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Sample records for pathway combinations predict

  1. Target Inhibition Networks: Predicting Selective Combinations of Druggable Targets to Block Cancer Survival Pathways

    PubMed Central

    Tang, Jing; Karhinen, Leena; Xu, Tao; Szwajda, Agnieszka; Yadav, Bhagwan; Wennerberg, Krister; Aittokallio, Tero

    2013-01-01

    A recent trend in drug development is to identify drug combinations or multi-target agents that effectively modify multiple nodes of disease-associated networks. Such polypharmacological effects may reduce the risk of emerging drug resistance by means of attacking the disease networks through synergistic and synthetic lethal interactions. However, due to the exponentially increasing number of potential drug and target combinations, systematic approaches are needed for prioritizing the most potent multi-target alternatives on a global network level. We took a functional systems pharmacology approach toward the identification of selective target combinations for specific cancer cells by combining large-scale screening data on drug treatment efficacies and drug-target binding affinities. Our model-based prediction approach, named TIMMA, takes advantage of the polypharmacological effects of drugs and infers combinatorial drug efficacies through system-level target inhibition networks. Case studies in MCF-7 and MDA-MB-231 breast cancer and BxPC-3 pancreatic cancer cells demonstrated how the target inhibition modeling allows systematic exploration of functional interactions between drugs and their targets to maximally inhibit multiple survival pathways in a given cancer type. The TIMMA prediction results were experimentally validated by means of systematic siRNA-mediated silencing of the selected targets and their pairwise combinations, showing increased ability to identify not only such druggable kinase targets that are essential for cancer survival either individually or in combination, but also synergistic interactions indicative of non-additive drug efficacies. These system-level analyses were enabled by a novel model construction method utilizing maximization and minimization rules, as well as a model selection algorithm based on sequential forward floating search. Compared with an existing computational solution, TIMMA showed both enhanced prediction accuracies in

  2. A novel model to combine clinical and pathway-based transcriptomic information for the prognosis prediction of breast cancer.

    PubMed

    Huang, Sijia; Yee, Cameron; Ching, Travers; Yu, Herbert; Garmire, Lana X

    2014-09-01

    Breast cancer is the most common malignancy in women worldwide. With the increasing awareness of heterogeneity in breast cancers, better prediction of breast cancer prognosis is much needed for more personalized treatment and disease management. Towards this goal, we have developed a novel computational model for breast cancer prognosis by combining the Pathway Deregulation Score (PDS) based pathifier algorithm, Cox regression and L1-LASSO penalization method. We trained the model on a set of 236 patients with gene expression data and clinical information, and validated the performance on three diversified testing data sets of 606 patients. To evaluate the performance of the model, we conducted survival analysis of the dichotomized groups, and compared the areas under the curve based on the binary classification. The resulting prognosis genomic model is composed of fifteen pathways (e.g., P53 pathway) that had previously reported cancer relevance, and it successfully differentiated relapse in the training set (log rank p-value = 6.25e-12) and three testing data sets (log rank p-value < 0.0005). Moreover, the pathway-based genomic models consistently performed better than gene-based models on all four data sets. We also find strong evidence that combining genomic information with clinical information improved the p-values of prognosis prediction by at least three orders of magnitude in comparison to using either genomic or clinical information alone. In summary, we propose a novel prognosis model that harnesses the pathway-based dysregulation as well as valuable clinical information. The selected pathways in our prognosis model are promising targets for therapeutic intervention. PMID:25233347

  3. Combining chemoinformatics with bioinformatics: in silico prediction of bacterial flavor-forming pathways by a chemical systems biology approach "reverse pathway engineering".

    PubMed

    Liu, Mengjin; Bienfait, Bruno; Sacher, Oliver; Gasteiger, Johann; Siezen, Roland J; Nauta, Arjen; Geurts, Jan M W

    2014-01-01

    The incompleteness of genome-scale metabolic models is a major bottleneck for systems biology approaches, which are based on large numbers of metabolites as identified and quantified by metabolomics. Many of the revealed secondary metabolites and/or their derivatives, such as flavor compounds, are non-essential in metabolism, and many of their synthesis pathways are unknown. In this study, we describe a novel approach, Reverse Pathway Engineering (RPE), which combines chemoinformatics and bioinformatics analyses, to predict the "missing links" between compounds of interest and their possible metabolic precursors by providing plausible chemical and/or enzymatic reactions. We demonstrate the added-value of the approach by using flavor-forming pathways in lactic acid bacteria (LAB) as an example. Established metabolic routes leading to the formation of flavor compounds from leucine were successfully replicated. Novel reactions involved in flavor formation, i.e. the conversion of alpha-hydroxy-isocaproate to 3-methylbutanoic acid and the synthesis of dimethyl sulfide, as well as the involved enzymes were successfully predicted. These new insights into the flavor-formation mechanisms in LAB can have a significant impact on improving the control of aroma formation in fermented food products. Since the input reaction databases and compounds are highly flexible, the RPE approach can be easily extended to a broad spectrum of applications, amongst others health/disease biomarker discovery as well as synthetic biology.

  4. Combining Chemoinformatics with Bioinformatics: In Silico Prediction of Bacterial Flavor-Forming Pathways by a Chemical Systems Biology Approach “Reverse Pathway Engineering”

    PubMed Central

    Liu, Mengjin; Bienfait, Bruno; Sacher, Oliver; Gasteiger, Johann; Siezen, Roland J.; Nauta, Arjen; Geurts, Jan M. W.

    2014-01-01

    The incompleteness of genome-scale metabolic models is a major bottleneck for systems biology approaches, which are based on large numbers of metabolites as identified and quantified by metabolomics. Many of the revealed secondary metabolites and/or their derivatives, such as flavor compounds, are non-essential in metabolism, and many of their synthesis pathways are unknown. In this study, we describe a novel approach, Reverse Pathway Engineering (RPE), which combines chemoinformatics and bioinformatics analyses, to predict the “missing links” between compounds of interest and their possible metabolic precursors by providing plausible chemical and/or enzymatic reactions. We demonstrate the added-value of the approach by using flavor-forming pathways in lactic acid bacteria (LAB) as an example. Established metabolic routes leading to the formation of flavor compounds from leucine were successfully replicated. Novel reactions involved in flavor formation, i.e. the conversion of alpha-hydroxy-isocaproate to 3-methylbutanoic acid and the synthesis of dimethyl sulfide, as well as the involved enzymes were successfully predicted. These new insights into the flavor-formation mechanisms in LAB can have a significant impact on improving the control of aroma formation in fermented food products. Since the input reaction databases and compounds are highly flexible, the RPE approach can be easily extended to a broad spectrum of applications, amongst others health/disease biomarker discovery as well as synthetic biology. PMID:24416282

  5. Early BMP, Wnt and Ca(2+)/PKC pathway activation predicts the bone forming capacity of periosteal cells in combination with calcium phosphates.

    PubMed

    Bolander, Johanna; Chai, Yoke Chin; Geris, Liesbet; Schrooten, Jan; Lambrechts, Dennis; Roberts, Scott J; Luyten, Frank P

    2016-04-01

    The development of osteoinductive calcium phosphate- (CaP) based biomaterials has, and continues to be, a major focus in the field of bone tissue engineering. However, limited insight into the spatiotemporal activation of signalling pathways has hampered the optimisation of in vivo bone formation and subsequent clinical translation. To gain further knowledge regarding the early molecular events governing bone tissue formation, we combined human periosteum derived progenitor cells with three types of clinically used CaP-scaffolds, to obtain constructs with a distinct range of bone forming capacity in vivo. Protein phosphorylation together with gene expression for key ligands and target genes were investigated 24 hours after cell seeding in vitro, and 3 and 12 days post ectopic implantation in nude mice. A computational modelling approach was used to deduce critical factors for bone formation 8 weeks post implantation. The combined Ca(2+)-mediated activation of BMP-, Wnt- and PKC signalling pathways 3 days post implantation were able to discriminate the bone forming from the non-bone forming constructs. Subsequently, a mathematical model able to predict in vivo bone formation with 96% accuracy was developed. This study illustrates the importance of defining and understanding CaP-activated signalling pathways that are required and sufficient for in vivo bone formation. Furthermore, we demonstrate the reliability of mathematical modelling as a tool to analyse and deduce key factors within an empirical data set and highlight its relevance to the translation of regenerative medicine strategies. PMID:26901484

  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. [Combining clinical pathway and patient education approaches].

    PubMed

    Bonnabel, Laurence; Huteau, Marie-Ève; Filhol, Nathalie; Clottes, Edwige; Massin, Julie; Quenet, François; Stoebner-Delbarre, Anne

    2016-01-01

    The integration of the therapeutic education of the patient into a clinical pathway approach helps to optimise nursing practice. Despite some limits, this method allows the position of the caregiver to evolve, going beyond the required methodological framework. It results in the emergence of several new educational facets which are essential for the patient and enable them to become a player in their own care. PMID:26743372

  8. Data-Driven Metabolic Pathway Compositions Enhance Cancer Survival Prediction

    PubMed Central

    Auslander, Noam; Wagner, Allon; Oberhardt, Matthew; Ruppin, Eytan

    2016-01-01

    Altered cellular metabolism is an important characteristic and driver of cancer. Surprisingly, however, we find here that aggregating individual gene expression using canonical metabolic pathways fails to enhance the classification of noncancerous vs. cancerous tissues and the prediction of cancer patient survival. This supports the notion that metabolic alterations in cancer rewire cellular metabolism through unconventional pathways. Here we present MCF (Metabolic classifier and feature generator), which incorporates gene expression measurements into a human metabolic network to infer new cancer-mediated pathway compositions that enhance cancer vs. adjacent noncancerous tissue classification across five different cancer types. MCF outperforms standard classifiers based on individual gene expression and on canonical human curated metabolic pathways. It successfully builds robust classifiers integrating different datasets of the same cancer type. Reassuringly, the MCF pathways identified lead to metabolites known to be associated with the pertaining specific cancer types. Aggregating gene expression through MCF pathways leads to markedly better predictions of breast cancer patients’ survival in an independent cohort than using the canonical human metabolic pathways (C-index = 0.69 vs. 0.52, respectively). Notably, the survival predictive power of individual MCF pathways strongly correlates with their power in predicting cancer vs. noncancerous samples. The more predictive composite pathways identified via MCF are hence more likely to capture key metabolic alterations occurring in cancer than the canonical pathways characterizing healthy human metabolism. PMID:27673682

  9. 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.

  10. Pathway-Based Genomics Prediction using Generalized Elastic Net

    PubMed Central

    Sokolov, Artem; Carlin, Daniel E.; Paull, Evan O.; Baertsch, Robert; Stuart, Joshua M.

    2016-01-01

    We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach. PMID:26960204

  11. Pathway-Based Genomics Prediction using Generalized Elastic Net.

    PubMed

    Sokolov, Artem; Carlin, Daniel E; Paull, Evan O; Baertsch, Robert; Stuart, Joshua M

    2016-03-01

    We present a novel regularization scheme called The Generalized Elastic Net (GELnet) that incorporates gene pathway information into feature selection. The proposed formulation is applicable to a wide variety of problems in which the interpretation of predictive features using known molecular interactions is desired. The method naturally steers solutions toward sets of mechanistically interlinked genes. Using experiments on synthetic data, we demonstrate that pathway-guided results maintain, and often improve, the accuracy of predictors even in cases where the full gene network is unknown. We apply the method to predict the drug response of breast cancer cell lines. GELnet is able to reveal genetic determinants of sensitivity and resistance for several compounds. In particular, for an EGFR/HER2 inhibitor, it finds a possible trans-differentiation resistance mechanism missed by the corresponding pathway agnostic approach.

  12. 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.

  13. 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...

  14. CINPER: an interactive web system for pathway prediction for prokaryotes.

    PubMed

    Mao, Xizeng; Chen, Xin; Zhang, Yu; Pangle, Spencer; Xu, Ying

    2012-01-01

    We present a web-based network-construction system, CINPER (CSBL INteractive Pathway BuildER), to assist a user to build a user-specified gene network for a prokaryotic organism in an intuitive manner. CINPER builds a network model based on different types of information provided by the user and stored in the system. CINPER's prediction process has four steps: (i) collection of template networks based on (partially) known pathways of related organism(s) from the SEED or BioCyc database and the published literature; (ii) construction of an initial network model based on the template networks using the P-Map program; (iii) expansion of the initial model, based on the association information derived from operons, protein-protein interactions, co-expression modules and phylogenetic profiles; and (iv) computational validation of the predicted models based on gene expression data. To facilitate easy applications, CINPER provides an interactive visualization environment for a user to enter, search and edit relevant data and for the system to display (partial) results and prompt for additional data. Evaluation of CINPER on 17 well-studied pathways in the MetaCyc database shows that the program achieves an average recall rate of 76% and an average precision rate of 90% on the initial models; and a higher average recall rate at 87% and an average precision rate at 28% on the final models. The reduced precision rate in the final models versus the initial models reflects the reality that the final models have large numbers of novel genes that have no experimental evidences and hence are not yet collected in the MetaCyc database. To demonstrate the usefulness of this server, we have predicted an iron homeostasis gene network of Synechocystis sp. PCC6803 using the server. The predicted models along with the server can be accessed at http://csbl.bmb.uga.edu/cinper/.

  15. 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.

  16. Earth Orientation Parameters Combination of Prediction Pilot Project

    NASA Astrophysics Data System (ADS)

    Shumate, N. A.; Luzum, B. J.; Kosek, W.

    2013-12-01

    The International Earth Rotation and Reference Systems Service (IERS) has been producing ensemble predictions of Earth Orientation Parameters (EOPs) on a daily basis as part of its Earth Orientation Parameters Combination of Prediction Pilot Project (EOPCPPP). By combining EOP predictions originating from a variety of different algorithms and initial conditions, the resulting ensemble predictions are expected to be more accurate and robust than any individual contribution. Since 2010, the Pilot Project has been collecting predictions of polar motion and UT1-UTC contributed by several organizations, and is currently combining seventeen different sets of EOP predictions on a daily basis with a simple arithmetic mean. This poster presents an analysis of the project comparing the EOPCPPP ensemble predictions and individual contributors' predictions as measured against the EOP 08 C04 series. Other informational diagnostics produced by the project to aid contributors and users will also be provided.

  17. Prediction of Drug Combinations by Integrating Molecular and Pharmacological Data

    PubMed Central

    Zhao, Xing-Ming; Iskar, Murat; Zeller, Georg; Kuhn, Michael; van Noort, Vera; Bork, Peer

    2011-01-01

    Combinatorial therapy is a promising strategy for combating complex disorders due to improved efficacy and reduced side effects. However, screening new drug combinations exhaustively is impractical considering all possible combinations between drugs. Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data. Specifically, drugs are represented by a set of their properties, such as their targets or indications. By integrating several of these features, we show that feature patterns enriched in approved drug combinations are not only predictive for new drug combinations but also provide insights into mechanisms underlying combinatorial therapy. Further analysis confirmed that among our top ranked predictions of effective combinations, 69% are supported by literature, while the others represent novel potential drug combinations. We believe that our proposed approach can help to limit the search space of drug combinations and provide a new way to effectively utilize existing drugs for new purposes. PMID:22219721

  18. 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.

  19. Combining clinical variables to optimize prediction of antidepressant treatment outcomes.

    PubMed

    Iniesta, Raquel; Malki, Karim; Maier, Wolfgang; Rietschel, Marcella; Mors, Ole; Hauser, Joanna; Henigsberg, Neven; Dernovsek, Mojca Zvezdana; Souery, Daniel; Stahl, Daniel; Dobson, Richard; Aitchison, Katherine J; Farmer, Anne; Lewis, Cathryn M; McGuffin, Peter; Uher, Rudolf

    2016-07-01

    The outcome of treatment with antidepressants varies markedly across people with the same diagnosis. A clinically significant prediction of outcomes could spare the frustration of trial and error approach and improve the outcomes of major depressive disorder through individualized treatment selection. It is likely that a combination of multiple predictors is needed to achieve such prediction. We used elastic net regularized regression to optimize prediction of symptom improvement and remission during treatment with escitalopram or nortriptyline and to identify contributing predictors from a range of demographic and clinical variables in 793 adults with major depressive disorder. A combination of demographic and clinical variables, with strong contributions from symptoms of depressed mood, reduced interest, decreased activity, indecisiveness, pessimism and anxiety significantly predicted treatment outcomes, explaining 5-10% of variance in symptom improvement with escitalopram. Similar combinations of variables predicted remission with area under the curve 0.72, explaining approximately 15% of variance (pseudo R(2)) in who achieves remission, with strong contributions from body mass index, appetite, interest-activity symptom dimension and anxious-somatizing depression subtype. Escitalopram-specific outcome prediction was more accurate than generic outcome prediction, and reached effect sizes that were near or above a previously established benchmark for clinical significance. Outcome prediction on the nortriptyline arm did not significantly differ from chance. These results suggest that easily obtained demographic and clinical variables can predict therapeutic response to escitalopram with clinically meaningful accuracy, suggesting a potential for individualized prescription of this antidepressant drug. PMID:27089522

  20. An efficient algorithm for de novo predictions of biochemical pathways between chemical compounds

    PubMed Central

    2012-01-01

    Background Prediction of biochemical (metabolic) pathways has a wide range of applications, including the optimization of drug candidates, and the elucidation of toxicity mechanisms. Recently, several methods have been developed for pathway prediction to derive a goal compound from a start compound. However, these methods require high computational costs, and cannot perform comprehensive prediction of novel metabolic pathways. Our aim of this study is to develop a de novo prediction method for reconstructions of metabolic pathways and predictions of unknown biosynthetic pathways in the sense that it does not require any initial network such as KEGG metabolic network to be explored. Results We formulated pathway prediction between a start compound and a goal compound as the shortest path search problem in terms of the number of enzyme reactions applied. We propose an efficient search method based on A* algorithm and heuristic techniques utilizing Linear Programming (LP) solution for estimation of the distance to the goal. First, a chemical compound is represented by a feature vector which counts frequencies of substructure occurrences in the structural formula. Second, an enzyme reaction is represented as an operator vector by detecting the structural changes to compounds before and after the reaction. By defining compound vectors as nodes and operator vectors as edges, prediction of the reaction pathway is reduced to the shortest path search problem in the vector space. In experiments on the DDT degradation pathway, we verify that the shortest paths predicted by our method are biologically correct pathways registered in the KEGG database. The results also demonstrate that the LP heuristics can achieve significant reduction in computation time. Furthermore, we apply our method to a secondary metabolite pathway of plant origin, and successfully find a novel biochemical pathway which cannot be predicted by the existing method. For the reconstruction of a known

  1. Rational combinations of immunotherapeutics that target discrete pathways

    PubMed Central

    2013-01-01

    An effective anti-tumor immune response requires the coordinated action of the innate and adaptive phases of the immune system. Critical processes include the activation of dendritic cells to present antigens, produce cytokines including type I interferons, and express multiple costimulatory ligands; induction of a productive T cell response within lymph nodes; migration of activated T cells to the tumor microenvironment in response to chemokines and homing receptor expression; and having effector T cells gain access to antigen-expressing tumor cells and maintain sufficient functionality to destroy them. However, tumors can become adept at escaping the immune response, developing multiple mechanisms to disrupt key processes. In general, tumors can be assigned into two different, major groups depending on whether the tumor there is an ‘inflamed’ or ‘non-inflamed’ tumor microenvironment. Improvements in our understanding of the interactions between the immune system and cancer have resulted in the development of various strategies to improve the immune-mediated control of tumors in both sub-groups. Categories of major immunotherapeutic intervention include methods to increase the frequency of tumor antigen-specific effector T cells in the circulation, strategies to block or uncouple a range of immune suppressive mechanisms within the tumor microenvironment, and tactics to induce de novo immune inflammation within the tumor microenvironment. The latter may be particularly important for eliciting immune recognition of non-inflamed tumor phenotypes. The premise put forth in this review is that synergistic therapeutic effects in vivo may be derived from combination therapies taken from distinct “bins” based on these mechanisms of action. Early data in both preclinical and some clinical studies provide support for this model. We also suggest that optimal application of these combinations may be aided by appropriate patient selection based on predictive

  2. Accuracy of genomic prediction when combining two related crossbred populations.

    PubMed

    Vallée, A; van Arendonk, J A M; Bovenhuis, H

    2014-10-01

    Charolais bulls are selected for their crossbreed performance when mated to Montbéliard or Holstein dams. To implement genomic prediction, one could build a reference population for each crossbred population independently. An alternative could be to combine both crossbred populations into a single reference population to increase size and accuracy of prediction. The objective of this study was to investigate the accuracy of genomic prediction by combining different crossbred populations. Three scenarios were considered: 1) using 1 crossbred population as reference to predict phenotype of animals from the same crossbred population, 2) combining the 2 crossbred populations into 1 reference to predict phenotype of animals from 1 crossbred population, and 3) using 1 crossbred population as reference to predict phenotype of animals from the other crossbred population. Traits studied were bone thinness, height, and muscular development. Phenotypes and 45,117 SNP genotypes were available for 1,764 Montbéliard × Charolais calves and 447 Holstein × Charolais calves. The population was randomly spilt into 10 subgroups, which were assigned to the validation one by one. To allow fair comparison between scenarios, size of the reference population was kept constant for all scenarios. Breeding values were estimated with BLUP and genomic BLUP. Accuracy of prediction was calculated as the correlation between the EBV and the phenotypic values of the calves in the validation divided by the square root of the heritability. Genomic BLUP showed higher accuracies (between 0.281 and 0.473) than BLUP (between 0.197 and 0.452). Accuracies tended to be highest when prediction was within 1 crossbred population, intermediate when populations were combined into the reference population, and lowest when prediction was across populations. Decrease in accuracy from a prediction within 1 population to a prediction across populations was more pronounced for bone thinness (-27%) and height (-29

  3. Signalling pathway for RKIP and Let-7 regulates and predicts metastatic breast cancer

    PubMed Central

    Yun, Jieun; Frankenberger, Casey A; Kuo, Wen-Liang; Boelens, Mirjam C; Eves, Eva M; Cheng, Nancy; Liang, Han; Li, Wen-Hsiung; Ishwaran, Hemant; Minn, Andy J; Rosner, Marsha Rich

    2011-01-01

    Tumour metastasis suppressors are inhibitors of metastasis but their mechanisms of action are generally not understood. We previously showed that the suppressor Raf kinase inhibitory protein (RKIP) inhibits breast tumour metastasis in part via let-7. Here, we demonstrate an integrated approach combining statistical analysis of breast tumour gene expression data and experimental validation to extend the signalling pathway for RKIP. We show that RKIP inhibits let-7 targets (HMGA2, BACH1) that in turn upregulate bone metastasis genes (MMP1, OPN, CXCR4). Our results reveal BACH1 as a novel let-7-regulated transcription factor that induces matrix metalloproteinase1 (MMP1) expression and promotes metastasis. An RKIP pathway metastasis signature (designated RPMS) derived from the complete signalling cascade predicts high metastatic risk better than the individual genes. These results highlight a powerful approach for identifying signalling pathways downstream of a key metastasis suppressor and indicate that analysis of genes in the context of their signalling environment is critical for understanding their predictive and therapeutic potential. PMID:21873975

  4. Combining physicochemical and evolutionary information for protein contact prediction.

    PubMed

    Schneider, Michael; Brock, Oliver

    2014-01-01

    We introduce a novel contact prediction method that achieves high prediction accuracy by combining evolutionary and physicochemical information about native contacts. We obtain evolutionary information from multiple-sequence alignments and physicochemical information from predicted ab initio protein structures. These structures represent low-energy states in an energy landscape and thus capture the physicochemical information encoded in the energy function. Such low-energy structures are likely to contain native contacts, even if their overall fold is not native. To differentiate native from non-native contacts in those structures, we develop a graph-based representation of the structural context of contacts. We then use this representation to train an support vector machine classifier to identify most likely native contacts in otherwise non-native structures. The resulting contact predictions are highly accurate. As a result of combining two sources of information--evolutionary and physicochemical--we maintain prediction accuracy even when only few sequence homologs are present. We show that the predicted contacts help to improve ab initio structure prediction. A web service is available at http://compbio.robotics.tu-berlin.de/epc-map/.

  5. Combining biophysical and bioinformatical approaches for predicting residue's contacts.

    NASA Astrophysics Data System (ADS)

    Alexov, Emil; Allardice, Amber; Kundrotas, Petras

    2006-03-01

    One of the most important task of the post genomics era is to utilize the enormous sequence information delivered from the genomes and to predict 3D structure of proteins. The quality of the predicted structure depends on many factors including the improvement made in ab initio, threading and homology modeling methods. Here we combine the method of correlated mutations with biophysical restrains in order to predict residue's contacts from amino acids sequence alone. The parameters of the protocol were optimized against a set of 21 proteins with known high resolution 3D structures. The effects of the degree of residue conservation, sequence similarity among the sequences within the multiple sequence alignment and conservation coefficient of two amino acids positions were studied. It was shown that the prediction accuracy of the method of correlated mutations alone is pure, on average only 10% of the contacts are predicted correctly. However, adding biophysical filters greatly improves the accuracy of the predictions. Thus, implying pairing rules for charged, polar and hydrophobic residues significantly reduces the total number of the predictions, e.g. reduces the coverage, however, most of the rejected predictions are false positives. As result, the relative rate of the correct predictions increases.

  6. 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…

  7. 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

    2014-11-01

    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.

  8. Combining differential expression, chromosomal and pathway analyses for the molecular characterization of renal cell carcinoma

    PubMed Central

    Furge, Kyle A; Dykema, Karl; Petillo, David; Westphal, Michael; Zhang, Zhongfa; Kort, Eric J; Teh, Bin Tean

    2007-01-01

    Using high-throughput gene-expression profiling technology, we can now gain a better understanding of the complex biology that is taking place in cancer cells. This complexity is largely dictated by the abnormal genetic makeup of the cancer cells. This abnormal genetic makeup can have profound effects on cellular activities such as cell growth, cell survival and other regulatory processes. Based on the pattern of gene expression, or molecular signatures of the tumours, we can distinguish or subclassify different types of cancers according to their cell of origin, behaviour, and the way they respond to therapeutic agents and radiation. These approaches will lead to better molecular subclassification of tumours, the basis of personalized medicine. We have, to date, done whole-genome microarray gene-expression profiling on several hundreds of kidney tumours. We adopt a combined bioinformatic approach, based on an integrative analysis of the gene-expression data. These data are used to identify both cytogenetic abnormalities and molecular pathways that are deregulated in renal cell carcinoma (RCC). For example, we have identified the deregulation of the VHL-hypoxia pathway in clear-cell RCC, as previously known, and the c-Myc pathway in aggressive papillary RCC. Besides the more common clear-cell, papillary and chromophobe RCCs, we are currently characterizing the molecular signatures of rarer forms of renal neoplasia such as carcinoma of the collecting ducts, mixed epithelial and stromal tumours, chromosome Xp11 translocations associated with papillary RCC, renal medullary carcinoma, mucinous tubular and spindle-cell carcinoma, and a group of unclassified tumours. Continued development and improvement in the field of molecular profiling will better characterize cancer and provide more accurate diagnosis, prognosis and prediction of drug response. PMID:18542781

  9. 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

  10. 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...

  11. Combining Microarray and Genomic Data to Predict DNA Binding Motifs

    SciTech Connect

    Mao, Linyong; Mackenzie, Ronald C.; Roh, J. H.; Eraso, Jesus M.; Kaplan, Samuel; Resat, Haluk

    2005-10-01

    The ability to detect regulatory elements within genome sequences is important in understanding how gene expression is controlled in biological systems. In this work, we combine microarray data analysis with genome sequence analysis to predict DNA sequences in the photosynthetic bacterium Rhodobacter sphaeroides that bind the regulators PrrA, PpsR and FnrL. These predictions were made by using hierarchical clustering to detect genes that share similar expression patterns. The DNA sequences upstream of these genes were then searched for possible transcription factor recognition motifs that may be involved in their co-regulation. The approach used promises to be widely applicable for the prediction of cis-acting DNA binding elements. Using this method we were independently able to detect and extend the previously described consensus sequences that have been suggested to bind FnrL and PpsR. In addition we have predicted sequences that may be recognized by the global regulator PrrA. Our results support the earlier suggestions that the DNA binding sequence of PrrA may have a variable sized gap between its conserved block elements. Using the predicted DNA binding sequences, we have performed a whole genome scale analysis to determine the relative importance of the interplay between these three regulators PpsR, FnrL and PrrA. Results of this analysis showed that, compared to the regulation by PpsR and FnrL, a much larger number of genes are candidates to be regulated by PrrA. Our study demonstrates by example that integration of multiple data types can be a powerful approach for inferring transcriptional regulatory patterns in microbial systems, and it allowed us to detect the photosynthesis related regulatory patterns in R. sphaeroides.

  12. 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.

  13. 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.

  14. Combining metabolic and protein engineering of a terpenoid biosynthetic pathway for overproduction and selectivity control

    PubMed Central

    Leonard, Effendi; Ajikumar, Parayil Kumaran; Thayer, Kelly; Xiao, Wen-Hai; Mo, Jeffrey D.; Tidor, Bruce; Stephanopoulos, Gregory; Prather, Kristala L. J.

    2010-01-01

    A common strategy of metabolic engineering is to increase the endogenous supply of precursor metabolites to improve pathway productivity. The ability to further enhance heterologous production of a desired compound may be limited by the inherent capacity of the imported pathway to accommodate high precursor supply. Here, we present engineered diterpenoid biosynthesis as a case where insufficient downstream pathway capacity limits high-level levopimaradiene production in Escherichia coli. To increase levopimaradiene synthesis, we amplified the flux toward isopentenyl diphosphate and dimethylallyl diphosphate precursors and reprogrammed the rate-limiting downstream pathway by generating combinatorial mutations in geranylgeranyl diphosphate synthase and levopimaradiene synthase. The mutant library contained pathway variants that not only increased diterpenoid production but also tuned the selectivity toward levopimaradiene. The most productive pathway, combining precursor flux amplification and mutant synthases, conferred approximately 2,600-fold increase in levopimaradiene levels. A maximum titer of approximately 700 mg/L was subsequently obtained by cultivation in a bench-scale bioreactor. The present study highlights the importance of engineering proteins along with pathways as a key strategy in achieving microbial biosynthesis and overproduction of pharmaceutical and chemical products. PMID:20643967

  15. Combination of Entner-Doudoroff Pathway with MEP Increases Isoprene Production in Engineered Escherichia coli

    PubMed Central

    Liu, Huaiwei; Sun, Yuanzhang; Ramos, Kristine Rose M.; Nisola, Grace M.; Valdehuesa, Kris Niño G.; Lee, Won–Keun; Park, Si Jae; Chung, Wook-Jin

    2013-01-01

    Embden-Meyerhof pathway (EMP) in tandem with 2-C-methyl-D-erythritol 4-phosphate pathway (MEP) is commonly used for isoprenoid biosynthesis in E. coli. However, this combination has limitations as EMP generates an imbalanced distribution of pyruvate and glyceraldehyde-3-phosphate (G3P). Herein, four glycolytic pathways—EMP, Entner-Doudoroff Pathway (EDP), Pentose Phosphate Pathway (PPP) and Dahms pathway were tested as MEP feeding modules for isoprene production. Results revealed the highest isoprene production from EDP containing modules, wherein pyruvate and G3P were generated simultaneously; isoprene titer and yield were more than three and six times higher than those of the EMP module, respectively. Additionally, the PPP module that generates G3P prior to pyruvate was significantly more effective than the Dahms pathway, in which pyruvate production precedes G3P. In terms of precursor generation and energy/reducing-equivalent supply, EDP+PPP was found to be the ideal feeding module for MEP. These findings may launch a new direction for the optimization of MEP-dependent isoprenoid biosynthesis pathways. PMID:24376679

  16. The Viability of Combining Academic and Career Pathways: A Study of Linked Learning

    ERIC Educational Resources Information Center

    Hubbard, Lea; McDonald, Mary

    2014-01-01

    In an attempt to reform high schools and prepare students with the knowledge and skills needed for the 21st century, educators and policymakers have turned to programs that combine career and academic pathways. One such program, Linked Learning, has taken up the reform challenge by relying on technical adjustments, rearranging students'…

  17. 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

  18. 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. PMID:27098033

  19. 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

  20. Meta-analysis of pathway enrichment: combining independent and dependent omics data sets.

    PubMed

    Kaever, Alexander; Landesfeind, Manuel; Feussner, Kirstin; Morgenstern, Burkhard; Feussner, Ivo; Meinicke, Peter

    2014-01-01

    A major challenge in current systems biology is the combination and integrative analysis of large data sets obtained from different high-throughput omics platforms, such as mass spectrometry based Metabolomics and Proteomics or DNA microarray or RNA-seq-based Transcriptomics. Especially in the case of non-targeted Metabolomics experiments, where it is often impossible to unambiguously map ion features from mass spectrometry analysis to metabolites, the integration of more reliable omics technologies is highly desirable. A popular method for the knowledge-based interpretation of single data sets is the (Gene) Set Enrichment Analysis. In order to combine the results from different analyses, we introduce a methodical framework for the meta-analysis of p-values obtained from Pathway Enrichment Analysis (Set Enrichment Analysis based on pathways) of multiple dependent or independent data sets from different omics platforms. For dependent data sets, e.g. obtained from the same biological samples, the framework utilizes a covariance estimation procedure based on the nonsignificant pathways in single data set enrichment analysis. The framework is evaluated and applied in the joint analysis of Metabolomics mass spectrometry and Transcriptomics DNA microarray data in the context of plant wounding. In extensive studies of simulated data set dependence, the introduced correlation could be fully reconstructed by means of the covariance estimation based on pathway enrichment. By restricting the range of p-values of pathways considered in the estimation, the overestimation of correlation, which is introduced by the significant pathways, could be reduced. When applying the proposed methods to the real data sets, the meta-analysis was shown not only to be a powerful tool to investigate the correlation between different data sets and summarize the results of multiple analyses but also to distinguish experiment-specific key pathways.

  1. Fault kinematics and retro-deformation analysis for prediction of potential leakage pathways - joint project PROTECT

    NASA Astrophysics Data System (ADS)

    Ziesch, Jennifer; Tanner, David C.; Dance, Tess; Beilecke, Thies; Krawczyk, Charlotte M.

    2014-05-01

    Within the context of long-term CO2 storage integrity, we determine the seismic and sub-seismic characteristics of potential fluid migration pathways between reservoir and surface. As a part of the PROTECT project we focus on the sub-seismic faults of the CO2CRC Otway Project pilot site in Australia. We carried out a detailed interpretation of 3D seismic data and have built a geological 3D model of 8 km x 7 km x 4.5 km (depth). The model comprises triangulated surfaces of 8 stratigraphic horizons and 24 large-scale faults with 75 m grid size. We have confirmed the site to comprise a complex system of south-dipping normal faults and north-dipping antithetic normal faults. Good knowledge of the kinematics of the large-scale faults is essential to predict sub-seismic structures. For this reason preconditioning analyses, such as thickness maps, fault curvature, cylindricity and connectivity studies, as well as Allan mapping were carried out. The most important aspect is that two different types of fault kinematics were simultaneously active: Dip-slip and a combination of dip-slip with dextral strike slip movement. Using these input parameters stratigraphic volumes are kinematically restored along the large-scale faults, taking fault topography into account (retro-deformation). The stratigraphic volumes are analyzed at the same time with respect to sub-seismic strain variation. Thereby we produce strain tensor maps to locate highly deformed or fractured zones and their orientation within the stratigraphic volumes. We will discuss the results in the framework of possible fluid/gas migration pathways and communication between storage reservoir and overburden. This will provide a tool to predict CO2 leakage and thus to adapt time-dependent monitoring strategies for subsurface storage in general. Acknowledgement: This work was sponsored in part by the Australian Commonwealth Government through the Cooperative Research Centre for Greenhouse Gas Technologies (CO2CRC). PROTECT

  2. 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

  3. An Algorithm Combining for Objective Prediction with Subjective Forecast Information

    NASA Astrophysics Data System (ADS)

    Choi, JunTae; Kim, SooHyun

    2016-04-01

    As direct or post-processed output from numerical weather prediction (NWP) models has begun to show acceptable performance compared with the predictions of human forecasters, many national weather centers have become interested in automatic forecasting systems based on NWP products alone, without intervention from human forecasters. The Korea Meteorological Administration (KMA) is now developing an automatic forecasting system for dry variables. The forecasts are automatically generated from NWP predictions using a post processing model (MOS). However, MOS cannot always produce acceptable predictions, and sometimes its predictions are rejected by human forecasters. In such cases, a human forecaster should manually modify the prediction consistently at points surrounding their corrections, using some kind of smart tool to incorporate the forecaster's opinion. This study introduces an algorithm to revise MOS predictions by adding a forecaster's subjective forecast information at neighbouring points. A statistical relation between two forecast points - a neighbouring point and a dependent point - was derived for the difference between a MOS prediction and that of a human forecaster. If the MOS prediction at a neighbouring point is updated by a human forecaster, the value at a dependent point is modified using a statistical relationship based on linear regression, with parameters obtained from a one-year dataset of MOS predictions and official forecast data issued by KMA. The best sets of neighbouring points and dependent point are statistically selected. According to verification, the RMSE of temperature predictions produced by the new algorithm was slightly lower than that of the original MOS predictions, and close to the RMSE of subjective forecasts. For wind speed and relative humidity, the new algorithm outperformed human forecasters.

