laura; Glackin, Yvonne; Almeida, Beverley; Marques, Raquel; Dowle, Stefanie; Papadopoulou, Charis; Murray, Kevin; Ioannou, John; Suffield, Linda; Al‐Obaidi, Muthana; Lee, Helen; Leach, Sam; Smith, Helen; McMahon, Anne‐Marie; Chisem, Heather; Kingshott, Ruth; Wilkinson, Nick; Inness, Emma; Kendall, Eunice; Mayers, David; Etherton, Ruth; Bailey, Kathryn; Clinch, Jacqui; Fineman, Natalie; Pluess‐Hall, Helen; Vallance, Lindsay; Akeroyd, Louise; Leahy, Alice; Collier, Amy; Cutts, Rebecca; De Graaf, Hans; Davidson, Brian; Hartfree, Sarah; Pratt, Danny

    2016-01-01

    , 1.61‐fold higher odds (95% CI 1.16–2.22; P = 0.004), and for the total biopsy score, 1.13‐fold higher odds (95% CI 1.03–1.24; P = 0.013). Conclusion Histopathologic severity, in combination with MSA subtype, is predictive of the risk of remaining on treatment in patients with juvenile DM and may be useful for discussing probable treatment length with parents and patients. Understanding these associations may identify patients at greater risk of severe disease. PMID:27214289

  6. A combined molecular docking-based and pharmacophore-based target prediction strategy with a probabilistic fusion method for target ranking.

    PubMed

    Li, Guo-Bo; Yang, Ling-Ling; Xu, Yong; Wang, Wen-Jing; Li, Lin-Li; Yang, Sheng-Yong

    2013-07-01

    Herein, a combined molecular docking-based and pharmacophore-based target prediction strategy is presented, in which a probabilistic fusion method is suggested for target ranking. Establishment and validation of the combined strategy are described. A target database, termed TargetDB, was firstly constructed, which contains 1105 drug targets. Based on TargetDB, the molecular docking-based target prediction and pharmacophore-based target prediction protocols were established. A probabilistic fusion method was then developed by constructing probability assignment curves (PACs) against a set of selected targets. Finally the workflow for the combined molecular docking-based and pharmacophore-based target prediction strategy was established. Evaluations of the performance of the combined strategy were carried out against a set of structurally different single-target compounds and a well-known multi-target drug, 4H-tamoxifen, which results showed that the combined strategy consistently outperformed the sole use of docking-based and pharmacophore-based methods. Overall, this investigation provides a possible way for improving the accuracy of in silico target prediction and a method for target ranking.

  7. Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences.

    PubMed

    An, Ji-Yong; Meng, Fan-Rong; You, Zhu-Hong; Fang, Yu-Hong; Zhao, Yu-Jun; Zhang, Ming

    2016-01-01

    We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments on Yeast and Human datasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the Yeast dataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research.

  8. Ductile Fracture Prediction in Rotational Incremental Forming for Magnesium Alloy Sheets Using Combined Kinematic/Isotropic Hardening Model

    NASA Astrophysics Data System (ADS)

    Nguyen, Duc-Toan; Park, Jin-Gee; Kim, Young-Suk

    2010-08-01

    To predict the ductile fracture of a magnesium alloy sheet when using rotational incremental forming, a combined kinematic and isotropic hardening law is implemented and evaluated from the histories of the ductile fracture value ( I) using a finite element analysis. Here, the criterion for a ductile fracture, as developed by Oyane ( J. Mech. Work. Technol., 1980, vol. 4, pp. 65-81), is applied via a user material based on a finite element analysis. To simulate the effect of the large amount of heat generation at elements in the contact area due to the friction energy of the rotational tool-specimen interface on the equivalent stress-strain evolution in incremental forming, the Johnson-Cook (JC) model was applied and the results compared with equivalent stress-strain curves obtained from tensile tests at elevated temperatures. The finite element (FE) simulation results for a ductile fracture were compared with the experimental results for a (80 mm × 80 mm × 25 mm) square shape with a 45 and 60 deg wall angle, respectively, and a (80 mm × 80 mm × 20 mm) square shape with a 70 deg wall angle. The trends of the FE simulation results agreed quite well with the experimental results. Finally, the effects of the process parameters, i.e., the tool down-step and tool radius, on the ductile fracture value and FLC at fracture (FLCF) were also investigated using the FE simulation results.

  9. Combined Wnt/β-catenin, Met, and CXCL12/CXCR4 signals characterize basal breast cancer and predict disease outcome.

    PubMed

    Holland, Jane D; Györffy, Balázs; Vogel, Regina; Eckert, Klaus; Valenti, Giovanni; Fang, Liang; Lohneis, Philipp; Elezkurtaj, Sefer; Ziebold, Ulrike; Birchmeier, Walter

    2013-12-12

    Prognosis for patients with estrogen-receptor (ER)-negative basal breast cancer is poor, and chemotherapy is currently the best therapeutic option. We have generated a compound-mutant mouse model combining the activation of β-catenin and HGF (Wnt-Met signaling), which produced rapidly growing basal mammary gland tumors. We identified the chemokine system CXCL12/CXCR4 as a crucial driver of Wnt-Met tumors, given that compound-mutant mice also deficient in the CXCR4 gene were tumor resistant. Wnt-Met activation rapidly expanded a population of cancer-propagating cells, in which the two signaling systems control different functions, self-renewal and differentiation. Molecular therapy targeting Wnt, Met, and CXCR4 in mice significantly delayed tumor development. The expression of a Wnt-Met 322 gene signature was found to be predictive of poor survival of human patients with ER-negative breast cancers. Thus, targeting CXCR4 and its upstream activators, Wnt and Met, might provide an efficient strategy for breast cancer treatment.

  10. Using the Relevance Vector Machine Model Combined with Local Phase Quantization to Predict Protein-Protein Interactions from Protein Sequences

    PubMed Central

    An, Ji-Yong; Meng, Fan-Rong; You, Zhu-Hong; Fang, Yu-Hong; Zhao, Yu-Jun; Zhang, Ming

    2016-01-01

    We propose a novel computational method known as RVM-LPQ that combines the Relevance Vector Machine (RVM) model and Local Phase Quantization (LPQ) to predict PPIs from protein sequences. The main improvements are the results of representing protein sequences using the LPQ feature representation on a Position Specific Scoring Matrix (PSSM), reducing the influence of noise using a Principal Component Analysis (PCA), and using a Relevance Vector Machine (RVM) based classifier. We perform 5-fold cross-validation experiments on Yeast and Human datasets, and we achieve very high accuracies of 92.65% and 97.62%, respectively, which is significantly better than previous works. To further evaluate the proposed method, we compare it with the state-of-the-art support vector machine (SVM) classifier on the Yeast dataset. The experimental results demonstrate that our RVM-LPQ method is obviously better than the SVM-based method. The promising experimental results show the efficiency and simplicity of the proposed method, which can be an automatic decision support tool for future proteomics research. PMID:27314023

  11. Evaluation of six channelized Hotelling observers in combination with a contrast sensitivity function to predict human observer performance

    NASA Astrophysics Data System (ADS)

    Goffi, Marco; Veldkamp, Wouter J. H.; van Engen, Ruben E.; Bouwman, Ramona W.