  4. Prediction of Metabolic Pathway Involvement in Prokaryotic UniProtKB Data by Association Rule Mining

    PubMed Central

    Hoehndorf, Robert; Martin, Maria J.; Solovyev, Victor

    2016-01-01

    The widening gap between known proteins and their functions has encouraged the development of methods to automatically infer annotations. Automatic functional annotation of proteins is expected to meet the conflicting requirements of maximizing annotation coverage, while minimizing erroneous functional assignments. This trade-off imposes a great challenge in designing intelligent systems to tackle the problem of automatic protein annotation. In this work, we present a system that utilizes rule mining techniques to predict metabolic pathways in prokaryotes. The resulting knowledge represents predictive models that assign pathway involvement to UniProtKB entries. We carried out an evaluation study of our system performance using cross-validation technique. We found that it achieved very promising results in pathway identification with an F1-measure of 0.982 and an AUC of 0.987. Our prediction models were then successfully applied to 6.2 million UniProtKB/TrEMBL reference proteome entries of prokaryotes. As a result, 663,724 entries were covered, where 436,510 of them lacked any previous pathway annotations. PMID:27390860

  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

    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 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

  8. Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach

    PubMed Central

    Kotera, Masaaki; Tabei, Yasuo; Yamanishi, Yoshihiro; Muto, Ai; Moriya, Yuki; Tokimatsu, Toshiaki; Goto, Susumu

    2014-01-01

    Motivation: Metabolic pathway analysis is crucial not only in metabolic engineering but also in rational drug design. However, the biosynthetic/biodegradation pathways are known only for a small portion of metabolites, and a vast amount of pathways remain uncharacterized. Therefore, an important challenge in metabolomics is the de novo reconstruction of potential reaction networks on a metabolome-scale. Results: In this article, we develop a novel method to predict the multistep reaction sequences for de novo reconstruction of metabolic pathways in the reaction-filling framework. We propose a supervised approach to learn what we refer to as ‘multistep reaction sequence likeness’, i.e. whether a compound–compound pair is possibly converted to each other by a sequence of enzymatic reactions. In the algorithm, we propose a recursive procedure of using step-specific classifiers to predict the intermediate compounds in the multistep reaction sequences, based on chemical substructure fingerprints/descriptors of compounds. We further demonstrate the usefulness of our proposed method on the prediction of enzymatic reaction networks from a metabolome-scale compound set and discuss characteristic features of the extracted chemical substructure transformation patterns in multistep reaction sequences. Our comprehensively predicted reaction networks help to fill the metabolic gap and to infer new reaction sequences in metabolic pathways. Availability and implementation: Materials are available for free at http://web.kuicr.kyoto-u.ac.jp/supp/kot/ismb2014/ Contact: goto@kuicr.kyoto-u.ac.jp Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24931980

  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. Combination of degradation pathways for naphthalene utilization in Rhodococcus sp. strain TFB

    PubMed Central

    Tomás-Gallardo, Laura; Gómez-Álvarez, Helena; Santero, Eduardo; Floriano, Belén

    2014-01-01

    Rhodococcus sp. strain TFB is a metabolic versatile bacterium able to grow on naphthalene as the only carbon and energy source. Applying proteomic, genetic and biochemical approaches, we propose in this paper that, at least, three coordinated but independently regulated set of genes are combined to degrade naphthalene in TFB. First, proteins involved in tetralin degradation are also induced by naphthalene and may carry out its conversion to salicylaldehyde. This is the only part of the naphthalene degradation pathway showing glucose catabolite repression. Second, a salicylaldehyde dehydrogenase activity that converts salicylaldehyde to salicylate is detected in naphthalene-grown cells but not in tetralin-or salicylate-grown cells. Finally, we describe the chromosomally located nag genes, encoding the gentisate pathway for salicylate conversion into fumarate and pyruvate, which are only induced by salicylate and not by naphthalene. This work shows how biodegradation pathways in Rhodococcus sp. strain TFB could be assembled using elements from different pathways mainly because of the laxity of the regulatory systems and the broad specificity of the catabolic enzymes. PMID:24325207

  11. Combination of degradation pathways for naphthalene utilization in Rhodococcus sp. strain TFB.

    PubMed

    Tomás-Gallardo, Laura; Gómez-Álvarez, Helena; Santero, Eduardo; Floriano, Belén

    2014-03-01

    Rhodococcus sp. strain TFB is a metabolic versatile bacterium able to grow on naphthalene as the only carbon and energy source. Applying proteomic, genetic and biochemical approaches, we propose in this paper that, at least, three coordinated but independently regulated set of genes are combined to degrade naphthalene in TFB. First, proteins involved in tetralin degradation are also induced by naphthalene and may carry out its conversion to salicylaldehyde. This is the only part of the naphthalene degradation pathway showing glucose catabolite repression. Second, a salicylaldehyde dehydrogenase activity that converts salicylaldehyde to salicylate is detected in naphthalene-grown cells but not in tetralin- or salicylate-grown cells. Finally, we describe the chromosomally located nag genes, encoding the gentisate pathway for salicylate conversion into fumarate and pyruvate, which are only induced by salicylate and not by naphthalene. This work shows how biodegradation pathways in Rhodococcus sp. strain TFB could be assembled using elements from different pathways mainly because of the laxity of the regulatory systems and the broad specificity of the catabolic enzymes.

  12. 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.

  13. A pathway-based data integration framework for prediction of disease progression

    PubMed Central

    Seoane, José A.; Day, Ian N. M.; Gaunt, Tom R.; Campbell, Colin

    2014-01-01

    Motivation: Within medical research there is an increasing trend toward deriving multiple types of data from the same individual. The most effective prognostic prediction methods should use all available data, as this maximizes the amount of information used. In this article, we consider a variety of learning strategies to boost prediction performance based on the use of all available data. Implementation: We consider data integration via the use of multiple kernel learning supervised learning methods. We propose a scheme in which feature selection by statistical score is performed separately per data type and by pathway membership. We further consider the introduction of a confidence measure for the class assignment, both to remove some ambiguously labeled datapoints from the training data and to implement a cautious classifier that only makes predictions when the associated confidence is high. Results: We use the METABRIC dataset for breast cancer, with prediction of survival at 2000 days from diagnosis. Predictive accuracy is improved by using kernels that exclusively use those genes, as features, which are known members of particular pathways. We show that yet further improvements can be made by using a range of additional kernels based on clinical covariates such as Estrogen Receptor (ER) status. Using this range of measures to improve prediction performance, we show that the test accuracy on new instances is nearly 80%, though predictions are only made on 69.2% of the patient cohort. Availability: https://github.com/jseoane/FSMKL Contact: J.Seoane@bristol.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24162466

  14. 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.

  15. Predicting chemotherapeutic drug combinations through gene network profiling

    PubMed Central

    Nguyen, Thi Thuy Trang; Chua, Jacqueline Kia Kee; Seah, Kwi Shan; Koo, Seok Hwee; Yee, Jie Yin; Yang, Eugene Guorong; Lim, Kim Kiat; Pang, Shermaine Yu Wen; Yuen, Audrey; Zhang, Louxin; Ang, Wee Han; Dymock, Brian; Lee, Edmund Jon Deoon; Chen, Ee Sin

    2016-01-01

    Contemporary chemotherapeutic treatments incorporate the use of several agents in combination. However, selecting the most appropriate drugs for such therapy is not necessarily an easy or straightforward task. Here, we describe a targeted approach that can facilitate the reliable selection of chemotherapeutic drug combinations through the interrogation of drug-resistance gene networks. Our method employed single-cell eukaryote fission yeast (Schizosaccharomyces pombe) as a model of proliferating cells to delineate a drug resistance gene network using a synthetic lethality workflow. Using the results of a previous unbiased screen, we assessed the genetic overlap of doxorubicin with six other drugs harboring varied mechanisms of action. Using this fission yeast model, drug-specific ontological sub-classifications were identified through the computation of relative hypersensitivities. We found that human gastric adenocarcinoma cells can be sensitized to doxorubicin by concomitant treatment with cisplatin, an intra-DNA strand crosslinking agent, and suberoylanilide hydroxamic acid, a histone deacetylase inhibitor. Our findings point to the utility of fission yeast as a model and the differential targeting of a conserved gene interaction network when screening for successful chemotherapeutic drug combinations for human cells. PMID:26791325

  16. Informatic prediction of Cheddar cheese flavor pathway changes due to sodium substitution.

    PubMed

    Ganesan, Balasubramanian; Brown, Kelly

    2014-01-01

    Increased interest in reduced and low sodium dairy foods generates flavor issues for cheeses. Sodium is partly replaced with potassium or calcium to sustain the salty flavor perception, but the other cations may also alter metabolic routes and the resulting flavor development in aged cheeses. The effect of some cations on selected metabolic enzyme activity and on lactic acid bacterial physiology and enzymology has been documented. Potassium, for example, is an activator of 40 enzymes and inhibits 25 enzymes. Currently, we can visualize the effects of these cations only as lists inside metabolic databases such as MetaCyc. By visualizing the impact of these activating and inhibitory activities as biochemical pathways inside a metabolic database, we can understand their relevance, predict, and eventually dictate the aging process of cheeses with cations that replace sodium. As examples, we reconstructed new metabolic databases that illustrate the effect of potassium on flavor-related enzymes as microbial pathways. After metabolic reconstruction and analysis, we found that 153 pathways of lactic acid bacteria are affected due to enzymes likely to be activated or inactivated by potassium. These pathways are primarily linked to sugar metabolism, acid production, and amino acid biosynthesis and degradation that relate to Cheddar cheese flavor. PMID:24246043

  17. 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.

  18. Pan-cancer network analysis identifies combinations of rare somatic mutations across pathways and protein complexes.

    PubMed

    Leiserson, Mark D M; Vandin, Fabio; Wu, Hsin-Ta; Dobson, Jason R; Eldridge, Jonathan V; Thomas, Jacob L; Papoutsaki, Alexandra; Kim, Younhun; Niu, Beifang; McLellan, Michael; Lawrence, Michael S; Gonzalez-Perez, Abel; Tamborero, David; Cheng, Yuwei; Ryslik, Gregory A; Lopez-Bigas, Nuria; Getz, Gad; Ding, Li; Raphael, Benjamin J

    2015-02-01

    Cancers exhibit extensive mutational heterogeneity, and the resulting long-tail phenomenon complicates the discovery of genes and pathways that are significantly mutated in cancer. We perform a pan-cancer analysis of mutated networks in 3,281 samples from 12 cancer types from The Cancer Genome Atlas (TCGA) using HotNet2, a new algorithm to find mutated subnetworks that overcomes the limitations of existing single-gene, pathway and network approaches. We identify 16 significantly mutated subnetworks that comprise well-known cancer signaling pathways as well as subnetworks with less characterized roles in cancer, including cohesin, condensin and others. Many of these subnetworks exhibit co-occurring mutations across samples. These subnetworks contain dozens of genes with rare somatic mutations across multiple cancers; many of these genes have additional evidence supporting a role in cancer. By illuminating these rare combinations of mutations, pan-cancer network analyses provide a roadmap to investigate new diagnostic and therapeutic opportunities across cancer types.

  19. 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

  20. 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

  1. 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

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

    PubMed

    Chen, Xing; Ren, Biao; Chen, Ming; Wang, Quanxin; Zhang, Lixin; Yan, Guiying

    2016-07-01

    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

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

    PubMed

    Chen, Xing; Ren, Biao; Chen, Ming; Wang, Quanxin; Zhang, Lixin; Yan, Guiying

    2016-07-01

    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.

  4. 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

  5. Predictability and epidemic pathways in global outbreaks of infectious diseases: the SARS case study

    PubMed Central

    Colizza, Vittoria; Barrat, Alain; Barthélemy, Marc; Vespignani, Alessandro

    2007-01-01

    Background The global spread of the severe acute respiratory syndrome (SARS) epidemic has clearly shown the importance of considering the long-range transportation networks in the understanding of emerging diseases outbreaks. The introduction of extensive transportation data sets is therefore an important step in order to develop epidemic models endowed with realism. Methods We develop a general stochastic meta-population model that incorporates actual travel and census data among 3 100 urban areas in 220 countries. The model allows probabilistic predictions on the likelihood of country outbreaks and their magnitude. The level of predictability offered by the model can be quantitatively analyzed and related to the appearance of robust epidemic pathways that represent the most probable routes for the spread of the disease. Results In order to assess the predictive power of the model, the case study of the global spread of SARS is considered. The disease parameter values and initial conditions used in the model are evaluated from empirical data for Hong Kong. The outbreak likelihood for specific countries is evaluated along with the emerging epidemic pathways. Simulation results are in agreement with the empirical data of the SARS worldwide epidemic. Conclusion The presented computational approach shows that the integration of long-range mobility and demographic data provides epidemic models with a predictive power that can be consistently tested and theoretically motivated. This computational strategy can be therefore considered as a general tool in the analysis and forecast of the global spreading of emerging diseases and in the definition of containment policies aimed at reducing the effects of potentially catastrophic outbreaks. PMID:18031574

  6. Population activity in the human dorsal pathway predicts the accuracy of visual motion detection.

    PubMed

    Donner, Tobias H; Siegel, Markus; Oostenveld, Robert; Fries, Pascal; Bauer, Markus; Engel, Andreas K

    2007-07-01

    A person's ability to detect a weak visual target stimulus varies from one viewing to the next. We tested whether the trial-to-trial fluctuations of neural population activity in the human brain are related to the fluctuations of behavioral performance in a "yes-no" visual motion-detection task. We recorded neural population activity with whole head magnetoencephalography (MEG) while subjects searched for a weak coherent motion signal embedded in spatiotemporal noise. We found that, during motion viewing, MEG activity in the 12- to 24-Hz ("beta") frequency range is higher, on average, before correct behavioral choices than before errors and that it predicts correct choices on a trial-by-trial basis. This performance-predictive activity is not evident in the prestimulus baseline and builds up slowly after stimulus onset. Source reconstruction revealed that the performance-predictive activity is expressed in the posterior parietal and dorsolateral prefrontal cortices and, less strongly, in the visual motion-sensitive area MT+. The 12- to 24-Hz activity in these key stages of the human dorsal visual pathway is correlated with behavioral choice in both target-present and target-absent conditions. Importantly, in the absence of the target, 12- to 24-Hz activity tends to be higher before "no" choices ("correct rejects") than before "yes" choices ("false alarms"). It thus predicts the accuracy, and not the content, of subjects' upcoming perceptual reports. We conclude that beta band activity in the human dorsal visual pathway indexes, and potentially controls, the efficiency of neural computations underlying simple perceptual decisions.

  7. 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

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

    NASA Astrophysics Data System (ADS)

    Zarkevich, N. A.; Johnson, D. D.

    2016-01-01

    As titanium is a highly utilized metal for structural lightweighting, 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 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.

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

    DOE PAGESBeta

    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.

  10. 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

  11. De novo prediction of protein folding pathways and structure using the principle of sequential stabilization

    PubMed Central

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

    2012-01-01

    Motivated by the relationship between the folding mechanism and the native structure, we develop a unified approach for predicting folding pathways and tertiary structure using only the primary sequence as input. Simulations begin from a realistic unfolded state devoid of secondary structure and use a chain representation lacking explicit side chains, rendering the simulations many orders of magnitude faster than molecular dynamics simulations. The multiple round nature of the algorithm mimics the authentic folding process and tests the effectiveness of sequential stabilization (SS) as a search strategy wherein 2° structural elements add onto existing structures in a process of progressive learning and stabilization of structure found in prior rounds of folding. Because no a priori knowledge is used, we can identify kinetically significant non-native interactions and intermediates, sometimes generated by only two mutations, while the evolution of contact matrices is often consistent with experiments. Moreover, structure prediction improves substantially by incorporating information from prior rounds. The success of our simple, homology-free approach affirms the validity of our description of the primary determinants of folding pathways and structure, and the effectiveness of SS as a search strategy. PMID:23045636

  12. Research of trace metals as markers of entry pathways in combined sewers.

    PubMed

    Gounou, C; Varrault, G; Amedzro, K; Gasperi, J; Moilleron, R; Garnaud, S; Chebbo, G

    2011-01-01

    Combined sewers receive high toxic trace metal loads emitted by various sources, such as traffic, industry, urban heating and building materials. During heavy rain events, Combined Sewer Overflows (CSO) can occur and, if so, are discharged directly into the aquatic system and therefore could have an acute impact on receiving waters. In this study, the concentrations of 18 metals have been measured in 89 samples drawn from the three pollutant Entry Pathways in Combined Sewers (EPCS): i) roof runoff, ii) street runoff, and iii) industrial and domestic effluents and also drawn from sewer deposits (SD). The aim of this research is to identify metallic markers for each EPCS; the data matrix was submitted to principal component analysis in order to determine metallic markers for the three EPCS and SD. This study highlights the fact that metallic content variability across samples from different EPCS and SD exceeds the spatio-temporal variability of samples from the same EPCS. In the catchment studied here, the most valuable EPCS and SD markers are lead, sodium, boron, antimony and zinc; these markers could be used in future studies to identify the contributions of each EPCS to CSO metallic loads.

  13. 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

  14. Slow pathway modification for atrioventricular node re-entrant tachycardia: fast junctional tachycardia predicts adverse prognosis

    PubMed Central

    Lipscomb, K; Zaidi, A; Fitzpatrick, A; LEFROY, D

    2001-01-01

    D LEFROY Deputy Editor OBJECTIVE—To examine the cycle length of the junctional tachycardia often seen during successful slow pathway ablation for atrioventricular (AV) node re-entrant tachycardia, to determine whether shorter cycle lengths predict imminent atrioventricular block.
DESIGN—Retrospective analysis of consecutive patients undergoing slow pathway modification. Intracardiac recordings were analysed after digital storage to determine the development of junctional tachycardia, its duration and maximum, minimum, and mean cycle length, occurrence of heart block, persistent slow pathway conduction, or later confirmed recurrence of AV node re-entrant tachycardia.
SETTING—Regional cardiac centre.
PATIENTS—136 consecutive patients undergoing electrophysiological study found to have typical "slow-fast" AV node re-entrant tachycardia and subject to 137 slow pathway modification procedures.
RESULTS—During successful temperature feedback controlled radiofrequency energy application, junctional tachycardia developed in 133 of 137 procedures. During ablation, 10 patients had evidence of AV block (first degree in seven patients and third degree in three), and 17 others had retrograde junctional atrial (JA) block. In these 27 patients, the junctional tachycardia was rapid, with a minimum (SD) cycle length 291 (47) ms. Conduction recovered quickly in all but two patients, one of whom required permanent pacing. Junctional tachycardia with normal AV and JA conduction in the other 111 patients was of a significantly slower minimum cycle length (537 (123) ms; p < 0.0001).
CONCLUSIONS—Fast junctional tachycardia with cycle lengths under 350 ms seen during slow pathway modification is a predictor of conduction block, suggesting proximity to the compact node. Radiofrequency energy application should be terminated immediately to prevent development of AV block. An "auto cut off" facility for cycle lengths shorter than 350 ms could be built into

  15. Systematic Analysis of Quantitative Logic Model Ensembles Predicts Drug Combination Effects on Cell Signaling Networks

    PubMed Central

    Morris, MK; Clarke, DC; Osimiri, LC

    2016-01-01

    A major challenge in developing anticancer therapies is determining the efficacies of drugs and their combinations in physiologically relevant microenvironments. We describe here our application of “constrained fuzzy logic” (CFL) ensemble modeling of the intracellular signaling network for predicting inhibitor treatments that reduce the phospho‐levels of key transcription factors downstream of growth factors and inflammatory cytokines representative of hepatocellular carcinoma (HCC) microenvironments. We observed that the CFL models successfully predicted the effects of several kinase inhibitor combinations. Furthermore, the ensemble predictions revealed ambiguous predictions that could be traced to a specific structural feature of these models, which we resolved with dedicated experiments, finding that IL‐1α activates downstream signals through TAK1 and not MEKK1 in HepG2 cells. We conclude that CFL‐Q2LM (Querying Quantitative Logic Models) is a promising approach for predicting effective anticancer drug combinations in cancer‐relevant microenvironments. PMID:27567007

  16. Integrating Publicly Available Data to Generate Computationally Predicted Adverse Outcome Pathways for Fatty Liver.

    PubMed

    Bell, Shannon M; Angrish, Michelle M; Wood, Charles E; Edwards, Stephen W

    2016-04-01

    Newin vitrotesting strategies make it possible to design testing batteries for large numbers of environmental chemicals. Full utilization of the results requires knowledge of the underlying biological networks and the adverse outcome pathways (AOPs) that describe the route from early molecular perturbations to an adverse outcome. Curation of a formal AOP is a time-intensive process and a rate-limiting step to designing these test batteries. Here, we describe a method for integrating publicly available data in order to generate computationally predicted AOP (cpAOP) scaffolds, which can be leveraged by domain experts to shorten the time for formal AOP development. A network-based workflow was used to facilitate the integration of multiple data types to generate cpAOPs. Edges between graph entities were identified through direct experimental or literature information, or computationally inferred using frequent itemset mining. Data from the TG-GATEs and ToxCast programs were used to channel large-scale toxicogenomics information into a cpAOP network (cpAOPnet) of over 20 000 relationships describing connections between chemical treatments, phenotypes, and perturbed pathways as measured by differential gene expression and high-throughput screening targets. The resulting fatty liver cpAOPnet is available as a resource to the community. Subnetworks of cpAOPs for a reference chemical (carbon tetrachloride, CCl4) and outcome (fatty liver) were compared with published mechanistic descriptions. In both cases, the computational approaches approximated the manually curated AOPs. The cpAOPnet can be used for accelerating expert-curated AOP development and to identify pathway targets that lack genomic markers or high-throughput screening tests. It can also facilitate identification of key events for designing test batteries and for classification and grouping of chemicals for follow up testing.

  17. Microbial regulation of terrestrial nitrous oxide formation: understanding the biological pathways for prediction of emission rates.

    PubMed

    Hu, Hang-Wei; Chen, Deli; He, Ji-Zheng

    2015-09-01

    The continuous increase of the greenhouse gas nitrous oxide (N2O) in the atmosphere due to increasing anthropogenic nitrogen input in agriculture has become a global concern. In recent years, identification of the microbial assemblages responsible for soil N2O production has substantially advanced with the development of molecular technologies and the discoveries of novel functional guilds and new types of metabolism. However, few practical tools are available to effectively reduce in situ soil N2O flux. Combating the negative impacts of increasing N2O fluxes poses considerable challenges and will be ineffective without successfully incorporating microbially regulated N2O processes into ecosystem modeling and mitigation strategies. Here, we synthesize the latest knowledge of (i) the key microbial pathways regulating N2O production and consumption processes in terrestrial ecosystems and the critical environmental factors influencing their occurrence, and (ii) the relative contributions of major biological pathways to soil N2O emissions by analyzing available natural isotopic signatures of N2O and by using stable isotope enrichment and inhibition techniques. We argue that it is urgently necessary to incorporate microbial traits into biogeochemical ecosystem modeling in order to increase the estimation reliability of N2O emissions. We further propose a molecular methodology oriented framework from gene to ecosystem scales for more robust prediction and mitigation of future N2O emissions. PMID:25934121

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

    PubMed Central

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

    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. PMID:27100653

  19. 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

  20. 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. PMID:27100653

  1. Studies of the Raman spectra of cyclic and acyclic molecules: Combination and prediction spectrum methods

    NASA Astrophysics Data System (ADS)

    Kim, Taejin; Assary, Rajeev S.; Marshall, Christopher L.; Gosztola, David J.; Curtiss, Larry A.; Stair, Peter C.

    2012-04-01

    A combination of Raman spectroscopy and density functional methods was employed to investigate the spectral features of selected molecules: furfural, 5-hydroxymethyl furfural (HMF), methanol, acetone, acetic acid, and levulinic acid. The computed spectra and measured spectra are in excellent agreement, consistent with previous studies. Using the combination and prediction spectrum method (CPSM), we were able to predict the important spectral features of two platform chemicals, HMF and levulinic acid. The results have shown that CPSM is a useful alternative method for predicting vibrational spectra of complex molecules in the biomass transformation process.

  2. Studies of the Raman Spectra of Cyclic and Acyclic Molecules: Combination and Prediction Spectrum Methods

    SciTech Connect

    Kim, Taijin; Assary, Rajeev S.; Marshall, Christopher L.; Gosztola, David J.; Curtiss, Larry A.; Stair, Peter C.

    2012-04-02

    A combination of Raman spectroscopy and density functional methods was employed to investigate the spectral features of selected molecules: furfural, 5-hydroxymethyl furfural (HMF), methanol, acetone, acetic acid, and levulinic acid. The computed spectra and measured spectra are in excellent agreement, consistent with previous studies. Using the combination and prediction spectrum method (CPSM), we were able to predict the important spectral features of two platform chemicals, HMF and levulinic acid.The results have shown that CPSM is a useful alternative method for predicting vibrational spectra of complex molecules in the biomass transformation process.

  3. Concomitant inactivation of the p53- and pRB- functional pathways predicts resistance to DNA damaging drugs in breast cancer in vivo.

    PubMed

    Knappskog, Stian; Berge, Elisabet O; Chrisanthar, Ranjan; Geisler, Stephanie; Staalesen, Vidar; Leirvaag, Beryl; Yndestad, Synnøve; de Faveri, Elise; Karlsen, Bård O; Wedge, David C; Akslen, Lars A; Lilleng, Peer K; Løkkevik, Erik; Lundgren, Steinar; Østenstad, Bjørn; Risberg, Terje; Mjaaland, Ingvild; Aas, Turid; Lønning, Per E

    2015-10-01

    Chemoresistance is the main obstacle to cancer cure. Contrasting studies focusing on single gene mutations, we hypothesize chemoresistance to be due to inactivation of key pathways affecting cellular mechanisms such as apoptosis, senescence, or DNA repair. In support of this hypothesis, we have previously shown inactivation of either TP53 or its key activators CHK2 and ATM to predict resistance to DNA damaging drugs in breast cancer better than TP53 mutations alone. Further, we hypothesized that redundant pathway(s) may compensate for loss of p53-pathway signaling and that these are inactivated as well in resistant tumour cells. Here, we assessed genetic alterations of the retinoblastoma gene (RB1) and its key regulators: Cyclin D and E as well as their inhibitors p16 and p27. In an exploratory cohort of 69 patients selected from two prospective studies treated with either doxorubicin monotherapy or 5-FU and mitomycin for locally advanced breast cancers, we found defects in the pRB-pathway to be associated with therapy resistance (p-values ranging from 0.001 to 0.094, depending on the cut-off value applied to p27 expression levels). Although statistically weaker, we observed confirmatory associations in a validation cohort from another prospective study (n = 107 patients treated with neoadjuvant epirubicin monotherapy; p-values ranging from 7.0 × 10(-4) to 0.001 in the combined data sets). Importantly, inactivation of the p53-and the pRB-pathways in concert predicted resistance to therapy more strongly than each of the two pathways assessed individually (exploratory cohort: p-values ranging from 3.9 × 10(-6) to 7.5 × 10(-3) depending on cut-off values applied to ATM and p27 mRNA expression levels). Again, similar findings were confirmed in the validation cohort, with p-values ranging from 6.0 × 10(-7) to 6.5 × 10(-5) in the combined data sets. Our findings strongly indicate that concomitant inactivation of the p53- and pRB- pathways predict

  4. 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.

  5. 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

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

    PubMed

    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

  7. 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.

  8. Combined blockade of signalling pathways shows marked anti-tumour potential in phaeochromocytoma cell lines

    PubMed Central

    Nölting, Svenja; Garcia, Edwin; Alusi, Ghassan; Giubellino, Alessio; Pacak, Karel; Korbonits, Márta; Grossman, Ashley B

    2016-01-01

    Currently, there is no completely effective therapy available for metastatic phaeochromocytomas (PCCs) and paragangliomas. In this study, we explore new molecular targeted therapies for these tumours, using one more benign (mouse phaeochromocytoma cell (MPC)) and one more malignant (mouse tumour tissue (MTT)) mouse PCC cell line –both generated from heterozygous neurofibromin 1 knockout mice. Several PCC-promoting gene mutations have been associated with aberrant activation of PI3K/AKT, mTORC1 and RAS/RAF/ERK signalling. We therefore investigated different agents that interfere specifically with these pathways, including antagonism of the IGF1 receptor by NVP-AEW541. We found that NVP-AEW541 significantly reduced MPC and MTT cell viability at relatively high doses but led to a compensatory up-regulation of ERK and mTORC1 signalling at suboptimal doses while PI3K/AKT inhibition remained stable. We subsequently investigated the effect of the dual PI3K/mTORC1/2 inhibitor NVP-BEZ235, which led to a significant decrease of MPC and MTT cell viability at doses down to 50 nM but again increased ERK signalling. Accordingly, we next examined the combination of NVP-BEZ235 with the established agent lovastatin, as this has been described to inhibit ERK signalling. Lovastatin alone significantly reduced MPC and MTT cell viability at therapeutically relevant doses and inhibited both ERK and AKT signalling, but increased mTORC1/p70S6K signalling. Combination treatment with NVP-BEZ235 and lovastatin showed a significant additive effect in MPC and MTT cells and resulted in inhibition of both AKT and mTORC1/p70S6K signalling without ERK up-regulation. Simultaneous inhibition of PI3K/AKT, mTORC1/2 and ERK signalling suggests a novel therapeutic approach for malignant PCCs. PMID:22715163

  9. Combined blockade of signalling pathways shows marked anti-tumour potential in phaeochromocytoma cell lines.

    PubMed

    Nölting, Svenja; Garcia, Edwin; Alusi, Ghassan; Giubellino, Alessio; Pacak, Karel; Korbonits, Márta; Grossman, Ashley B

    2012-10-01

    Currently, there is no completely effective therapy available for metastatic phaeochromocytomas (PCCs) and paragangliomas. In this study, we explore new molecular targeted therapies for these tumours, using one more benign (mouse phaeochromocytoma cell (MPC)) and one more malignant (mouse tumour tissue (MTT)) mouse PCC cell line - both generated from heterozygous neurofibromin 1 knockout mice. Several PCC-promoting gene mutations have been associated with aberrant activation of PI3K/AKT, mTORC1 and RAS/RAF/ERK signalling. We therefore investigated different agents that interfere specifically with these pathways, including antagonism of the IGF1 receptor by NVP-AEW541. We found that NVP-AEW541 significantly reduced MPC and MTT cell viability at relatively high doses but led to a compensatory up-regulation of ERK and mTORC1 signalling at suboptimal doses while PI3K/AKT inhibition remained stable. We subsequently investigated the effect of the dual PI3K/mTORC1/2 inhibitor NVP-BEZ235, which led to a significant decrease of MPC and MTT cell viability at doses below 50 nM but again increased ERK signalling. Accordingly, we next examined the combination of NVP-BEZ235 with the established agent lovastatin, as this has been described to inhibit ERK signalling. Lovastatin alone significantly reduced MPC and MTT cell viability at therapeutically relevant doses and inhibited both ERK and AKT signalling, but increased mTORC1/p70S6K signalling. Combination treatment with NVP-BEZ235 and lovastatin showed a significant additive effect in MPC and MTT cells and resulted in inhibition of both AKT and mTORC1/p70S6K signalling without ERK up-regulation. Simultaneous inhibition of PI3K/AKT, mTORC1/2 and ERK signalling suggests a novel therapeutic approach for malignant PCCs.

  10. Combining Radon and heat as tracers to characterise surface water and groundwater exchange pathways

    NASA Astrophysics Data System (ADS)

    Rau, Gabriel C.; Frecker, Jonathan; Andersen, Martin S.; Unland, Nicolaas P.; Hofmann, Harald; Gilfedder, Ben S.; Atkinson, Alex; Cuthberrt, Mark O.; McCallum, Andrew M.; Roshan, Hamid; Cartwright, Ian; Hollins, Suzanne; Acworth, Ian

    2014-05-01

    Heat and Radon (222-Rn) have both been used separately as natural tracers to quantify vertical streambed fluxes and to calculate water residence times in shallow alluvial systems. Both tracers have different advantages and limitations: Heat transport is measured through temperature changes at discrete spatial points in the streambed, and methods for the calculation of vertical flux time-series exist. By contrast, grab sampled Radon activities represent integration along a flow path but the discrete sampling means that only a snapshot in time can be obtained. A pumping test was conducted at Maules Creek (Australia) in order to artificially stress the stream-aquifer system. Water was continuously pumped from an extraction well located 40 m from the creek for 8 days. A flood event occurred during the pumping test adding another level of complexity to the system. The stream-aquifer response was monitored with a transect of 25 observation bores, of which 15 were regularly sampled for Radon activities. Additionally, a total of 4 temperature arrays, consisting of 4 temperature loggers each, were installed in the streambed to measure the sediment temperature over time. Vertical streambed fluxes were calculated using the temperature data. A joint interpretation of heat and Radon results reveals subsurface heterogeneity and distinct exchange pathways. This study shows the advantage of combining at least two different tracers in order to characterise a connected system.

  11. Combined Scintigraphy and Tumor Marker Analysis Predicts Unfavorable Histopathology of Neuroblastic Tumors with High Accuracy

    PubMed Central

    Fendler, Wolfgang Peter; Wenter, Vera; Thornton, Henriette Ingrid; Ilhan, Harun; von Schweinitz, Dietrich; Coppenrath, Eva; Schmid, Irene; Bartenstein, Peter; Pfluger, Thomas

    2015-01-01

    Objectives Our aim was to improve the prediction of unfavorable histopathology (UH) in neuroblastic tumors through combined imaging and biochemical parameters. Methods 123I-MIBG SPECT and MRI was performed before surgical resection or biopsy in 47 consecutive pediatric patients with neuroblastic tumor. Semi-quantitative tumor-to-liver count-rate ratio (TLCRR), MRI tumor size and margins, urine catecholamine and NSE blood levels of neuron specific enolase (NSE) were recorded. Accuracy of single and combined variables for prediction of UH was tested by ROC analysis with Bonferroni correction. Results 34 of 47 patients had UH based on the International Neuroblastoma Pathology Classification (INPC). TLCRR and serum NSE both predicted UH with moderate accuracy. Optimal cut-off for TLCRR was 2.0, resulting in 68% sensitivity and 100% specificity (AUC-ROC 0.86, p < 0.001). Optimal cut-off for NSE was 25.8 ng/ml, resulting in 74% sensitivity and 85% specificity (AUC-ROC 0.81, p = 0.001). Combination of TLCRR/NSE criteria reduced false negative findings from 11/9 to only five, with improved sensitivity and specificity of 85% (AUC-ROC 0.85, p < 0.001). Conclusion Strong 123I-MIBG uptake and high serum level of NSE were each predictive of UH. Combined analysis of both parameters improved the prediction of UH in patients with neuroblastic tumor. MRI parameters and urine catecholamine levels did not predict UH. PMID:26177109

  12. 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.

  13. 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

  14. Seismic and sub-seismic deformation prediction for the assessment of possible pathways - the joint project PROTECT

    NASA Astrophysics Data System (ADS)

    Krawczyk, C. M.; Tanner, D. C.

    2014-12-01

    In the joint project PROTECT (PRediction Of deformation To Ensure Carbon Traps), we determine the specific potential of communicating systems that occur between reservoir and surface in the framework of CO2 underground storage. The development of a new seismo-mechanical workflow permits an estimation of the long-term storage integrity. The study target in the Otway Basin in south-western Victoria is Australia´s first demonstration of the deep geological storage of CO2, operated by the CO2CRC. Our objective is to predict and quantify the distribution and the amount of sub-/seismic strain caused by fault movement in the proximity of the reservoir. For this purpose, we applied three independent approaches to fill the sub-seismic space, and validated them. Firstly, we built a geometrical kinematic 3-D depth model based on 2-D and 3-D seismic data that are provided by the CO2CRC Consortium. This interpretation was stabilized by additional seismic attribute processing that images small-scale lineaments with high resolution by multi-attribute displays that combine curvature and coherency. Retro-deformation, i.e. kinematically restoring the strata in 3-D, was performed on the reservoir seal. The highest strain magnitudes are around 4-5%. They are not observed where the fault displacements are highest, but where the fault morphologies were the most complex, i.e., there are rapid changes in displacement along the fault plane. Benchmarking this approach by numerical forward modelling yields further constraints on stress variation. In areas where we had preliminary predicted critical deformation we carried out new reflection seismic measurements to calibrate our predictions. This not only yields high-resolution structural images, but, in addition, also allows to determine petrophysical parameters by the acquisition of shear-wave reflection seismic data. With this seismo-mechanical workflow we obtain a better overview of possible fluid migration pathways and communication

  15. Pathway: a dynamic food-chain model to predict radionuclide ingestion after fallout deposition.

    PubMed

    Whicker, F W; Kirchner, T B

    1987-06-01

    This manuscript describes the structure and basis for parameter values of a computerized food-chain transport model for radionuclides. The model, called "PATHWAY," estimates the time-integrated ingestion intake by humans of 20 radionuclides after a single deposition from the atmosphere to the landscape. The model solves a set of linear, coupled differential equations to estimate the inventories and concentrations of radionuclides in soil, vegetation, animal tissues and animal products as a function of time following deposition. Dynamic processes in the model include foliar interception, weathering and absorption; plant growth, uptake, harvest and senescence; soil resuspension, percolation, leaching and tillage; radioactive decay; and livestock ingestion, absorption and excretion. Human dietary data are included to permit calculation of time-dependent radionuclide ingestion rates, which are then numerically integrated. The model considers seasonal changes in the biomass of vegetation and animal diets, as well as specific plowing and crop-harvest dates; thus the integrated radionuclide intakes by humans are dependent on the seasonal timing of deposition. The agricultural data base represents the arid and semi-arid regions of the western United States. The foliar deposition parameters apply to regional fallout out to a few hundred miles from nuclear detonations at the Nevada Test Site. With modification, the model could be applied to chronic or other acute releases, providing the ground deposition in Bq m-2 could be estimated. The output of PATHWAY (Bq ingested per Bq m-2 deposited) may be multiplied by the deposition and a dose conversion factor (Gy Bq-1) to yield an organ-specific dose estimate. The model may be run deterministically to yield single estimates or stochastically ("Monte-Carlo" mode) to provide distributional output that reflects uncertainty in the output due to uncertainty in parameters. Tests of the predictive accuracy are briefly described and work

  16. 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

    2015-02-01

    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.

  17. 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

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

    PubMed

    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.

  19. Combined computational metabolite prediction and automated structure-based analysis of mass spectrometric data.

    PubMed

    Stranz, David D; Miao, Shichang; Campbell, Scott; Maydwell, George; Ekins, Sean

    2008-01-01

    ABSTRACT As high-throughput technologies have developed in the pharmaceutical industry, the demand for identification of possible metabolites using predominantly liquid chromatographic/mass spectrometry-mass spectrometry/mass spectrometry (LC/MS-MS/MS) for a large number of molecules in drug discovery has also increased. In parallel, computational technologies have also been developed to generate predictions for metabolites alongside methods to predict MS spectra and score the quality of the match with experimental spectra. The goal of the current study was to generate metabolite predictions from molecular structure with a software product, MetaDrug. In vitro microsomal incubations were used to ultimately produce MS data that could be used to verify the predictions with Apex, which is a new software tool that can predict the molecular ion spectrum and a fragmentation spectrum, automating the detailed examination of both MS and MS/MS spectra. For the test molecule imipramine used to illustrate the combined in vitro/in silico process proposed, MetaDrug predicts 16 metabolites. Following rat microsomal incubations with imipramine and analysis of the MS(n) data using the Apex software, strong evidence was found for imipramine and five metabolites and weaker evidence for five additional metabolites. This study suggests a new approach to streamline MS data analysis using a combination of predictive computational approaches with software capable of comparing the predicted metabolite output with empirical data when looking at drug metabolites.

  20. Combining Models is More Likely to Give Better Predictions than Single Models.

    PubMed

    Hu, Xiaoping; Madden, Laurence V; Edwards, Simon; Xu, Xiangming

    2015-09-01

    In agricultural research, it is often difficult to construct a single "best" predictive model based on data collected under field conditions. We studied the relative prediction performance of combining empirical linear models over the single best model in relation to number of models to be combined, number of variates in the models, magnitude of residual errors, and weighting schemes. Two scenarios were simulated: the modeler did or did not know the relative of performance of the models to be combined. For the former case, model averaging is achieved either through weights based on the Akaike Information Criterion (AIC) statistic or with arithmetic averaging; for the latter case, only the arithmetic averaging is possible (because the relative model predictive performance is not known for a common dataset). In addition to two experimental datasets on oat mycotoxins in relation to environmental variables, two datasets were generated assuming a consistent correlation structure among explanatory variates with two magnitudes of residual errors. For the majority of cases, model averaging resulted in improved prediction performance over the single-model predictions, especially when a modeler does not have the information of relative model performance. The fewer variates in the models to be combined, the greater is improvement of model averaging over the single-model predictions. Combining models led to very little improvement over individual models when there were many variates in individual models. Overall, simple arithmetic averaging resulted in slightly better performance than the AIC-based weighted averaging. The advantage in model averaging is also noticeable for larger residual errors. This study suggests that model averaging generally performs better than single-model predictions, especially when a modeler does not have information on the relative performance of the candidate models.

  1. Identification of novel components of NAD-utilizing metabolic pathways and prediction of their biochemical functions.

    PubMed

    de Souza, Robson Francisco; Aravind, L

    2012-06-01

    Nicotinamide adenine dinucleotide (NAD) is a ubiquitous cofactor participating in numerous redox reactions. It is also a substrate for regulatory modifications of proteins and nucleic acids via the addition of ADP-ribose moieties or removal of acyl groups by transfer to ADP-ribose. In this study, we use in-depth sequence, structure and genomic context analysis to uncover new enzymes and substrate-binding proteins in NAD-utilizing metabolic and macromolecular modification systems. We predict that Escherichia coli YbiA and related families of domains from diverse bacteria, eukaryotes, large DNA viruses and single strand RNA viruses are previously unrecognized components of NAD-utilizing pathways that probably operate on ADP-ribose derivatives. Using contextual analysis we show that some of these proteins potentially act in RNA repair, where NAD is used to remove 2'-3' cyclic phosphodiester linkages. Likewise, we predict that another family of YbiA-related enzymes is likely to comprise a novel NAD-dependent ADP-ribosylation system for proteins, in conjunction with a previously unrecognized ADP-ribosyltransferase. A similar ADP-ribosyltransferase is also coupled with MACRO or ADP-ribosylglycohydrolase domain proteins in other related systems, suggesting that all these novel systems are likely to comprise pairs of ADP-ribosylation and ribosylglycohydrolase enzymes analogous to the DraG-DraT system, and a novel group of bacterial polymorphic toxins. We present evidence that some of these coupled ADP-ribosyltransferases/ribosylglycohydrolases are likely to regulate certain restriction modification enzymes in bacteria. The ADP-ribosyltransferases found in these, the bacterial polymorphic toxin and host-directed toxin systems of bacteria such as Waddlia also throw light on the evolution of this fold and the origin of eukaryotic polyADP-ribosyltransferases and NEURL4-like ARTs, which might be involved in centrosomal assembly. We also infer a novel biosynthetic pathway that

  2. 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. PMID:25147789

  3. Dynamic Inlet Distortion Prediction with a Combined Computational Fluid Dynamics and Distortion Synthesis Approach

    NASA Technical Reports Server (NTRS)

    Norby, W. P.; Ladd, J. A.; Yuhas, A. J.

    1996-01-01

    A procedure has been developed for predicting peak dynamic inlet distortion. This procedure combines Computational Fluid Dynamics (CFD) and distortion synthesis analysis to obtain a prediction of peak dynamic distortion intensity and the associated instantaneous total pressure pattern. A prediction of the steady state total pressure pattern at the Aerodynamic Interface Plane is first obtained using an appropriate CFD flow solver. A corresponding inlet turbulence pattern is obtained from the CFD solution via a correlation linking root mean square (RMS) inlet turbulence to a formulation of several CFD parameters representative of flow turbulence intensity. This correlation was derived using flight data obtained from the NASA High Alpha Research Vehicle flight test program and several CFD solutions at conditions matching the flight test data. A distortion synthesis analysis is then performed on the predicted steady state total pressure and RMS turbulence patterns to yield a predicted value of dynamic distortion intensity and the associated instantaneous total pressure pattern.

  4. 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.

  5. Towards Direct Synthesis of Alane: A Predicted Defect-Mediated Pathway Confirmed Experimentally.

    PubMed

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

    2016-09-01

    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 10(4)  bar expected from bulk thermodynamics.

  6. Towards Direct Synthesis of Alane: A Predicted Defect-Mediated Pathway Confirmed Experimentally.

    PubMed

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

    2016-09-01

    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 10(4)  bar expected from bulk thermodynamics. PMID:27535100

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

    DOE PAGESBeta

    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 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

  8. 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...

  9. 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.

  10. 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

  11. KeyPathwayMiner 4.0: condition-specific pathway analysis by combining multiple omics studies and networks with Cytoscape

    PubMed Central

    2014-01-01

    Background Over the last decade network enrichment analysis has become popular in computational systems biology to elucidate aberrant network modules. Traditionally, these approaches focus on combining gene expression data with protein-protein interaction (PPI) networks. Nowadays, the so-called omics technologies allow for inclusion of many more data sets, e.g. protein phosphorylation or epigenetic modifications. This creates a need for analysis methods that can combine these various sources of data to obtain a systems-level view on aberrant biological networks. Results We present a new release of KeyPathwayMiner (version 4.0) that is not limited to analyses of single omics data sets, e.g. gene expression, but is able to directly combine several different omics data types. Version 4.0 can further integrate existing knowledge by adding a search bias towards sub-networks that contain (avoid) genes provided in a positive (negative) list. Finally the new release now also provides a set of novel visualization features and has been implemented as an app for the standard bioinformatics network analysis tool: Cytoscape. Conclusion With KeyPathwayMiner 4.0, we publish a Cytoscape app for multi-omics based sub-network extraction. It is available in Cytoscape’s app store http://apps.cytoscape.org/apps/keypathwayminer or via http://keypathwayminer.mpi-inf.mpg.de. PMID:25134827

  12. In Silico Reconstruction of the Metabolic Pathways of Lactobacillus plantarum: Comparing Predictions of Nutrient Requirements with Those from Growth Experiments

    PubMed Central

    Teusink, Bas; van Enckevort, Frank H. J.; Francke, Christof; Wiersma, Anne; Wegkamp, Arno; Smid, Eddy J.; Siezen, Roland J.

    2005-01-01

    On the basis of the annotated genome we reconstructed the metabolic pathways of the lactic acid bacterium Lactobacillus plantarum WCFS1. After automatic reconstruction by the Pathologic tool of Pathway Tools (http://bioinformatics.ai.sri.com/ptools/), the resulting pathway-genome database, LacplantCyc, was manually curated extensively. The current database contains refinements to existing routes and new gram-positive bacterium-specific reactions that were not present in the MetaCyc database. These reactions include, for example, reactions related to cell wall biosynthesis, molybdopterin biosynthesis, and transport. At present, LacplantCyc includes 129 pathways and 704 predicted reactions involving some 670 chemical species and 710 enzymes. We tested vitamin and amino acid requirements of L. plantarum experimentally and compared the results with the pathways present in LacplantCyc. In the majority of cases (32 of 37 cases) the experimental results agreed with the final reconstruction. LacplantCyc is the most extensively curated pathway-genome database for gram-positive bacteria and is open to the microbiology community via the World Wide Web (www.lacplantcyc.nl). It can be used as a reference pathway-genome database for gram-positive microbes in general and lactic acid bacteria in particular. PMID:16269766

  13. Combined dynamic and static optical tomography for prediction of treatment outcome in breast cancer patients

    NASA Astrophysics Data System (ADS)

    Gunther, Jacqueline; Lim, Emerson; Kim, Hyun Keol; Flexman, Molly; Zweck, Lukas; Arora, Sindhiya; Refice, Susan; Brown, Mindy; Kalinsky, Kevin; Hershman, Dawn; Hielscher, Andreas H.

    2015-07-01

    We explored evidence that a combination of dynamic and static diffuse optical tomography can be used to predict treatment response in patients undergoing neo adjuvant chemotherapy. Both blood chromophore concentrations and hemodynamic signatures were measured over the 5-month course of treatment.

  14. Combining Expressed Vocational Choice and Measures of Career Development to Predict Future Occupational Field.

    ERIC Educational Resources Information Center

    Noeth, Richard J.

    A study was designed to test predictability of actual occupation from expressed vocational choice when combined separately with measures of career development. Subjects were 1,994 members of a national study of high school career development who were working more than half-time three years later (1976). Expressed vocational choice and measures of…

  15. 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),…

  16. Disjoint combinations profiling (DCP): a new method for the prediction of antibody CDR conformation from sequence.

    PubMed

    Nikoloudis, Dimitris; Pitts, Jim E; Saldanha, José W

    2014-01-01

    The accurate prediction of the conformation of Complementarity-Determining Regions (CDRs) is important in modelling antibodies for protein engineering applications. Specifically, the Canonical paradigm has proved successful in predicting the CDR conformation in antibody variable regions. It relies on canonical templates which detail allowed residues at key positions in the variable region framework or in the CDR itself for 5 of the 6 CDRs. While no templates have as yet been defined for the hypervariable CDR-H3, instead, reliable sequence rules have been devised for predicting the base of the CDR-H3 loop. Here a new method termed Disjoint Combinations Profiling (DCP) is presented, which contributes a considerable advance in the prediction of CDR conformations. This novel method is explained and compared with canonical templates and sequence rules in a 3-way blind prediction. DCP achieved 93% accuracy over 951 blind predictions and showed an improvement in cumulative accuracy compared to predictions with canonical templates or sequence rules. In addition to its overall improvement in prediction accuracy, it is suggested that DCP is open to better implementations in the future and that it can improve as more antibody structures are deposited in the databank. In contrast, it is argued that canonical templates and sequence rules may have reached their peak.

  17. Combining Evolutionary Information and an Iterative Sampling Strategy for Accurate Protein Structure Prediction.

    PubMed

    Braun, Tatjana; Koehler Leman, Julia; Lange, Oliver F

    2015-12-01

    Recent work has shown that the accuracy of ab initio structure prediction can be significantly improved by integrating evolutionary information in form of intra-protein residue-residue contacts. Following this seminal result, much effort is put into the improvement of contact predictions. However, there is also a substantial need to develop structure prediction protocols tailored to the type of restraints gained by contact predictions. Here, we present a structure prediction protocol that combines evolutionary information with the resolution-adapted structural recombination approach of Rosetta, called RASREC. Compared to the classic Rosetta ab initio protocol, RASREC achieves improved sampling, better convergence and higher robustness against incorrect distance restraints, making it the ideal sampling strategy for the stated problem. To demonstrate the accuracy of our protocol, we tested the approach on a diverse set of 28 globular proteins. Our method is able to converge for 26 out of the 28 targets and improves the average TM-score of the entire benchmark set from 0.55 to 0.72 when compared to the top ranked models obtained by the EVFold web server using identical contact predictions. Using a smaller benchmark, we furthermore show that the prediction accuracy of our method is only slightly reduced when the contact prediction accuracy is comparatively low. This observation is of special interest for protein sequences that only have a limited number of homologs.

  18. The NFL combine: does it predict performance in the National Football League?

    PubMed

    Kuzmits, Frank E; Adams, Arthur J

    2008-11-01

    The authors investigate the correlation between National Football League (NFL) combine test results and NFL success for players drafted at three different offensive positions (quarterback, running back, and wide receiver) during a recent 6-year period, 1999-2004. The combine consists of series of drills, exercises, interviews, aptitude tests, and physical exams designed to assess the skills of promising college football players and to predict their performance in the NFL. Combine measures examined in this study include 10-, 20-, and 40-yard dashes, bench press, vertical jump, broad jump, 20- and 60-yard shuttles, three-cone drill, and the Wonderlic Personnel Test. Performance criteria include 10 variables: draft order; 3 years each of salary received and games played; and position-specific data. Using correlation analysis, we find no consistent statistical relationship between combine tests and professional football performance, with the notable exception of sprint tests for running backs. We put forth possible explanations for the general lack of statistical relations detected, and, consequently, we question the overall usefulness of the combine. We also offer suggestions for improving the prediction of success in the NFL, primarily the use of more rigorous psychological tests and the examination of collegiate performance as a job sample test. Finally, from a practical standpoint, the results of the study should encourage NFL team personnel to reevaluate the usefulness of the combine's physical tests and exercises as predictors of player performance. This study should encourage team personnel to consider the weighting and importance of various combine measures and the potential benefits of overhauling the combine process, with the goal of creating a more valid system for predicting player success.

  19. Predicting targeted drug combinations based on Pareto optimal patterns of coexpression network connectivity

    PubMed Central

    2014-01-01

    Background Molecularly targeted drugs promise a safer and more effective treatment modality than conventional chemotherapy for cancer patients. However, tumors are dynamic systems that readily adapt to these agents activating alternative survival pathways as they evolve resistant phenotypes. Combination therapies can overcome resistance but finding the optimal combinations efficiently presents a formidable challenge. Here we introduce a new paradigm for the design of combination therapy treatment strategies that exploits the tumor adaptive process to identify context-dependent essential genes as druggable targets. Methods We have developed a framework to mine high-throughput transcriptomic data, based on differential coexpression and Pareto optimization, to investigate drug-induced tumor adaptation. We use this approach to identify tumor-essential genes as druggable candidates. We apply our method to a set of ER+ breast tumor samples, collected before (n = 58) and after (n = 60) neoadjuvant treatment with the aromatase inhibitor letrozole, to prioritize genes as targets for combination therapy with letrozole treatment. We validate letrozole-induced tumor adaptation through coexpression and pathway analyses in an independent data set (n = 18). Results We find pervasive differential coexpression between the untreated and letrozole-treated tumor samples as evidence of letrozole-induced tumor adaptation. Based on patterns of coexpression, we identify ten genes as potential candidates for combination therapy with letrozole including EPCAM, a letrozole-induced essential gene and a target to which drugs have already been developed as cancer therapeutics. Through replication, we validate six letrozole-induced coexpression relationships and confirm the epithelial-to-mesenchymal transition as a process that is upregulated in the residual tumor samples following letrozole treatment. Conclusions To derive the greatest benefit from molecularly targeted drugs it is

  20. Combination bortezomib and rituximab treatment affects multiple survival and death pathways to promote apoptosis in mantle cell lymphoma

    PubMed Central

    Alinari, Lapo; White, Valerie L; Earl, Christian T; Ryan, Timothy P; Johnston, Jeffrey S; Dalton, James T; Ferketich, Amy K; Lai, Raymond; Lucas, David M; Porcu, Pierluigi; Blum, Kristie A; Byrd, John C

    2009-01-01

    Mantle cell lymphoma (MCL) is a distinct histologic subtype of B cell non-Hodgkins lymphoma (NHL) associated with an aggressive clinical course. Inhibition of the ubiquitin-proteasome pathway modulates survival and proliferation signals in MCL and has shown clinical benefit in this disease. This has provided rationale for exploring combination regimens with B-cell selective immunotherapies such as rituximab. In this study, we examined the effects of combined treatment with bortezomib and rituximab on patient-derived MCL cell lines (Jeko, Mino, SP53) and tumor samples from patients with MCL where we validate reversible proteasome inhibition concurrent with cell cycle arrest and additive induction of apoptosis. When MCL cells were exposed to single agent bortezomib or combination bortezomib/rituximab, caspase dependent and independent apoptosis was observed. Single agent bortezomib or rituximab treatment of Mino and Jeko cell lines and patient samples resulted in decreased levels of nuclear NFκB complex(es) capable of binding p65 consensus oligonucleotides, and this decrease was enhanced by the combination. Constitutive activation of the Akt pathway was also diminished with bortezomib alone or in combination with rituximab. On the basis of in vitro data demonstrating additive apoptosis and enhanced NFκB and phosphorylated Akt depletion in MCL with combination bortezomib plus rituximab, a phase II trial of bortezomib-rituximab in patients with relapsed/refractory MCL is underway. PMID:20046572

  1. Combined biosynthetic pathway for de novo production of UDP-galactose: catalysis with multiple enzymes immobilized on agarose beads.

    PubMed

    Liu, Ziye; Zhang, Jianbo; Chen, Xi; Wang, Peng G

    2002-04-01

    Regeneration of sugar nucleotides is a critical step in the biosynthetic pathway for the formation of oligosaccharides. To alleviate the difficulties in the production of sugar nucleotides, we have developed a method to produce uridine diphosphate galactose (UDP-galactose). The combined biosynthetic pathway, which involves seven enzymes, is composed of three parts: i) the main pathway to form UDP-galactose from galactose, with the enzymes galactokinase, galactose-1-phosphate uridyltransferase, UDP-glucose pyrophosphorylase, and inorganic pyrophosphatase, ii) the uridine triphosphate supply pathway catalyzed by uridine monophosphate (UMP) kinase and nucleotide diphosphate kinase, and iii) the adenosine triphosphate (ATP) regeneration pathway catalyzed by polyphosphate kinase with polyphosphate added as an energy resource. All of the enzymes were expressed individually and immobilized through their hexahistidine tags onto nickel agarose beads ("super beads"). The reaction requires a stoichiometric amount of UMP and galactose, and catalytic amounts of ATP and glucose 1-phosphate, all inexpensive starting materials. After continuous circulation of the reaction mixture through the super-bead column for 48 h, 50 % of the UMP was converted into UDP-galactose. The results show that de novo production of UDP-galactose on the super-bead column is more efficient than in solution because of the stability of the immobilized enzymes.

  2. Improved Heterosis Prediction by Combining Information on DNA- and Metabolic Markers

    PubMed Central

    Gärtner, Tanja; Steinfath, Matthias; Andorf, Sandra; Lisec, Jan; Meyer, Rhonda C.; Altmann, Thomas; Willmitzer, Lothar; Selbig, Joachim

    2009-01-01

    Background Hybrids represent a cornerstone in the success story of breeding programs. The fundamental principle underlying this success is the phenomenon of hybrid vigour, or heterosis. It describes an advantage of the offspring as compared to the two parental lines with respect to parameters such as growth and resistance against abiotic or biotic stress. Dominance, overdominance or epistasis based models are commonly used explanations. Conclusion/Significance The heterosis level is clearly a function of the combination of the parents used for offspring production. This results in a major challenge for plant breeders, as usually several thousand combinations of parents have to be tested for identifying the best combinations. Thus, any approach to reliably predict heterosis levels based on properties of the parental lines would be highly beneficial for plant breeding. Methodology/Principal Findings Recently, genetic data have been used to predict heterosis. Here we show that a combination of parental genetic and metabolic markers, identified via feature selection and minimum-description-length based regression methods, significantly improves the prediction of biomass heterosis in resulting offspring. These findings will help furthering our understanding of the molecular basis of heterosis, revealing, for instance, the presence of nonlinear genotype-phenotype relationships. In addition, we describe a possible approach for accelerated selection in plant breeding. PMID:19370148

  3. 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

  4. 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).

  5. 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.

  6. Combining mid infrared and total X-ray fluorescence spectroscopy for prediction of soil properties

    NASA Astrophysics Data System (ADS)

    Towett, Erick; Shepherd, Keith; Sila, Andrew; Aynekulu, Ermias; Cadisch, Georg

    2015-04-01

    Mid-infrared diffuse reflectance spectroscopy (MIR) can predict many soil properties but extractable nutrients are often predicted poorly. We evaluated the potential of MIR and total elemental analysis using total X-ray fluorescence spectroscopy (TXRF), both individually and combined, to predict results of conventional soil tests. Total multi-elemental analysis provides a fingerprint of soil mineralogy and could predict some soil properties and help improve MIR predictions. A set of 700 georeferenced soil samples associated with the Africa Soil Information Service (AfSIS) (www.africasoils.net) from 44 stratified randomly-located 100-km2 sentinel sites distributed across sub-Saharan Africa were analysed for physico-chemical composition using conventional reference methods, and compared to MIR and TXRF spectra using the Random Forests regression algorithm and an internal out-of-bag validation. MIR spectra resulted in good prediction models (R2 >0.80) for organic C and total N, Mehlich-3 Ca and Al, and pH. To test the combined spectroscopic approach, TXRF element concentration data was included as a property predictor along with the first derivative of MIR spectral data using the RF algorithm. Including TXRF did not improve prediction of these properties. TXRF was poorer (R2 0.86) as these elements are not directly determined with TXRF, however the variance explained is still quite high and may be attributable to TXRF signatures relating to mineralogy correlated with protection of soil organic matter. TXRF model for Mehlich-3 Al had excellent prediction capability explaining 81% of the observed variation in extractable Al content and was comparable to that of MIR (R2 = 0.86). However, models for pH and Mehlich-3 exchangeable Ca exhibited R2 values of 0.74 and 0.79 respectively and thus had moderate predictive accuracy, compared to MIR alone with R2 values of 0.82 and 0.84 respectively. Both MIR and TXRF methods predicted soil properties that relate to nutrient

  7. 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.

  8. Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables

    PubMed Central

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

    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. PMID:25610454

  9. 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.

  10. PD-L1 (B7-H1) and PD-1 pathway blockade for cancer therapy: Mechanisms, response biomarkers, and combinations.

    PubMed

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

    2016-03-01

    PD-L1 and PD-1 (PD) pathway blockade is a highly promising therapy and has elicited durable antitumor 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 substantial insight into this issue. Here, we focus on tumor microenvironment and 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, and microbiota. We further clarify the mechanisms of action of these markers and their 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

  11. 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.

  12. PreProPath: An Uncertainty-Aware Algorithm for Identifying Predictable Profitable Pathways in Biochemical Networks.

    PubMed

    Ullah, Ehsan; Walker, Mark; Lee, Kyongbum; Hassoun, Soha

    2015-01-01

    Pathway analysis is a powerful approach to enable rational design or redesign of biochemical networks for optimizing metabolic engineering and synthetic biology objectives such as production of desired chemicals or biomolecules from specific nutrients. While experimental methods can be quite successful, computational approaches can enhance discovery and guide experimentation by efficiently exploring very large design spaces. We present a computational algorithm, Predictably Profitable Path (PreProPath), to identify target pathways best suited for engineering modifications. The algorithm utilizes uncertainties about the metabolic networks operating state inherent in the underdetermined linear equations representing the stoichiometric model. Flux Variability Analysis is used to determine the operational flux range. PreProPath identifies a path that is predictable in behavior, exhibiting small flux ranges, and profitable, containing the least restrictive flux-limiting reaction in the network. The algorithm is computationally efficient because it does not require enumeration of pathways. The results of case studies show that PreProPath can efficiently analyze variances in metabolic states and model uncertainties to suggest pathway engineering strategies that have been previously supported by experimental data.

  13. 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.

  14. Combining information from ancestors and personal experiences to predict individual differences in developmental trajectories.

    PubMed

    Stamps, Judy A; Krishnan, V V

    2014-11-01

    A persistent question in biology is how information from ancestors combines with personal experiences over the lifetime to affect the developmental trajectories of phenotypic traits. We address this question by modeling individual differences in behavioral developmental trajectories on the basis of two assumptions: (1) differences among individuals in the behavior expressed at birth or hatching are based on information from their ancestors (via genes, epigenes, and prenatal maternal effects), and (2) information from ancestors is combined with information from personal experiences over ontogeny via Bayesian updating. The model predicts relationships between the means and the variability of the behavior expressed by neonates and the subsequent developmental trajectories of their behavior when every individual is reared under the same environmental conditions. Several predictions of the model are supported by data from previous studies of behavioral development, for example, that the temporal stability of personality will increase with age and that the intercepts and slopes of developmental trajectories for boldness will be negatively correlated across individuals or genotypes when subjects are raised in safe environments. We describe how other specific predictions of the model can be used to test the hypothesis that information from ancestors and information from personal experiences are combined via nonadditive, Bayesian-like processes. PMID:25325748

  15. 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.

  16. Combining information from ancestors and personal experiences to predict individual differences in developmental trajectories.

    PubMed

    Stamps, Judy A; Krishnan, V V

    2014-11-01

    A persistent question in biology is how information from ancestors combines with personal experiences over the lifetime to affect the developmental trajectories of phenotypic traits. We address this question by modeling individual differences in behavioral developmental trajectories on the basis of two assumptions: (1) differences among individuals in the behavior expressed at birth or hatching are based on information from their ancestors (via genes, epigenes, and prenatal maternal effects), and (2) information from ancestors is combined with information from personal experiences over ontogeny via Bayesian updating. The model predicts relationships between the means and the variability of the behavior expressed by neonates and the subsequent developmental trajectories of their behavior when every individual is reared under the same environmental conditions. Several predictions of the model are supported by data from previous studies of behavioral development, for example, that the temporal stability of personality will increase with age and that the intercepts and slopes of developmental trajectories for boldness will be negatively correlated across individuals or genotypes when subjects are raised in safe environments. We describe how other specific predictions of the model can be used to test the hypothesis that information from ancestors and information from personal experiences are combined via nonadditive, Bayesian-like processes.

  17. GO-At: in silico prediction of gene function in Arabidopsis thaliana by combining heterogeneous data.

    PubMed

    Bradford, James R; Needham, Chris J; Tedder, Philip; Care, Matthew A; Bulpitt, Andrew J; Westhead, David R

    2010-02-01

    Despite recent advances, accurate gene function prediction remains an elusive goal, with very few methods directly applicable to the plant Arabidopsis thaliana. In this study, we present GO-At (gene ontology prediction in A. thaliana), a method that combines five data types (co-expression, sequence, phylogenetic profile, interaction and gene neighbourhood) to predict gene function in Arabidopsis. Using a simple, yet powerful two-step approach, GO-At first generates a list of genes ranked in descending order of probability of functional association with the query gene. Next, a prediction score is automatically assigned to each function in this list based on the assumption that functions appearing most frequently at the top of the list are most likely to represent the function of the query gene. In this way, the second step provides an effective alternative to simply taking the 'best hit' from the first list, and achieves success rates of up to 79%. GO-At is applicable across all three GO categories: molecular function, biological process and cellular component, and can assign functions at multiple levels of annotation detail. Furthermore, we demonstrate GO-At's ability to predict functions of uncharacterized genes by identifying ten putative golgins/Golgi-associated proteins amongst 8219 genes of previously unknown cellular component and present independent evidence to support our predictions. A web-based implementation of GO-At (http://www.bioinformatics.leeds.ac.uk/goat) is available, providing a unique resource for plant researchers to make predictions for uncharacterized genes and predict novel functions in Arabidopsis.

  18. 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.

  19. 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. PMID:23299827

  20. Prediction and Biochemical Demonstration of a Catabolic Pathway for the Osmoprotectant Proline Betaine

    PubMed Central

    Kumar, Ritesh; Zhao, Suwen; Vetting, Matthew W.; Wood, B. McKay; Sakai, Ayano; Cho, Kyuil; Solbiati, José; Almo, Steven C.; Sweedler, Jonathan V.; Jacobson, Matthew P.; Gerlt, John A.; Cronan, John E.

    2014-01-01

    ABSTRACT Through the use of genetic, enzymatic, metabolomic, and structural analyses, we have discovered the catabolic pathway for proline betaine, an osmoprotectant, in Paracoccus denitrificans and Rhodobacter sphaeroides. Genetic and enzymatic analyses showed that several of the key enzymes of the hydroxyproline betaine degradation pathway also function in proline betaine degradation. Metabolomic analyses detected each of the metabolic intermediates of the pathway. The proline betaine catabolic pathway was repressed by osmotic stress and cold stress, and a regulatory transcription factor was identified. We also report crystal structure complexes of the P. denitrificans HpbD hydroxyproline betaine epimerase/proline betaine racemase with l-proline betaine and cis-hydroxyproline betaine. PMID:24520058

  1. 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...

  2. Seasonal streamflow prediction by a combined climate-hydrologic system for river basins of Taiwan

    NASA Astrophysics Data System (ADS)

    Kuo, Chun-Chao; Gan, Thian Yew; Yu, Pao-Shan

    2010-06-01

    SummaryA combined, climate-hydrologic system with three components to predict the streamflow of two river basins of Taiwan at one season (3-month) lead time for the NDJ and JFM seasons was developed. The first component consists of the wavelet-based, ANN-GA model (Artificial Neural Network calibrated by Genetic Algorithm) which predicts the seasonal rainfall by using selected sea surface temperature (SST) as predictors, given that SST are generally predictable by climate models up to 6-month lead time. For the second component, three disaggregation models, Valencia and Schaake (VS), Lane, and Canonical Random Cascade Model (CRCM), were tested to compare the accuracy of seasonal rainfall disaggregated by these three models to 3-day time scale rainfall data. The third component consists of the continuous rainfall-runoff model modified from HBV (called the MHBV) and calibrated by a global optimization algorithm against the observed rainfall and streamflow data of the Shihmen and Tsengwen river basins of Taiwan. The proposed system was tested, first by disaggregating the predicted seasonal rainfall of ANN-GA to rainfall of 3-day time step using the Lane model; then the disaggregated rainfall data was used to drive the calibrated MHBV to predict the streamflow for both river basins at 3-day time step up to a season's lead time. Overall, the streamflow predicted by this combined system for the NDJ season, which is better than that of the JFM season, will be useful for the seasonal planning and management of water resources of these two river basins of Taiwan.

  3. [Insulin combined with selenium inhibit p38MAPK/CBP pathway and suppresses cardiomyocyte apoptosis in rats with diabetic cardiomyopathy].

    PubMed

    Xu, Tianjiao; Liu, Yong; Deng, Yating; Meng, Jin; Li, Ping; Xu, Xiaoli; Zeng, Jurong

    2016-07-01

    Objective To investigate the effect of insulin in combination with selenium on p38-mitogen-activated protein kinase/CREB-binding protein (p38MAPK/CBP) pathway in rats with diabetic cardiomyopathy. Methods Fifty SD rats were randomly grouped into control group, diabetic cardiomyopathy (DCM) group, diabetic cardiomyopathy with insulin treatment (DCM-In) group, diabetic cardiomyopathy with selenium treatment (DCM-Se) group, and diabetic cardiomyopathy with insulin and selenium combination treatment (DCM-In-Se) group. Flow cytometry was used to analyze cell cycle. TUNEL staining was used to detect cardiomyocyte apoptosis. Western blotting was used to examine the levels of cyclin D1, cyclin E, Bax, Bcl-2, p38MAPK, p-p38MAPK, CBP and Ku70. Co-immunoprecipitation was used to examine the acetylation status of Ku70. Results Insulin in combination with selenium significantly inhibited cardiomyocyte apoptosis, increased Bcl-2 levels and decreased Bax, cyclin D1, cyclin E, p38MAPK, p-p38MAPK, CBP, Ku70 and acetylated Ku70 levels. Conclusion The combined treatment of insulin and selenium suppresses cardiomyocyte apoptosis by inhibiting p38MAPK/CBP pathway. PMID:27363274

  4. Multi-Marker Strategy in Heart Failure: Combination of ST2 and CRP Predicts Poor Outcome

    PubMed Central

    Dupuy, Anne Marie; Curinier, Corentin; Kuster, Nils; Huet, Fabien; Leclercq, Florence; Davy, Jean Marc; Cristol, Jean Paul; Roubille, François

    2016-01-01

    Natriuretic peptides (BNP and NT-proBNP) are recognized as gold-standard predictive markers in Heart Failure (HF). However, currently ST2 (member of the interleukin 1 receptor family) has emerged as marker of inflammation, fibrosis and cardiac stress. We evaluated ST2 and CRP as prognostic markers in 178 patients with chronic heart failure in comparison with other classical markers such as clinical established parameters but also biological markers: NT-proBNP, hs-cTnT alone or in combination. In multivariate analysis, subsequent addition of ST2 led to age, CRP and ST2 as the only remaining predictors of all-cause mortality (HR 1.03, HR 1.61 and HR 2.75, respectively) as well as of cardiovascular mortality (HR 1.00, HR 2.27 and HR 3.78, respectively). The combined increase of ST2 and CRP was significant for predicting worsened outcomes leading to identify a high risk subgroup that individual assessment of either marker. The same analysis was performed with ST2 in combination with Barcelona score. Overall, our findings extend previous data demonstrating that ST2 in combination with CRP as a valuable tool for identifying patients at risk of death. PMID:27311068

  5. Mild depletion of dietary folate combined with other B vitamins alters multiple components of the Wnt pathway in mouse colon.

    PubMed

    Liu, Zhenhua; Choi, Sang-Woon; Crott, Jimmy W; Keyes, Mary K; Jang, Hyeran; Smith, Donald E; Kim, Myungjin; Laird, Peter W; Bronson, Roderick; Mason, Joel B

    2007-12-01

    Preclinical and clinical studies suggest that diminished folate status increases the risk of colorectal carcinogenesis. However, many biochemical functions of folate are dependent on the adequate availability of other 1-carbon nutrients, including riboflavin, vitamin B-6, and vitamin B-12. Aberrations in the Wnt pathway are thought to play an important role in human colorectal cancers. This study therefore investigated if mild depletion of folate combined with depletion of riboflavin, vitamin B-6, and vitamin B-12 could induce alterations in the Wnt pathway in the colonic mucosa. Ninety-six mice were pair-fed diets with different combinations of B vitamin depletion for 10 wk. Genomic DNA methylation and uracil misincorporation were measured by LC/MS and GC/MS. Gene-specific methylation, strand breaks, and expressions were measured by real-time PCR and immunoblotting. Proliferation and apoptosis were determined by immunohistochemistry. DNA strand breaks within the Apc mutation cluster region were induced by folate depletion combined with inadequacies of riboflavin, vitamin B-6, and vitamin B-12 (P < 0.05), but such effects were not induced by folate depletion alone. Similarly, minor changes in the expression of Apc, beta-catenin, and cyclin D1 produced by mild folate depletion were significantly magnified by multiple vitamin depletion. Apoptosis, which can be suppressed by increased Wnt-signaling, was attenuated by the combined deficiency state (P < 0.05) but not by singlet or doublet deficiencies. These findings indicate that a mild depletion of folate that is of insufficient magnitude by itself to induce alterations in components of the Wnt pathway may produce such effects when present in conjunction with mild inadequacies of other 1-carbon nutrients.