    2015-03-01

    Standard methods to quantify image quality (IQ) may not be adequate for clinical images since they depend on uniform backgrounds and linearity. Statistical model observers are not restricted to these limitations and might be suitable for IQ evaluation of clinical images. One of these statistical model observers is the channelized Hotelling observer (CHO), where the images are filtered by a set of channels. The aim of this study was to evaluate six different channel sets, with an additional filter to simulate the human contrast sensitivity function (CSF), in their ability to predict human observer performance. For this evaluation a two alternative forced choice experiment was performed with two types of background structures (white noise (WN) and clustered lumpy background (CLB)), 5 disk-shaped objects with different diameters and 3 different signal energies. The results show that the correlation between human and model observers have a diameter dependency for some channel sets in combination with CLBs. The addition of the CSF reduces this diameter dependency and in some cases improves the correlation coefficient between human- and model observer. For the CLB the Partial Least Squares channel set shows the highest correlation with the human observer (r2=0.71) and for WN backgrounds it was the Gabor-channel set with CSF (r2=0.72). This study showed that for some channels there is a high correlation between human and model observer, which suggests that the CHO has potential as a tool for IQ analysis of digital mammography systems.

  12. CS-31A NOVEL YAP-DRIVEN MIGRATION AND INVASION SIGNALING PATHWAY PREDICTS POOR OUTCOME IN GLIOBLASTOMA

    PubMed Central

    Shah, Sagar R; Tippens, Nathaniel D; Park, Jinseok; Mohyeldin, Ahmed; Vela, Guillermo; Levchenko, Andre; Quinones-Hinojosa, Alfredo

    2014-01-01

    One of the hallmarks of tumor malignancy is the ability of cells to not only locally invade its surrounding parenchyma but also distally metastasize. Aggressive tumors such as glioblastoma (GBM) often display a collective sheet of migrating cells which may eventually disseminate and migrate in a single cell manner. By integrating extracellular cues and intracellular signaling, cell polarization and the persistence and speed of locomotion is tightly governed. Given the diverse inputs that may modulate this intricate cell migration pathway, it is of interest to identify critical modulators of this network. Yes-associated protein (YAP), a transcriptional regulator, has been suggested to potentiate migration, invasion, and metastasis; however, it is not known how or whether YAP expression and activity can regulate the molecular networks controlling cell migration and invasion, and whether this function of YAP may be consequential to the progression of aggressive cancers. We thus explored mechanisms of YAP-mediated migration and invasion in normal cells as well as cancer cells where YAP is hyperactive (p < 0.05). We found that YAP plays a pivotal role in regulation of this complex migratory and invasive behavior through a novel small Rho-GTPase-dependent signaling mechanism. As with GBM, metastatic cancers often evade detection because individual cells spread from the primary bulk tumor; thus, making complete resection and treatment virtually impossible. Congruent with our in vitro studies, our murine intracranial xenograft model argue the role of YAP in driving invasive tumor growth (p < 0.05). Moreover, we demonstrate that these YAP-driven cell dispersal mechanisms confer poor patient prognosis in the TCGA and REMBRANDT GBM databases (p < 0.05). Thus, our findings provide new insights into the biology of aggressive cancers with particular prognostic relevance of this YAP-driven pro-motility cascade in glioblastoma. In addition, our studies suggest these YAP

  13. WNT-pathway components as predictive markers useful for diagnosis, prevention and therapy in inflammatory bowel disease and sporadic colorectal cancer

    PubMed Central

    Serafino, Annalucia; Moroni, Noemi; Zonfrillo, Manuela; Andreola, Federica; Mercuri, Luana; Nicotera, Giuseppe; Nunziata, Joseph; Ricci, Riccardo; Antinori, Armando; Rasi, Guido; Pierimarchi, Pasquale

    2014-01-01

    The key role of the Wnt/β-catenin signaling in colorectal cancer (CRC) insurgence and progression is now recognized and several therapeutic strategies targeting this pathway are currently in developing. Wnt/β-catenin signaling not only dominates the early stages of sporadic colorectal cancer (SCC), but could also represent the connection between inflammatory bowel diseases (IBD) and increased risk of developing SCC. The knowledge on the sequential molecular events of Wnt-signaling cascade in IBD and during colorectal carcinogenesis, might provide new diagnostic/prognostic markers and could be helpful for optimizing the treatment protocols, thus improving the efficacy of Wnt-targeting therapies. We performed a comparative evaluation of the expression of some crucial molecules participating to Wnt signaling in an animal model of chemically-induced CRC and in human tissues obtained from patients suffering from IBD or at sequential stages of SCC. Specifically, we analyzed upstream events of Wnt signaling including β-catenin nuclear translocation and loss of E-cadherin and APC functions, and downstream events including c-Myc and Cyclin-D1 expression. We demonstrated that these crucial components of the Wnt/β-catenin pathway, when evaluated by immunohistochemistry using a multiparametric approach that includes the analyses of both expression and localization, could be potent markers for diagnosis, prevention and therapy in IBD and SCC, also possessing a predictive value for responsiveness to Wnt-targeting therapies. Furthermore, we showed that the animal model of chemically-induced CRC mimics the molecular events of Wnt signaling during IBD and SCC development in humans and may therefore be suitable for testing chemopreventive or therapeutic drugs targeting this pathway. PMID:24657851

  14. Paeonol and danshensu combination attenuates apoptosis in myocardial infarcted rats by inhibiting oxidative stress: Roles of Nrf2/HO-1 and PI3K/Akt pathway

    PubMed Central

    Li, Hua; Song, Fan; Duan, Lin-Rui; Sheng, Juan-Juan; Xie, Yan-Hua; Yang, Qian; Chen, Ying; Dong, Qian-Qian; Zhang, Bang-Le; Wang, Si-Wang

    2016-01-01

    Paeonol and danshensu is the representative active ingredient of traditional Chinese medicinal herbs Cortex Moutan and Radix Salviae Milthiorrhizae, respectively. Paeonol and danshensu combination (PDSS) has putative cardioprotective effects in treating ischemic heart disease (IHD). However, the evidence for the protective effect is scarce and the pharmacological mechanisms of the combination remain unclear. The present study was designed to investigate the protective effect of PDSS on isoproterenol (ISO)-induced myocardial infarction in rats and to elucidate the potential mechanism. Assays of creatine kinase-MB, cardiac troponin I and T and histopathological analysis revealed PDSS significantly prevented myocardial injury induced by ISO. The ISO-induced profound elevation of oxidative stress was also suppressed by PDSS. TUNEL and caspase-3 activity assay showed that PDSS significantly inhibited apoptosis in myocardia. In exploring the underlying mechanisms of PDSS, we found PDSS enhanced the nuclear translocation of Nrf2 in myocardial injured rats. Furthermore, PDSS increased phosphorylated PI3K and Akt, which may in turn activate antioxidative and antiapoptotic signaling events in rat. These present findings demonstrated that PDSS exerts significant cardioprotective effects against ISO-induced myocardial infarction in rats. The protective effect is, at least partly, via activation of Nrf2/HO-1 signaling and involvement of the PI3K/Akt cell survival signaling pathway. PMID:27021411

  15. Temporal Uncertainty and Temporal Estimation Errors Affect Insular Activity and the Frontostriatal Indirect Pathway during Action Update: A Predictive Coding Study

    PubMed Central

    Limongi, Roberto; Pérez, Francisco J.; Modroño, Cristián; González-Mora, José L.