  6. 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

  7. Protein structure prediction: combining de novo modeling with sparse experimental data.

    PubMed

    Latek, Dorota; Ekonomiuk, Dariusz; Kolinski, Andrzej

    2007-07-30

    Routine structure prediction of new folds is still a challenging task for computational biology. The challenge is not only in the proper determination of overall fold but also in building models of acceptable resolution, useful for modeling the drug interactions and protein-protein complexes. In this work we propose and test a comprehensive approach to protein structure modeling supported by sparse, and relatively easy to obtain, experimental data. We focus on chemical shift-based restraints from NMR, although other sparse restraints could be easily included. In particular, we demonstrate that combining the typical NMR software with artificial intelligence-based prediction of secondary structure enhances significantly the accuracy of the restraints for molecular modeling. The computational procedure is based on the reduced representation approach implemented in the CABS modeling software, which proved to be a versatile tool for protein structure prediction during the CASP (CASP stands for critical assessment of techniques for protein structure prediction) experiments (see http://predictioncenter/CASP6/org). The method is successfully tested on a small set of representative globular proteins of different size and topology, including the two CASP6 targets, for which the required NMR data already exist. The method is implemented in a semi-automated pipeline applicable to a large scale structural annotation of genomic data. Here, we limit the computations to relatively small set. This enabled, without a loss of generality, a detailed discussion of various factors determining accuracy of the proposed approach to the protein structure prediction.

  8. 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.

  9. Evaluation of two treatment outcome prediction models for restoration of visual fields in patients with postchiasmatic visual pathway lesions.

    PubMed

    Gall, Carolin; Steger, Benedikt; Koehler, Juergen; Sabel, Bernhard A

    2013-09-01

    Visual functions of patients with visual field defects after acquired brain injury affecting the primary visual pathway can be improved by means of vision restoration training. Since the extent of the restored visual field varies between patients, the prediction of treatment outcome and its visualization may help patients to decide for or against participating in therapies aimed at vision restoration. For this purpose, two treatment outcome prediction models were established based on either self-organizing maps (SOMs) or categorical regression (CR) to predict visual field change after intervention by several features that were hypothesized to be associated with vision restoration. Prediction was calculated for visual field changes recorded with High Resolution Perimetry (HRP). Both models revealed a similar predictive quality with the CR model being slightly more beneficial. Predictive quality of the SOM model improved when using only a small number of features that exhibited a higher association with treatment outcome than the remaining features, i.e. neighborhood activity and homogeneity within the surrounding 5° visual field of a given position, together with its residual function and distance to the scotoma border. Although both models serve their purpose, these were not able to outperform a primitive prediction rule that attests the importance of areas of residual vision, i.e. regions with partial visual field function, for vision restoration.

  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). PMID:26544602

  11. Atomistic Molecular-Dynamics Simulations Enable Prediction of the Arginine Permeation Pathway through OccD1/OprD from Pseudomonas aeruginosa

    PubMed Central

    Parkin, Jamie; Khalid, Syma

    2014-01-01

    Pseudomonas aeruginosa is a Gram-negative bacterium that does not contain large, nonspecific porins in its outer membrane. Consequently, the outer membrane is highly impermeable to polar solutes and serves as a barrier against the penetration of antimicrobial agents. This is one of the reasons why such bacteria are intrinsically resistant to antibiotics. Polar molecules that permeate across the outer membrane do so through substrate-specific channels proteins. To design antibiotics that target substrate-channel proteins, it is essential to first identify the permeation pathways of their natural substrates. In P. aeruginosa, the largest family of substrate-specific proteins is the OccD (previously reported under the name OprD) family. Here, we employ equilibrium and steered molecular-dynamics simulations to study OccD1/OprD, the archetypical member of the OccD family. We study the permeation of arginine, one of the natural substrates of OccD1, through the protein. The combination of simulation methods allows us to predict the pathway taken by the amino acid, which is enabled by conformational rearrangements of the extracellular loops of the protein. Furthermore, we show that arginine adopts a specific orientation to form the molecular interactions that facilitate its passage through part of the protein. We predict a three-stage permeation process for arginine. PMID:25418166

  12. 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

  13. Non-canonical pathway network modelling and ubiquitination site prediction through homology modelling of NF-κB.

    PubMed

    Ghosh, Sayantan; Febin Prabhu Dass, J

    2016-04-25

    Given the fact that NF-κB stays as a dormant molecule in the cytoplasm in steady state, one common step in all the metabolic activities comprising NF-κB is its activation. Consequently there are two pathways of interest related to NF-κB activation: Canonical and alternate. Both the pathways involve ubiquitination of its repressors, that is to say ubiquitination of I-κB by NEMO/IKK-α/IKK-β complex in case of NF-κB1 and that of p100 by IKK-α homodimer in case of NF-κB2. This paper attempts to figure out the ubiquitination sites in alternate pathway of NF-κB activation using a purely computational approach. We initiated the work by acquiring the genes involved in NF kappa B alternate pathway through Agilent literature search. For this we employed the Cytoscape and STRING database. Secondly, the MSA was built using the sequences obtained through BLAST search, and the results were used to update the original sequence list, which was further refined using HMMER. Structural alignment was achieved via Modeller libraries. The final model has been refined using loop_model and asses_dope functions of Modeller. Ubiquitination site is predicted to be comprised of residues 'SPECLDLLVDS' between sites 178 and 188, both positions inclusive. Unlike the classical pathway, due to absence of parallel studies for p100/RelB, a quality match could not be performed, but future studies are in pipeline to replicate the methodology for NF-κB1 activation and compare the results with existing observations. The study can be used to understand the cofactors involved and ubiquitination sites employed during the activation process during drug designing activities. The methodology can be easily scaled and adapted for classical pathway as well. PMID:26784652

  14. 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.

  15. 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.

  16. 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

  17. 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.

  18. 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

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

    PubMed Central

    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. PMID:26880879

  20. 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. PMID:26880879

  1. 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.

  2. Extensions to In Silico Bioactivity Predictions Using Pathway Annotations and Differential Pharmacology Analysis: Application to Xenopus laevis Phenotypic Readouts.

    PubMed

    Liggi, Sonia; Drakakis, Georgios; Hendry, Adam E; Hanson, Kimberley M; Brewerton, Suzanne C; Wheeler, Grant N; Bodkin, Michael J; Evans, David A; Bender, Andreas

    2013-12-01

    The simultaneous increase of computational power and the availability of chemical and biological data have contributed to the recent popularity of in silico bioactivity prediction algorithms. Such methods are commonly used to infer the 'Mechanism of Action' of small molecules and they can also be employed in cases where full bioactivity profiles have not been established experimentally. However, protein target predictions by themselves do not necessarily capture information about the effect of a compound on a biological system, and hence merging their output with a systems biology approach can help to better understand the complex network modulation which leads to a particular phenotype. In this work, we review approaches and applications of target prediction, as well as their shortcomings, and demonstrate two extensions of this concept which are exemplified using phenotypic readouts from a chemical genetic screen in Xenopus laevis. In particular, the experimental observations are linked to their predicted bioactivity profiles. Predicted targets are annotated with pathways, which lead to further biological insight. Moreover, we subject the prediction to further machine learning algorithms, namely decision trees, to capture the differential pharmacology of ligand-target interactions in biological systems. Both methodologies hence provide new insight into understanding the Mechanism of Action of compound activities from phenotypic screens.

  3. Combining many interaction networks to predict gene function and analyze gene lists.

    PubMed

    Mostafavi, Sara; Morris, Quaid

    2012-05-01

    In this article, we review how interaction networks can be used alone or in combination in an automated fashion to provide insight into gene and protein function. We describe the concept of a "gene-recommender system" that can be applied to any large collection of interaction networks to make predictions about gene or protein function based on a query list of proteins that share a function of interest. We discuss these systems in general and focus on one specific system, GeneMANIA, that has unique features and uses different algorithms from the majority of other systems.

  4. Combining Dissimilarities in a Hyper Reproducing Kernel Hilbert Space for Complex Human Cancer Prediction

    PubMed Central

    Martín-Merino, Manuel; Blanco, Ángela; De Las Rivas, Javier

    2009-01-01

    DNA microarrays provide rich profiles that are used in cancer prediction considering the gene expression levels across a collection of related samples. Support Vector Machines (SVM) have been applied to the classification of cancer samples with encouraging results. However, they rely on Euclidean distances that fail to reflect accurately the proximities among sample profiles. Then, non-Euclidean dissimilarities provide additional information that should be considered to reduce the misclassification errors. In this paper, we incorporate in the ν-SVM algorithm a linear combination of non-Euclidean dissimilarities. The weights of the combination are learnt in a (Hyper Reproducing Kernel Hilbert Space) HRKHS using a Semidefinite Programming algorithm. This approach allows us to incorporate a smoothing term that penalizes the complexity of the family of distances and avoids overfitting. The experimental results suggest that the method proposed helps to reduce the misclassification errors in several human cancer problems. PMID:19584909

  5. A combined approach to buffet response analyses and fatigue life prediction

    NASA Astrophysics Data System (ADS)

    Jacobs, J. H.; Perez, R.

    1994-03-01

    Experimental measurement and neural network based prediction of wind tunnel model empennage random pressures are discussed. Artificially generated neural network power spectral densities of surface pressures are used to augment existing data and then load an elastic finite element model to obtain response spectra. Details on the use of actual response spectra from flight test data are also discussed. A random spectra fatigue method is described which effectively combines buffet and maneuver loads into a time series based on aircraft usage data. A peak-valley damage analysis procedure is employed to compute the aggregate fatigue life of the structure based on five combined load time series information. Applications of the method as a continual learning tool for buffet response spectra is elaborated.

  6. Predicted and observed early effects of combined alpha and beta lung irradiation

    SciTech Connect

    Scott, B.R.; Hahn, F.F.; Snipes, M.B.; Newton, G.J.; Eidson, A.F.; Mauderly, J.L.; Boecker, B.B. )

    1990-12-01

    The nonstochastic radiobiological effects of combined alpha and beta irradiation of the lungs of rats from inhaled radionuclides were studied. Both respiratory functional morbidity at 18 mo and mortality from radiation pneumonitis within 18 mo after exposure were examined for rats exposed to the beta-emitter 147Pm, the alpha-emitter 238Pu, or both combined. The results were used to validate hazard-function models that were developed (1) for respiratory functional morbidity at 18 mo and (2) for lethality from radiation pneumonitis within 18 mo. Both models were found to adequately predict the experimental observations for chronic alpha plus beta irradiation of the lung. Based on this 18-mo study, a relative biological effectiveness of approximately seven was obtained for 238Pu alpha radiation compared to 147Pm beta radiation for both respiratory functional morbidity and lethality from radiation pneumonitis. However, the relative biological effectiveness for the alpha radiation is likely to increase with longer follow-up.

  7. Exploring rearrangements along the fragmentation pathways of diuron anion: A combined experimental and computational investigation

    NASA Astrophysics Data System (ADS)

    Kanawati, Basem; Harir, Mourad; Schmitt-Kopplin, Philippe

    2009-12-01

    Diuron (3-(3,4-dichlorophenyl)-1,1-dimethylurea), a common herbicide from phenyl urea class, was investigated by studying the formation of several negative ions [M-H]- in the gas phase and the fragmentation behaviour of the thermodynamically most probably formed isomeric anions upon linear ion acceleration/collision experiments. The collision induced dissociation experiments (CID) were carried out in a hexapole-quadrupole-hexapole hybrid system coupled to 12 T magnet with infinity ICR cell for high resolution measurements. Two distinctive main pathways were observed in the MS/MS spectrum. Sustained off-resonance irradiation (SORI) experiments inside the ICR cell reinforce the fragmentation channels obtained from linear ion acceleration experiments. The fragmentation pathways were also completely investigated by the use of B3LYP/6-311+G(2d,p)//B3LYP/6-31+G(d) level of theory. Elimination of dimethylamine takes place in a two-step process, by which two successive 1,3 proton shifts occur. The second 1,3 proton shift is concerted with the departure of dimethylamine. The driving force for the (CH3)2NH elimination is the formation of isocyanate group. The formed primary product ion can further decompose to release HCl through a new transition state. A stable new aromatic product ion is formed with 10[pi] electrons. Loss of C3H5NO neutral from another anionic isomer of the precursor ion was also observed and is characteristic for the amide terminal of the diamide functional group. A concerted mechanism is proposed, by which N-C bond breakage and cyclization of the eliminated neutral fragment C3H5NO takes place simultaneously to form 1-methyl-aziridin-2-one.

  8. 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.

  9. Toremifene – Atamestane; Alone or In Combination: Predictions from the Preclinical Intratumoral Aromatase Model

    PubMed Central

    Sabnis, Gauri J; Macedo, Luciana; Goloubeva, Olga; Schayowitz, Adam; Zhu, Yue; Brodie, Angela

    2011-01-01

    Since most breast cancers occur in post-menopausal women and are hormone dependent, we developed a model system that mimics this situation. In this model, tumors of human estrogen receptor ER positive breast cancer cells stably transfected with aromatase (Ac-1) are grown in immune compromised mice. Using this model we have explored a number of therapeutic strategies to maximize the antitumor efficacy of antiestrogens (AEs) and aromatase inhibitors (AIs). This intratumoral aromatase xenograft model has proved accurate in predicting the outcome of several clinical trials. In this current study we compared the effect of an AE toremifene and steroidal AI atamestane, alone or in combination, on growth of hormone dependent human breast cancer. We have also compared toremifene plus atamestane combination with tamoxifen in this study. The growth of Ac-1 cells was inhibited by tamoxifen, toremifene and atamestane in vitro with IC50 values of 1.8±1.3μM, 1±0.3μM and 60.4±17.2μM, respectively. The combination of toremifene plus atamestane was found to be better than toremifene or atamestane alone in vitro. The effect of this combination was then studied in vivo using Ac-1 xenografts grown in ovariectomized female SCID mice. The mice were injected with toremifene (1000μg/day), atamestane (1000μg/day), tamoxifen (100μg/day), or the combination of toremifene plus atamestane. In this study, our results indicate that the combination of toremifene plus atamestane was as effective as toremifene or tamoxifen alone but may not provide any additional benefit over toremifene alone or tamoxifen alone. PMID:17942301

  10. 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

    2015-10-01

    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. PMID:26320181

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

    PubMed Central

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

    2015-01-01

    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. PMID:26320181

  12. Comparison of Predicted Epimerases and Reductases of the Campylobacter jejuni d-altro- and l-gluco-Heptose Synthesis Pathways*

    PubMed Central

    McCallum, Matthew; Shaw, Gary S.; Creuzenet, Carole

    2013-01-01

    Uniquely modified heptoses found in surface carbohydrates of bacterial pathogens are potential therapeutic targets against such pathogens. Our recent biochemical characterization of the GDP-6-deoxy-d-manno- and GDP-6-deoxy-d-altro-heptose biosynthesis pathways has provided the foundation for elucidation of the more complex l-gluco-heptose synthesis pathway of Campylobacter jejuni strain NCTC 11168. In this work we use GDP-4-keto,6-deoxy-d-lyxo-heptose as a surrogate substrate to characterize three enzymes predicted to be involved in this pathway: WcaGNCTC (also known as Cj1427), MlghB (Cj1430), and MlghC (Cj1428). We compare them with homologues involved in d-altro-heptose production: WcaG81176 (formerly WcaG), DdahB (Cjj1430), and DdahC (Cjj1427). We show that despite high levels of similarity, the enzymes have pathway-specific catalytic activities and substrate specificities. MlghB forms three products via C3 and C5 epimerization activities, whereas its DdahB homologue only had C3 epimerase activity along its cognate pathway. MlghC is specific for the double C3/C5 epimer generated by MlghB and produces l-gluco-heptose via stereospecific C4 reductase activity. In contrast, its homologue DdahC only uses the C3 epimer to yield d-altro-heptose via C4 reduction. Finally, we show that WcaGNCTC is not necessary for l-gluco-heptose synthesis and does not affect its production by MlghB and MlghC, in contrast to its homologue WcaG81176, that has regulatory activity on d-altro-heptose synthesis. These studies expand our fundamental understanding of heptose modification, provide new glycobiology tools to synthesize novel heptose derivatives with biomedical applications, and provide a foundation for the structure function analysis of these enzymes. PMID:23689373

  13. Combining local- and large-scale models to predict the distributions of invasive plant species.

    PubMed

    Jones, Chad C; Acker, Steven A; Halpern, Charles B

    2010-03-01

    Habitat distribution models are increasingly used to predict the potential distributions of invasive species and to inform monitoring. However, these models assume that species are in equilibrium with the environment, which is clearly not true for most invasive species. Although this assumption is frequently acknowledged, solutions have not been adequately addressed. There are several potential methods for improving habitat distribution models. Models that require only presence data may be more effective for invasive species, but this assumption has rarely been tested. In addition, combining modeling types to form "ensemble" models may improve the accuracy of predictions. However, even with these improvements, models developed for recently invaded areas are greatly influenced by the current distributions of species and thus reflect near- rather than long-term potential for invasion. Larger scale models from species' native and invaded ranges may better reflect long-term invasion potential, but they lack finer scale resolution. We compared logistic regression (which uses presence/absence data) and two presence-only methods for modeling the potential distributions of three invasive plant species on the Olympic Peninsula in Washington, USA. We then combined the three methods to create ensemble models. We also developed climate envelope models for the same species based on larger scale distributions and combined models from multiple scales to create an index of near- and long-term invasion risk to inform monitoring in Olympic National Park (ONP). Neither presence-only nor ensemble models were more accurate than logistic regression for any of the species. Larger scale models predicted much greater areas at risk of invasion. Our index of near- and long-term invasion risk indicates that < 4% of ONP is at high near-term risk of invasion while 67-99% of the Park is at moderate or high long-term risk of invasion. We demonstrate how modeling results can be used to guide the

  14. 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. ...

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

    PubMed Central

    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. PMID:27034651

  16. Combined microbial, seismic surveys predict oil and gas occurrences in Bolivia

    SciTech Connect

    Lopez, J.P. ); Hitzman, D.; Tucker, J. )

    1994-10-24

    Microbial and geophysical surveys in the jungles of Bolivia's extensive Sub-Andean region have combined for three successful predictions of deep oil and gas reserves in as many tries. Hydrocarbon microseepage measured by microbial soil samples predicted the Carrasco, Katari, and Surubi structures of Bolivia's Chapare region in 1991--92, detecting traps with reserves at depths exceeding 4,500 m. Approximately 800 km of seismic lines covering 3,500 sq km was completed by Yacimientos Petroliferos Fiscales Bolivianos (YPFB) for evaluation of the YPFB reserve block. For 1 month each year at the end of the field season, seismic lines were quickly traversed by several microbial sampling teams. Using hand augers or shovels, the teams collected more than 3,200 samples approximately 20 cm (8 in.) deep at intervals of 250 m next to staked seismic locations. Microbial results were directly compared with seismic profiles for identification and ranking of traps and structures. The paper discusses the survey predictions and the microbial approach.

  17. Prediction of Preeclampsia by First Trimester Combined Test and Simple Complete Blood Count Parameters

    PubMed Central

    Ersoy, Ali Ozgur; Daglar, Korkut; Dikici, Turkan; Biberoglu, Ebru Hacer; Kirbas, Ozgur; Danisman, Nuri

    2015-01-01

    Introduction Preeclampsia is a serious disease which may result in maternal and neonatal mortality and morbidity. Improving the outcome for preeclampsia necessitates early prediction of the disease to identify women at high risk. Measuring blood cell subtype ratios, such as the neutrophil to lymphocyte (NLR) and platelet to lymphocyte (PLR) ratios, might provide prognostic and diagnostic clues to diseases. Aim To investigate hematological changes in early pregnancy, using simple complete blood count (CBC) and blood concentrations of beta-human chorionic gonadotropin (β-hCG) and pregnancy-associated plasma protein-A (PAPP-A) to determine whether these measures are of any value in the prediction and early diagnosis of preeclampsia. Materials and Methods Six hundred fourteen consecutive pregnant women with preeclampsia (288 with mild disease and 326 with severe disease) and 320 uncomplicated pregnant women were included in the study. Blood samples for routine CBC and first trimester screen, which combines PAPP-A and free β-hCG blood concentrations, were analyzed. Results The NLR values were significantly higher in the severe preeclampsia group compared with the control group (p<0.001). We also confirmed that levels of PAPP-A were lower in patients who developed preeclampsia. Conclusion Because measuring CBC parameters, particularly NLR, is fast and easily applicable, they may be used to predict preeclampsia. PMID:26674673

  18. Taller-than-wide sign for predicting thyroid microcarcinoma: comparison and combination of two ultrasonographic planes.

    PubMed

    Chen, Shun-Ping; Hu, Yuan-Ping; Chen, Bin

    2014-09-01

    The aims of this study were to investigate the accuracy of using the taller-than-wide (TTW) sign in two ultrasonographic planes to predict thyroid microcarcinoma, and to confirm the hypothesis that the presence of a TTW sign in both the transverse and longitudinal ultrasonographic planes strongly suggests thyroid microcarcinoma. Nine hundred forty-two thyroid nodules ≤1 cm were submitted to surgical-histopathologic and ultrasonographic examination. TTW signs were divided into three types based on their detection only in the transverse plane (TTTW type, n = 100), only in the longitudinal plane (LTTW type, n = 61) or in both planes (BTTW type, n = 131). The areas under the receiver operating characteristic curves (A(z)) for the three different TTW signs, as well as for the combination of all TTW signs (ATTW, n = 292), were compared. The results indicated that the A(z) values of the TTTW, LTTW, BTTW and ATTW signs in predicting thyroid microcarcinoma were 0.544, 0.531, 0.627 and 0.702, respectively. The ATTW sign was the most accurate (p < 0.05), and the BTTW sign was 100% accurate for predicting thyroid microcarcinoma. However, there was no significant difference between the A(z) values for the TTTW and LTTW signs (p > 0.05). Therefore, both the LTTW and TTTW signs are reliable markers of thyroid microcarcinoma. The BTTW sign strongly suggests thyroid microcarcinoma.

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

    PubMed

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

    2015-01-01

    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. PMID:25634021

  20. 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.

  1. 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/).

  2. Comparative motif discovery combined with comparative transcriptomics yields accurate targetome and enhancer predictions.

    PubMed

    Naval-Sánchez, Marina; Potier, Delphine; Haagen, Lotte; Sánchez, Máximo; Munck, Sebastian; Van de Sande, Bram; Casares, Fernando; Christiaens, Valerie; Aerts, Stein

    2013-01-01

    The identification of transcription factor binding sites, enhancers, and transcriptional target genes often relies on the integration of gene expression profiling and computational cis-regulatory sequence analysis. Methods for the prediction of cis-regulatory elements can take advantage of comparative genomics to increase signal-to-noise levels. However, gene expression data are usually derived from only one species. Here we investigate tissue-specific cross-species gene expression profiling by high-throughput sequencing, combined with cross-species motif discovery. First, we compared different methods for expression level quantification and cross-species integration using Tag-seq data. Using the optimal pipeline, we derived a set of genes with conserved expression during retinal determination across Drosophila melanogaster, Drosophila yakuba, and Drosophila virilis. These genes are enriched for binding sites of eye-related transcription factors including the zinc-finger Glass, a master regulator of photoreceptor differentiation. Validation of predicted Glass targets using RNA-seq in homozygous glass mutants confirms that the majority of our predictions are expressed downstream from Glass. Finally, we tested nine candidate enhancers by in vivo reporter assays and found eight of them to drive GFP in the eye disc, of which seven colocalize with the Glass protein, namely, scrt, chp, dpr10, CG6329, retn, Lim3, and dmrt99B. In conclusion, we show for the first time the combined use of cross-species expression profiling with cross-species motif discovery as a method to define a core developmental program, and we augment the candidate Glass targetome from a single known target gene, lozenge, to at least 62 conserved transcriptional targets. PMID:23070853

  3. Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators.

    PubMed

    Hieronymus, Haley; Lamb, Justin; Ross, Kenneth N; Peng, Xiao P; Clement, Cristina; Rodina, Anna; Nieto, Maria; Du, Jinyan; Stegmaier, Kimberly; Raj, Srilakshmi M; Maloney, Katherine N; Clardy, Jon; Hahn, William C; Chiosis, Gabriela; Golub, Todd R

    2006-10-01

    Although androgen receptor (AR)-mediated signaling is central to prostate cancer, the ability to modulate AR signaling states is limited. Here we establish a chemical genomic approach for discovery and target prediction of modulators of cancer phenotypes, as exemplified by AR signaling. We first identify AR activation inhibitors, including a group of structurally related compounds comprising celastrol, gedunin, and derivatives. To develop an in silico approach for target pathway identification, we apply a gene expression-based analysis that classifies HSP90 inhibitors as having similar activity to celastrol and gedunin. Validating this prediction, we demonstrate that celastrol and gedunin inhibit HSP90 activity and HSP90 clients, including AR. Broadly, this work identifies new modes of HSP90 modulation through a gene expression-based strategy. PMID:17010675

  4. A Fatigue Life Prediction Model of Welded Joints under Combined Cyclic Loading

    NASA Astrophysics Data System (ADS)

    Goes, Keurrie C.; Camarao, Arnaldo F.; Pereira, Marcos Venicius S.; Ferreira Batalha, Gilmar

    2011-01-01

    A practical and robust methodology is developed to evaluate the fatigue life in seam welded joints when subjected to combined cyclic loading. The fatigue analysis was conducted in virtual environment. The FE stress results from each loading were imported to fatigue code FE-Fatigue and combined to perform the fatigue life prediction using the S x N (stress x life) method. The measurement or modelling of the residual stresses resulting from the welded process is not part of this work. However, the thermal and metallurgical effects, such as distortions and residual stresses, were considered indirectly through fatigue curves corrections in the samples investigated. A tube-plate specimen was submitted to combined cyclic loading (bending and torsion) with constant amplitude. The virtual durability analysis result was calibrated based on these laboratory tests and design codes such as BS7608 and Eurocode 3. The feasibility and application of the proposed numerical-experimental methodology and contributions for the technical development are discussed. Major challenges associated with this modelling and improvement proposals are finally presented.

  5. Postprandial Regulation of Hepatic MicroRNAs Predicted to Target the Insulin Pathway in Rainbow Trout

    PubMed Central

    Mennigen, Jan A.; Panserat, Stéphane; Larquier, Mélanie; Plagnes-Juan, Elisabeth; Medale, Françoise; Seiliez, Iban; Skiba-Cassy, Sandrine

    2012-01-01

    Rainbow trout are carnivorous fish and poor metabolizers of carbohydrates, which established this species as a model organism to study the comparative physiology of insulin. Following the recent characterisation of key roles of several miRNAs in the insulin action on hepatic intermediary metabolism in mammalian models, we investigated the hypothesis that hepatic miRNA expression is postprandially regulated in the rainbow trout and temporally coordinated in the context of insulin-mediated regulation of metabolic gene expression in the liver. To address this hypothesis, we used a time-course experiment in which rainbow trout were fed a commercial diet after short-term fasting. We investigated hepatic miRNA expression, activation of the insulin pathway, and insulin regulated metabolic target genes at several time points. Several miRNAs which negatively regulate hepatic insulin signaling in mammalian model organisms were transiently increased 4 h after the meal, consistent with a potential role in acute postprandial negative feed-back regulation of the insulin pathway and attenuation of gluconeogenic gene expression. We equally observed a transient increase in omy- miRNA-33 and omy-miRNA-122b 4 h after feeding, whose homologues have potent lipogenic roles in the liver of mammalian model systems. A concurrent increase in the activity of the hepatic insulin signaling pathway and the expression of lipogenic genes (srebp1c, fas, acly) was equally observed, while lipolytic gene expression (cpt1a and cpt1b) decreased significantly 4 h after the meal. This suggests lipogenic roles of omy-miRNA-33 and omy-miRNA-122b may be conserved between rainbow trout and mammals and that these miRNAs may furthermore contribute to acute postprandial regulation of de novo hepatic lipid synthesis in rainbow trout. These findings provide a framework for future research of miRNA regulation of hepatic metabolism in trout and will help to further elucidate the metabolic phenotype of rainbow trout

  6. A Panel of Novel Biomarkers Representing Different Disease Pathways Improves Prediction of Renal Function Decline in Type 2 Diabetes

    PubMed Central

    Pena, Michelle J.; Heinzel, Andreas; Heinze, Georg; Alkhalaf, Alaa; Bakker, Stephan J. L.; Nguyen, Tri Q.; Goldschmeding, Roel; Bilo, Henk J. G.; Perco, Paul; Mayer, Bernd; de Zeeuw, Dick; Lambers Heerspink, Hiddo J.

    2015-01-01

    Objective We aimed to identify a novel panel of biomarkers predicting renal function decline in type 2 diabetes, using biomarkers representing different disease pathways speculated to contribute to the progression of diabetic nephropathy. Research Design and Methods A systematic data integration approach was used to select biomarkers representing different disease pathways. Twenty-eight biomarkers were measured in 82 patients seen at an outpatient diabetes center in The Netherlands. Median follow-up was 4.0 years. We compared the cross-validated explained variation (R2) of two models to predict eGFR decline, one including only established risk markers, the other adding a novel panel of biomarkers. Least absolute shrinkage and selection operator (LASSO) was used for model estimation. The C-index was calculated to assess improvement in prediction of accelerated eGFR decline defined as <-3.0 mL/min/1.73m2/year. Results Patients’ average age was 63.5 years and baseline eGFR was 77.9 mL/min/1.73m2. The average rate of eGFR decline was -2.0 ± 4.7 mL/min/1.73m2/year. When modeled on top of established risk markers, the biomarker panel including matrix metallopeptidases, tyrosine kinase, podocin, CTGF, TNF-receptor-1, sclerostin, CCL2, YKL-40, and NT-proCNP improved the explained variability of eGFR decline (R2 increase from 37.7% to 54.6%; p=0.018) and improved prediction of accelerated eGFR decline (C-index increase from 0.835 to 0.896; p=0.008). Conclusions A novel panel of biomarkers representing different pathways of renal disease progression including inflammation, fibrosis, angiogenesis, and endothelial function improved prediction of eGFR decline on top of established risk markers in type 2 diabetes. These results need to be confirmed in a large prospective cohort. PMID:25973922

  7. Use of combinations of in vitro quality assessments to predict fertility of bovine semen.

    PubMed

    Sellem, E; Broekhuijse, M L W J; Chevrier, L; Camugli, S; Schmitt, E; Schibler, L; Koenen, E P C

    2015-12-01

    Predicting in vivo fertility of bull ejaculates using in vitro-assessed semen quality criteria remains challenging for the breeding industry. New technologies such as computer-assisted semen analysis (CASA) and flow cytometry may provide accurate and objective methods to improve semen quality control. The aim of this study was to evaluate the relationship between semen quality parameters and field fertility of bull ejaculates. A total of 153 ejaculates from 19 Holstein bulls have been analyzed using CASA (postthawing semen motility and morphology) and several flow cytometric tests, including sperm DNA integrity, viability (estimated by membrane integrity), acrosomal integrity, mitochondria aerobic functionality and oxidation. Samples were analyzed both immediately after thawing and after 4 hours at 37 °C. A fertility value (FV), based on nonreturn rate at 56 days after insemination and adjusted for environment factors, was calculated for each ejaculate. Simple and multiple regressions have been used to correlate FV with CASA and flow cytometric parameters. Significant simple correlations have been observed between some parameters and FV (e.g., straight line velocity [μm/s], r(2) = -0.12; polarized mitochondria sperm (%), r(2) = 0.07), but the relation between simple parameter and FV was too week to predict the fertility. Partial least square procedure identified several mathematical models combining flow cytometer and CASA variables and had better correlations with FV (adjusted r(2) ranging between 0.24 and 0.40 [P < 0.0001], depending on the number of included variables). In conclusion, this study suggests that quality assessment of thawed bull sperm using CASA and flow cytometry may provide a reasonable prediction of bovine semen fertility. Additional work will be required to increase the prediction reliability and promote this technology in routine artificial insemination laboratory practice.

  8. 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.

  9. 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.

  10. 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.

  11. Simplified protein models can rival all atom simulations in predicting folding pathways and structure

    PubMed Central

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

    2014-01-01

    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 native-like 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 native-like propensities in the unfolded state do not necessarily determine the order of structure formation, a departure from a major conclusion of the MD 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. PMID:23889448

  12. 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

  13. 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.

  14. Mimicking the folding pathway to improve homology-free protein structure prediction

    NASA Astrophysics Data System (ADS)

    Freed, Karl; Debartolo, Joe; Colubri, Andres; Jha, Abhishek; Fitzgerald, James; Sosnick, Tobin

    2010-03-01

    Since demonstrating that a protein's sequence encodes its structure, the prediction of structure from sequence remains an outstanding problem that impacts numerous scientific disciplines including many genome projects. By iteratively fixing secondary structure assignments of residues during Monte Carlo simulations of folding, our coarse grained model without information concerning homology or explicit side chains outperforms current homology-based secondary structure prediction methods for many proteins. The computationally rapid algorithm using only single residue (phi, psi) dihedral angle moves also generates tertiary structures of comparable accuracy to existing all-atom methods for many small proteins, particularly ones with low homology. Hence, given appropriate search strategies and scoring functions, reduced representations can be used for accurately predicting secondary structure as well as providing three-dimensional structures, thereby increasing the size of proteins approachable by homology-free methods and the accuracy of template methods whose accuracy depends on the quality of the input secondary structure. Inclusion of information from evolutionarily related sequences enhances the statistics and the accuracy of the predictions.

  15. 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.

  16. 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.

  17. MicroRNA-related polymorphisms in apoptosis pathway genes are predictive of clinical outcome in patients with limited disease small cell lung cancer

    PubMed Central

    Jiang, Wei; Bi, Nan; Zhang, Wen-Jue; Wu, Li-Hong; Liu, Li-Pin; Men, Yu; Wang, Jing-Bo; Liang, Jun; Hui, Zhou-Guang; Zhou, Zong-Mei; Wang, Lu-Hua

    2016-01-01

    We examined the impact of single nucleotide polymorphisms (SNPs) at miRNA binding sites in the 3′-UTRs of genes in the apoptosis pathway on the prognosis of patients with limited disease-small cell lung cancer (LD-SCLC). Twelve tagSNPs in seven genes were genotyped using blood samples from 146 LD-SCLC patients treated with chemoradiotherapy. Cox proportional hazard regression models and recursive partitioning analysis were performed to identify SNPs significantly associated with overall survival. Three SNPs, CASP8: rs1045494 (C > T), PIK3R1: rs3756668 (A > G) and CASP7: rs4353229 (T > C), were associated with longer overall survival in LD-SCLC patients after chemoradiotherapy. The adjusted hazard ratios (95% confidence intervals) were 0.480 (0.258–0.894), 0.405 (0.173–0.947) and 0.446 (0.247–0.802), respectively, and remained significant after multiple comparison correction. Moreover, subset analysis showed these SNPs were still predictive of overall survival in stage III patients. Recursive partitioning analysis enabled patients to be classified into three risk subgroups based on unfavorable genotype combinations of the rs1045494 and rs4353229 SNPs. These findings suggest miRNA-related polymorphisms in the apoptosis pathway may be useful biomarkers for selection of LD-SCLC patients likely to benefit from chemoradiotherapy. PMID:26988918

  18. A light load model combining surface roughness and waviness to predict thermal contact conductance

    NASA Astrophysics Data System (ADS)

    Yovanovich, M. M.; Fisher, N. J.; Saabas, H. J.

    1983-06-01

    Thermal contact conductance data of wavy-rough stainless steel surfaces are compared with the theoretical values of the Clausing-Chao point contact model (PCM) and of the Cooper-Mikic-Yovanovich conforming, rough surface model (CRM). Neither model accurately predicts the conductances over all contact pressures; however they appear to represent the light and heavy load bounds on the conductances. The PCM is modified to include the effects of surface roughness, the lateral boundaries of the test specimen, and the constriction parameter. The CRM is modified to include the effectiveness of waviness. Both modified PCM and CRM are combined in a contact conductance correlation which is in very good agreement with the data.

  19. Understanding how children’s engagement and teachers’ interactions combine to predict school readiness

    PubMed Central

    Williford, Amanda P.; Maier, Michelle F.; Downer, Jason T.; Pianta, Robert C.; Howes, Carolee

    2015-01-01

    This study examined the quality of preschool classroom experiences through the combination of teachers’ interactions at the classroom level and children’s individual patterns of engagement in predicting children’s gains in school readiness. A sample of 605 children and 309 teachers participated. The quality of children’s engagement and teacher interactions was directly observed in the classroom setting, and direct assessments of children’s school readiness skills were obtained in the fall and again in the spring. The quality of teacher interactions was associated with gains across all school readiness skills. The effect of children’s individual classroom engagement on their gains in school readiness skills (specifically phonological awareness and expressive vocabulary) was moderated by classroom level teacher interactions. The results suggest that if teachers provide highly responsive interactions at the classroom level, children may develop more equitable school readiness skills regardless of their individual engagement patterns. PMID:26722137

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

    SciTech Connect

    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.

  1. 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.

  2. Positive and negative family emotional climate differentially predict youth anxiety and depression via distinct affective pathways.

    PubMed

    Luebbe, Aaron M; Bell, Debora J

    2014-08-01

    A socioaffective specificity model was tested in which positive and negative affect differentially mediated relations of family emotional climate to youth internalizing symptoms. Participants were 134 7(th)-9(th) grade adolescents (65 girls; 86 % Caucasian) and mothers who completed measures of emotion-related family processes, experienced affect, anxiety, and depression. Results suggested that a family environment characterized by maternal psychological control and family negative emotion expressiveness predicted greater anxiety and depression, and was mediated by experienced negative affect. Conversely, a family emotional environment characterized by low maternal warmth and low positive emotion expressiveness predicted only depression, and was mediated through lowered experienced positive affect. This study synthesizes a theoretical model of typical family emotion socialization with an extant affect-based model of shared and unique aspects of anxiety and depression symptom expression.

  3. Cloud computing approaches for prediction of ligand binding poses and pathways.

    PubMed

    Lawrenz, Morgan; Shukla, Diwakar; Pande, Vijay S

    2015-01-22

    We describe an innovative protocol for ab initio prediction of ligand crystallographic binding poses and highly effective analysis of large datasets generated for protein-ligand dynamics. We include a procedure for setup and performance of distributed molecular dynamics simulations on cloud computing architectures, a model for efficient analysis of simulation data, and a metric for evaluation of model convergence. We give accurate binding pose predictions for five ligands ranging in affinity from 7 nM to > 200 μM for the immunophilin protein FKBP12, for expedited results in cases where experimental structures are difficult to produce. Our approach goes beyond single, low energy ligand poses to give quantitative kinetic information that can inform protein engineering and ligand design.

  4. Combining Donor Characteristics with Immunohistological Data Improves the Prediction of Islet Isolation Success

    PubMed Central

    Girman, Peter; Zacharovova, Klara; Kriz, Jan; Fabryova, Eva; Leontovyc, Ivan; Koblas, Tomas; Kosinova, Lucie; Neskudla, Tomas; Vavrova, Ema; Habart, David; Loukotova, Sarka; Zahradnicka, Martina; Lipar, Kvetoslav; Voska, Ludek; Skibova, Jelena

    2016-01-01

    Variability of pancreatic donors may significantly impact the success of islet isolation. The aim of this study was to evaluate donor factors associated with isolation failure and to investigate whether immunohistology could contribute to organ selection. Donor characteristics were evaluated for both successful (n = 61) and failed (n = 98) islet isolations. Samples of donor pancreatic tissue (n = 78) were taken for immunohistochemical examination. Islet isolations with 250000 islet equivalents were considered successful. We confirmed that BMI of less than 25 kg/m2 (P < 0.001), cold ischemia time more than 8 hours (P < 0.01), hospitalization longer than 96 hours (P < 0.05), higher catecholamine doses (P < 0.05), and edematous pancreases (P < 0.01) all unfavorably affected isolation outcome. Subsequent immunohistochemical examination of donor pancreases confirmed significant differences in insulin-positive areas (P < 0.001). ROC analyses then established that the insulin-positive area in the pancreas could be used to predict the likely success of islet isolation (P < 0.001). At the optimal cutoff point (>1.02%), sensitivity and specificity were 89% and 76%, respectively. To conclude, while the insulin-positive area, determined preislet isolation, as a single variable, is sufficient to predict isolation outcome and helps to improve the success of this procedure, its combination with the established donor scoring system might further improve organ selection. PMID:27803935

  5. 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

  6. Pressure and fluid saturation prediction in a multicomponent reservoir, using combined seismic and electromagnetic imaging

    SciTech Connect

    Hoversten, G.M.; Gritto, Roland; Washbourne, John; Daley, Tom

    2002-06-10

    This paper presents a method for combining seismic and electromagnetic measurements to predict changes in water saturation, pressure, and CO{sub 2} gas/oil ratio in a reservoir undergoing CO{sub 2} flood. Crosswell seismic and electromagnetic data sets taken before and during CO{sub 2} flooding of an oil reservoir are inverted to produce crosswell images of the change in compressional velocity, shear velocity, and electrical conductivity during a CO{sub 2} injection pilot study. A rock properties model is developed using measured log porosity, fluid saturations, pressure, temperature, bulk density, sonic velocity, and electrical conductivity. The parameters of the rock properties model are found by an L1-norm simplex minimization of predicted and observed differences in compressional velocity and density. A separate minimization, using Archie's law, provides parameters for modeling the relations between water saturation, porosity, and the electrical conductivity. The rock-properties model is used to generate relationships between changes in geophysical parameters and changes in reservoir parameters. Electrical conductivity changes are directly mapped to changes in water saturation; estimated changes in water saturation are used along with the observed changes in shear wave velocity to predict changes in reservoir pressure. The estimation of the spatial extent and amount of CO{sub 2} relies on first removing the effects of the water saturation and pressure changes from the observed compressional velocity changes, producing a residual compressional velocity change. This velocity change is then interpreted in terms of increases in the CO{sub 2}/oil ratio. Resulting images of the CO{sub 2}/oil ratio show CO{sub 2}-rich zones that are well correlated to the location of injection perforations, with the size of these zones also correlating to the amount of injected CO{sub 2}. The images produced by this process are better correlated to the location and amount of injected

  7. Combining multidimensional genomic measurements for predicting cancer prognosis: observations from TCGA

    PubMed Central

    Zhao, Qing; Shi, Xingjie; Xie, Yang; Huang, Jian; Shia, BenChang

    2015-01-01

    With accumulating research on the interconnections among different types of genomic regulations, researchers have found that multidimensional genomic studies outperform one-dimensional studies in multiple aspects. Among many sources of multidimensional genomic data, The Cancer Genome Atlas (TCGA) provides the public with comprehensive profiling data on >30 cancer types, making it an ideal test bed for conducting and comparing different analyses. In this article, the analysis goal is to apply several existing methods and associate multidimensional genomic measurements with cancer outcomes in particular prognosis, with special focus on the predictive power of genomic signatures. We exploit clinical data and four types of genomic measurement including mRNA gene expression, DNA methylation, microRNA and copy number alterations for breast invasive carcinoma, glioblastoma multiforme, acute myeloid leukemia and lung squamous cell carcinoma collected by TCGA. To accommodate the high dimensionality, we extract important features using Principal Component Analysis, Partial Least Squares and Least Absolute Shrinkage and Selection Operator (Lasso), which are representative of dimension reduction and variable selection techniques and have been extensively adopted, and fit Cox survival models with combined important features. We calibrate the predictive power of each type of genomic measurement for the prognosis of four cancer types and find that the results vary across cancers. Our analysis also suggests that for most of the cancers in our study and the adopted methods, there is no substantial improvement in prediction when adding other genomic measurement after gene expression and clinical covariates have been included in the model. This is consistent with the findings that molecular features measured at the transcription level affect clinical outcomes more directly than those measured at the DNA/epigenetic level. PMID:24632304

  8. 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.

  9. 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

  10. Combining features in a graphical model to predict protein binding sites.

    PubMed

    Wierschin, Torsten; Wang, Keyu; Welter, Marlon; Waack, Stephan; Stanke, Mario

    2015-05-01

    Large efforts have been made in classifying residues as binding sites in proteins using machine learning methods. The prediction task can be translated into the computational challenge of assigning each residue the label binding site or non-binding site. Observational data comes from various possibly highly correlated sources. It includes the structure of the protein but not the structure of the complex. The model class of conditional random fields (CRFs) has previously successfully been used for protein binding site prediction. Here, a new CRF-approach is presented that models the dependencies of residues using a general graphical structure defined as a neighborhood graph and thus our model makes fewer independence assumptions on the labels than sequential labeling approaches. A novel node feature "change in free energy" is introduced into the model, which is then denoted by ΔF-CRF. Parameters are trained with an online large-margin algorithm. Using the standard feature class relative accessible surface area alone, the general graph-structure CRF already achieves higher prediction accuracy than the linear chain CRF of Li et al. ΔF-CRF performs significantly better on a large range of false positive rates than the support-vector-machine-based program PresCont of Zellner et al. on a homodimer set containing 128 chains. ΔF-CRF has a broader scope than PresCont since it is not constrained to protein subgroups and requires no multiple sequence alignment. The improvement is attributed to the advantageous combination of the novel node feature with the standard feature and to the adopted parameter training method.

  11. Prediction of drug clearance by glucuronidation from in vitro data: use of combined cytochrome P450 and UDP-glucuronosyltransferase cofactors in alamethicin-activated human liver microsomes.

    PubMed

    Kilford, Peter J; Stringer, Rowan; Sohal, Bindi; Houston, J Brian; Galetin, Aleksandra

    2009-01-01

    Glucuronidation via UDP-glucuronosyltransferase (UGT) is an increasingly important clearance pathway. In this study intrinsic clearance (CL(int)) values for buprenorphine, carvedilol, codeine, diclofenac, gemfibrozil, ketoprofen, midazolam, naloxone, raloxifene, and zidovudine were determined in pooled human liver microsomes using the substrate depletion approach. The in vitro clearance data indicated a varying contribution of glucuronidation to the clearance of the compounds studied, ranging from 6 to 79% for midazolam and gemfibrozil, respectively. The CL(int) was obtained using either individual or combined cofactors for cytochrome P450 (P450) and UGT enzymes with alamethicin activation and in the presence and absence of 2% bovine serum albumin (BSA). In the presence of combined P450 and UGT cofactors, CL(int) ranged from 2.8 to 688 microl/min/mg for zidovudine and buprenorphine, respectively; the clearance was approximately equal to the sum of the CL(int) values obtained in the presence of individual cofactors. The unbound intrinsic clearance (CL(int, u)) was scaled to provide an in vivo predicted CL(int); the data obtained in the presence of combined cofactors resulted in 5-fold underprediction on average. Addition of 2% BSA to the incubation with both P450 and UGT cofactors reduced the bias in the clearance prediction, with 8 of 10 compounds predicted within 2-fold of in vivo values with the exception of raloxifene and gemfibrozil. The current study indicates the applicability of combined cofactor conditions in the assessment of clearance for compounds with a differential contribution of P450 and UGT enzymes to their elimination. In addition, improved predictability of microsomal data is observed in the presence of BSA, in particular for UGT2B7 substrates.

  12. Prediction of Primary Slow-Pathway Ablation Success Rate according to the Characteristics of Junctional Rhythm Developed during the Radiofrequency Catheter Ablation of Atrioventricular Nodal Reentrant Tachycardia

    PubMed Central

    Bagherzadeh, Ataallah; Rezaee, Mohammad Esmaeel; Farahani, Maryam Moshkani

    2011-01-01

    Background Nowadays, developed junctional rhythm (JR) that occurs during slow-pathway radiofrequency (RF) catheter ablation of atrioventricular nodal reentrant tachycardia (AVNRT) has been focused upon as a highly sensitive surrogate end point for successful radiofrequency ablation. This study was conducted to assess the relationship between the presence and pattern of developed JR during the RF ablation of AVNRT and a successful outcome. Methods Seventy-five patients aged between 14 and 88 who underwent slow-pathway RF ablation due to symptomatic AVNRT were enrolled into the study and received a total of 162 RF energy applications. Combined anatomic and electrogram mapping approach was used for slow-pathway RF ablation. The ablation procedure consisted of 60-second, 60 °C temperature-controlled energy delivery. After each ablation pulse, successful ablation was assessed according to the loss of AVNRT inducibility via isoproterenol infusion. Four different patterns were considered for the developed JR, namely sparse, intermittent, continuous, and transient block. Success ablation rate was assessed with respect to the position, pattern, and number of junctional beats. Results Successful RF ablation with a loss of AVNRT inducibility was achieved in 43 (57.3%) patients using 119 RF energy applications (73.5%). JR developed in 133 of the 162 (82.1%) applications with a given sensitivity of 90.8% and low specificity of 41.9% as an end point of successful RF ablation, with a negative predictive value of 62.1%. The mean number of the developed junctional beats was significantly higher in the successful ablations (p value < 0.001), and the ROC analysis revealed that the best cut-off point of the cumulative junctional beats for identifying accurate AVNRT ablation therapy is 14 beats with 90.76 % sensitivity and 90.70% specificity. There were no significant differences in terms of successful ablation rates according to the four different patterns of JR and its positions (p

  13. A Combined Covalent-Electrostatic Model of Hydrogen Bonding Improves Structure Prediction with Rosetta

    PubMed Central

    O’Meara, Matthew J.; Leaver-Fay, Andrew; Tyka, Mike; Stein, Amelie; Houlihan, Kevin; DiMaio, Frank; Bradley, Philip; Kortemme, Tanja; Baker, David; Snoeyink, Jack; Kuhlman, Brian

    2015-01-01

    Interactions between polar atoms are challenging to model because at very short ranges they form hydrogen bonds (H-bonds) that are partially covalent in character and exhibit strong orientation preferences; at longer ranges the orientation preferences are lost, but significant electrostatic interactions between charged and partially charged atoms remain. To simultaneously model these two types of behavior, we refined an orientation dependent model of hydrogen bonds [Kortemme et al. 2003] used by the molecular modeling program Rosetta and then combined it with a distance-dependent Coulomb model of electrostatics. The functional form of the H-bond potential is physically motivated and parameters are fit so that H-bond geometries that Rosetta generates closely resemble H-bond geometries in high-resolution crystal structures. The combined potentials improve performance in a variety of scientific benchmarks including decoy discrimination, side chain prediction, and native sequence recovery in protein design simulations, and establishes a new standard energy function for Rosetta. PMID:25866491

  14. Complex hybrid models combining deterministic and machine learning components for numerical climate modeling and weather prediction.

    PubMed

    Krasnopolsky, Vladimir M; Fox-Rabinovitz, Michael S

    2006-03-01

    A new practical application of neural network (NN) techniques to environmental numerical modeling has been developed. Namely, a new type of numerical model, a complex hybrid environmental model based on a synergetic combination of deterministic and machine learning model components, has been introduced. Conceptual and practical possibilities of developing hybrid models are discussed in this paper for applications to climate modeling and weather prediction. The approach presented here uses NN as a statistical or machine learning technique to develop highly accurate and fast emulations for time consuming model physics components (model physics parameterizations). The NN emulations of the most time consuming model physics components, short and long wave radiation parameterizations or full model radiation, presented in this paper are combined with the remaining deterministic components (like model dynamics) of the original complex environmental model--a general circulation model or global climate model (GCM)--to constitute a hybrid GCM (HGCM). The parallel GCM and HGCM simulations produce very similar results but HGCM is significantly faster. The speed-up of model calculations opens the opportunity for model improvement. Examples of developed HGCMs illustrate the feasibility and efficiency of the new approach for modeling complex multidimensional interdisciplinary systems.

  15. Aberrant DNA damage response pathways may predict the outcome of platinum chemotherapy in ovarian cancer.

    PubMed

    Stefanou, Dimitra T; Bamias, Aristotelis; Episkopou, Hara; Kyrtopoulos, Soterios A; Likka, Maria; Kalampokas, Theodore; Photiou, Stylianos; Gavalas, Nikos; Sfikakis, Petros P; Dimopoulos, Meletios A; Souliotis, Vassilis L

    2015-01-01

    Ovarian carcinoma (OC) is the most lethal gynecological malignancy. Despite the advances in the treatment of OC with combinatorial regimens, including surgery and platinum-based chemotherapy, patients generally exhibit poor prognosis due to high chemotherapy resistance. Herein, we tested the hypothesis that DNA damage response (DDR) pathways are involved in resistance of OC patients to platinum chemotherapy. Selected DDR signals were evaluated in two human ovarian carcinoma cell lines, one sensitive (A2780) and one resistant (A2780/C30) to platinum treatment as well as in peripheral blood mononuclear cells (PBMCs) from OC patients, sensitive (n = 7) or resistant (n = 4) to subsequent chemotherapy. PBMCs from healthy volunteers (n = 9) were studied in parallel. DNA damage was evaluated by immunofluorescence γH2AX staining and comet assay. Higher levels of intrinsic DNA damage were found in A2780 than in A2780/C30 cells. Moreover, the intrinsic DNA damage levels were significantly higher in OC patients relative to healthy volunteers, as well as in platinum-sensitive patients relative to platinum-resistant ones (all P<0.05). Following carboplatin treatment, A2780 cells showed lower DNA repair efficiency than A2780/C30 cells. Also, following carboplatin treatment of PBMCs ex vivo, the DNA repair efficiency was significantly higher in healthy volunteers than in platinum-resistant patients and lowest in platinum-sensitive ones (t1/2 for loss of γH2AX foci: 2.7±0.5h, 8.8±1.9h and 15.4±3.2h, respectively; using comet assay, t1/2 of platinum-induced damage repair: 4.8±1.4h, 12.9±1.9h and 21.4±2.6h, respectively; all P<0.03). Additionally, the carboplatin-induced apoptosis rate was higher in A2780 than in A2780/C30 cells. In PBMCs, apoptosis rates were inversely correlated with DNA repair efficiencies of these cells, being significantly higher in platinum-sensitive than in platinum-resistant patients and lowest in healthy volunteers (all P<0.05). We conclude that

  16. An integrated pathway interaction network for the combination of four effective compounds from ShengMai preparations in the treatment of cardio-cerebral ischemic diseases

    PubMed Central

    Li, Fang; Lv, Yan-ni; Tan, Yi-sha; Shen, Kai; Zhai, Ke-feng; Chen, Hong-lin; Kou, Jun-ping; Yu, Bo-yang

    2015-01-01

    Aim: SMXZF (a combination of ginsenoside Rb1, ginsenoside Rg1, schizandrin and DT-13) derived from Chinese traditional medicine formula ShengMai preparations) is capable of alleviating cerebral ischemia-reperfusion injury in mice. In this study we used network pharmacology approach to explore the mechanisms of SMXZF in the treatment of cardio-cerebral ischemic diseases. Methods: Based upon the chemical predictors, such as chemical structure, pharmacological information and systems biology functional data analysis, a target-pathway interaction network was constructed to identify potential pathways and targets of SMXZF in the treatment of cardio-cerebral ischemia. Furthermore, the most related pathways were verified in TNF-α-treated human vascular endothelial EA.hy926 cells and H2O2-treated rat PC12 cells. Results: Three signaling pathways including the NF-κB pathway, oxidative stress pathway and cytokine network pathway were demonstrated to be the main signaling pathways. The results from the gene ontology analysis were in accordance with these signaling pathways. The target proteins were found to be associated with other diseases such as vision, renal and metabolic diseases, although they exerted therapeutic actions on cardio-cerebral ischemic diseases. Furthermore, SMXZF not only dose-dependently inhibited the phosphorylation of NF-κB, p50, p65 and IKKα/β in TNF-α-treated EA.hy926 cells, but also regulated the Nrf2/HO-1 pathway in H2O2-treated PC12 cells. Conclusion: NF-κB signaling pathway, oxidative stress pathway and cytokine network pathway are mainly responsible for the therapeutic actions of SMXZF against cardio-cerebral ischemic diseases. PMID:26456587

  17. Nitrate enrichment in groundwater from long-term intensive agriculture: its mechanistic pathways and prediction through modeling.

    PubMed

    Kundu, Manik Chandra; Mandal, Biswapati

    2009-08-01

    Nitrate (NO(3-)N) contamination of drinking groundwater is a serious worldwide problem. We studied the mechanistic pathways of the nitrate enrichment in a drinking groundwater system of an intensively cultivated district in India and predicted the enrichment through modeling. Analysis of groundwater samples (3472) showed that the nitrate content during the postmonsoon season (0.87 mg L(-1)) was higher than the nitrate content during the premonsoon season (0.58 mg L(-1)). It decreased with increasing depth of the aquifers sampled (r = -0.38), decreasing N-fertilizer application rate (r = 0.74), increasing average root length of the cropping systems followed (r = -0.54), and their efficacy for N-utilization (r = -0.61). Soil properties (136 representative samples) like bulk density (r = -0.72), hydraulic conductivity (r = 0.56), clay (r = -0.29), organic carbon (r = 0.72), NO(3-)N (r = 0.82), and potentially plantavailable soil N (pAvN) (r = 0.82) added to the variability of its enrichment. Prediction of nitrate enrichment by multiple regression equations with selected mastervariables explained 83.6-85.8% of the variability. Results indicate that potentially plant available soil nitrogen, commonly measured for fertilizer recommendation, may help in predicting nitrate enrichment under long-term intensively cultivated alluvial agroecosystems.

  18. 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

  19. 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

  20. 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.

  1. MAPK pathway activation leads to Bim loss and histone deacetylase inhibitor resistance: rationale to combine romidepsin with an MEK inhibitor.

    PubMed

    Chakraborty, Arup R; Robey, Robert W; Luchenko, Victoria L; Zhan, Zhirong; Piekarz, Richard L; Gillet, Jean-Pierre; Kossenkov, Andrew V; Wilkerson, Julia; Showe, Louise C; Gottesman, Michael M; Collie, Nathan L; Bates, Susan E

    2013-05-16

    To identify molecular determinants of histone deacetylase inhibitor (HDI) resistance, we selected HuT78 cutaneous T-cell lymphoma (CTCL) cells with romidepsin in the presence of P-glycoprotein inhibitors to prevent transporter upregulation. Resistant sublines were 250- to 385-fold resistant to romidepsin and were resistant to apoptosis induced by apicidin, entinostat, panobinostat, belinostat, and vorinostat. A custom TaqMan array identified increased insulin receptor (INSR) gene expression; immunoblot analysis confirmed increased protein expression and a four- to eightfold increase in mitogen-activated protein kinase (MAPK) kinase (MEK) phosphorylation in resistant cells compared with parental cells. Resistant cells were exquisitely sensitive to MEK inhibitors, and apoptosis correlated with restoration of proapoptotic Bim. Romidepsin combined with MEK inhibitors yielded greater apoptosis in cells expressing mutant KRAS compared with romidepsin treatment alone. Gene expression analysis of samples obtained from patients with CTCL enrolled on the NCI1312 phase 2 study of romidepsin in T-cell lymphoma suggested perturbation of the MAPK pathway by romidepsin. Immunohistochemical analysis of Bim expression demonstrated decreased expression in some skin biopsies at disease progression. These findings implicate increased activation of MEK and decreased Bim expression as a resistance mechanism to HDIs, supporting combination of romidepsin with MEK inhibitors in clinical trials.

  2. Structure prediction and molecular simulation of gases diffusion pathways in hydrogenase.

    PubMed

    Sundaram, Shanthy; Tripathi, Ashutosh; Gupta, Vipul

    2010-01-01

    Although hydrogen is considered to be one of the most promising future energy sources and the technical aspects involved in using it have advanced considerably, the future supply of hydrogen from renewable sources is still unsolved. The [Fe]- hydrogenase enzymes are highly efficient H(2) catalysts found in ecologically and phylogenetically diverse microorganisms, including the photosynthetic green alga, Chlamydomonas reinhardtii. While these enzymes can occur in several forms, H(2) catalysis takes place at a unique [FeS] prosthetic group or H-cluster, located at the active site. 3D structure of the protein hydA1 hydrogenase from Chlamydomonas reinhardtti was predicted using the MODELER 8v2 software. Conserved region was depicted from the NCBI CDD Search. Template selection was done on the basis NCBI BLAST results. For single template 1FEH was used and for multiple templates 1FEH and 1HFE were used. The result of the Homology modeling was verified by uploading the file to SAVS server. On the basis of the SAVS result 3D structure predicted using single template was chosen for performing molecular simulation. For performing molecular simulation three strategies were used. First the molecular simulation of the protein was performed in solvated box containing bulk water. Then 100 H(2) molecules were randomly inserted in the solvated box and two simulations of 50 and 100 ps were performed. Similarly 100 O(2) molecules were randomly placed in the solvated box and again 50 and 100 ps simulation were performed. Energy minimization was performed before each simulation was performed. Conformations were saved after each simulation. Analysis of the gas diffusion was done on the basis of RMSD, Radius of Gyration and no. of gas molecule/ps plot. PMID:21364783

  3. Structure prediction and molecular simulation of gases diffusion pathways in hydrogenase.

    PubMed

    Sundaram, Shanthy; Tripathi, Ashutosh; Gupta, Vipul

    2010-10-06

    Although hydrogen is considered to be one of the most promising future energy sources and the technical aspects involved in using it have advanced considerably, the future supply of hydrogen from renewable sources is still unsolved. The [Fe]- hydrogenase enzymes are highly efficient H(2) catalysts found in ecologically and phylogenetically diverse microorganisms, including the photosynthetic green alga, Chlamydomonas reinhardtii. While these enzymes can occur in several forms, H(2) catalysis takes place at a unique [FeS] prosthetic group or H-cluster, located at the active site. 3D structure of the protein hydA1 hydrogenase from Chlamydomonas reinhardtti was predicted using the MODELER 8v2 software. Conserved region was depicted from the NCBI CDD Search. Template selection was done on the basis NCBI BLAST results. For single template 1FEH was used and for multiple templates 1FEH and 1HFE were used. The result of the Homology modeling was verified by uploading the file to SAVS server. On the basis of the SAVS result 3D structure predicted using single template was chosen for performing molecular simulation. For performing molecular simulation three strategies were used. First the molecular simulation of the protein was performed in solvated box containing bulk water. Then 100 H(2) molecules were randomly inserted in the solvated box and two simulations of 50 and 100 ps were performed. Similarly 100 O(2) molecules were randomly placed in the solvated box and again 50 and 100 ps simulation were performed. Energy minimization was performed before each simulation was performed. Conformations were saved after each simulation. Analysis of the gas diffusion was done on the basis of RMSD, Radius of Gyration and no. of gas molecule/ps plot.

  4. Precise Classification of Cervical Carcinomas Combined with Somatic Mutation Profiling Contributes to Predicting Disease Outcome

    PubMed Central

    Spaans, Vivian M.; Trietsch, Marjolijn D.; Peters, Alexander A. W.; Osse, Michelle; ter Haar, Natalja; Fleuren, Gert J.; Jordanova, Ekaterina S.

    2015-01-01

    Introduction Squamous cell carcinoma (SCC), adenocarcinoma (AC), and adenosquamous carcinoma (ASC) are the most common histological subtypes of cervical cancer. Differences in the somatic mutation profiles of these subtypes have been suggested. We investigated the prevalence of somatic hot-spot mutations in three well-defined cohorts of SCC, AC, and ASC and determined the additional value of mutation profiling in predicting disease outcome relative to well-established prognostic parameters. Materials and Methods Clinicopathological data were collected for 301 cervical tumors classified as SCC (n=166), AC (n=55), or ASC (n=80). Mass spectrometry was used to analyze 171 somatic hot-spot mutations in 13 relevant genes. Results In 103 (34%) tumors, 123 mutations were detected (36% in SCC, 38% in AC, and 28% in ASC), mostly in PIK3CA (20%) and KRAS (7%). PIK3CA mutations occurred more frequently in SCC than AC (25% vs. 11%, P=0.025), whereas KRAS mutations occurred more frequently in AC than SCC (24% vs. 3%, P<0.001) and ASC (24% vs. 3%, P<0.001). A positive mutation status correlated with worse disease-free survival (HR 1.57, P=0.043). In multivariate analysis, tumor diameter, parametrial infiltration, and lymph node metastasis, but not the presence of a somatic mutation, were independent predictors of survival. Conclusion Potentially targetable somatic mutations occurred in 34% of cervical tumors with different distributions among histological subtypes. Precise classification of cervical carcinomas in combination with mutation profiling is valuable for predicting disease outcome and may guide the development and selection of tumor-specific treatment approaches. PMID:26197069

  5. Combination of honokiol and magnolol inhibits hepatic steatosis through AMPK-SREBP-1 c pathway

    PubMed Central

    Lee, Ju-Hee; Jung, Ji Yun; Jang, Eun Jeong; Jegal, Kyung Hwan; Moon, Soo Young; Ku, Sae Kwang; Kang, Seung Ho; Cho, Il Je; Park, Sook Jahr; Lee, Jong Rok; Zhao, Rong Jie; Kim, Sang Chan

    2015-01-01

    Honokiol and magnolol, as pharmacological biphenolic compounds of Magnolia officinalis, have been reported to have antioxidant and anti-inflammatory properties. Sterol regulatory element binding protein-1 c (SREBP-1 c) plays an important role in the development and processing of steatosis in the liver. In the present study, we investigated the effects of a combination of honokiol and magnolol on SREBP-1 c-dependent lipogenesis in hepatocytes as well as in mice with fatty liver due to consumption of high-fat diet (HFD). Liver X receptor α (LXRα) agonists induced activation of SREBP-1 c and expression of lipogenic genes, which were blocked by co-treatment of honokiol and magnolol (HM). Moreover, a combination of HM potently increased mRNA of fatty acid oxidation genes. HM induced AMP-activated protein kinase (AMPK), an inhibitory kinase of the LXRα-SREBP-1 c pathway. The role of AMPK activation induced by HM was confirmed using an inhibitor of AMPK, Compound C, which reversed the ability of HM to both inhibit SREBP-1 c induction as well as induce genes for fatty acid oxidation. In mice, HM administration for four weeks ameliorated HFD-induced hepatic steatosis and liver dysfunction, as indicated by plasma parameters and Oil Red O staining. Taken together, our results demonstrated that a combination of HM has beneficial effects on inhibition of fatty liver and SREBP-1 c-mediated hepatic lipogenesis, and these events may be mediated by AMPK activation. PMID:25125496

  6. Common Variation in Vitamin D Pathway Genes Predicts Circulating 25-Hydroxyvitamin D Levels among African Americans

    PubMed Central

    Signorello, Lisa B.; Shi, Jiajun; Cai, Qiuyin; Zheng, Wei; Williams, Scott M.; Long, Jirong; Cohen, Sarah S.; Li, Guoliang; Hollis, Bruce W.; Smith, Jeffrey R.; Blot, William J.

    2011-01-01

    Vitamin D is implicated in a wide range of health outcomes, and although environmental predictors of vitamin D levels are known, the genetic drivers of vitamin D status remain to be clarified. African Americans are a group at particularly high risk for vitamin D insufficiency but to date have been virtually absent from studies of genetic predictors of circulating vitamin D levels. Within the Southern Community Cohort Study, we investigated the association between 94 single nucleotide polymorphisms (SNPs) in five vitamin D pathway genes (GC, VDR, CYP2R1, CYP24A1, CYP27B1) and serum 25-hydroxyvitamin D (25(OH)D) levels among 379 African American and 379 Caucasian participants. We found statistically significant associations with three SNPs (rs2298849 and rs2282679 in the GC gene, and rs10877012 in the CYP27B1 gene), although only for African Americans. A genotype score, representing the number of risk alleles across the three SNPs, alone accounted for 4.6% of the variation in serum vitamin D among African Americans. A genotype score of 5 (vs. 1) was also associated with a 7.1 ng/mL reduction in serum 25(OH)D levels and a six-fold risk of vitamin D insufficiency (<20 ng/mL) (odds ratio 6.0, p = 0.01) among African Americans. With African ancestry determined from a panel of 276 ancestry informative SNPs, we found that high risk genotypes did not cluster among those with higher African ancestry. This study is one of the first to investigate common genetic variation in relation to vitamin D levels in African Americans, and the first to evaluate how vitamin D-associated genotypes vary in relation to African ancestry. These results suggest that further evaluation of genetic contributors to vitamin D status among African Americans may help provide insights regarding racial health disparities or enable the identification of subgroups especially in need of vitamin D-related interventions. PMID:22205958

  7. Combinations of biomarkers and Milan criteria for predicting hepatocellular carcinoma recurrence after liver transplantation.

    PubMed

    Chaiteerakij, Roongruedee; Zhang, Xiaodan; Addissie, Benyam D; Mohamed, Essa A; Harmsen, William S; Theobald, Paul J; Peters, Brian E; Balsanek, Joseph G; Ward, Melissa M; Giama, Nasra H; Moser, Catherine D; Oseini, Abdul M; Umeda, Naoki; Venkatesh, Sudhakar; Harnois, Denise M; Charlton, Michael R; Yamada, Hiroyuki; Satomura, Shinji; Algeciras-Schimnich, Alicia; Snyder, Melissa R; Therneau, Terry M; Roberts, Lewis R

    2015-05-01

    Growing evidence suggests that pretransplant alpha-fetoprotein (AFP) predicts outcomes of hepatocellular carcinoma (HCC) patients treated with liver transplantation. We aimed to determine whether pretransplant AFP, Lens culinaris agglutinin-reactive alpha-fetoprotein (AFP-L3), and des-gamma-carboxyprothrombin (DCP) predicted HCC recurrence after transplantation. A retrospective cohort study of 313 HCC patients undergoing transplantation between 2000 and 2008 was conducted, and 48 (15.3%) developed recurrence during a median follow-up of 90.8 months. The 127 patients with available serum drawn before transplantation were included; they included 86 without recurrence and 41 with recurrence. Serum was tested for AFP, AFP-L3%, and DCP in a blinded fashion with the μTASWako i30 immunoanalyzer. All biomarkers were significantly associated with HCC recurrence. The hazard ratios (HRs) were 3.5 [95% confidence interval (CI), 1.9-6.7; P < 0.0001] for DCP ≥ 7.5 ng/mL and 2.8 (95% CI, 1.4-5.4; P = 0.002) for AFP ≥ 250 ng/mL. The HR increased to 5.2 (95% CI, 2.3-12.0; P < 0.0001) when AFP ≥ 250 ng/mL and DCP ≥7.5 ng/mL were considered together. When they were combined with the Milan criteria, the HR increased from 2.6 (95% CI, 1.4-4.7; P = 0.003) for outside the Milan criteria to 8.6 (95% CI, 3.0-24.6; P < 0.0001) for outside the Milan criteria and AFP ≥ 250 ng/mL and to 7.2 (95% CI, 2.8-18.1; P < 0.0001) for outside the Milan criteria and DCP ≥7.5 ng/mL. Our findings suggest that biomarkers are useful for predicting the risk of HCC recurrence after transplantation. Using both biomarkers and the Milan criteria may be better than using the Milan criteria alone in optimizing the decision of liver transplantation eligibility. PMID:25789635

  8. 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. PMID:23377928

  9. 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

  10. 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.

  11. 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

  12. Application of a GRNN oracle to the intelligent combination of several breast cancer benign/malignant predictive paradigms

    NASA Astrophysics Data System (ADS)

    Land, Walker H., Jr.; Masters, Timothy D.; Lo, Joseph Y.

    2000-06-01

    The General Regression Neural Network (GRNN) is well known to be an extremely effective prediction model in a wide variety of problems. It has been recently established that in many prediction problems, the results obtained by intelligently combining the outputs of several different prediction models are generally superior to the results obtained by using any one of the models. An overseer model that combines predictions from other independently trained prediction models is often called an oracle. This paper describes how the GRNN is modified to serve as a powerful oracle for combining decisions from four different breast cancer benign/malignant prediction models using mammogram data. Specifically, the GRNN oracle combines decisions from an evolutionary programming derived neural network, a probabilistic neural network, a fully- interconnected three-layer, feed-forward, error backpropagation network, and a linear discriminant analysis model. In all experiments conducted, the oracle consistently provided superior benign/malignant classification discrimination as measured by the receiver operator characteristic curve Az index values.

  13. 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

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

    PubMed

    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

  15. 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.

  16. Structure predicts function: Combining non-invasive electrophysiology with in-vivo histology

    PubMed Central

    Helbling, Saskia; Teki, Sundeep; Callaghan, Martina F.; Sedley, William; Mohammadi, Siawoosh; Griffiths, Timothy D.; Weiskopf, Nikolaus; Barnes, Gareth R.

    2015-01-01

    We present an approach for combining high resolution MRI-based myelin mapping with functional information from electroencephalography (EEG) or magnetoencephalography (MEG). The main contribution to the primary currents detectable with EEG and MEG comes from ionic currents in the apical dendrites of cortical pyramidal cells, aligned perpendicularly to the local cortical surface. We provide evidence from an in-vivo experiment that the variation in MRI-based myeloarchitecture measures across the cortex predicts the variation of the current density over individuals and thus is of functional relevance. Equivalent current dipole locations and moments due to pitch onset evoked response fields (ERFs) were estimated by means of a variational Bayesian algorithm. The myeloarchitecture was estimated indirectly from individual high resolution quantitative multi-parameter maps (MPMs) acquired at 800 μm isotropic resolution. Myelin estimates across cortical areas correlated positively with dipole magnitude. This correlation was spatially specific: regions of interest in the auditory cortex provided significantly better models than those covering whole hemispheres. Based on the MPM data we identified the auditory cortical area TE1.2 as the most likely origin of the pitch ERFs measured by MEG. We can now proceed to exploit the higher spatial resolution of quantitative MPMs to identify the cortical origin of M/EEG signals, inform M/EEG source reconstruction and explore structure–function relationships at a fine structural level in the living human brain. PMID:25529007

  17. 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

  18. PRBP: Prediction of RNA-Binding Proteins Using a Random Forest Algorithm Combined with an RNA-Binding Residue Predictor.