    2016-01-01

    Action update, substituting a prepotent behavior with a new action, allows the organism to counteract surprising environmental demands. However, action update fails when the organism is uncertain about when to release the substituting behavior, when it faces temporal uncertainty. Predictive coding states that accurate perception demands minimization of precise prediction errors. Activity of the right anterior insula (rAI) is associated with temporal uncertainty. Therefore, we hypothesize that temporal uncertainty during action update would cause the AI to decrease the sensitivity to ascending prediction errors. Moreover, action update requires response inhibition which recruits the frontostriatal indirect pathway associated with motor control. Therefore, we also hypothesize that temporal estimation errors modulate frontostriatal connections. To test these hypotheses, we collected fMRI data when participants performed an action-update paradigm within the context of temporal estimation. We fit dynamic causal models to the imaging data. Competing models comprised the inferior occipital gyrus (IOG), right supramarginal gyrus (rSMG), rAI, right presupplementary motor area (rPreSMA), and the right striatum (rSTR). The winning model showed that temporal uncertainty drove activity into the rAI and decreased insular sensitivity to ascending prediction errors, as shown by weak connectivity strength of rSMG→rAI connections. Moreover, temporal estimation errors weakened rPreSMA→rSTR connections and also modulated rAI→rSTR connections, causing the disruption of action update. Results provide information about the neurophysiological implementation of the so-called horse-race model of action control. We suggest that, contrary to what might be believed, unsuccessful action update could be a homeostatic process that represents a Bayes optimal encoding of uncertainty. PMID:27445737

  16. Temporal Uncertainty and Temporal Estimation Errors Affect Insular Activity and the Frontostriatal Indirect Pathway during Action Update: A Predictive Coding Study.

    PubMed

    Limongi, Roberto; Pérez, Francisco J; Modroño, Cristián; González-Mora, José L

    2016-01-01

    Action update, substituting a prepotent behavior with a new action, allows the organism to counteract surprising environmental demands. However, action update fails when the organism is uncertain about when to release the substituting behavior, when it faces temporal uncertainty. Predictive coding states that accurate perception demands minimization of precise prediction errors. Activity of the right anterior insula (rAI) is associated with temporal uncertainty. Therefore, we hypothesize that temporal uncertainty during action update would cause the AI to decrease the sensitivity to ascending prediction errors. Moreover, action update requires response inhibition which recruits the frontostriatal indirect pathway associated with motor control. Therefore, we also hypothesize that temporal estimation errors modulate frontostriatal connections. To test these hypotheses, we collected fMRI data when participants performed an action-update paradigm within the context of temporal estimation. We fit dynamic causal models to the imaging data. Competing models comprised the inferior occipital gyrus (IOG), right supramarginal gyrus (rSMG), rAI, right presupplementary motor area (rPreSMA), and the right striatum (rSTR). The winning model showed that temporal uncertainty drove activity into the rAI and decreased insular sensitivity to ascending prediction errors, as shown by weak connectivity strength of rSMG→rAI connections. Moreover, temporal estimation errors weakened rPreSMA→rSTR connections and also modulated rAI→rSTR connections, causing the disruption of action update. Results provide information about the neurophysiological implementation of the so-called horse-race model of action control. We suggest that, contrary to what might be believed, unsuccessful action update could be a homeostatic process that represents a Bayes optimal encoding of uncertainty.

  17. Predicting Lymph Node Metastasis in Endometrial Cancer Using Serum CA125 Combined with Immunohistochemical Markers PR and Ki67, and a Comparison with Other Prediction Models

    PubMed Central

    Xue, Xiaohong; Wang, Huaying; Shan, Weiwei; Ning, Chengcheng; Zhou, Qiongjie; Chen, Xiaojun; Luo, Xuezhen

    2016-01-01

    We aimed to evaluate the value of immunohistochemical markers and serum CA125 in predicting the risk of lymph node metastasis (LNM) in women with endometrial cancer and to identify a low-risk group of LNM. The medical records of 370 patients with endometrial endometrioid adenocarcinoma who underwent surgical staging in the Obstetrics & Gynecology Hospital of Fudan University were collected and retrospectively reviewed. Immunohistochemical markers were screened. A model using serum cancer antigen 125 (CA125) level, the immunohistochemical markers progesterone receptor (PR) and Ki67 was created for prediction of LNM. A predicted probability of 4% among these patients was defined as low risk. The developed model was externally validated in 200 patients from Shanghai Cancer Center. The efficiency of the model was compared with three other reported prediction models. Patients with serum CA125 < 30.0 IU/mL, either or both of positive PR staining > 50% and Ki67 < 40% in cancer lesion were defined as low risk for LNM. The model showed good discrimination with an area under the receiver operating characteristic curve of 0.82. The model classified 61.9% (229/370) of patients as being at low risk for LNM. Among these 229 patients, 6 patients (2.6%) had LNM and the negative predictive value was 97.4% (223/229). The sensitivity and specificity of the model were 84.6% and 67.4% respectively. In the validation cohort, the model classified 59.5% (119/200) of patients as low-risk, 3 out of these 119 patients (2.5%) has LNM. Our model showed a predictive power similar to those of two previously reported prediction models. The prediction model using serum CA125 and the immunohistochemical markers PR and Ki67 is useful to predict patients with a low risk of LNM and has the potential to provide valuable guidance to clinicians in the treatment of patients with endometrioid endometrial cancer. PMID:27163153

  18. Combination of plasma-soluble fms-like tyrosine kinase 1 and uterine artery Doppler for the prediction of preeclampsia in cases of elderly gravida.

    PubMed

    Kulmala, Lalita; Phupong, Vorapong

    2014-06-01

    The aim of this study was to determine the predictive value of the combination of plasma-soluble fms-like tyrosine kinase 1 (sFlt-1) and uterine artery Doppler for the detection of preeclampsia in women of advanced age at 16-18 weeks of gestation and to identify associations between other pregnancy complications and abnormalities of these combined tests. The maternal plasma sFlt-1 level was measured, and uterine artery Doppler was performed at 16-18 weeks of gestation in 314 cases of elderly gravida. The main outcome was preeclampsia. Fourteen women (4.46%) developed preeclampsia. The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of uterine artery Doppler combined with plasma sFlt-1 for preeclampsia prediction were 28.6, 95.7, 23.5 and 96.6%, respectively. For the prediction of early-onset preeclampsia, the sensitivity, specificity, PPV and NPV were 80, 95.8, 23.5 and 99.7%, respectively. Patients with abnormal uterine artery Doppler findings and an abnormal plasma s Flt-1 level (greater than 1724.5 pg ml(-1)) had a higher risk of preterm delivery (relative risk (RR)=3.38, 95% confidence interval (CI) 1.47-7.59), neonatal respiratory distress syndrome (RR=52.06, 95% CI 5.71-474.45) and perinatal death (RR=17.35, 95% CI 1.13-265.64). Our findings indicate that the combination of uterine artery Doppler and sFlt-1 level at 16-18 weeks of gestation in cases of elderly gravida has a high predictive value for early-onset preeclampsia, but not for overall preeclampsia. This combination test may be a useful early second trimester screening test for the prediction of early-onset preeclampsia in cases of elderly gravida.

  19. KDR Amplification Is Associated with VEGF-Induced Activation of the mTOR and Invasion Pathways but does not Predict Clinical Benefit to the VEGFR TKI Vandetanib

    PubMed Central

    Nilsson, Monique B.; Giri, Uma; Gudikote, Jayanthi; Tang, Ximing; Lu, Wei; Tran, Hai; Fan, Youhong; Koo, Andrew; Diao, Lixia; Tong, Pan; Wang, Jing; Herbst, Roy; Johnson, Bruce E.; Ryan, Andy; Webster, Alan; Rowe, Philip; Wistuba, Ignacio I.; Heymach, John V.