    PubMed

    Ma, Xin; Guo, Jing; Xiao, Ke; Sun, Xiao

    2015-01-01

    The prediction of RNA-binding proteins is an incredibly challenging problem in computational biology. Although great progress has been made using various machine learning approaches with numerous features, the problem is still far from being solved. In this study, we attempt to predict RNA-binding proteins directly from amino acid sequences. A novel approach, PRBP predicts RNA-binding proteins using the information of predicted RNA-binding residues in conjunction with a random forest based method. For a given protein, we first predict its RNA-binding residues and then judge whether the protein binds RNA or not based on information from that prediction. If the protein cannot be identified by the information associated with its predicted RNA-binding residues, then a novel random forest predictor is used to determine if the query protein is a RNA-binding protein. We incorporated features of evolutionary information combined with physicochemical features (EIPP) and amino acid composition feature to establish the random forest predictor. Feature analysis showed that EIPP contributed the most to the prediction of RNA-binding proteins. The results also showed that the information from the RNA-binding residue prediction improved the overall performance of our RNA-binding protein prediction. It is anticipated that the PRBP method will become a useful tool for identifying RNA-binding proteins. A PRBP Web server implementation is freely available at http://www.cbi.seu.edu.cn/PRBP/.

  19. Combined overexpression of genes involved in pentose phosphate pathway enables enhanced D-xylose utilization by Clostridium acetobutylicum.

    PubMed

    Jin, Lin; Zhang, Hui; Chen, Liwen; Yang, Chen; Yang, Sheng; Jiang, Weihong; Gu, Yang

    2014-03-10

    D-Xylose utilization by Clostridium acetobutylicum, an important industrial microorganism used in ABE (Acetone, Butanol and Ethanol) production, has attracted increasing interests. We demonstrated previously that co-overexpression of genes, encoding d-xylose symporter, D-xylose isomerase and xylulokinase, improved D-xylose utilization by C. acetobutylicum (Xiao, H., et al., 2011. Applied and Environmental Microbiology 77, 7886-7895). Here, we further identified genes involved in PPP (Pentose Phosphate Pathway) in C. acetobutylicum and evaluated their contribution to d-xylose utilization. Among all the candidate genes, the CAC1347, CAC1348, CAC1730 and CAC2880 were validated to encode genes tal, tkl, rpe and rpi, four key genes involved in PPP, respectively. The following combined overexpression of these genes conferred a significantly improved xylose-utilizing ability to the recombinant strain, reaching a solvent titer 42% higher than that of the wild-type strain. This finding offers a useful strategy to optimize d-xylose utilization by C. acetobutylicum.

  20. 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.

  1. DARTAB: a program to combine airborne radionuclide environmental exposure data with dosimetric and health effects data to generate tabulations of predicted health impacts

    SciTech Connect

    Begovich, C.L.; Eckerman, K.F.; Schlatter, E.C.; Ohr, S.Y.; Chester, R.O.

    1981-08-01

    The DARTAB computer code combines radionuclide environmental exposure data with dosimetric and health effects data to generate tabulations of the predicted impact of radioactive airborne effluents. DARTAB is independent of the environmental transport code used to generate the environmental exposure data and the codes used to produce the dosimetric and health effects data. Therefore human dose and risk calculations need not be added to every environmental transport code. Options are included in DARTAB to permit the user to request tabulations by various topics (e.g., cancer site, exposure pathway, etc.) to facilitate characterization of the human health impacts of the effluents. The DARTAB code was written at ORNL for the US Environmental Protection Agency, Office of Radiation Programs.

  2. 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...

  3. 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...

  4. 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

  5. The prediction of T- and B-combined epitope and tertiary structure of the Eg95 antigen of Echinococcus granulosus

    PubMed Central

    MA, XIUMIN; ZHOU, XIAOTAO; ZHU, YUEJIE; LI, YANHUA; WANG, HONGYING; MAMUTI, WULAMU; LI, YUJIAO; WEN, HAO; DING, JIANBING

    2013-01-01

    Echinococcosis, also known as hydatid disease, is a type of zoonotic parasitic disease caused by the Echinococcus larvae infection. The disease is severely harmful to both humans and animals. Research and development of an epitope vaccine is crucial. To determine the dominant epitopes of the Eg95 antigen, the tertiary structure and the T- and B-combined epitope of the Eg95 protein for Echinococcus granulosus were predicted and analyzed in the present study. The tertiary structure of the Eg95 protein was predicted using the 3DLigandsite server and RasMol software. The T- and B-combined epitope of the Eg95 antigen was analyzed using the DNAStar (V5.0), IEDB, SYFPEITHI and BIMAS. Tertiary structure prediction results showed that there were potential epitopes in Eg95 antigen. Bioinformatics analysis revealed the T- and B-combined epitopes of Eg95 antigen. Four and six T- and B-combined epitopes induced immune responses in humans and mice. Additionally, four T- and B-combined epitopes induced immune responses in both humans and mice. The tertiary structure and T- and B-combined epitopes of the Eg95 protein were also determined. The results obtained in the present study may be beneficial in the investigation of Eg95 antigenicity and the development of dominant epitope vaccines. PMID:24137242

  6. 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

  7. Comparison of the Combined Obesity Indices to Predict Cardiovascular Diseases Risk Factors and Metabolic Syndrome in Northeast China

    PubMed Central

    Tao, Yuchun; Yu, Jianxing; Tao, Yuhui; Pang, Hui; Yu, Yang; Yu, Yaqin; Jin, Lina

    2016-01-01

    Background: Obesity is associated with cardiovascular disease (CVD) risk factors (hypertension, dyslipidemia and diabetes) and metabolic syndrome (MetS), and it may be flawed that most studies only use one obesity index to predict these risk factors. Therefore, our study aims to compare the various combined obesity indices systematically, and to find the optimal combined obesity indices to predict CVD risk factors and MetS. Methods: A total of 16,766 participants aged 18–79 years old were recruited in Jilin Province in 2012. Receiver operating characteristic curve (ROC) curves and multiple logistic regressions were used to evaluate the predictive capacity of the combined obesity indices for CVD risk factors and MetS. Results: The adjusted area under receiver operating characteristic (AUROC) with two combined obesity indices had been improved up to 19.45%, compared with one single obesity index. In addition, body mass index (BMI) and waist circumference (WC) were the optimal combinations, where the AUROC (95% confidence interval (CI)) for hypertension, dyslipidemia, diabetes and MetS in males were 0.730 (0.718, 0.740), 0.694 (0.682, 0.706), 0.725 (0.709, 0.742) and 0.820 (0.810, 0.830), and in females were 0.790 (0.780, 0.799), 0.727 (0.717, 0.738), 0.746 (0.731, 0.761) and 0.828 (0.820, 0.837), respectively. Conclusions: The more abnormal obesity indices that one has the higher the risk for CVD risk factors and MetS, especially in males. In addition, the combined obesity indices have better predictions than one obesity index, where BMI and WC are the optimal combinations. PMID:27517940

  8. Combining metabolic pathway analysis with Evolutionary Game Theory: explaining the occurrence of low-yield pathways by an analytic optimization approach.

    PubMed

    Schuster, Stefan; de Figueiredo, Luis F; Schroeter, Anja; Kaleta, Christoph

    2011-08-01

    Elementary-mode analysis is a powerful method for detecting all potential pathways in a metabolic network and computing the associated molar yields. Metabolic pathways can be interpreted as different strategies of organisms. Thus, methods from Evolutionary Game Theory can be employed. In Flux Balance Analysis (FBA), it is usually assumed that molar yields of relevant products (such as biomass or ATP) have been maximized during evolution. This has been questioned on game-theoretical grounds. In particular, in situations that can be characterized as a Prisoner's Dilemma, maximization of flux is not in line with maximization of yield. Under other conditions (that is, for other parameter values of maximal velocities), a Harmony game can result, where the above two maximization criteria give the same result. Here, we analyse the optimal situations under varying conditions. In particular, we consider the case where the cell can allocate a certain amount of protein on several enzymes in a varying distribution and model this by a linear programming problem in which not only the rates but also the maximal velocities are variable. It turns out that in the case of low or moderate synthesis costs for the enzymes of the high-yield pathway, maximizing pathway flux is in line with maximizing molar yield while in the case of high costs, it is not. This may explain the observation that many cells such as striated muscle cells, tumour cells, activated lymphocytes and several yeasts do not reallocate protein away from glycolytic enzymes towards TCA cycle and respiratory chain enzymes, in spite of the higher efficiency of respiration. This provides a straightforward explanation of the Warburg effect in tumour cells.

  9. Computation and Experiment: A Powerful Combination to Understand and Predict Reactivities.

    PubMed

    Sperger, Theresa; Sanhueza, Italo A; Schoenebeck, Franziska

    2016-06-21

    discussed. Additional combined experimental and computational studies are described for alternative metals, these include the discussion of the factors that control C-H versus C-C activation in the aerobic Cu-catalyzed oxidation of ketones, and ligand and additive effects on the nature and favored oxidation state of the active catalyst in Ni-catalyzed trifluoromethylthiolations of aryl chlorides. Examples of successful computational reactivity predictions along with experimental verifications are then presented. This includes the design of a fluorinated ligand [(CF3)2P(CH2)2P(CF3)2] for the challenging reductive elimination of ArCF3 from Pd(II) as well as the guidance of substrate scope (functional group tolerance and suitable leaving group) in the Ni-catalyzed trifluoromethylthiolation of C(sp(2))-O bonds. In summary, this account aims to convey the benefits of integrating computational studies in experimental research to increase understanding of observed phenomena and guide future experiments. PMID:27171796

  10. Serum biomarkers combined with uterine artery Doppler in prediction of preeclampsia

    PubMed Central

    Li, Lijie; Zheng, Yanmei; Zhu, Ying; Li, Jianchun

    2016-01-01

    First-trimester screening may be a major advantage over a second-trimester approach since it opens prospects for early and more efficient interventions. The aim of the current study was to evaluate whether the measurement of maternal serum inhibin A, activin A and placental growth factor (PlGF) at three to four months gestation with the second-trimester uterine artery pulsatility index (PI) are useful in predicting preeclampsia in a group of nulliparous women. All the patients also underwent uterine artery Doppler examination to measure the PI at 22–24 weeks gestation. Inhibin A, activin A and PlGF were measured using an ELISA by an examiner who was blinded to the pregnancy outcome. Thirty-eight cases with preeclampsia and 100 controls were analyzed. Second-trimester uterine artery PI and marker levels were expressed as multiples of the median (MoM). The uterine artery PI was increased in pregnancies with preeclampsia compared with controls. In pregnancies that developed preeclampsia, the uterine artery PI was increased (1.61±0.047 vs. 1.02±0.049, P<0.001), as was the level of inhibin A (1.72±0.023 vs. 1.03±0.063, P<0.001) and the level of activin A (1.68±0.38 vs. 1.06±0.42, P<0.001) compared with the controls. In contrast, the level of PlGF was decreased in pregnancies that developed preeclampsia compared with the controls (0.69±0.23 vs. 1.00±0.26, P<0.001). A combination of activin A, PlGF and uterine artery PI gave an AUC of 0.915 (95% CI, 0.812–0.928; P<0.001) with a sensitivity of 91% at a specificity of 82%. In our study, we demonstrated that both serum inhibin A and activin A levels were increased, while the PlGF level was decreased in the early second-trimester in women who developed preeclampsia.

  11. Serum biomarkers combined with uterine artery Doppler in prediction of preeclampsia

    PubMed Central

    Li, Lijie; Zheng, Yanmei; Zhu, Ying; Li, Jianchun

    2016-01-01

    First-trimester screening may be a major advantage over a second-trimester approach since it opens prospects for early and more efficient interventions. The aim of the current study was to evaluate whether the measurement of maternal serum inhibin A, activin A and placental growth factor (PlGF) at three to four months gestation with the second-trimester uterine artery pulsatility index (PI) are useful in predicting preeclampsia in a group of nulliparous women. All the patients also underwent uterine artery Doppler examination to measure the PI at 22–24 weeks gestation. Inhibin A, activin A and PlGF were measured using an ELISA by an examiner who was blinded to the pregnancy outcome. Thirty-eight cases with preeclampsia and 100 controls were analyzed. Second-trimester uterine artery PI and marker levels were expressed as multiples of the median (MoM). The uterine artery PI was increased in pregnancies with preeclampsia compared with controls. In pregnancies that developed preeclampsia, the uterine artery PI was increased (1.61±0.047 vs. 1.02±0.049, P<0.001), as was the level of inhibin A (1.72±0.023 vs. 1.03±0.063, P<0.001) and the level of activin A (1.68±0.38 vs. 1.06±0.42, P<0.001) compared with the controls. In contrast, the level of PlGF was decreased in pregnancies that developed preeclampsia compared with the controls (0.69±0.23 vs. 1.00±0.26, P<0.001). A combination of activin A, PlGF and uterine artery PI gave an AUC of 0.915 (95% CI, 0.812–0.928; P<0.001) with a sensitivity of 91% at a specificity of 82%. In our study, we demonstrated that both serum inhibin A and activin A levels were increased, while the PlGF level was decreased in the early second-trimester in women who developed preeclampsia. PMID:27698752

  12. 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. PMID:27100168

  13. 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.

  14. 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. PMID:27209454

  15. Wavelet and ANN combination model for prediction of daily suspended sediment load in rivers.

    PubMed

    Rajaee, Taher

    2011-07-01

    In this research, a new wavelet artificial neural network (WANN) model was proposed for daily suspended sediment load (SSL) prediction in rivers. In the developed model, wavelet analysis was linked to an artificial neural network (ANN). For this purpose, daily observed time series of river discharge (Q) and SSL in Yadkin River at Yadkin College, NC station in the USA were decomposed to some sub-time series at different levels by wavelet analysis. Then, these sub-time series were imposed to the ANN technique for SSL time series modeling. To evaluate the model accuracy, the proposed model was compared with ANN, multi linear regression (MLR), and conventional sediment rating curve (SRC) models. The comparison of prediction accuracy of the models illustrated that the WANN was the most accurate model in SSL prediction. Results presented that the WANN model could satisfactorily simulate hysteresis phenomenon, acceptably estimate cumulative SSL, and reasonably predict high SSL values.

  16. 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

  17. 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.

  18. 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

  19. Using Combined Computational Techniques to Predict the Glass Transition Temperatures of Aromatic Polybenzoxazines

    PubMed Central

    Mhlanga, Phumzile; Wan Hassan, Wan Aminah; Hamerton, Ian; Howlin, Brendan J.

    2013-01-01

    The Molecular Operating Environment software (MOE) is used to construct a series of benzoxazine monomers for which a variety of parameters relating to the structures (e.g. water accessible surface area, negative van der Waals surface area, hydrophobic volume and the sum of atomic polarizabilities, etc.) are obtained and quantitative structure property relationships (QSPR) models are formulated. Three QSPR models (formulated using up to 5 descriptors) are first used to make predictions for the initiator data set (n = 9) and compared to published thermal data; in all of the QSPR models there is a high level of agreement between the actual data and the predicted data (within 0.63–1.86 K of the entire dataset). The water accessible surface area is found to be the most important descriptor in the prediction of Tg. Molecular modelling simulations of the benzoxazine polymer (minus initiator) carried out at the same time using the Materials Studio software suite provide an independent prediction of Tg. Predicted Tg values from molecular modelling fall in the middle of the range of the experimentally determined Tg values, indicating that the structure of the network is influenced by the nature of the initiator used. Hence both techniques can provide predictions of glass transition temperatures and provide complementary data for polymer design. PMID:23326419

  20. 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

  1. Combining quantitative trait loci and heterogeneous microarray data analyses reveals putative candidate pathways affecting mastitis in cattle.

    PubMed

    Lewandowska-Sabat, A M; Günther, J; Seyfert, H M; Olsaker, I

    2012-12-01

    Mastitis is a frequent disease and considerable problem for the global dairy industry. Identification of solutions leading to the development of new control strategies is therefore of high importance. In this study, we have integrated genomic data from genome-wide association mapping in cattle with transcriptomic data from microarray studies of several mastitis pathogens and host species in vitro and in vivo. To identify significant candidate pathways directly and indirectly involved in the immune response to mastitis, ingenuity pathway analysis (ipa) and database for annotation, visualization and integrated discovery bioinformatic (david) were applied. Several candidate pathways were found. Of great interest are IL-17 and IL-8 signalling pathways, responsible for the recruitment and migration of inflammatory cells into tissue during inflammation and infection. These results may emphasize further functional studies for identification of factors contributing to resistance to mastitis pathogens in cattle.

  2. Computing reward-prediction error: an integrated account of cortical timing and basal-ganglia pathways for appetitive and aversive learning.

    PubMed

    Morita, Kenji; Kawaguchi, Yasuo

    2015-08-01

    There are two prevailing notions regarding the involvement of the corticobasal ganglia system in value-based learning: (i) the direct and indirect pathways of the basal ganglia are crucial for appetitive and aversive learning, respectively, and (ii) the activity of midbrain dopamine neurons represents reward-prediction error. Although (ii) constitutes a critical assumption of (i), it remains elusive how (ii) holds given (i), with the basal-ganglia influence on the dopamine neurons. Here we present a computational neural-circuit model that potentially resolves this issue. Based on the latest analyses of the heterogeneous corticostriatal neurons and connections, our model posits that the direct and indirect pathways, respectively, represent the values of upcoming and previous actions, and up-regulate and down-regulate the dopamine neurons via the basal-ganglia output nuclei. This explains how the difference between the upcoming and previous values, which constitutes the core of reward-prediction error, is calculated. Simultaneously, it predicts that blockade of the direct/indirect pathway causes a negative/positive shift of reward-prediction error and thereby impairs learning from positive/negative error, i.e. appetitive/aversive learning. Through simulation of reward-reversal learning and punishment-avoidance learning, we show that our model could indeed account for the experimentally observed features that are suggested to support notion (i) and could also provide predictions on neural activity. We also present a behavioral prediction of our model, through simulation of inter-temporal choice, on how the balance between the two pathways relates to the subject's time preference. These results indicate that our model, incorporating the heterogeneity of the cortical influence on the basal ganglia, is expected to provide a closed-circuit mechanistic understanding of appetitive/aversive learning.

  3. Computing reward-prediction error: an integrated account of cortical timing and basal-ganglia pathways for appetitive and aversive learning.

    PubMed

    Morita, Kenji; Kawaguchi, Yasuo

    2015-08-01

    There are two prevailing notions regarding the involvement of the corticobasal ganglia system in value-based learning: (i) the direct and indirect pathways of the basal ganglia are crucial for appetitive and aversive learning, respectively, and (ii) the activity of midbrain dopamine neurons represents reward-prediction error. Although (ii) constitutes a critical assumption of (i), it remains elusive how (ii) holds given (i), with the basal-ganglia influence on the dopamine neurons. Here we present a computational neural-circuit model that potentially resolves this issue. Based on the latest analyses of the heterogeneous corticostriatal neurons and connections, our model posits that the direct and indirect pathways, respectively, represent the values of upcoming and previous actions, and up-regulate and down-regulate the dopamine neurons via the basal-ganglia output nuclei. This explains how the difference between the upcoming and previous values, which constitutes the core of reward-prediction error, is calculated. Simultaneously, it predicts that blockade of the direct/indirect pathway causes a negative/positive shift of reward-prediction error and thereby impairs learning from positive/negative error, i.e. appetitive/aversive learning. Through simulation of reward-reversal learning and punishment-avoidance learning, we show that our model could indeed account for the experimentally observed features that are suggested to support notion (i) and could also provide predictions on neural activity. We also present a behavioral prediction of our model, through simulation of inter-temporal choice, on how the balance between the two pathways relates to the subject's time preference. These results indicate that our model, incorporating the heterogeneity of the cortical influence on the basal ganglia, is expected to provide a closed-circuit mechanistic understanding of appetitive/aversive learning. PMID:26095906

  4. 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

  5. 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

  6. Combined effect of water loss and wounding stress on gene activation of metabolic pathways associated with phenolic biosynthesis in carrot

    PubMed Central

    Becerra-Moreno, Alejandro; Redondo-Gil, Mónica; Benavides, Jorge; Nair, Vimal; Cisneros-Zevallos, Luis; Jacobo-Velázquez, Daniel A.

    2015-01-01

    The application of postharvest abiotic stresses is an effective strategy to activate the primary and secondary metabolism of plants inducing the accumulation of antioxidant phenolic compounds. In the present study, the effect of water stress applied alone and in combination with wounding stress on the activation of primary (shikimic acid) and secondary (phenylpropanoid) metabolic pathways related with the accumulation of phenolic compound in plants was evaluated. Carrot (Daucus carota) was used as model system for this study, and the effect of abiotic stresses was evaluated at the gene expression level and on the accumulation of metabolites. As control of the study, whole carrots were stored under the same conditions. Results demonstrated that water stress activated the primary and secondary metabolism of carrots, favoring the lignification process. Likewise, wounding stress induced higher activation of the primary and secondary metabolism of carrots as compared to water stress alone, leading to higher accumulation of shikimic acid, phenolic compounds, and lignin. Additional water stress applied on wounded carrots exerted a synergistic effect on the wound-response at the gene expression level. For instance, when wounded carrots were treated with water stress, the tissue showed 20- and 14-fold increases in the relative expression of 3-deoxy-D-arabino-heptulosanate synthase and phenylalanine ammonia-lyase genes, respectively. However, since lignification was increased, lower accumulation of phenolic compounds was detected. Indicatively, at 48 h of storage, wounded carrots treated with water stress showed ~31% lower levels of phenolic compounds and ~23% higher lignin content as compared with wounded controls. In the present study, it was demonstrated that water stress is one of the pivotal mechanism of the wound-response in carrot. Results allowed the elucidation of strategies to induce the accumulation of specific primary or secondary metabolites when plants are

  7. Combined effect of water loss and wounding stress on gene activation of metabolic pathways associated with phenolic biosynthesis in carrot.

    PubMed

    Becerra-Moreno, Alejandro; Redondo-Gil, Mónica; Benavides, Jorge; Nair, Vimal; Cisneros-Zevallos, Luis; Jacobo-Velázquez, Daniel A

    2015-01-01

    The application of postharvest abiotic stresses is an effective strategy to activate the primary and secondary metabolism of plants inducing the accumulation of antioxidant phenolic compounds. In the present study, the effect of water stress applied alone and in combination with wounding stress on the activation of primary (shikimic acid) and secondary (phenylpropanoid) metabolic pathways related with the accumulation of phenolic compound in plants was evaluated. Carrot (Daucus carota) was used as model system for this study, and the effect of abiotic stresses was evaluated at the gene expression level and on the accumulation of metabolites. As control of the study, whole carrots were stored under the same conditions. Results demonstrated that water stress activated the primary and secondary metabolism of carrots, favoring the lignification process. Likewise, wounding stress induced higher activation of the primary and secondary metabolism of carrots as compared to water stress alone, leading to higher accumulation of shikimic acid, phenolic compounds, and lignin. Additional water stress applied on wounded carrots exerted a synergistic effect on the wound-response at the gene expression level. For instance, when wounded carrots were treated with water stress, the tissue showed 20- and 14-fold increases in the relative expression of 3-deoxy-D-arabino-heptulosanate synthase and phenylalanine ammonia-lyase genes, respectively. However, since lignification was increased, lower accumulation of phenolic compounds was detected. Indicatively, at 48 h of storage, wounded carrots treated with water stress showed ~31% lower levels of phenolic compounds and ~23% higher lignin content as compared with wounded controls. In the present study, it was demonstrated that water stress is one of the pivotal mechanism of the wound-response in carrot. Results allowed the elucidation of strategies to induce the accumulation of specific primary or secondary metabolites when plants are

  8. 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...

  9. Combined treatment with SAHA, bortezomib, and clarithromycin for concomitant targeting of aggresome formation and intracellular proteolytic pathways enhances ER stress-mediated cell death in breast cancer cells.

    PubMed

    Komatsu, Seiichiro; Moriya, Shota; Che, Xiao-Fang; Yokoyama, Tomohisa; Kohno, Norio; Miyazawa, Keisuke

    2013-07-19

    The ubiquitin-proteasome pathway and the autophagy-lysosome pathway are two major intracellular protein degradation systems. We previously reported that clarithromycin (CAM) blocks autophagy flux, and that combined treatment with CAM and proteasome inhibitor bortezomib (BZ) enhances ER-stress-mediated apoptosis in breast cancer cells, whereas treatment with CAM alone results in almost no cytotoxicity. Since HDAC6 is involved in aggresome formation, which is recognized as a cytoprotective response serving to sequester misfolded proteins and facilitate their clearance by autophagy, we further investigated the combined effect of vorinostat (suberoylanilide hydroxamic acid (SAHA)), which has a potent inhibitory effect for HDAC6, with CAM and BZ in breast cancer cell lines. SAHA exhibited some cytotoxicity along with an increased acetylation level of α-tubulin, a substrate of HDAC6. Combined treatment of SAHA, CAM, and BZ potently enhanced the apoptosis-inducing effect compared with treatment using each reagent alone or a combination of two of the three. Expression levels of ER-stress-related genes, including the pro-apoptotic transcription factor CHOP (GADD153), were maximally induced by the simultaneous combination of three reagents. Like breast cancer cell lines, a wild-type murine embryonic fibroblast (MEF) cell line exhibited enhanced cytotoxicity and maximally up-regulated Chop after combined treatment with SAHA, CAM, and BZ; however, a Chop knockout MEF cell line almost completely canceled this enhanced effect. The specific HDAC6 inhibitor tubacin also exhibited a pronounced cytocidal effect with a combination of CAM plus BZ. These data suggest that simultaneous targeting of intracellular proteolytic pathways and HDAC6 enhances ER-stress-mediated apoptosis in breast cancer cells.

  10. Metabolic engineering of Escherichia coli for production of fatty acid short-chain esters through combination of the fatty acid and 2-keto acid pathways.

    PubMed

    Guo, Daoyi; Zhu, Jing; Deng, Zixin; Liu, Tiangang

    2014-03-01

    Fatty acid short-chain esters (FASEs) are biodiesels that are renewable, nontoxic, and biodegradable biofuels. A novel approach for the biosynthesis of FASEs has been developed using metabolically-engineered E. coli through combination of the fatty acid and 2-keto acid pathways. Several genetic engineering strategies were also developed to increase fatty acyl-CoA availability to improve FASEs production. Fed-batch cultivation of the engineered E. coli resulted in a titer of 1008 mg/L FASEs. Since the fatty acid and 2-keto acid pathways are native microbial synthesis pathways, this strategy can be implemented in a variety of microorganisms to produce various FASEs from cheap and readily-available, renewable, raw materials such as sugars and cellulose in the future.

  11. 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

  12. 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

  13. 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.

  14. Factors predictive of adolescents' intentions to use birth control pills, condoms, and birth control pills in combination with condoms.

    PubMed

    Craig, D M; Wade, K E; Allison, K R; Irving, H M; Williams, J I; Hlibka, C M

    2000-01-01

    Using the Theory of Planned Behaviour (Ajzen, 1988) as a conceptual framework, 705 secondary school students were surveyed to identify their intentions to use birth control pills, condoms, and birth control pills in combination with condoms. Hierarchical multiple regression revealed that the theory explained between 23.5% and 45.8% of the variance in intentions. Variables external to the model such as past use, age, and ethnicity exhibited some independent effects. Attitudes were consistently predictive of intentions to use condoms, pills, and condoms in combination with pills for both male and female students. However, there were differences by gender in the degree to which subjective norms and perceived behavioural control predicted intentions. The findings suggest that programs should focus on: creation of positive attitudes regarding birth control pills and condoms; targeting important social influences, particularly regarding males' use of condoms; and developing strategies to increase students' control over the use of condoms.

  15. Factors predictive of adolescents' intentions to use birth control pills, condoms, and birth control pills in combination with condoms.

    PubMed

    Craig, D M; Wade, K E; Allison, K R; Irving, H M; Williams, J I; Hlibka, C M

    2000-01-01

    Using the Theory of Planned Behaviour (Ajzen, 1988) as a conceptual framework, 705 secondary school students were surveyed to identify their intentions to use birth control pills, condoms, and birth control pills in combination with condoms. Hierarchical multiple regression revealed that the theory explained between 23.5% and 45.8% of the variance in intentions. Variables external to the model such as past use, age, and ethnicity exhibited some independent effects. Attitudes were consistently predictive of intentions to use condoms, pills, and condoms in combination with pills for both male and female students. However, there were differences by gender in the degree to which subjective norms and perceived behavioural control predicted intentions. The findings suggest that programs should focus on: creation of positive attitudes regarding birth control pills and condoms; targeting important social influences, particularly regarding males' use of condoms; and developing strategies to increase students' control over the use of condoms. PMID:11089290

  16. Comparison of single and combination diuretics on glucose tolerance (PATHWAY-3): protocol for a randomised double-blind trial in patients with essential hypertension

    PubMed Central

    Brown, Morris J; Williams, Bryan; MacDonald, Thomas M; Caulfield, Mark; Cruickshank, J Kennedy; McInnes, Gordon; Sever, Peter; Webb, David J; Salsbury, Jackie; Morant, Steve; Ford, Ian

    2015-01-01

    Introduction Thiazide diuretics are associated with increased risk of diabetes mellitus. This risk may arise from K+-depletion. We hypothesised that a K+-sparing diuretic will improve glucose tolerance, and that combination of low-dose thiazide with K+-sparing diuretic will improve both blood pressure reduction and glucose tolerance, compared to a high-dose thiazide. Methods and analysis This is a parallel-group, randomised, double-blind, multicentre trial, comparing hydrochlorothiazide 25–50 mg, amiloride 10–20 mg and combination of both diuretics at half these doses. A single-blind placebo run-in of 1 month is followed by 24 weeks of blinded active treatment. There is forced dose-doubling after 3 months. The Primary end point is the blood glucose 2 h after oral ingestion of a 75 g glucose drink (OGTT), following overnight fasting. The primary outcome is the difference between 2 h glucose at weeks 0, 12 and 24. Secondary outcomes include the changes in home systolic blood pressure (BP) and glycated haemoglobin and prediction of response by baseline plasma renin. Eligibility criteria are: age 18–79, systolic BP on permitted background treatment ≥140 mm Hg and home BP ≥130 mm Hg and one component of the metabolic syndrome additional to hypertension. Principal exclusions are diabetes, estimated-glomerular filtration rate <45 mL/min, abnormal plasma K+, clinic SBP >200 mm Hg or DBP >120 mm Hg (box 2). The sample size calculation indicates that 486 patients will give 80% power at α=0.01 to detect a difference in means of 1 mmol/L (SD=2.2) between 2 h glucose on hydrochlorothiazide and comparators. Ethics and dissemination PATHWAY-3 was approved by Cambridge South Ethics Committee, number 09/H035/19. The trial results will be published in a peer-reviewed scientific journal. Trial registration numbers Eudract number 2009-010068-41 and clinical trials registration number: NCT02351973. PMID:26253567

  17. 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…

  18. COMPLEMENTARY CO-KRIGING: SPATIAL PREDICTION USING DATA COMBINED FROM, SEVERAL POLLUTION MONITORING NETWORKS

    EPA Science Inventory

    We consider the problem of optimal spatial prediction of an environmental variable using data from more than one sampling network. A model incorporating spatial dependence and measurement errors with network-specific biases and variances serves as the basis for the analysis of th...

  19. 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.

  20. 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.

  1. 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...

  2. 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

  3. Combination of cyclopamine and tamoxifen promotes survival and migration of mcf-7 breast cancer cells--interaction of hedgehog-gli and estrogen receptor signaling pathways.

    PubMed

    Sabol, Maja; Trnski, Diana; Uzarevic, Zvonimir; Ozretic, Petar; Musani, Vesna; Rafaj, Maja; Cindric, Mario; Levanat, Sonja

    2014-01-01

    Hedgehog-Gli (Hh-Gli) signaling pathway is one of the new molecular targets found upregulated in breast tumors. Estrogen receptor alpha (ERα) signaling has a key role in the development of hormone-dependent breast cancer. We aimed to investigate the effects of inhibiting both pathways simultaneously on breast cancer cell survival and the potential interactions between these two signaling pathways. ER-positive MCF-7 cells show decreased viability after treatment with cyclopamine, a Hh-Gli pathway inhibitor, as well as after tamoxifen (an ERα inhibitor) treatment. Simultaneous treatment with cyclopamine and tamoxifen on the other hand, causes short-term survival of cells, and increased migration. We found upregulated Hh-Gli signaling under these conditions and protein profiling revealed increased expression of proteins involved in cell proliferation and migration. Therefore, even though Hh-Gli signaling seems to be a good potential target for breast cancer therapy, caution must be advised, especially when combining therapies. In addition, we also show a potential direct interaction between the Shh protein and ERα in MCF-7 cells. Our data suggest that the Shh protein is able to activate ERα independently of the canonical Hh-Gli signaling pathway. Therefore, this may present an additional boost for ER-positive cells that express Shh, even in the absence of estrogen.

  4. Combination of Cyclopamine and Tamoxifen Promotes Survival and Migration of MCF-7 Breast Cancer Cells – Interaction of Hedgehog-Gli and Estrogen Receptor Signaling Pathways

    PubMed Central

    Uzarevic, Zvonimir; Ozretic, Petar; Musani, Vesna; Rafaj, Maja; Cindric, Mario; Levanat, Sonja

    2014-01-01

    Hedgehog-Gli (Hh-Gli) signaling pathway is one of the new molecular targets found upregulated in breast tumors. Estrogen receptor alpha (ERα) signaling has a key role in the development of hormone-dependent breast cancer. We aimed to investigate the effects of inhibiting both pathways simultaneously on breast cancer cell survival and the potential interactions between these two signaling pathways. ER-positive MCF-7 cells show decreased viability after treatment with cyclopamine, a Hh-Gli pathway inhibitor, as well as after tamoxifen (an ERα inhibitor) treatment. Simultaneous treatment with cyclopamine and tamoxifen on the other hand, causes short-term survival of cells, and increased migration. We found upregulated Hh-Gli signaling under these conditions and protein profiling revealed increased expression of proteins involved in cell proliferation and migration. Therefore, even though Hh-Gli signaling seems to be a good potential target for breast cancer therapy, caution must be advised, especially when combining therapies. In addition, we also show a potential direct interaction between the Shh protein and ERα in MCF-7 cells. Our data suggest that the Shh protein is able to activate ERα independently of the canonical Hh-Gli signaling pathway. Therefore, this may present an additional boost for ER-positive cells that express Shh, even in the absence of estrogen. PMID:25503972

  5. Combining global and local measures for structure-based druggability predictions.

    PubMed

    Volkamer, Andrea; Kuhn, Daniel; Grombacher, Thomas; Rippmann, Friedrich; Rarey, Matthias

    2012-02-27

    Predicting druggability and prioritizing certain disease modifying targets for the drug development process is of high practical relevance in pharmaceutical research. DoGSiteScorer is a fully automatic algorithm for pocket and druggability prediction. Besides consideration of global properties of the pocket, also local similarities shared between pockets are reflected. Druggability scores are predicted by means of a support vector machine (SVM), trained, and tested on the druggability data set (DD) and its nonredundant version (NRDD). The DD consists of 1069 targets with assigned druggable, difficult, and undruggable classes. In 90% of the NRDD, the SVM model based on global descriptors correctly classifies a target as either druggable or undruggable. Nevertheless, global properties suffer from binding site changes due to ligand binding and from the pocket boundary definition. Therefore, local pocket properties are additionally investigated in terms of a nearest neighbor search. Local similarities are described by distance dependent histograms between atom pairs. In 88% of the DD pocket set, the nearest neighbor and the structure itself conform with their druggability type. A discriminant feature between druggable and undruggable pockets is having less short-range hydrophilic-hydrophilic pairs and more short-range lipophilic-lipophilic pairs. Our findings for global pocket descriptors coincide with previously published methods affirming that size, shape, and hydrophobicity are important global pocket descriptors for automatic druggability prediction. Nevertheless, the variety of pocket shapes and their flexibility upon ligand binding limit the automatic projection of druggable features onto descriptors. Incorporating local pocket properties is another step toward a reliable descriptor-based druggability prediction. PMID:22148551

  6. 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.

  7. 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.

  8. Autofocus using adaptive prediction approximation combined search for the fluorescence microscope in second-generation DNA sequencing system.