    2016-01-01

    Purpose VEGF pathway inhibitors have been investigated as therapeutic agents in the treatment of non–small cell lung cancer (NSCLC) because of its central role in angiogenesis. These agents have improved survival in patients with advanced NSCLC, but the effects have been modest. Although VEGFR2/KDR is typically localized to the vasculature, amplification of KDR has reported to occur in 9% to 30% of the DNA from different lung cancers. We investigated the signaling pathways activated downstream of KDR and whether KDR amplification is associated with benefit in patients with NSCLC treated with the VEGFR inhibitor vandetanib. Methods NSCLC cell lines with or without KDR amplification were studied for the effects of VEGFR tyrosine kinase inhibitors (TKI) on cell viability and migration. Archival tumor samples collected from patients with platinum-refractory NSCLC in the phase III ZODIAC study of vandetanib plus docetaxel or placebo plus docetaxel (N = 294) were screened for KDR amplification by FISH. Results KDR amplification was associated with VEGF-induced activation of mTOR, p38, and invasiveness in NSCLC cell lines. However, VEGFR TKIs did not inhibit proliferation of NSCLC cell lines with KDR amplification. VEGFR inhibition decreased cell motility as well as expression of HIF1α in KDR-amplified NSCLC cells. In the ZODIAC study, KDR amplification was observed in 15% of patients and was not associated with improved progression-free survival, overall survival, or objective response rate for the vandetanib arm. Conclusions Preclinical studies suggest KDR activates invasion but not survival pathways in KDR-amplified NSCLC models. Patients with NSCLC whose tumor had KDR amplification were not associated with clinical benefit for vandetanib in combination with docetaxel. PMID:26578684

  20. Genetic variants of genes in the Notch signaling pathway predict overall survival of non-small cell lung cancer patients in the PLCO study

    PubMed Central

    Xu, Yinghui; Wang, Yanru; Liu, Hongliang; Kang, Xiaozheng; Li, Wei; Wei, Qingyi

    2016-01-01

    The Notch signaling pathway has been shown to have biological significance and therapeutic application in non-small cell lung cancer (NSCLC). We hypothesize that genetic variants of genes in the Notch signaling pathway are associated with overall survival (OS) of NSCLC patients. To test this hypothesis, we performed multivariate Cox proportional hazards regression analysis to evaluate associations of 19,571 single nucleotide polymorphisms (SNPs) in 132 Notch pathway genes with OS of 1,185 NSCLC patients available from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. We found that five potentially functional tagSNPs in four genes (i.e., ADAM12 rs10794069 A > G, DTX1 rs1732793 G > A, TLE1 rs199731120 C > CA, TLE1 rs35970494 T > TC and E2F3 rs3806116 G > T) were associated with a poor OS, with a variant-allele attributed hazards ratio (HR) of 1.27 [95% confidence interval (95% CI) = 1.13–1.42, P = 3.62E-05], 1.30 (1.14–1.49, 8.16E-05), 1.40 (1.16–1.68, 3.47E-04), 1.27 (1.11–1.44, 3.38E-04), and 1.21 (1.09–1.33, 2.56E-04), respectively. Combined analysis of these five risk genotypes revealed that the genetic score 0–5 was associated with the adjusted HR in a dose-response manner (Ptrend = 3.44E-13); individuals with 2–5 risk genotypes had an adjusted HR of 1.56 (1.34–1.82, 1.46E-08), compared with those with 0–1 risk genotypes. Larger studies are needed to validate our findings. PMID:27557513

  1. Combined Inhibition of the BMP pathway and the RANK-RANKL axis in a Mixed Lytic/blastic Prostate Cancer Lesion

    PubMed Central

    Virk, Mandeep S.; Alaee, Farhang; Petrigliano, Frank A.; Sugiyama, Osamu; Chatziioannou, Arion F.; Stout, David; Dougall, William C.; Lieberman, Jay R.

    2010-01-01

    The purpose of this study was to investigate the influence of combined inhibition of RANKL (receptor activator of nuclear factor kappa-B ligand) and bone morphogenetic protein (BMP) activity in a mixed lytic/blastic prostate cancer lesion in bone. Human prostate cancer cells (C4 2b) were injected into immunocompromised mice using an intratibial injection model to create mixed lytic/blastic lesions. RANK-Fc, a recombinant RANKL antagonist, was injected subcutaneously three times a week (10mg/kg) to inhibit RANKL and subsequent formation, function and survival of osteoclasts. Inhibition of BMP activity was achieved by transducing prostate cancer cells ex vivo with a retroviral vector expressing noggin (retronoggin; RN). There were three treatment groups (RANK-Fc treatment, RN treatment and combined RN and RANK-Fc treatment) and two control groups (untreated control and empty vector control for the RN treatment group). The progression of bone lesion and tumor growth was evaluated using plain radiographs, hind limb tumor size, 18F-Fluorodeoxyglucose and 18F-fluoride micro PET-CT, histology and histomorphometry. Treatment with RANK-Fc alone inhibited osteolysis and transformed a mixed lytic/blastic lesion into an osteoblastic phenotype. Treatment with RN alone inhibited the osteoblastic component in a mixed lytic/blastic lesion and resulted in formation of smaller osteolytic bone lesion with smaller soft tissue size. The animals treated with both RN and RANK-Fc demonstrated delayed development of bone lesions, inhibition of osteolysis, small soft tissue tumors and preservation of bone architecture with less tumor induced new bone formation. This study suggests that combined inhibition of the RANKL and the BMP pathway may be an effective biologic therapy to inhibit the progression of established mixed lytic/blastic prostate cancer lesions in bone. PMID:21073986

  2. A combination of genistein and magnesium enhances the vasodilatory effect via an eNOS pathway and BK(Ca) current amplification.

    PubMed

    Sun, Lina; Hou, Yunlong; Zhao, Tingting; Zhou, Shanshan; Wang, Xiaoran; Zhang, Liming; Yu, Guichun

    2015-04-01

    The phytoestrogen genistein (GST) and magnesium have been independently shown to regulate vascular tone; however, their individual vasodilatory effects are limited. The aim of this study was to examine the combined effects of GST plus magnesium on vascular tone in mesenteric arteries. The effects of pretreatment with GST (0-200 μmol/L), MgCl2 (0-4.8 mmol/L) and GST plus MgCl2 on 10 μmol/L phenylephrine (PE) precontracted mesenteric arteries in rats were assessed by measuring isometric force. BK(Ca) currents were detected by the patch clamp method. GST caused concentration- and partial endothelium-dependent relaxation. Magnesium resulted in dual adjustment of vascular tone. Magnesium-free solution eliminated the vasodilatation of GST in both endothelium-intact and denuded rings. GST (50 μmol/L) plus magnesium (4.8 mmol/L) caused stronger relaxation in both endothelium-intact and denuded rings. Pretreatment with the nitric oxide synthase (NOS) inhibitor L-N-nitroarginine methyl ester (L-NAME, 100 μmol/L) significantly inhibited the effects of GST, high magnesium, and the combination of GST and magnesium. BK(Ca) currents were amplified to a greater extent when GST (50 μmol/L) was combined with 4.8 versus 1.2 mmol/L Mg(2+). Our data suggest that GST plus magnesium provides enhanced vasodilatory effects in rat mesenteric arteries compared with that observed when either is used separately, which was related to an eNOS pathway and BK(Ca) current amplification.

  3. Effects of elevated dissolved carbon dioxide and perfluorooctane sulfonic acid, given singly and in combination, on steroidogenic and biotransformation pathways of Atlantic cod.

    PubMed

    Preus-Olsen, Gunnhild; Olufsen, Marianne O; Pedersen, Sindre Andre; Letcher, Robert J; Arukwe, Augustine

    2014-10-01

    In the aquatic environments, the predicted changes in water temperature, pO2 and pCO2 could result in hypercapnic and hypoxic conditions for aquatic animals. These conditions are thought to affect several basic cellular and physiological mechanisms. Yet, possible adverse effects of elevated CO2 (hypercapnia) on teleost fish, as well as combined effects with emerging and legacy environmental contaminants are poorly investigated. In this study, juvenile Atlantic cod (Gadus morhua) were divided into groups and exposed to three different water bath PFOS exposure regimes (0 (control), 100 and 200 μg L(-1)) for 5 days at 1h/day, followed by three different CO2-levels (normocapnia, moderate (0.3%) and high (0.9%)). The moderate CO2 level is the predicted near future (within year 2300) level, while 0.9% represent severe hypercapnia. Tissue samples were collected at 3, 6 and 9 days after initiated CO2 exposure. Effects on the endocrine and biotransformation systems were examined by analyzing levels of sex steroid hormones (E2, T, 11-KT) and transcript expression of estrogen responsive genes (ERα, Vtg-α, Vtg-β, ZP2 and ZP3). In addition, transcripts for genes encoding xenobiotic metabolizing enzymes (cyp1a and cyp3a) and hypoxia-inducible factor (HIF-1α) were analyzed. Hypercapnia alone produced increased levels of sex steroid hormones (E2, T, 11-KT) with concomitant mRNA level increase of estrogen responsive genes, while PFOS produced weak and time-dependent effects on E2-inducible gene transcription. Combined PFOS and hypercapnia exposure produced increased effects on sex steroid levels as compared to hypercapnia alone, with transcript expression patterns that are indicative of time-dependent interactive effects. Exposure to hypercapnia singly or in combination with PFOS produced modulations of the biotransformation and hypoxic responses that were apparently concentration- and time-dependent. Loading plots of principal component analysis (PCA) produced a significant

  4. Exploring transition pathway and free-energy profile of large-scale protein conformational change by combining normal mode analysis and umbrella sampling molecular dynamics.

    PubMed

    Wang, Jinan; Shao, Qiang; Xu, Zhijian; Liu, Yingtao; Yang, Zhuo; Cossins, Benjamin P; Jiang, Hualiang; Chen, Kaixian; Shi, Jiye; Zhu, Weiliang

    2014-01-09

    Large-scale conformational changes of proteins are usually associated with the binding of ligands. Because the conformational changes are often related to the biological functions of proteins, understanding the molecular mechanisms of these motions and the effects of ligand binding becomes very necessary. In the present study, we use the combination of normal-mode analysis and umbrella sampling molecular dynamics simulation to delineate the atomically detailed conformational transition pathways and the associated free-energy landscapes for three well-known protein systems, viz., adenylate kinase (AdK), calmodulin (CaM), and p38α kinase in the absence and presence of respective ligands. For each protein under study, the transient conformations along the conformational transition pathway and thermodynamic observables are in agreement with experimentally and computationally determined ones. The calculated free-energy profiles reveal that AdK and CaM are intrinsically flexible in structures without obvious energy barrier, and their ligand binding shifts the equilibrium from the ligand-free to ligand-bound conformation (population shift mechanism). In contrast, the ligand binding to p38α leads to a large change in free-energy barrier (ΔΔG ≈ 7 kcal/mol), promoting the transition from DFG-in to DFG-out conformation (induced fit mechanism). Moreover, the effect of the protonation of D168 on the conformational change of p38α is also studied, which reduces the free-energy difference between the two functional states of p38α and thus further facilitates the conformational interconversion. Therefore, the present study suggests that the detailed mechanism of ligand binding and the associated conformational transition is not uniform for all kinds of proteins but correlated to their respective biological functions.

  5. Integrated Genomics Identifies miR-32/MCL-1 Pathway as a Critical Driver of Melanomagenesis: Implications for miR-Replacement and Combination Therapy

    PubMed Central

    Mishra, Prasun J.; Mishra, Pravin J.; Merlino, Glenn

    2016-01-01

    Aims Cutaneous malignant melanoma is among the deadliest human cancers, broadly resistant to most clinical therapies. A majority of patients with BRAFV600E melanomas respond well to inhibitors such as vemurafenib, but all ultimately relapse. Moreover, there are no viable treatment options available for other non-BRAF melanoma subtypes in the clinic. A key to improving treatment options lies in a better understanding of mechanisms underlying melanoma progression, which are complex and heterogeneous. Methods In this study we integrated gene and microRNA (miRNA) expression data from genetically engineered mouse models of highly and poorly malignant melanocytic tumors, as well as available human melanoma databases, and discovered an important role for a pathway centered on a tumor suppressor miRNA, miR-32. Results Malignant tumors frequently exhibited poor expression of miR-32, whose targets include NRAS, PI3K and notably, MCL-1. Accordingly, MCL-1 was often highly expressed in melanomas, and when knocked down diminished oncogenic potential. Forced MCL-1 overexpression transformed immortalized primary mouse melanocytes, but only when also expressing activating mutations in BRAF, CRAF or PI3K. Importantly, both miR-32 replacement therapy and the MCL-1-specific antagonist sabutoclax demonstrated single-agent efficacy, and acted synergistically in combination with vemurafenib in preclinical melanoma models. Conclusions We here identify miR-32/MCL-1 pathway members as key early genetic events driving melanoma progression, and suggest that their inhibition may be an effective anti-melanoma strategy irrespective of NRAS, BRAF, and PTEN status. PMID:27846237

  6. Gene Expression Profiling and Pathway Network Analysis Predicts a Novel Antitumor Function for a Botanical-Derived Drug, PG2

    PubMed Central

    Kuo, Yu-Lun; Chen, Chun-Houh; Chuang, Tsung-Hsien; Hua, Wei-Kai; Lin, Wey-Jinq; Hsu, Wei-Hsiang; Chang, Peter Mu-Hsin; Hsu, Shih-Lan; Huang, Tse-Hung; Kao, Cheng-Yan; Huang, Chi-Ying F.

    2015-01-01

    PG2 is a botanical drug that is mostly composed of Astragalus polysaccharides (APS). Its role in hematopoiesis and relieving cancer-related fatigue has recently been clinically investigated in cancer patients. However, systematic analyses of its functions are still limited. The aim of this study was to use microarray-based expression profiling to evaluate the quality and consistency of PG2 from three different product batches and to study biological mechanisms of PG2. An integrative molecular analysis approach has been designed to examine significant PG2-induced signatures in HL-60 leukemia cells. A quantitative analysis of gene expression signatures was conducted for PG2 by hierarchical clustering of correlation coefficients. The results showed that PG2 product batches were consistent and of high quality. These batches were also functionally equivalent to each other with regard to how they modulated the immune and hematopoietic systems. Within the PG2 signature, there were five genes associated with doxorubicin: IL-8, MDM4, BCL2, PRODH2, and BIRC5. Moreover, the combination of PG2 and doxorubicin had a synergistic effect on induced cell death in HL-60 cells. Together with the bioinformatics-based approach, gene expression profiling provided a quantitative measurement for the quality and consistency of herbal medicines and revealed new roles (e.g., immune modulation) for PG2 in cancer treatment. PMID:25972907