    PubMed

    Xu, Hancong; Liu, Jinfeng; Li, Yang; Yin, Yan; Zhu, Chenxu; Lu, Hua

    2014-07-10

    Autofocus is an important technique for high-speed image acquisition in the second-generation DNA sequencing system, and this paper studies the passive focus algorithm for the system, which consists of two parts: focus measurement (FM) and focus search (FS). Based on the properties of DNA chips' images, we choose the normalized variance as the FM algorithm and develop a new robust FS named adaptive prediction approximation combined search (APACS). APACS utilizes golden section search (GSS) to approximate the focus position and engages the curve-fitting search (CFS) to predict the position simultaneously in every step of GSS. When the difference between consecutive predictions meets the set precision, the search finishes. Otherwise, it ends as GSS. In APACS, we also propose an estimation method, named the combination of centroid estimation and overdetermined equations estimation by least squares solution, to calculate the initial vector for the nonlinear equations in APACS prediction, which reduces the iterations and accelerates the search. The simulation and measured results demonstrate that APACS not only maintains the stability but also reduces the focus time compared with GSS and CFS, which indicates APACS is a robust and fast FS for the fluorescence microscope in a sequencing system.

  9. 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

  10. 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.

  11. 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

  12. Combination of a Selective HSP90α/β Inhibitor and a RAS-RAF-MEK-ERK Signaling Pathway Inhibitor Triggers Synergistic Cytotoxicity in Multiple Myeloma Cells

    PubMed Central

    Mimura, Naoya; Minami, Jiro; Ohguchi, Hiroto; Yoshida, Yasuhiro; Sagawa, Morihiko; Gorgun, Gullu; Cirstea, Diana; Cottini, Francesca; Jakubikova, Jana; Tai, Yu-Tzu; Chauhan, Dharminder; Richardson, Paul G.; Munshi, Nikhil; Ando, Kiyoshi; Utsugi, Teruhiro; Hideshima, Teru; Anderson, Kenneth C.

    2015-01-01

    Heat shock protein (HSP)90 inhibitors have shown significant anti-tumor activities in preclinical settings in both solid and hematological tumors. We previously reported that the novel, orally available HSP90α/β inhibitor TAS-116 shows significant anti-MM activities. In this study, we further examined the combination effect of TAS-116 with a RAS-RAF-MEK-ERK signaling pathway inhibitor in RAS- or BRAF-mutated MM cell lines. TAS-116 monotherapy significantly inhibited growth of RAS-mutated MM cell lines and was associated with decreased expression of downstream target proteins of the RAS-RAF-MEK-ERK signaling pathway. Moreover, TAS-116 showed synergistic growth inhibitory effects with the farnesyltransferase inhibitor tipifarnib, the BRAF inhibitor dabrafenib, and the MEK inhibitor selumetinib. Importantly, treatment with these inhibitors paradoxically enhanced p-C-Raf, p-MEK, and p-ERK activity, which was abrogated by TAS-116. TAS-116 also enhanced dabrafenib-induced MM cytotoxicity associated with mitochondrial damage-induced apoptosis, even in the BRAF-mutated U266 MM cell line. This enhanced apoptosis in RAS-mutated MM triggered by combination treatment was observed even in the presence of bone marrow stromal cells. Taken together, our results provide the rationale for novel combination treatment with HSP90α/β inhibitor and RAS-RAF-MEK-ERK signaling pathway inhibitors to improve outcomes in patients with in RAS- or BRAF-mutated MM. PMID:26630652

  13. Combination of a Selective HSP90α/β Inhibitor and a RAS-RAF-MEK-ERK Signaling Pathway Inhibitor Triggers Synergistic Cytotoxicity in Multiple Myeloma Cells.

    PubMed

    Suzuki, Rikio; Kikuchi, Shohei; Harada, Takeshi; Mimura, Naoya; Minami, Jiro; Ohguchi, Hiroto; Yoshida, Yasuhiro; Sagawa, Morihiko; Gorgun, Gullu; Cirstea, Diana; Cottini, Francesca; Jakubikova, Jana; Tai, Yu-Tzu; Chauhan, Dharminder; Richardson, Paul G; Munshi, Nikhil; Ando, Kiyoshi; Utsugi, Teruhiro; Hideshima, Teru; Anderson, Kenneth C

    2015-01-01

    Heat shock protein (HSP)90 inhibitors have shown significant anti-tumor activities in preclinical settings in both solid and hematological tumors. We previously reported that the novel, orally available HSP90α/β inhibitor TAS-116 shows significant anti-MM activities. In this study, we further examined the combination effect of TAS-116 with a RAS-RAF-MEK-ERK signaling pathway inhibitor in RAS- or BRAF-mutated MM cell lines. TAS-116 monotherapy significantly inhibited growth of RAS-mutated MM cell lines and was associated with decreased expression of downstream target proteins of the RAS-RAF-MEK-ERK signaling pathway. Moreover, TAS-116 showed synergistic growth inhibitory effects with the farnesyltransferase inhibitor tipifarnib, the BRAF inhibitor dabrafenib, and the MEK inhibitor selumetinib. Importantly, treatment with these inhibitors paradoxically enhanced p-C-Raf, p-MEK, and p-ERK activity, which was abrogated by TAS-116. TAS-116 also enhanced dabrafenib-induced MM cytotoxicity associated with mitochondrial damage-induced apoptosis, even in the BRAF-mutated U266 MM cell line. This enhanced apoptosis in RAS-mutated MM triggered by combination treatment was observed even in the presence of bone marrow stromal cells. Taken together, our results provide the rationale for novel combination treatment with HSP90α/β inhibitor and RAS-RAF-MEK-ERK signaling pathway inhibitors to improve outcomes in patients with in RAS- or BRAF-mutated MM.

  14. 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

    2014-12-01

    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.

  15. 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.

  16. 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. PMID:25751095

  17. JAK2 inhibitor combined with DC-activated AFP-specific T-cells enhances antitumor function in a Fas/FasL signal-independent pathway

    PubMed Central

    Liu, Yang; Wang, Yue-ru; Ding, Guang-hui; Yang, Ting-song; Yao, Le; Hua, Jie; He, Zhi-gang; Qian, Ming-ping

    2016-01-01

    Objective Combination therapy for cancer is more effective than using only standard chemo- or radiotherapy. Our previous results showed that dendritic cell-activated α-fetoprotein (AFP)-specific T-cells inhibit tumor in vitro and in vivo. In this study, we focused on antitumor function of CD8+ T-cells combined with or without JAK2 inhibitor. Methods Proliferation and cell cycle were analyzed by CCK-8 and flow cytometry. Western blot was used to analyze the expression level of related protein and signaling pathway. Results We demonstrated reduced viability and induction of apoptosis of tumor cells with combination treatment. Intriguingly, cell cycle was blocked at the G1 phase by using AFP-specific CD8+ T-cells combined with JAK2 inhibitor (AG490). Furthermore, an enhanced expression of BAX but no influence on Fas/FasL was detected from the tumor cells. Conclusion These results indicate a Fas/FasL-independent pathway for cellular apoptosis in cancer therapies with the treatment of AFP-specific CD8+ T-cells combined with JAK2 inhibitor. PMID:27499636

  18. Augmented efficacy with the combination of blockade of the Notch-1 pathway, bortezomib and romidepsin in a murine MT-1 adult T-cell leukemia model.

    PubMed

    Yu, P; Petrus, M N; Ju, W; Zhang, M; Conlon, K C; Nakagawa, M; Maeda, M; Bamford, R N; Waldmann, T A

    2015-03-01

    Adult T-cell leukemia (ATL) is an aggressive malignancy caused by human T-cell lymphotropic virus-1. There is no accepted curative therapy for ATL. We have reported that certain ATL patients have increased Notch-1 signaling along with constitutive activation of the nuclear factor-κB pathway. Physical and functional interaction between these two pathways provides the rationale to combine the γ-secretase inhibitor compound E with the proteasome inhibitor bortezomib. Moreover, romidepsin, a histone deacetylase inhibitor, has demonstrated major antitumor action in leukemia/lymphoma. In this study, we investigated the therapeutic efficacy of the single agents and the combination of these agents in a murine model of human ATL, the MT-1 model. Single and double agents inhibited tumor growth as monitored by tumor size (P<0.05), and prolonged survival of leukemia-bearing mice (P<0.05) compared with the control group. The combination of three agents significantly enhanced the antitumor efficacy as assessed by tumor size, tumor markers in the serum (human soluble interleukin-2 receptor-α and β2-microglobulin) and survival of the MT-1 tumor-bearing mice, compared with all other treatment groups (P<0.05). Improved therapeutic efficacy obtained by combining compound E, bortezomib and romidepsin supports a clinical trial of this combination in the treatment of ATL.

  19. 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.

  20. 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...

  1. Total ion chromatographic fingerprints combined with chemometrics and mass defect filter to predict antitumor components of Picrasma quassioids.

    PubMed

    Shi, Yuanyuan; Zhan, Hao; Zhong, Liuyi; Yan, Fangrong; Feng, Feng; Liu, Wenyuan; Xie, Ning

    2016-07-01

    A method of total ion chromatogram combined with chemometrics and mass defect filter was established for the prediction of active ingredients in Picrasma quassioides samples. The total ion chromatogram data of 28 batches were pretreated with wavelet transformation and correlation optimized warping to correct baseline drifts and retention time shifts. Then partial least squares regression was applied to construct a regression model to bridge the total ion chromatogram fingerprints and the antitumor activity of P. quassioides. Finally, the regression coefficients were used to predict the active peaks in total ion chromatogram fingerprints. In this strategy, mass defect filter was employed to classify and characterize the active peaks from a chemical point of view. A total of 17 constituents were predicted as the potential active compounds, 16 of which were identified as alkaloids by this developed approach. The results showed that the established method was not only simple and easy to operate, but also suitable to predict ultraviolet undetectable compounds and provide chemical information for the prediction of active compounds in herbs.

  2. Total ion chromatographic fingerprints combined with chemometrics and mass defect filter to predict antitumor components of Picrasma quassioids.

    PubMed

    Shi, Yuanyuan; Zhan, Hao; Zhong, Liuyi; Yan, Fangrong; Feng, Feng; Liu, Wenyuan; Xie, Ning

    2016-07-01

    A method of total ion chromatogram combined with chemometrics and mass defect filter was established for the prediction of active ingredients in Picrasma quassioides samples. The total ion chromatogram data of 28 batches were pretreated with wavelet transformation and correlation optimized warping to correct baseline drifts and retention time shifts. Then partial least squares regression was applied to construct a regression model to bridge the total ion chromatogram fingerprints and the antitumor activity of P. quassioides. Finally, the regression coefficients were used to predict the active peaks in total ion chromatogram fingerprints. In this strategy, mass defect filter was employed to classify and characterize the active peaks from a chemical point of view. A total of 17 constituents were predicted as the potential active compounds, 16 of which were identified as alkaloids by this developed approach. The results showed that the established method was not only simple and easy to operate, but also suitable to predict ultraviolet undetectable compounds and provide chemical information for the prediction of active compounds in herbs. PMID:27135885

  3. 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. PMID:27558075

  4. Benefit of hepatitis C virus core antigen assay in prediction of therapeutic response to interferon and ribavirin combination therapy.

    PubMed

    Takahashi, Masahiko; Saito, Hidetsugu; Higashimoto, Makiko; Atsukawa, Kazuhiro; Ishii, Hiromasa

    2005-01-01

    A highly sensitive second-generation hepatitis C virus (HCV) core antigen assay has recently been developed. We compared viral disappearance and first-phase kinetics between commercially available core antigen (Ag) assays, Lumipulse Ortho HCV Ag (Lumipulse-Ag), and a quantitative HCV RNA PCR assay, Cobas Amplicor HCV Monitor test, version 2 (Amplicor M), to estimate the predictive benefit of a sustained viral response (SVR) and non-SVR in 44 genotype 1b patients treated with interferon (IFN) and ribavirin. HCV core Ag negativity could predict SVR on day 1 (sensitivity = 100%, specificity = 85.0%, accuracy = 86.4%), whereas RNA negativity could predict SVR on day 7 (sensitivity = 100%, specificity = 87.2%, accuracy = 88.6%). None of the patients who had detectable serum core Ag or RNA on day 14 achieved SVR (specificity = 100%). The predictive accuracy on day 14 was higher by RNA negativity (93.2%) than that by core Ag negativity (75.0%). The combined predictive criterion of both viral load decline during the first 24 h and basal viral load was also predictive for SVR; the sensitivities of Lumipulse-Ag and Amplicor-M were 45.5 and 47.6%, respectively, and the specificity was 100%. Amplicor-M had better predictive accuracy than Lumipulse-Ag in 2-week disappearance tests because it had better sensitivity. On the other hand, estimates of kinetic parameters were similar regardless of the detection method. Although the correlations between Lumipulse-Ag and Amplicor-M were good both before and 24 h after IFN administration, HCV core Ag seemed to be relatively lower 24 h after IFN administration than before administration. Lumipulse-Ag seems to be useful for detecting the HCV concentration during IFN therapy; however, we still need to understand the characteristics of the assay.

  5. Effect of IL-17 monoclonal antibody Secukinumab combined with IL-35 blockade of Notch signaling pathway on the invasive capability of hepatoma cells.

    PubMed

    Li, H Ch; Zhang, Y X; Liu, Y; Wang, Q Sh

    2016-07-14

    We investigated the effect of the IL-17 monoclonal antibody Secukinumab combined with IL-35 in the blockade of the Notch signaling pathway on the invasive capability of hepatoma cells. We examined the effects of IL-17 antibody or IL-35 treatment alone or in combination on cell invasion and migration capabilities with Transwell chambers. The mRNA levels of Hes1, Hes5, and Hey1 were tested using quantitative polymerase chain reaction. The protein expression of N1ICD, Snail, and E-cadherin protein expressions were measured with western blot. The expression of Hes1, Hes5, Hey1 and N1ICD were all very high in hepatoma cell lines, and were positively correlated with the invasive migration capabilities of the cells. The combination of IL-17 monoclonal antibody Secukinumab with IL-35 could effectively inhibit the Notch signaling pathway, as well as the invasive migration of the cells. Snail and E-cadherin are involved in the migration of hepatoma cells, and it has been established that Snail can regulate the expression of E-cadherin. IL-17 monoclonal antibody Secukinumab combined with IL-35 can increase E-cadherin and decrease Snail expression, which are positively correlated with cell invasive migration capabilities. Overall, treatment with both IL-17 antibody and IL-35 is more effective than each treatment alone. Notch signaling is activated in hepatoma cell lines and increases with the enhancement of cell invasive migration capabilities. IL-17 monoclonal antibody Secukinumab combined with IL-35 can block the Notch signaling pathway, simultaneously reducing the invasive migration capability of hepatoma cells.

  6. Different combinations of atomic interactions predict protein-small molecule and protein-DNA/RNA affinities with similar accuracy.

    PubMed

    Dias, Raquel; Kolazckowski, Bryan

    2015-11-01

    Interactions between proteins and other molecules play essential roles in all biological processes. Although it is widely held that a protein's ligand specificity is determined primarily by its three-dimensional structure, the general principles by which structure determines ligand binding remain poorly understood. Here we use statistical analyses of a large number of protein-ligand complexes with associated binding-affinity measurements to quantitatively characterize how combinations of atomic interactions contribute to ligand affinity. We find that there are significant differences in how atomic interactions determine ligand affinity for proteins that bind small chemical ligands, those that bind DNA/RNA and those that interact with other proteins. Although protein-small molecule and protein-DNA/RNA binding affinities can be accurately predicted from structural data, models predicting one type of interaction perform poorly on the others. Additionally, the particular combinations of atomic interactions required to predict binding affinity differed between small-molecule and DNA/RNA data sets, consistent with the conclusion that the structural bases determining ligand affinity differ among interaction types. In contrast to what we observed for small-molecule and DNA/RNA interactions, no statistical models were capable of predicting protein-protein affinity with >60% correlation. We demonstrate the potential usefulness of protein-DNA/RNA binding prediction as a possible tool for high-throughput virtual screening to guide laboratory investigations, suggesting that quantitative characterization of diverse molecular interactions may have practical applications as well as fundamentally advancing our understanding of how molecular structure translates into function.

  7. Different combinations of atomic interactions predict protein‐small molecule and protein‐DNA/RNA affinities with similar accuracy

    PubMed Central

    Dias, Raquel

    2015-01-01

    ABSTRACT Interactions between proteins and other molecules play essential roles in all biological processes. Although it is widely held that a protein's ligand specificity is determined primarily by its three‐dimensional structure, the general principles by which structure determines ligand binding remain poorly understood. Here we use statistical analyses of a large number of protein−ligand complexes with associated binding‐affinity measurements to quantitatively characterize how combinations of atomic interactions contribute to ligand affinity. We find that there are significant differences in how atomic interactions determine ligand affinity for proteins that bind small chemical ligands, those that bind DNA/RNA and those that interact with other proteins. Although protein‐small molecule and protein‐DNA/RNA binding affinities can be accurately predicted from structural data, models predicting one type of interaction perform poorly on the others. Additionally, the particular combinations of atomic interactions required to predict binding affinity differed between small‐molecule and DNA/RNA data sets, consistent with the conclusion that the structural bases determining ligand affinity differ among interaction types. In contrast to what we observed for small‐molecule and DNA/RNA interactions, no statistical models were capable of predicting protein−protein affinity with >60% correlation. We demonstrate the potential usefulness of protein‐DNA/RNA binding prediction as a possible tool for high‐throughput virtual screening to guide laboratory investigations, suggesting that quantitative characterization of diverse molecular interactions may have practical applications as well as fundamentally advancing our understanding of how molecular structure translates into function. Proteins 2015; 83:2100–2114. © 2015 The Authors. Proteins: Structure, Function, and Bioinformatics Published by Wiley Periodicals, Inc. PMID:26370248

  8. Prediction of post-treatment outcome after combined treatment with maxillary protraction and chincap appliances.

    PubMed

    Yoshida, Ikue; Yamaguchi, Nobuhito; Mizoguchi, Itaru

    2006-02-01

    The aims of this study were to identify differences in the initial skeletal morphology between successful and unsuccessful groups and to establish a novel method for predicting the final outcome of treatment with a maxillary protraction appliance (MPA) and chincap. The cephalograms used in this study were taken from 32 Japanese girls (mean age 10.2 years) with a Class III malocclusion at the beginning of treatment with an MPA and chincap (T1), at removal of the appliance (T2), and during the final post-treatment period (T3). The subjects were divided into two groups according to the treatment outcome at T3. Lower face height (ANS-Me), total face height (N-Me), ratio of face height (ANS-Me/N-ANS), maxillary position, mandibular plane and gonial angle at T1 were all significantly larger in the unsuccessful group, compared with the successful group. Discriminant analysis indicated that lower face height and gonial angle were significant determinants for distinguishing between the two groups at T1. From T1 to T2, while the anterior displacement of the maxilla was almost the same in the two groups, SNB decreased by 1.6 degrees in the successful group and 0.4 degrees in the unsuccessful group. After orthopaedic treatment, a second phase of treatment with a multibracket system was performed (T2 to T3). From T2 to T3, SNA increased by 0.4 degrees in the successful group and decreased by 0.7 degrees in the unsuccessful group. These results indicate that the vertical dimensions of the craniofacial skeleton are important for predicting the prognosis of skeletal Class III patients treated with a MPA and chincap and that the discriminant formula established in this study is effective in predicting the final treatment outcome.

  9. 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.

  10. 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

    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

  11. 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

  12. Linear combinations of nonlinear models for predicting human-machine interface forces.

    PubMed

    Patton, James L; Mussa-Ivaldi, Ferdinando A

    2002-01-01

    This study presents a computational framework that capitalizes on known human neuromechanical characteristics during limb movements in order to predict human-machine interactions. A parallel-distributed approach, the mixture of nonlinear models, fits the relationship between the measured kinematics and kinetics at the handle of a robot. Each element of the mixture represented the arm and its controller as a feedforward nonlinear model of inverse dynamics plus a linear approximation of musculotendonous impedance. We evaluated this approach with data from experiments where subjects held the handle of a planar manipulandum robot and attempted to make point-to-point reaching movements. We compared the performance to the more conventional approach of a constrained, nonlinear optimization of the parameters. The mixture of nonlinear models accounted for 79 +/- 11% (mean +/- SD) of the variance in measured force, and force errors were 0.73 +/- 0.20% of the maximum exerted force. Solutions were acquired in half the time with a significantly better fit. However, both approaches suffered equally from the simplifying assumptions, namely that the human neuromechanical system consisted of a feedforward controller coupled with linear impedances and a moving state equilibrium. Hence, predictability was best limited to the first half of the movement. The mixture of nonlinear models may be useful in human-machine tasks such as in telerobotics, fly-by-wire vehicles, robotic training, and rehabilitation.

  13. Storm diagnostic/predictive images derived from a combination of lightning and satellite imagery

    NASA Technical Reports Server (NTRS)

    Goodman, Steven J.; Buechler, Dennis E.; Meyer, Paul J.

    1988-01-01

    A technique is presented for generating trend or convective tendency images using a combination of GOES satellite imagery and cloud-to-ground lightning observations. The convective tendency images can be used for short term forecasting of storm development. A conceptual model of cloud electrical development and an example of the methodology used to generate lightning/satellite convective tendency imagery are given. Successive convective tendency images can be looped or animated to show the previous growth or decay of thunderstorms and their associated lighting activity. It is suggested that the convective tendency image may also be used to indicate potential microburst producing storms.

  14. 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.

  15. 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

  16. Combining Phylogenetic Profiling-Based and Machine Learning-Based Techniques to Predict Functional Related Proteins

    PubMed Central

    Lin, Tzu-Wen; Wu, Jian-Wei; Chang, Darby Tien-Hao

    2013-01-01

    Annotating protein functions and linking proteins with similar functions are important in systems biology. The rapid growth rate of newly sequenced genomes calls for the development of computational methods to help experimental techniques. Phylogenetic profiling (PP) is a method that exploits the evolutionary co-occurrence pattern to identify functional related proteins. However, PP-based methods delivered satisfactory performance only on prokaryotes but not on eukaryotes. This study proposed a two-stage framework to predict protein functional linkages, which successfully enhances a PP-based method with machine learning. The experimental results show that the proposed two-stage framework achieved the best overall performance in comparison with three PP-based methods. PMID:24069454

  17. 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.

  18. The combination of HDAC and aminopeptidase inhibitors is highly synergistic in myeloma and leads to disruption of the NFκB signalling pathway.

    PubMed

    Smith, Emma M; Zhang, Lei; Walker, Brian A; Davenport, Emma L; Aronson, Lauren I; Krige, David; Hooftman, Leon; Drummond, Alan H; Morgan, Gareth J; Davies, Faith E

    2015-07-10

    There is a growing body of evidence supporting the use of epigenetic therapies in the treatment of multiple myeloma. We show the novel HDAC inhibitor CHR-3996 induces apoptosis in myeloma cells at concentrations in the nanomolar range and with apoptosis mediated by p53 and caspase pathways. In addition, HDAC inhibitors are highly synergistic, both in vitro and in vivo, with the aminopeptidase inhibitor tosedostat (CHR-2797). We demonstrate that the basis for this synergy is a consequence of changes in the levels of NFκB regulators BIRC3/cIAP2, A20, CYLD, and IκB, which were markedly affected by the combination. When co-administered the HDAC and aminopeptidase inhibitors caused rapid nuclear translocation of NFκB family members p65 and p52, following activation of both canonical and non-canonical NFκB signalling pathways. The subsequent up-regulation of inhibitors of NFκB activation (most significantly BIRC3/cIAP2) turned off the cytoprotective effects of the NFκB signalling response in a negative feedback loop. These results provide a rationale for combining HDAC and aminopeptidase inhibitors clinically for the treatment of myeloma patients and support the disruption of the NFκB signalling pathway as a therapeutic strategy.

  19. Chloroquine or Chloroquine-PI3K/Akt Pathway Inhibitor Combinations Strongly Promote γ-Irradiation-Induced Cell Death in Primary Stem-Like Glioma Cells

    PubMed Central

    Firat, Elke; Weyerbrock, Astrid; Gaedicke, Simone; Grosu, Anca-Ligia; Niedermann, Gabriele

    2012-01-01

    We asked whether inhibitors of the phosphatidylinositol 3-kinase (PI3K)/Akt pathway, which is highly active in cancer stem cells (CSCs) and upregulated in response to genotoxic treatments, promote γ-irradiationγIR)-induced cell death in highly radioresistant, patient-derived stem-like glioma cells (SLGCs). Surprisingly, in most cases the inhibitors did not promote γIR-induced cell death. In contrast, the strongly cytostatic Ly294002 and PI-103 even tended to reduce it. Since autophagy was induced we examined whether addition of the clinically applicable autophagy inhibitor chloroquine (CQ) would trigger cell death in SLGCs. Triple therapy with CQ at doses as low as 5 to 10 µM indeed caused strong apoptosis. At slightly higher doses, CQ alone strongly promoted γIR-induced apoptosis in all SLGC lines examined. The strong apoptosis in combinations with CQ was invariably associated with strong accumulation of the autophagosomal marker LC3-II, indicating inhibition of late autophagy. Thus, autophagy-promoting effects of PI3K/Akt pathway inhibitors apparently hinder cell death induction in γ-irradiated SLGCs. However, as we show here for the first time, the late autophagy inhibitor CQ strongly promotes γIR-induced cell death in highly radioresistant CSCs, and triple combinations of CQ, γIR and a PI3K/Akt pathway inhibitor permit reduction of the CQ dose required to trigger cell death. PMID:23091617

  20. 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.

  1. Prediction of the spectral reflectance of laser-generated color prints by combination of an optical model and learning methods.

    PubMed

    Nébouy, David; Hébert, Mathieu; Fournel, Thierry; Larina, Nina; Lesur, Jean-Luc

    2015-09-01

    Recent color printing technologies based on the principle of revealing colors on pre-functionalized achromatic supports by laser irradiation offer advanced functionalities, especially for security applications. However, for such technologies, the color prediction is challenging, compared to classic ink-transfer printing systems. The spectral properties of the coloring materials modified by the lasers are not precisely known and may strongly vary, depending on the laser settings, in a nonlinear manner. We show in this study, through the example of the color laser marking (CLM) technology, based on laser bleaching of a mixture of pigments, that the combination of an adapted optical reflectance model and learning methods to get the model's parameters enables prediction of the spectral reflectance of any printable color with rather good accuracy. Even though the pigment mixture is formulated from three colored pigments, an analysis of the dimensionality of the spectral space generated by CLM printing, thanks to a principal component analysis decomposition, shows that at least four spectral primaries are needed for accurate spectral reflectance predictions. A polynomial interpolation is then used to relate RGB laser intensities with virtual coordinates of new basis vectors. By studying the influence of the number of calibration patches on the prediction accuracy, we can conclude that a reasonable number of 130 patches are enough to achieve good accuracy in this application. PMID:26367434

  2. Prediction of the spectral reflectance of laser-generated color prints by combination of an optical model and learning methods.

    PubMed

    Nébouy, David; Hébert, Mathieu; Fournel, Thierry; Larina, Nina; Lesur, Jean-Luc

    2015-09-01

    Recent color printing technologies based on the principle of revealing colors on pre-functionalized achromatic supports by laser irradiation offer advanced functionalities, especially for security applications. However, for such technologies, the color prediction is challenging, compared to classic ink-transfer printing systems. The spectral properties of the coloring materials modified by the lasers are not precisely known and may strongly vary, depending on the laser settings, in a nonlinear manner. We show in this study, through the example of the color laser marking (CLM) technology, based on laser bleaching of a mixture of pigments, that the combination of an adapted optical reflectance model and learning methods to get the model's parameters enables prediction of the spectral reflectance of any printable color with rather good accuracy. Even though the pigment mixture is formulated from three colored pigments, an analysis of the dimensionality of the spectral space generated by CLM printing, thanks to a principal component analysis decomposition, shows that at least four spectral primaries are needed for accurate spectral reflectance predictions. A polynomial interpolation is then used to relate RGB laser intensities with virtual coordinates of new basis vectors. By studying the influence of the number of calibration patches on the prediction accuracy, we can conclude that a reasonable number of 130 patches are enough to achieve good accuracy in this application.

  3. Affiliation buffers stress: cumulative genetic risk in oxytocin–vasopressin genes combines with early caregiving to predict PTSD in war-exposed young children

    PubMed Central

    Feldman, R; Vengrober, A; Ebstein, R P

    2014-01-01

    Research indicates that risk for post-traumatic stress disorder (PTSD) is shaped by the interaction between genetic vulnerability and early caregiving experiences; yet, caregiving has typically been assessed by adult retrospective accounts. Here, we employed a prospective longitudinal design with real-time observations of early caregiving combined with assessment of genetic liability along the axis of vasopressin–oxytocin (OT) gene pathways to test G × E contributions to PTSD. Participants were 232 young Israeli children (1.5–5 years) and their parents, including 148 living in zones of continuous war and 84 controls. A cumulative genetic risk factor was computed for each family member by summing five risk alleles across three genes (OXTR, CD38 and AVPR1a) previously associated with psychopathology, sociality and caregiving. Child PTSD was diagnosed and mother–child interactions were observed in multiple contexts. In middle childhood (7–8 years), child psychopathology was re-evaluated. War exposure increased propensity to develop Axis-I disorder by threefold: 60% of exposed children displayed a psychiatric disorder by middle childhood and 62% of those showed several comorbid disorders. On the other hand, maternal sensitive support reduced risk for psychopathology. G × E effect was found for child genetic risk: in the context of war exposure, greater genetic risk on the vasopressin–OT pathway increased propensity for psychopathology. Among exposed children, chronicity of PTSD from early to middle childhood was related to higher child, maternal and paternal genetic risk, low maternal support and greater initial avoidance symptoms. Child avoidance was predicted by low maternal support and reduced mother–child reciprocity. These findings underscore the saliency of both genetic and behavioral facets of the human affiliation system in shaping vulnerability to PTSD as well as providing an underlying mechanism of post-traumatic resilience. PMID:24618689

  4. Combining Information from Common Type 2 Diabetes Risk Polymorphisms Improves Disease Prediction

    PubMed Central

    Weedon, Michael N; McCarthy, Mark I; Hitman, Graham; Walker, Mark; Groves, Christopher J; Zeggini, Eleftheria; Rayner, N. William; Shields, Beverley; Owen, Katharine R; Hattersley, Andrew T; Frayling, Timothy M

    2006-01-01

    Background A limited number of studies have assessed the risk of common diseases when combining information from several predisposing polymorphisms. In most cases, individual polymorphisms only moderately increase risk (~20%), and they are thought to be unhelpful in assessing individuals' risk clinically. The value of analyzing multiple alleles simultaneously is not well studied. This is often because, for any given disease, very few common risk alleles have been confirmed. Methods and Findings Three common variants (Lys23 of KCNJ11, Pro12 of PPARG, and the T allele at rs7903146 of TCF7L2) have been shown to predispose to type 2 diabetes mellitus across many large studies. Risk allele frequencies ranged from 0.30 to 0.88 in controls. To assess the combined effect of multiple susceptibility alleles, we genotyped these variants in a large case-control study (3,668 controls versus 2,409 cases). Individual allele odds ratios (ORs) ranged from 1.14 (95% confidence interval [CI], 1.05 to 1.23) to 1.48 (95% CI, 1.36 to 1.60). We found no evidence of gene-gene interaction, and the risks of multiple alleles were consistent with a multiplicative model. Each additional risk allele increased the odds of type 2 diabetes by 1.28 (95% CI, 1.21 to 1.35) times. Participants with all six risk alleles had an OR of 5.71 (95% CI, 1.15 to 28.3) compared to those with no risk alleles. The 8.1% of participants that were double-homozygous for the risk alleles at TCF7L2 and Pro12Ala had an OR of 3.16 (95% CI, 2.22 to 4.50), compared to 4.3% with no TCF7L2 risk alleles and either no or one Glu23Lys or Pro12Ala risk alleles. Conclusions Combining information from several known common risk polymorphisms allows the identification of population subgroups with markedly differing risks of developing type 2 diabetes compared to those obtained using single polymorphisms. This approach may have a role in future preventative measures for common, polygenic diseases. PMID:17020404

  5. 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

  6. 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.

  7. 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. PMID:27379394

  8. Conceptual Knowledge Discovery in Databases for Drug Combinations Predictions in Malignant Melanoma.

    PubMed

    Regan, Kelly; Raje, Satyajeet; Saravanamuthu, Cartik; Payne, Philip R O

    2015-01-01

    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.

  9. 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

  10. 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

  11. 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.

  12. A highly conserved family of domains related to the DNA-glycosylase fold helps predict multiple novel pathways for RNA modifications

    PubMed Central

    Burroughs, A Maxwell; Aravind, L

    2014-01-01

    A protein family including mammalian NEMF, Drosophila caliban, yeast Tae2, and bacterial FpbA-like proteins was first defined over a decade ago and found to be universally distributed across the three domains/superkingdoms of life. Since its initial characterization, this family of proteins has been tantalizingly linked to a wide range of biochemical functions. Tapping the enormous wealth of genome information that has accumulated since the initial characterization of these proteins, we perform a detailed computational analysis of the family, identifying multiple conserved domains. Domains identified include an enzymatic domain related to the formamidopyrimidine (Fpg), MutM, and Nei/EndoVIII family of DNA glycosylases, a novel, predicted RNA-binding domain, and a domain potentially mediating protein–protein interactions. Through this characterization, we predict that the DNA glycosylase-like domain catalytically operates on double-stranded RNA, as part of a hitherto unknown base modification mechanism that probably targets rRNAs. At least in archaea, and possibly eukaryotes, this pathway might additionally include the AMMECR1 family of proteins. The predicted RNA-binding domain associated with this family is also observed in distinct architectural contexts in other proteins across phylogenetically diverse prokaryotes. Here it is predicted to play a key role in a new pathway for tRNA 4-thiouridylation along with TusA-like sulfur transfer proteins. PMID:24646681

  13. Predicting Air-Water Geysers and Their Implications on Reducing Combined Sewer Overflows

    NASA Astrophysics Data System (ADS)

    Choi, Y.; Leon, A.; Apte, S.

    2014-12-01

    An air-water geyser in a closed conduit system is characterized by an explosive jetting of a mixture of air and water through drop-shafts. In this study, three scenarios of geysers are numerically simulated using a 3D computational fluid dynamics (CFD) model. The three tested scenarios are comprised of a drop shaft that is closed at its bottom and partially or fully open at the top. Initially, the lower section of the drop shaft is filled with pressurized air, the middle section with stagnant water and the upper section with air at atmospheric pressure. The pressure and volume of the pressurized air, and hence the stored energy, is different for all three test cases. The volume of the stagnant water and the air at atmospheric pressure are kept constant in the tests. The numerical simulations aim to identify the correlation between dimensionless energy stored in the pressurized air pocket and dimensionless maximum pressure reached at the outlet. This dimensionless correlation could be used to determine the energy threshold that does not produce air-water geyser, which in turn could be used in the design of combined sewer systems for minimizing geysers.

  14. 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.

  15. 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.

  16. 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

  17. Distal end of the atrioventricular nodal artery predicts the risk of atrioventricular block during slow pathway catheter ablation of atrioventricular nodal re-entrant tachycardia

    PubMed Central

    Lin, J; Huang, S; Lai, L; Lin, L; Chen, J; Tseng, Y; Lien, W

    2000-01-01

    OBJECTIVE—To search for a reliable anatomical landmark within Koch's triangle to predict the risk of atrioventricular (AV) block during radiofrequency slow pathway catheter ablation of AV nodal re-entrant tachycardia (AVNRT).
PATIENTS AND METHODS—To test the hypothesis that the distal end of the AV nodal artery represents the anatomical location of the AV node, and thus could be a useful landmark for predicting the risk of AV block, 128 consecutive patients with AVNRT receiving slow pathway catheter ablation were prospectively studied in two phases. In phase I (77 patients), angiographic demonstration of the AV nodal artery and its ending was performed at the end of the ablation procedure, whereas in the subsequent phase II study (51 patients), the angiography was performed immediately before catheter ablation to assess the value of identifying this new landmark in reducing the risk of AV block. Multiple electrophysiologic and anatomical parameters were analysed. The former included the atrial activation sequence between the His bundle recording site (HBE) and the coronary sinus orifice or the catheter ablation site, either during AVNRT or during sinus rhythm. The latter included the spatial distances between the distal end of the AV nodal artery and the HBE and the final catheter ablation site, and the distance between the HBE and the tricuspid border at the coronary sinus orifice floor.
RESULTS—In phase I, nine of the 77 patients had complications of transient (seven patients) or permanent (two patients) complete AV block during stepwise, anatomy guided slow pathway catheter ablation. These nine patients had a wider distance between the HBE and the distal end of the AV nodal artery, and a closer approximation of the catheter ablation site to the distal end of the AV nodal artery, which independently predicted the risk of AV block. In contrast, none of the available electrophysiologic parameters were shown to be reliable. When the distance between

  18. 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 ...

  19. Prediction of 18-month survival in patients with primary myelodysplastic syndrome. A regression model and scoring system based on the combination of chromosome findings and the Bournemouth score.