  7. Stimulation of PBMC and Monocyte-Derived Macrophages via Toll-Like Receptor Activates Innate Immune Pathways in HIV-Infected Patients on Virally Suppressive Combination Antiretroviral Therapy

    PubMed Central

    Merlini, Esther; Tincati, Camilla; Biasin, Mara; Saulle, Irma; Cazzaniga, Federico Angelo; d’Arminio Monforte, Antonella; Cappione, Amedeo J.; Snyder-Cappione, Jennifer; Clerici, Mario; Marchetti, Giulia Carla

    2016-01-01

    In HIV-infected, combination antiretroviral therapy (cART)-treated patients, immune activation and microbial translocation persist and associate with inadequate CD4 recovery and morbidity/mortality. We analyzed whether alterations in the toll-like receptor (TLR) pathway could be responsible for the immune hyperactivation seen in these patients. PBMC/monocyte-derived macrophages (MDMs) of 28 HIV+ untreated and 35 cART-treated patients with HIV-RNA < 40 cp/mL [20 Full Responders (FRs): CD4 ≥ 350; 15 Immunological Non-Responders (INRs): CD4 < 350], as well as of 16 healthy controls were stimulated with a panel of TLR agonists. We measured: CD4/CD8/CD14/CD38/HLA-DR/Ki67/AnnexinV/CD69/TLR4/8 (Flow Cytometry); PBMC expression of 84 TLR pathway genes (qPCR); PBMC/MDM cytokine release (Multiplex); and plasma lipopolysaccharide (LPS)/sCD14 (LAL/ELISA). PBMC/MDM from cART patients responded weakly to LPS stimulation but released high amounts of pro-inflammatory cytokines. MDM from these patients were characterized by a reduced expression of HLA-DR+ MDM and failed to expand activated HLA-DR+ CD38+ T-lymphocytes. PBMC/MDM from cART patients responded more robustly to ssRNA stimulation; this resulted in a significant expansion of activated CD38 + CD8 and the release of amounts of pro-inflammatory cytokines comparable to those seen in untreated viremic patients. Despite greater constitutive TLR pathway gene expression, PBMC from INRs seemed to upregulate only type I IFN genes following TLR stimulation, whereas PBMC from full responders showed a broader response. Systemic exposure to microbial antigens drives immune activation during cART by triggering TLRs. Bacterial stimulation modifies MDM function/pro-inflammatory profile in cART patients without affecting T-lymphocytes; this suggests translocating bacteria as selective stimulus to chronic innate activation during cART. High constitutive TLR activation is seen in patients lacking CD4 recovery, suggesting

  8. The innate oxygen dependant immune pathway as a sensitive parameter to predict the performance of biological graft materials.

    PubMed

    Bryan, Nicholas; Ashwin, Helen; Smart, Neil; Bayon, Yves; Scarborough, Nelson; Hunt, John A

    2012-09-01

    Clinical performance of a biomaterial is decided early after implantation as leukocytes interrogate the graft throughout acute inflammation. High degrees of leukocyte activation lead to poor material/patient compliance, accelerated degeneration and graft rejection. A number reactive oxygen species (ROS) are released by leukocytes throughout their interaction with a material, which can be used as a sensitive measure of leukocyte activation. The aim of this study was to compare leukocyte activation by commercially available biologic surgical materials and define the extent manufacturing variables influence down-stream ROS response. Chemiluminescence assays were performed using modifications to a commercially available kit (Knight Scientific, UK). Whole blood was obtained from 4 healthy human adults at 7 day intervals for 4 weeks, combined with Adjuvant K, Pholasin (a highly sensitive ROS excitable photoprotein) and biomaterial, and incubated for 60 min with continuous chemiluminescent measurements. Leukocyte ROS inducers fMLP and PMA were added as controls. Xeno- and allogeneic dermal and small intestinal submucosal (SIS) derived biomaterials were produced commercially (Surgisis Biodesign™, Alloderm(®), Strattice(®)Firm & Pliable & Permacol™) or fabricated in house to induce variations in decellularisation and cross-linking. Statistics were performed using Waller-Duncan post hoc ranking. Materials demonstrated significant differences in leukocyte activation as a function of decellularisation reagent and tissue origin. The data demonstrated SIS was significantly more pro-inflammatory than dermis. Additionally it was deduced that SDS during decellularisation induced pro-inflammatory changes to dermal materials. Furthermore, it was possible to conclude inter-patient variation in leukocyte response. The in vitro findings were validated in vivo which confirmed the chemiluminescence observations, highlighting the potential for translation of this technique as a

  9. Predicting dermal penetration for ToxCast chemicals using in silico estimates for diffusion in combination with physiologically based pharmacokinetic (PBPK) modeling.

    EPA Science Inventory

    Predicting dermal penetration for ToxCast chemicals using in silico estimates for diffusion in combination with physiologically based pharmacokinetic (PBPK) modeling.Evans, M.V., Sawyer, M.E., Isaacs, K.K, and Wambaugh, J.With the development of efficient high-throughput (HT) in ...

  10. Decomposition of acetaminophen in water by a gas phase dielectric barrier discharge plasma combined with TiO2-rGO nanocomposite: Mechanism and degradation pathway.

    PubMed

    Zhang, Guyu; Sun, Yabing; Zhang, Chunxiao; Yu, Zhongqing

    2017-02-05

    Acetaminophen (APAP) served as the model pollutant to evaluate the feasibility of pollutant removal by gas phase dielectric barrier discharge plasma combined with the titanium dioxide-reduced Graphene Oxide (TiO2-rGO) nanocomposite. TiO2-rGO nanocomposite was prepared using the modified hydrothermal method and characterized by TEM and XPS before and after plasma process. The results indicated that the APAP degradation efficiency was significantly improved to 92% after 18min of discharge plasma treatment coupling 0.25gL(-1) TiO2-rGO 5%wt at 18kV, compared with the plasma alone and plasma combined with P25 TiO2. The degradation mechanism for APAP in this system was studied by investigating the effects of the operational variables (e.g. discharge voltage and pH value) and the amount of the generated active species; and the results showed that O3 and H2O2 yields were influenced notably by adding TiO2-rGO. Also, it was observed that, compared with unused TiO2-rGO, the photocatalytic performance of used TiO2-rGO declined after several recirculation times due to the further reduction of Graphene Oxide in plasma system. Finally, intermediate products were analyzed by UV-vis spectrometry and HPLC/MS, and possible transformation pathways were identified with the support of theoretically calculating the frontier electron density of APAP.

  11. Reaction pathways of proton transfer in hydrogen-bonded phenol-carboxylate complexes explored by combined UV-vis and NMR spectroscopy.

    PubMed

    Koeppe, Benjamin; Tolstoy, Peter M; Limbach, Hans-Heinrich

    2011-05-25

    Combined low-temperature NMR/UV-vis spectroscopy (UVNMR), where optical and NMR spectra are measured in the NMR spectrometer under the same conditions, has been set up and applied to the study of H-bonded anions A··H··X(-) (AH = 1-(13)C-2-chloro-4-nitrophenol, X(-) = 15 carboxylic acid anions, 5 phenolates, Cl(-), Br(-), I(-), and BF(4)(-)). In this series, H is shifted from A to X, modeling the proton-transfer pathway. The (1)H and (13)C chemical shifts and the H/D isotope effects on the latter provide information about averaged H-bond geometries. At the same time, red shifts of the π-π* UV-vis absorption bands are observed which correlate with the averaged H-bond geometries. However, on the UV-vis time scale, different tautomeric states and solvent configurations are in slow exchange. The combined data sets indicate that the proton transfer starts with a H-bond compression and a displacement of the proton toward the H-bond center, involving single-well configurations A-H···X(-). In the strong H-bond regime, coexisting tautomers A··H···X(-) and A(-)···H··X are observed by UV. Their geometries and statistical weights change continuously when the basicity of X(-) is increased. Finally, again a series of single-well structures of the type A(-)···H-X is observed. Interestingly, the UV-vis absorption bands are broadened inhomogeneously because of a distribution of H-bond geometries arising from different solvent configurations.

  12. Modified mRNA for BMP-2 in Combination with Biomaterials Serves as a Transcript-Activated Matrix for Effectively Inducing Osteogenic Pathways in Stem Cells.

    PubMed

    Balmayor, Elizabeth R; Geiger, Johannes P; Koch, Christian; Aneja, Manish K; van Griensven, Martijn; Rudolph, Carsten; Plank, Christian

    2017-01-01

    Bone regeneration using stem cells and growth factors has disadvantages while needing to use supraphysiological growth factors concentrations. Gene therapy has been proposed as alternative, but also has limitation. Messenger RNA (mRNA)-based transcript therapy is a novel approach that may solve plasmid DNA-based gene therapy limitations. Although much more efficient in delivering genes into the cell, mRNA is unfortunately unstable and immunogenic. However, recent reports indicated that chemical modifications of the mRNA molecule can improve stability and toxicity. In this study, we have combined biomaterials and chemically modified mRNA (cmRNA) encoding Metridia luciferase, eGFP, and bone morphogenetic protein (BMP)-2 to develop transcript-activated matrices (TAMs) for gene transfer to stem cells. BMP-2 cmRNA was produced to evaluate its feasibility in stimulating osteogenic differentiation. Fibrin gel and micro-macro biphasic calcium phosphate (MBCP) granules were used as biomaterials. A sustained release of hBMP-2 cmRNA from both biomaterials was observed during 7 days. This occurred significantly faster from the MBCP granules compared to fibrin gels (92% from MBCP and 43% from fibrin after 7 days). Stem cells cultured in hBMP-2 cmRNA/fibrin or on hBMP-2 cmRNA/MBCP were transfected and able to secrete significant amounts of hBMP-2. Furthermore, transfected cells expressed osteogenic markers in vitro. Interestingly, although both TAMs promoted gene expression at the same level, hBMP-2 cmRNA/MBCP granules induced significantly higher collagen I and osteocalcin gene expression. This matrix also induced more mineral deposition. Overall, our results demonstrated the feasibility of developing efficient TAMs for bone regeneration by combining biomaterials and cmRNAs. MBCP synergistically enhances the hBMP-2 cmRNA-induced osteogenic pathway.

  13. Spin-transfer pathways in paramagnetic lithium transition-metal phosphates from combined broadband isotropic solid-state MAS NMR spectroscopy and DFT calculations.

    PubMed

    Clément, Raphaële J; Pell, Andrew J; Middlemiss, Derek S; Strobridge, Fiona C; Miller, Joel K; Whittingham, M Stanley; Emsley, Lyndon; Grey, Clare P; Pintacuda, Guido

    2012-10-17

    Substituted lithium transition-metal (TM) phosphate LiFe(x)Mn(1-x)PO(4) materials with olivine-type structures are among the most promising next generation lithium ion battery cathodes. However, a complete atomic-level description of the structure of such phases is not yet available. Here, a combined experimental and theoretical approach to the detailed assignment of the (31)P NMR spectra of the LiFe(x)Mn(1-x)PO(4) (x = 0, 0.25, 0.5, 0.75, 1) pure and mixed TM phosphates is developed and applied. Key to the present work is the development of a new NMR experiment enabling the characterization of complex paramagnetic materials via the complete separation of the individual isotropic chemical shifts, along with solid-state hybrid DFT calculations providing the separate hyperfine contributions of all distinct Mn-O-P and Fe-O-P bond pathways. The NMR experiment, referred to as aMAT, makes use of short high-powered adiabatic pulses (SHAPs), which can achieve 100% inversion over a range of isotropic shifts on the order of 1 MHz and with anisotropies greater than 100 kHz. In addition to complete spectral assignments of the mixed phases, the present study provides a detailed insight into the differences in electronic structure driving the variations in hyperfine parameters across the range of materials. A simple model delimiting the effects of distortions due to Mn/Fe substitution is also proposed and applied. The combined approach has clear future applications to TM-bearing battery cathode phases in particular and for the understanding of complex paramagnetic phases in general.

  14. Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis.

    PubMed

    Ivorra, Eugenio; Verdu, Samuel; Sánchez, Antonio J; Grau, Raúl; Barat, José M

    2016-10-19

    A technique that combines the spatial resolution of a 3D structured-light (SL) imaging system with the spectral analysis of a hyperspectral short-wave near infrared system was developed for freshness predictions of gilthead sea bream on the first storage days (Days 0-6). This novel approach allows the hyperspectral analysis of very specific fish areas, which provides more information for freshness estimations. The SL system obtains a 3D reconstruction of fish, and an automatic method locates gilthead's pupils and irises. Once these regions are positioned, the hyperspectral camera acquires spectral information and a multivariate statistical study is done. The best region is the pupil with an R² of 0.92 and an RMSE of 0.651 for predictions. We conclude that the combination of 3D technology with the hyperspectral analysis offers plenty of potential and is a very promising technique to non destructively predict gilthead freshness.

  15. Predicting Gilthead Sea Bream (Sparus aurata) Freshness by a Novel Combined Technique of 3D Imaging and SW-NIR Spectral Analysis

    PubMed Central

    Ivorra, Eugenio; Verdu, Samuel; Sánchez, Antonio J.; Grau, Raúl; Barat, José M.

    2016-01-01

    A technique that combines the spatial resolution of a 3D structured-light (SL) imaging system with the spectral analysis of a hyperspectral short-wave near infrared system was developed for freshness predictions of gilthead sea bream on the first storage days (Days 0–6). This novel approach allows the hyperspectral analysis of very specific fish areas, which provides more information for freshness estimations. The SL system obtains a 3D reconstruction of fish, and an automatic method locates gilthead’s pupils and irises. Once these regions are positioned, the hyperspectral camera acquires spectral information and a multivariate statistical study is done. The best region is the pupil with an R2 of 0.92 and an RMSE of 0.651 for predictions. We conclude that the combination of 3D technology with the hyperspectral analysis offers plenty of potential and is a very promising technique to non destructively predict gilthead freshness. PMID:27775556

  16. A combination of epitope prediction and molecular docking allows for good identification of MHC class I restricted T-cell epitopes.

    PubMed

    Zhang, Xue Wu

    2013-08-01

    In silico identification of T-cell epitopes is emerging as a new methodology for the study of epitope-based vaccines against viruses and cancer. In order to improve accuracy of prediction, we designed a novel approach, using epitope prediction methods in combination with molecular docking techniques, to identify MHC class I restricted T-cell epitopes. Analysis of the HIV-1 p24 protein and influenza virus matrix protein revealed that the present approach is effective, yielding prediction accuracy of over 80% with respect to experimental data. Subsequently, we applied such a method for prediction of T-cell epitopes in SARS coronavirus (SARS-CoV) S, N and M proteins. Based on available experimental data, the prediction accuracy is up to 90% for S protein. We suggest the use of epitope prediction methods in combination with 3D structural modelling of peptide-MHC-TCR complex to identify MHC class I restricted T-cell epitopes for use in epitope based vaccines like HIV and human cancers, which should provide a valuable step forward for the design of better vaccines and may provide in depth understanding about activation of T-cell epitopes by MHC binding peptides.

  17. Feasibility of combining spectra with texture data of multispectral imaging to predict heme and non-heme iron contents in pork sausages.

    PubMed

    Ma, Fei; Qin, Hao; Shi, Kefu; Zhou, Cunliu; Chen, Conggui; Hu, Xiaohua; Zheng, Lei

    2016-01-01

    To precisely determine heme and non-heme iron contents in meat product, the feasibility of combining spectral with texture features extracted from multispectral imaging data (405-970 nm) was assessed. In our study, spectra and textures of 120 pork sausages (PSs) treated by different temperatures (30-80 °C) were analyzed using different calibration models including partial least squares regression (PLSR) and LIB support vector machine (Lib-SVM) for predicting heme and non-heme iron contents in PSs. Based on a combination of spectral and textural features, optimized PLSR models were obtained with determination coefficient (R(2)) of 0.912 for heme and of 0.901 for non-heme iron prediction, which demonstrated the superiority of combining spectra with texture data. Results of satisfactory determination and visualization of heme and non-heme iron contents indicated that multispectral imaging could serve as a feasible approach for online industrial applications in the future.

  18. Predicting the Future as Bayesian Inference: People Combine Prior Knowledge with Observations when Estimating Duration and Extent

    ERIC Educational Resources Information Center

    Griffiths, Thomas L.; Tenenbaum, Joshua B.

    2011-01-01

    Predicting the future is a basic problem that people have to solve every day and a component of planning, decision making, memory, and causal reasoning. In this article, we present 5 experiments testing a Bayesian model of predicting the duration or extent of phenomena from their current state. This Bayesian model indicates how people should…

  19. Investigating Marine Boundary Layer Parameterizations for Improved Off-Shore Wind Predictions by Combining Observations with Models via State Estimation

    NASA Astrophysics Data System (ADS)

    Delle Monache, Luca; Hacker, Josh; Kosovic, Branko; Lee, Jared; Vandenberghe, Francois; Wu, Yonghui; Clifton, Andrew; Hawkins, Sam; Nissen, Jesper; Rostkier-Edelstein, Dorita

    2014-05-01

    Despite advances in model representation of the spatial and temporal evolution of the atmospheric boundary layer (ABL) a fundamental understanding of the processes shaping the Marine Boundary Layer (MBL) is still lacking. As part of a project funded by the U.S. Department of Energy, we are tackling this problem by combining available atmosphere and ocean observations with advanced coupled atmosphere-wave models, and via state estimation (SE) methodologies. The over-arching goal is to achieve significant advances in the scientific understanding and prediction of the underlying physical processes of the MBL, with an emphasis on the coupling between the atmosphere and the ocean via momentum and heat fluxes. We are using the single-column model (SCM) and three-dimensional (3D) versions of the Weather Research and Forecasting (WRF) model, observations of MBL structure as provided by coastal and offshore remote sensing platforms and meteorological towers, and probabilistic SE. We are systematically investigating the errors in the treatment of the surface layer of the MBL, identifying structural model inadequacies associated with its representation. We expect one key deficiency of current model representations of the surface layer of the MBL that can have a profound effect on fluxes estimates: the use of Monin-Obukhov similarity theory (MOST). This theory was developed for continental ABLs using land-based measurements, which accounts for mechanical and thermal forcing on turbulence but neglects the influence of ocean waves. We have developed an atmosphere-wave coupled modeling system by interfacing WRF with a wave model (Wavewatch III - WWIII), which is used for evaluating errors in the representation of wave-induced forcing on the energy balance at the interface between atmosphere and ocean. The Data Assimilation Research Testbed (DART) includes the SE algorithms that provide the framework for obtaining spatial and temporal statistics of wind-error evolution (and hence

  20. A Method for Finding Metabolic Pathways Using Atomic Group Tracking

    PubMed Central

    Zhong, Cheng; Lin, Hai Xiang; Wang, Jianyi

    2017-01-01

    A fundamental computational problem in metabolic engineering is to find pathways between compounds. Pathfinding methods using atom tracking have been widely used to find biochemically relevant pathways. However, these methods require the user to define the atoms to be tracked. This may lead to failing to predict the pathways that do not conserve the user-defined atoms. In this work, we propose a pathfinding method called AGPathFinder to find biochemically relevant metabolic pathways between two given compounds. In AGPathFinder, we find alternative pathways by tracking the movement of atomic groups through metabolic networks and use combined information of reaction thermodynamics and compound similarity to guide the search towards more feasible pathways and better performance. The experimental results show that atomic group tracking enables our method to find pathways without the need of defining the atoms to be tracked, avoid hub metabolites, and obtain biochemically meaningful pathways. Our results also demonstrate that atomic group tracking, when incorporated with combined information of reaction thermodynamics and compound similarity, improves the quality of the found pathways. In most cases, the average compound inclusion accuracy and reaction inclusion accuracy for the top resulting pathways of our method are around 0.90 and 0.70, respectively, which are better than those of the existing methods. Additionally, AGPathFinder provides the information of thermodynamic feasibility and compound similarity for the resulting pathways. PMID:28068354

  1. Statistical monitoring and dynamic simulation of a wastewater treatment plant: A combined approach to achieve model predictive control.

    PubMed

    Wang, Xiaodong; Ratnaweera, Harsha; Holm, Johan Abdullah; Olsbu, Vibeke

    2017-02-07

    The on-line monitoring of Chemical oxygen demand (COD) and total phosphorus (TP) restrains wastewater treatment plants to achieve better control of aeration and chemical dosing. In this study, we applied principal components analysis (PCA) to find out significant variables for COD and TP prediction. Multiple regression method applied the variables suggested by PCA to predict influent COD and TP. Moreover, a model of full-scale wastewater treatment plant with moving bed bioreactor (MBBR) and ballasted separation process was developed to simulate the performance of wastewater treatment. The predicted COD and TP data by multiple regression served as model input for dynamic simulation. Besides, the wastewater characteristic of the wastewater treatment plant and MBBR model parameters were given for model calibration. As a result, R(2) of predicted COD and TP versus measured data are 81.6% and 77.2%, respectively. The model output in terms of sludge production and effluent COD based on predicted input data fitted measured data well, which provides possibility to enabled model predictive control of aeration and coagulant dosing in practice. This study provide a feasible and economical approach to overcome monitoring and modelling restrictions that limits model predictive control of wastewater treatment plant.

  2. Extended evaluation on the ES-D3 cell differentiation assay combined with the BeWo transport model, to predict relative developmental toxicity of triazole compounds.

    PubMed

    Li, Hequn; Flick, Burkhard; Rietjens, Ivonne M C M; Louisse, Jochem; Schneider, Steffen; van Ravenzwaay, Bennard

    2016-05-01

    The mouse embryonic stem D3 (ES-D3) cell differentiation assay is based on the morphometric measurement of cardiomyocyte differentiation and is a promising tool to detect developmental toxicity of compounds. The BeWo transport model, consisting of BeWo b30 cells grown on transwell inserts and mimicking the placental barrier, is useful to determine relative placental transport velocities of compounds. We have previously demonstrated the usefulness of the ES-D3 cell differentiation assay in combination with the in vitro BeWo transport model to predict