    PubMed

    Parlier, V; van Melle, G; Beris, P; Schmidt, P M; Tobler, A; Haller, E; Bellomo, M J

    1995-06-01

    The predictive potential of six selected factors was assessed in 72 patients with primary myelodysplastic syndrome using univariate and multivariate logistic regression analysis of survival at 18 months. Factors were age (above median of 69 years), dysplastic features in the three myeloid bone marrow cell lineages, presence of chromosome defects, all metaphases abnormal, double or complex chromosome defects (C23), and a Bournemouth score of 2, 3, or 4 (B234). In the multivariate approach, B234 and C23 proved to be significantly associated with a reduction in the survival probability. The similarity of the regression coefficients associated with these two factors means that they have about the same weight. Consequently, the model was simplified by counting the number of factors (0, 1, or 2) present in each patient, thus generating a scoring system called the Lausanne-Bournemouth score (LB score). The LB score combines the well-recognized and easy-to-use Bournemouth score (B score) with the chromosome defect complexity, C23 constituting an additional indicator of patient outcome. The predicted risk of death within 18 months calculated from the model is as follows: 7.1% (confidence interval: 1.7-24.8) for patients with an LB score of 0, 60.1% (44.7-73.8) for an LB score of 1, and 96.8% (84.5-99.4) for an LB score of 2. The scoring system presented here has several interesting features. The LB score may improve the predictive value of the B score, as it is able to recognize two prognostic groups in the intermediate risk category of patients with B scores of 2 or 3. It has also the ability to identify two distinct prognostic subclasses among RAEB and possibly CMML patients. In addition to its above-described usefulness in the prognostic evaluation, the LB score may bring new insights into the understanding of evolution patterns in MDS. We used the combination of the B score and chromosome complexity to define four classes which may be considered four possible states of

  20. Combined P16 and human papillomavirus testing predicts head and neck cancer survival.

    PubMed

    Salazar, Christian R; Anayannis, Nicole; Smith, Richard V; Wang, Yanhua; Haigentz, Missak; Garg, Madhur; Schiff, Bradley A; Kawachi, Nicole; Elman, Jordan; Belbin, Thomas J; Prystowsky, Michael B; Burk, Robert D; Schlecht, Nicolas F

    2014-11-15

    While its prognostic significance remains unclear, p16(INK4a) 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 (IHC). 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 nonoropharyngeal tumors. However, when both p16 and HPV DNA test results were considered, concordantly positive nonoropharyngeal tumors had significantly better disease-specific survival than concordantly negative nonoropharyngeal 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 nonoropharyngeal HNSCC, p16(+)/HPV16(-) nonoropharyngeal 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 IHC alone has potential as a prognostic test for oropharyngeal cancer survival, but combined p16/HPV testing is necessary to identify HPV-associated nonoropharyngeal HNSCC with better prognosis. PMID:24706381

  1. Use of Health Plan Combined with Registry Data to Predict Clinical Trial Recruitment

    PubMed Central

    Curtis, Jeffrey R; Wright, Nicole C; Xie, Fenglong; Chen, Lang; Zhang, Jie; Saag, Kenneth G; Bharat, Aseem; Kremer, Joel; Cofield, Stacey; Winthrop, Kevin; Delzell, Elizabeth

    2014-01-01

    Background Large pragmatic clinical trials (PCTs) are increasingly used to conduct comparative effectiveness research. In the context of planning a safety PCT of the live herpes zoster vaccine in rheumatoid arthritis (RA) patients age ≥ 50 receiving anti- tumor necrosis factor (TNF) therapy, we evaluated the use of health plan combined with registry data to assess the feasibility of recruiting the 4,000 patients needed for the trial and to facilitate site selection. Methods Using national United States data from Medicare, we identified older RA patients who received anti-TNF therapy in the last quarter of 2009. Extrapolations were made from the Medicare patient population to younger patients and those with other types of insurance using the Consortium of Rheumatology Researchers of North America (CORRONA) disease registry. Patients’ treating rheumatologists were grouped into practices and sorted by size from the greatest to the least number of eligible patients. Results Approximately 50,000 RA patients receiving anti-TNF therapy were identified in the Medicare data, distributed across 1,980 physician practices. After augmenting Medicare data with information from CORRONA and extrapolating to younger patients and those with other types of insurance, more than 12,000 potentially eligible study subjects were identified from the 40-45 largest rheumatology practices. Conclusion Health plan and registry databases appear useful to assess feasibility of large pragmatic trials and to assist in selection of recruitment sites with the greatest number of potentially eligible patients. This novel approach is applicable to trials with simple inclusion/exclusion criteria that can be readily assessed in these data sources. PMID:24346611

  2. mzGroupAnalyzer--predicting pathways and novel chemical structures from untargeted high-throughput metabolomics data.

    PubMed

    Doerfler, Hannes; Sun, Xiaoliang; 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

  3. Network spatio-temporal analysis predicts disease stage-related genes and pathways in renal cell carcinoma.

    PubMed

    Li1, X H; Yang, C Z; Wang, J

    2016-01-01

    The purpose of this study was to screen the key genes and pathways of renal cell carcinoma (RCC) and lay the foundation for its diagnosis and therapy. Microarray data of normal subjects and RCC patients at different stages of disease were used to screen differentially expressed genes (DEGs). Based on the DEGs in the four disease stages, four co-expression networks were constructed using the Empirical Bayes method and hub genes were obtained by centrality analysis. The enriched pathways of the DEGs and the mutual hub genes in the cluster of each disease stage were investigated. The mutual hub genes of the four disease stages in RCC tissue were validated using reverse transcription-polymerase chain reaction (RT-PCR) and western blot analysis. A total of 432 DEGs were screened, including 233 upregulated and 199 downregulated genes, by statistical analysis. Centrality analysis of co-expression networks in different disease stages suggested that PLXDC1, IKZF1, RUNX2, and RNF125 were mutual hub genes. Pathway analysis showed that the DEGs were significantly enriched in seven terms. The hub modules in stage I disease were significantly enriched in the complement coagulation cascade pathway and the hub modules of the other three disease stages were enriched in natural killer cell-mediated cytotoxicity. The expression levels of PLXDC1, IKZF1, RUNX2, and RNF125 were significantly different between normal subjects and RCC patients by RT-PCR and western blot. Our study revealed four hub genes (PLXDC1, IKZF1, RUNX2, and RNF125) and two biological pathways that might be underlying biomarkers involved in RCC. PMID:27173324

  4. Wnt Pathway Activation Predicts Increased Risk of Tumor Recurrence in Patients with Stage I Non-Small Cell Lung Cancer

    PubMed Central

    Shapiro, Mark; Akiri, Gal; Chin, Cynthia; Wisnivesky, Juan P.; Beasley, Mary B.; Weiser, Todd S.; Swanson, Scott J.; Aaronson, Stuart A.

    2012-01-01

    Objective To determine the prevalence of Wnt pathway activation in patients with stage I NSCLC and its influence on lung cancer recurrence. Background Despite resection, the 5 year recurrence with localized stage I non-small cell lung cancer (NSCLC) is 18.4–24%. Aberrant Wnt signaling activation plays an important role in a wide variety of tumor types. However, there is not much known about the role Wnt pathway plays in patients with stage I lung cancer Methods Tumor and normal lung tissues from 55 patients following resection for stage I NSCLC were subjected to glutathione-S-transferase (GST) E-cadherin pull-down and immunoblot analysis to assess levels of uncomplexed β-catenin, a reliable measure of Wnt signaling activation. The β-catenin gene was also screened for oncogenic mutations in tumors with activated Wnt signaling. Cancer recurrence rates were correlated in a blinded manner in patients with Wnt pathway positive and negative tumors. Results Tumors in twenty patients (36.4%) scored as Wnt positive with only one exhibiting a β-catenin oncogenic mutation. Patients with Wnt positive tumors experienced a significantly higher rate of overall cancer recurrence than those with Wnt negative tumors (30.0% vs. 5.7%, p=0.02), with 25.0% exhibiting distal tumor recurrence compared to 2.9% in the Wnt negative group (p=0.02). Conclusions Wnt pathway activation was present in a substantial fraction of Stage I NSCLCs, which was rarely due to mutations. Moreover, Wnt pathway activation was associated with a significantly higher rate of tumor recurrence. These findings suggest that Wnt activation reflects a more aggressive tumor phenotype and identifies patients who may benefit from more aggressive therapy in addition to resection. PMID:23011390

  5. Targeting of multiple oncogenic signaling pathways by Hsp90 inhibitor alone or in combination with berberine for treatment of colorectal cancer.

    PubMed

    Su, Yen-Hao; Tang, Wan-Chun; Cheng, Ya-Wen; Sia, Peik; Huang, Chi-Chen; Lee, Yi-Chao; Jiang, Hsin-Yi; Wu, Ming-Heng; Lai, I-Lu; Lee, Jun-Wei; Lee, Kuen-Haur

    2015-10-01

    There is a wide range of drugs and combinations under investigation and/or approved over the last decade to treat colorectal cancer (CRC), but the 5-year survival rate remains poor at stages II-IV. Therefore, new, more-efficient drugs still need to be developed that will hopefully be included in first-line therapy or overcome resistance when it appears, as part of second- or third-line treatments in the near future. In this study, we revealed that heat shock protein 90 (Hsp90) inhibitors have high therapeutic potential in CRC according to combinative analysis of NCBI's Gene Expression Omnibus (GEO) repository and chemical genomic database of Connectivity Map (CMap). We found that second generation Hsp90 inhibitor, NVP-AUY922, significantly downregulated the activities of a broad spectrum of kinases involved in regulating cell growth arrest and death of NVP-AUY922-sensitive CRC cells. To overcome NVP-AUY922-induced upregulation of survivin expression which causes drug insensitivity, we found that combining berberine (BBR), a herbal medicine with potency in inhibiting survivin expression, with NVP-AUY922 resulted in synergistic antiproliferative effects for NVP-AUY922-sensitive and -insensitive CRC cells. Furthermore, we demonstrated that treatment of NVP-AUY922-insensitive CRC cells with the combination of NVP-AUY922 and BBR caused cell growth arrest through inhibiting CDK4 expression and induction of microRNA-296-5p (miR-296-5p)-mediated suppression of Pin1-β-catenin-cyclin D1 signaling pathway. Finally, we found that the expression level of Hsp90 in tumor tissues of CRC was positively correlated with CDK4 and Pin1 expression levels. Taken together, these results indicate that combination of NVP-AUY922 and BBR therapy can inhibit multiple oncogenic signaling pathways of CRC. PMID:25982393

  6. Predicting species cover of marine macrophyte and invertebrate species combining hyperspectral remote sensing, machine learning and regression techniques.

    PubMed

    Kotta, Jonne; Kutser, Tiit; Teeveer, Karolin; Vahtmäe, Ele; Pärnoja, Merli

    2014-01-01

    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.

  7. 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

  8. 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

  9. Behavior and neuroimaging at baseline predict individual response to combined mathematical and working memory training in children.

    PubMed

    Nemmi, Federico; Helander, Elin; Helenius, Ola; Almeida, Rita; Hassler, Martin; Räsänen, Pekka; Klingberg, Torkel

    2016-08-01

    Mathematical performance is highly correlated with several general cognitive abilities, including working memory (WM) capacity. Here we investigated the effect of numerical training using a number-line (NLT), WM training (WMT), or the combination of the two on a composite score of mathematical ability. The aim was to investigate if the combination contributed to the outcome, and determine if baseline performance or neuroimaging predict the magnitude of improvement. We randomly assigned 308, 6-year-old children to WMT, NLT, WMT+NLT or a control intervention. Overall, there was a significant effect of NLT but not WMT. The WMT+NLT was the only group that improved significantly more than the controls, although the interaction NLTxWM was non-significant. Higher WM and maths performance predicted larger benefits for WMT and NLT, respectively. Neuroimaging at baseline also contributed significant information about training gain. Different individuals showed as much as a three-fold difference in their responses to the same intervention. These results show that the impact of an intervention is highly dependent on individual characteristics of the child. If differences in responses could be used to optimize the intervention for each child, future interventions could be substantially more effective. PMID:27399278

  10. Predicting transient particle transport in enclosed environments with the combined computational fluid dynamics and Markov chain method.

    PubMed

    Chen, C; Lin, C-H; Long, Z; Chen, Q

    2014-02-01

    To quickly obtain information about airborne infectious disease transmission in enclosed environments is critical in reducing the infection risk to the occupants. This study developed a combined computational fluid dynamics (CFD) and Markov chain method for quickly predicting transient particle transport in enclosed environments. The method first calculated a transition probability matrix using CFD simulations. Next, the Markov chain technique was applied to calculate the transient particle concentration distributions. This investigation used three cases, particle transport in an isothermal clean room, an office with an underfloor air distribution system, and the first-class cabin of an MD-82 airliner, to validate the combined CFD and Markov chain method. The general trends of the particle concentrations vs. time predicted by the Markov chain method agreed with the CFD simulations for these cases. The proposed Markov chain method can provide faster-than-real-time information about particle transport in enclosed environments. Furthermore, for a fixed airflow field, when the source location is changed, the Markov chain method can be used to avoid recalculation of the particle transport equation and thus reduce computing costs. PMID:23789964

  11. Combination of a proteomics approach and reengineering of meso scale network models for prediction of mode-of-action for tyrosine kinase inhibitors.

    PubMed

    Balabanov, Stefan; Wilhelm, Thomas; Venz, Simone; Keller, Gunhild; Scharf, Christian; Pospisil, Heike; Braig, Melanie; Barett, Christine; Bokemeyer, Carsten; Walther, Reinhard; Brümmendorf, Tim H; Schuppert, Andreas

    2013-01-01

    In drug discovery, the characterisation of the precise modes of action (MoA) and of unwanted off-target effects of novel molecularly targeted compounds is of highest relevance. Recent approaches for identification of MoA have employed various techniques for modeling of well defined signaling pathways including structural information, changes in phenotypic behavior of cells and gene expression patterns after drug treatment. However, efficient approaches focusing on proteome wide data for the identification of MoA including interference with mutations are underrepresented. As mutations are key drivers of drug resistance in molecularly targeted tumor therapies, efficient analysis and modeling of downstream effects of mutations on drug MoA is a key to efficient development of improved targeted anti-cancer drugs. Here we present a combination of a global proteome analysis, reengineering of network models and integration of apoptosis data used to infer the mode-of-action of various tyrosine kinase inhibitors (TKIs) in chronic myeloid leukemia (CML) cell lines expressing wild type as well as TKI resistance conferring mutants of BCR-ABL. The inferred network models provide a tool to predict the main MoA of drugs as well as to grouping of drugs with known similar kinase inhibitory activity patterns in comparison to drugs with an additional MoA. We believe that our direct network reconstruction approach, demonstrated on proteomics data, can provide a complementary method to the established network reconstruction approaches for the preclinical modeling of the MoA of various types of targeted drugs in cancer treatment. Hence it may contribute to the more precise prediction of clinically relevant on- and off-target effects of TKIs. PMID:23326482

  12. Epigenetic age predictions based on buccal swabs are more precise in combination with cell type-specific DNA methylation signatures

    PubMed Central

    Eipel, Monika; Mayer, Felix; Arent, Tanja; Ferreira, Marcelo R. P.; Birkhofer, Carina; Gerstenmaier, Uwe; Costa, Ivan G.; Ritz-Timme, Stefanie; Wagner, Wolfgang

    2016-01-01

    Aging is reflected by highly reproducible DNA methylation (DNAm) changes that open new perspectives for estimation of chronological age in legal medicine. DNA can be harvested non-invasively from cells at the inside of a person's cheek using buccal swabs – but these specimens resemble heterogeneous mixtures of buccal epithelial cells and leukocytes with different epigenetic makeup. In this study, we have trained an age predictor based on three age-associated CpG sites (associated with the genes PDE4C, ASPA, and ITGA2B) for swab samples to reach a mean absolute deviation (MAD) between predicted and chronological age of 4.3 years in a training set and of 7.03 years in a validation set. Subsequently, the composition of buccal epithelial cells versus leukocytes was estimated by two additional CpGs (associated with the genes CD6 and SERPINB5). Results of this “Buccal-Cell-Signature” correlated with cell counts in cytological stains (R2 = 0.94). Combination of cell type-specific and age-associated CpGs into one multivariate model enabled age predictions with MADs of 5.09 years and 5.12 years in two independent validation sets. Our results demonstrate that the cellular composition in buccal swab samples can be determined by DNAm at two cell type-specific CpGs to improve epigenetic age predictions. PMID:27249102

  13. 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.

  14. 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

  15. 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.

  16. [PI3K-AKT-mTOR pathway: Description, therapeutic development, resistance, predictive/prognostic biomarkers and therapeutic applications for cancer].

    PubMed

    Brotelle, Thibault; Bay, Jacques-Olivier

    2016-01-01

    Among many cancer cells signaling pathways, PI3K-AKT-mTOR plays a major role in growth, proliferation and cellular survival. This is a complex pathway activated either by an extracellular way (receptors with tyrosine kinase activity) or by an intracellular way with transformed or overexpressed proteins involved in the signal transduction. To date, there are many applications of mTOR inhibitors in oncology with an expanding development rapidly. However, resistances appear to mTOR inhibitors which lead to 2nd generation mTOR inhibitors development. A better knowledge of predictive and prognostic biomarkers will allow to specify the group of patients who may benefit from these treatments and help to the choice. PMID:26582734

  17. A Novel Combination of Calprotectin and CXCL12 for Predicting Malignancy in Patients with Exudative Pleural Effusion

    PubMed Central

    Luo, Jian; Wang, Maoyun; Li, Chuntao; Liang, Binmiao; Liu, Dan; Shi, Chaoli; Jiang, Faming; Wang, Ting; Li, Peijun; Liang, Zongan

    2015-01-01

    Abstract 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. PMID:26632726

  18. Species-specific diversity of novel bacterial lineages and differential abundance of predicted pathways for toxic compound degradation in scorpion gut microbiota.

    PubMed

    Bolaños, Luis M; Rosenblueth, Mónica; Castillo-Ramírez, Santiago; Figuier-Huttin, Gilles; Martínez-Romero, Esperanza

    2016-05-01

    Scorpions are considered 'living fossils' that have conserved ancestral anatomical features and have adapted to numerous habitats. However, their gut microbiota diversity has not been studied. Here, we characterized the gut microbiota of two scorpion species, Vaejovis smithi and Centruroides limpidus. Our results indicate that scorpion gut microbiota is species-specific and that food deprivation reduces bacterial diversity. 16S rRNA gene phylogenetic analysis revealed novel bacterial lineages showing a low level of sequence identity to any known bacteria. Furthermore, these novel bacterial lineages were each restricted to a different scorpion species. Additionally, our results of the predicted metagenomic profiles revealed a core set of pathways that were highly abundant in both species, and mostly related to amino acid, carbohydrate, vitamin and cofactor metabolism. Notably, the food-deprived V. smithi shotgun metagenome matched almost completely the metabolic features of the prediction. Finally, comparisons among predicted metagenomic profiles showed that toxic compound degradation pathways were more abundant in recently captured C. limpidus scorpions. This study gives a first insight into the scorpion gut microbiota and provides a reference for future studies on the gut microbiota from other arachnid species. PMID:26058415

  19. 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

  20. Hematoporphyrin monomethyl ether combined with He–Ne laser irradiation-induced apoptosis in canine breast cancer cells through the mitochondrial pathway

    PubMed Central

    Li, Huatao; Tong, Jinjin; Bao, Jun; Tang, Damu; Tian, Wenru

    2016-01-01

    Hematoporphyrin monomethyl ether (HMME) combined with He-Ne laser irradiation is a novel and promising photodynamic therapy (PDT)-induced apoptosis that can be applied in vitro on canine breast cancer cells. However, the exact pathway responsible for HMME-PDT in canine breast cancer cells remains unknown. CHMm cells morphology and apoptosis were analyzed using optical microscope, terminal deoxynucleotidyl transferase dUTP nick end labeling fluorescein staining and DNA ladder assays. Apoptotic pathway was further confirmed by Real-time-polymerase chain reaction and Western blotting assays. Our results showed that HMME-PDT induced significant changes in cell morphology, such as formation of cytoplasmic vacuoles and the gradual rounding of cells coupled with decreased size and detachment. DNA fragmentation and cell death was shown to occur in a time-dependent manner. Furthermore, HMME-PDT increased the activities of caspase-9 and caspase-3, and released cytochrome c from mitochondria into the cytoplasm. HMME-PDT also significantly increased both mRNA and protein levels of Bax and decreased P53 gene expression in a time-dependent manner, while the mRNA and protein expression of Bcl-2 were repressed. These alterations suggest that HMME-PDT induced CHMm cell apoptosis via the mitochondrial apoptosis pathway and had anti-canine breast cancer effects in vitro. PMID:26645330

  1. The combination of 5-fluorouracil plus p53 pathway restoration is associated with depletion of p53-deficient or mutant p53-expressing putative colon cancer stem cells.

    PubMed

    Huang, Catherine; Zhang, Xiang M; Tavaluc, Raluca T; Hart, Lori S; Dicker, David T; Wang, Wenge; El-Deiry, Wafik S

    2009-11-01

    The cancer stem cell hypothesis suggests that rare populations of tumor-initiating cells may be resistant to therapy, lead to tumor relapse and contribute to poor prognosis for cancer patients. We previously demonstrated the feasibility of p53 pathway restoration in p53-deficient tumor cell populations using small molecules including ellipticine or its derivatives. We now establish a single cell p53-regulated green fluorescent protein (EGFP)-reporter system in human DLD1 colon tumor cells expressing mutant p53 protein. We use these p53-EGFP reporter DLD1 cells to investigate the status of p53 transcriptional activity in putative colon cancer stem cell populations following exposure to p53 pathway-restoring drugs and/or classical chemotherapy. We demonstrate induction of p53-specific EGFP reporter fluorescence following overexpression of p53 family member p73 by an Adenovirus vector. We further show that p53-reporter activity is induced in DLD1 putative cancer stem cell side-populations analyzed by their Hoechst dye efflux properties following treatment with the p53 pathway restoring drug ellipticine. Combination of ellipticine with the cytotoxic agent 5-fluorouracil resulted in increased cytotoxicity as compared to either agent alone and this was associated with depletion of putative cancer stem cell populations as compared with 5-FU alone treatment. Our results support the feasibility of therapeutic targeting of mutant p53 in putative cancer stem cells as well as the potential to enhance cytotoxic chemotherapy. PMID:19923910

  2. Combining solvent thermodynamic profiles with functionality maps of the Hsp90 binding site to predict the displacement of water molecules.

    PubMed

    Haider, Kamran; Huggins, David J

    2013-10-28

    Intermolecular interactions in the aqueous phase must compete with the interactions between the two binding partners and their solvating water molecules. In biological systems, water molecules in protein binding sites cluster at well-defined hydration sites and can form strong hydrogen-bonding interactions with backbone and side-chain atoms. Displacement of such water molecules is only favorable when the ligand can form strong compensating hydrogen bonds. Conversely, water molecules in hydrophobic regions of protein binding sites make only weak interactions, and the requirements for favorable displacement are less stringent. The propensity of water molecules for displacement can be identified using inhomogeneous fluid solvation theory (IFST), a statistical mechanical method that decomposes the solvation free energy of a solute into the contributions from different spatial regions and identifies potential binding hotspots. In this study, we employed IFST to study the displacement of water molecules from the ATP binding site of Hsp90, using a test set of 103 ligands. The predicted contribution of a hydration site to the hydration free energy was found to correlate well with the observed displacement. Additionally, we investigated if this correlation could be improved by using the energetic scores of favorable probe groups binding at the location of hydration sites, derived from a multiple copy simultaneous search (MCSS) method. The probe binding scores were not highly predictive of the observed displacement and did not improve the predictivity when used in combination with IFST-based hydration free energies. The results show that IFST alone can be used to reliably predict the observed displacement of water molecules in Hsp90. However, MCSS can augment IFST calculations by suggesting which functional groups should be used to replace highly displaceable water molecules. Such an approach could be very useful in improving the hit-to-lead process for new drug targets.

  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. Remarkable effect of bimetallic nanocluster catalysts for aerobic oxidation of alcohols: combining metals changes the activities and the reaction pathways to aldehydes/carboxylic acids or esters.

    PubMed

    Kaizuka, Kosuke; Miyamura, Hiroyuki; Kobayashi, Shū

    2010-11-01

    Selective oxidation of alcohols catalyzed by novel carbon-stabilized polymer-incarcerated bimetallic nanocluster catalysts using molecular oxygen has been developed. The reactivity and the selectivity were strongly dependent on the combination of metals and solvent systems; aldehydes and ketones were obtained by the gold/platinum catalyst in benzotrifluoride, and esters were formed by the gold/palladium catalyst in methanol. To the best of our knowledge, this is the first example that the reaction pathway has been changed dramatically in gold catalysis by combining with a second metal. The differences in the activity and the selectivity are considered to be derived from the difference in the structure of the bimetallic clusters.

  5. Divergent responses of the amygdala and ventral striatum predict stress-related problem drinking in young adults: Possible differential markers of affective and impulsive pathways of risk for alcohol use disorder

    PubMed Central

    Nikolova, Yuliya S.; Knodt, Annchen R.; Radtke, Spenser R.; Hariri, Ahmad R.

    2015-01-01

    Prior work suggests there may be two distinct pathways of alcohol use disorder (AUD) risk: one associated with positive emotion enhancement and behavioral impulsivity, and one associated with negative emotion relief and coping. We sought to map these two pathways onto individual differences in neural reward and threat processing assessed using BOLD fMRI in a sample of 759 undergraduate students (426 women, mean age 19.65±1.24) participating in the Duke Neurogenetics Study. We demonstrate that problem drinking is highest in the context of stress and in those with one of two distinct neural phenotypes: 1) a combination of relatively low reward-related activity of the ventral striatum (VS) and high threat-related reactivity of the amygdala; or 2) a combination of relatively high VS activity and low amygdala reactivity. In addition, we demonstrate that the relationship between stress and problem alcohol use is mediated by impulsivity, as reflected in monetary delay discounting rates, for those with high VS-low amygdala reactivity, and by anxious/depressive symptomatology for those with the opposite neural risk phenotype. Across both neural phenotypes, we found that greater divergence between VS and amygdala reactivity predicted greater risk for problem drinking. Finally, for those individuals with the low VS-high amygdala risk phenotype we found that stress not only predicted the presence of a DSM-IV diagnosed AUD at the time of neuroimaging, but also subsequent problem drinking reported three months following study completion. These results offer new insight into the neural basis of AUD risk and suggest novel biological targets for early individualized treatment or prevention. PMID:26122584

  6. 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 ...

  7. Combined inhibition of IL1, CXCR1/2, and TGFβ signaling pathways modulates in-vivo resistance to anti-VEGF treatment.

    PubMed

    Carbone, Carmine; Tamburrino, Anna; Piro, Geny; Boschi, Federico; Cataldo, Ivana; Zanotto, Marco; Mina, Maria M; Zanini, Silvia; Sbarbati, Andrea; Scarpa, Aldo; Tortora, Giampaolo; Melisi, Davide

    2016-01-01

    Resistance of tumors to antiangiogenic therapies is becoming increasingly relevant. We recently identified interleukin-1 (IL1), CXC receptors (CXCR)1/2 ligands, and transforming growth factor β (TGFβ) among the proinflammatory factors that were expressed at higher levels in murine models resistant to the antivascular endothelial growth factor (anti-VEGF) antibody bevacizumab. Here, we hypothesized that the combined inhibition of these proinflammatory signaling pathways might reverse this anti-VEGF resistance. Bevacizumab-resistant FGBR pancreatic cancer cells were treated in vitro with bevacizumab, the recombinant human IL1 receptor antagonist anakinra, the monoclonal antibody against TGFβ receptor type II TR1, and a novel recombinant antibody binding CXCR1/2 ligands. The FGBR cells treated with these agents in combination had significantly higher levels of E-cadherin and lower levels of vimentin, IL6, phosphorylated p65, and SMAD2, and showed significantly lower migration rates than did their controls treated with the same agents without bevacizumab or with a single agent bevacizumab as a control. Consistently, the combination of these agents with bevacizumab reduced the FGBR tumor burden and significantly prolonged mice survival compared with bevacizumab in monotherapy. Tumors from mice receiving the combination treatment showed significantly lower expression of IL6 and phosphorylated SMAD2, higher expression of E-cadherin and lower levels of vimentin, and a significantly lower infiltration by CD11b cells compared with bevacizumab-treated controls. This study suggests that inhibition of IL1, CXCR1/2, and TGFβ signaling pathways is a potential therapeutic approach to modulate the acquired resistance to anti-VEGF treatment by reversing epithelial-mesenchymal transition and inhibiting CD11b proangiogenic myeloid cells' tumor infiltration.

  8. 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

  9. Identification of combined genetic determinants of liver stiffness within the SREBP1c-PNPLA3 pathway.

    PubMed

    Krawczyk, Marcin; Grünhage, Frank; Lammert, Frank

    2013-01-01

    The common PNPLA3 (adiponutrin) variant, p.I148M, was identified as a genetic determinant of liver fibrosis. Since the expression of PNPLA3 is induced by sterol regulatory element binding protein 1c (SREBP1c), we investigate two common SREBP1c variants (rs2297508 and rs11868035) for their association with liver stiffness. In 899 individuals (aged 17-83 years, 547 males) with chronic liver diseases, hepatic fibrosis was non-invasively phenotyped by transient elastography (TE). The SREBP1c single nucleotide polymorphisms (SNPs) were genotyped using PCR-based assays with 5'-nuclease and fluorescence detection. The SREBP1c rs11868035 variant affected liver fibrosis significantly (p = 0.029): median TE levels were 7.2, 6.6 and 6.0 kPa in carriers of (TT) (n = 421), (CT) (n = 384) and (CC) (n = 87) genotypes, respectively. Overall, the SREBP1c SNP was associated with low TE levels (5.0-8.0 kPa). Carriers of both PNPLA3 and SREBP1c risk genotypes displayed significantly (p = 0.005) higher median liver stiffness, as compared to patients carrying none of these variants. The common SREBP1c variant may affect early stages of liver fibrosis. Our study supports a role of the SREBP1c-PNPLA3 pathway as a "disease module" that promotes hepatic fibrogenesis.

  10. Precise prediction for the light MSSM Higgs-boson mass combining effective field theory and fixed-order calculations

    NASA Astrophysics Data System (ADS)

    Bahl, Henning; Hollik, Wolfgang

    2016-09-01

    In the Minimal Supersymmetric Standard Model heavy superparticles introduce large logarithms in the calculation of the lightest {CP}-even Higgs-boson mass. These logarithmic contributions can be resummed using effective field theory techniques. For light superparticles, however, fixed-order calculations are expected to be more accurate. To gain a precise prediction also for intermediate mass scales, the two approaches have to be combined. Here, we report on an improvement of this method in various steps: the inclusion of electroweak contributions, of separate electroweakino and gluino thresholds, as well as resummation at the NNLL level. These improvements can lead to significant numerical effects. In most cases, the lightest {CP}-even Higgs-boson mass is shifted downwards by about 1 GeV. This is mainly caused by higher-order corrections to the {overline{ {MS}}} top-quark mass. We also describe the implementation of the new contributions in the code FeynHiggs.

  11. 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

  12. Uncoupling of the Pathway of Methanogenesis in Northern Wetlands: Connection to Vegetation, and Implications for Variability and Predictability.

    NASA Astrophysics Data System (ADS)

    Hines, M. E.; Duddleston, K. N.; Chanton, J. P.

    2006-12-01

    Typical methanogenic decomposition pathways include near terminal carbon intermediates that turn over rapidly with small pool sizes. However, incubation and field experiments demonstrated that these organic intermediates accumulate in northern wetlands due to the lack of consumption by methanogenic bacteria. Acetate is the major organic end product of decomposition rather than CH4, and methanogenesis can be insignificant. The ratio of CO2:acetate:CH4 varied with vegetation type, and habitats dominated by non-vascular plants (Sphagnum) produced more acetate-C than CO2 or CH4. This ratio correlated well with stable C isotope alpha values used to delineate the path of CH4 formation. We suggest that methanogenesis in general is inhibited in oligotrophic wetlands, but that the conversion of acetate to CH4 is more sensitive, which increases the importance of the conversion of H2/CO2 to CH4. The relative importance of CH4 as an end product increased greatly in sites containing even small populations of Carex compared to sites inhabited only by Sphagnum, suggesting that subtle vegetation changes expected to occur during warming could lead to changes in the path of methanogenesis, increasing production. In addition, depth profiles revealed an active surficial (0-7 cm) C cycle that is sensitive to hydrology that may also greatly affect variability of CH4 formation. Acetate production represented a terminal process and was a sink for a large portion of metabolized C whose ultimate fate was aerobic oxidation to CO2. C destined for CH4 is thus bypassed to CO2 and does not contribute to atmospheric CH4. However, the connection and sensitivity of the pathway of methanogenesis to even small vegetation changes suggests that pathways can be mapped, they vary greatly over small distances, and they can change drastically with relatively small temperature increases.

  13. 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.

  14. 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

    2016-01-01

    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

  15. 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

    2016-01-01

    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

  16. 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.

  17. A combination of anatomical and functional evaluations improves the prediction of cardiac event in patients with coronary artery bypass

    PubMed Central

    Kawai, Hideki; Sarai, Masayoshi; Motoyama, Sadako; Ito, Hajime; Takada, Kayoko; Harigaya, Hiroto; Takahashi, Hiroshi; Hashimoto, Shuji; Takagi, Yasushi; Ando, Motomi; Anno, Hirofumi; Ishii, Junichi; Murohara, Toyoaki; Ozaki, Yukio

    2013-01-01

    Objective To study the usefulness of combined risk stratification of coronary CT angiography (CTA) and myocardial perfusion imaging (MPI) in patients with previous coronary-artery-bypass grafting (CABG). Design A retrospective, observational, single centre study. Setting and patients 204 patients (84.3% men, mean age 68.7±7.6) undergoing CTA and MPI. Main outcome measures CTA defined unprotected coronary territories (UCT; 0, 1, 2 or 3) by evaluating the number of significant stenoses which were defined as the left main trunk ≥50% diameter stenosis, other native vessel stenosis ≥70% or graft stenosis ≥70%. Using a cut-off value with receiver-operating characteristics analysis, all patients were divided into four groups: group A (UCT=0, summed stress score (SSS)<4), group B (UCT≥1, SSS<4), group C (UCT=0, SSS≥4) and group D (UCT≥1, SSS≥4). Results Cardiac events, as a composite end point including cardiac death, non-fatal myocardial infarction, unstable angina requiring revascularisation and heart-failure hospitalisation, were observed in 27 patients for a median follow-up of 27.5 months. The annual event rates were 1.1%, 2%, 5.7% and 12.9% of patients in groups A, B, C and D, respectively (log rank p value <0.0001). Adding UCT or SSS to a model with significant clinical factors including left ventricular ejection fraction, time since CABG and Euro SCORE II improved the prediction of events, while adding UCT and SSS to the model improved it greatly with increasing C-index, net reclassification improvement and integrated discrimination improvement. Conclusions The combination of anatomical and functional evaluations non-invasively enhances the predictive accuracy of cardiac events in patients with CABG. PMID:24220113

  18. 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.

  19. 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

  20. Treatment with bone marrow mesenchymal stem cells combined with plumbagin alleviates spinal cord injury by affecting oxidative stress, inflammation, apoptotis and the activation of the Nrf2 pathway.

    PubMed

    Yang, Wencheng; Yang, Yan; Yang, Jian-Yi; Liang, Ming; Song, Jiangtao

    2016-04-01

    The aim of the present study was to investigate the protective effect exerted by bone marrow mesenchymal stem cells (BMSCs) in combination with plumbagin on spinal cord injury (SCI) and explore the mechanism behind this protective effect. Firstly, BMSCs were extracted from male Sprague-Dawley rats, cultured in vitro, and identified by hematoxylin. Sprague-Dawley rats were then randomly divided into a control group, SCI model group, BMSC-treated group, a plumbagin-treated group, and a BMSC and plumbagin-treated group. After treatment with BMSCs combined with plumbagin, a Basso, Beattie and Bresnahan (BBB) test was carried out and the spinal cord water content was examined in order to analyze the effect of BMSCs combined with plumbagin on SCI. The myeloperoxidase (MPO), superoxide dismutase (SOD), malondialdehyde (MDA), nuclear factor-κB (NF-κB) p65 unit, tumor necrosis factor-α (TNF-α) levels were also detected. Moreover, nuclear factor erythroid 2‑related factor 2 (Nrf2), phosphoinositide 3-kinase (PI3K), phosphorylated (p-)Akt, p-p38 mitogen-activated protein kinase (MAPK), and p-extracellular-signal-regulated kinase (ERK) protein expression levels were measured using western blot analysis. Treatment with BMSCs combined with plumbagin significantly improved locomotor recovery and reduced the spinal cord water content after SCI. The increased MPO, MDA, NF-κB p65 and TNF-α levels were significantly suppressed and the decreased SOD was significantly increased in SCI rats. The suppression of Nrf2, p-Akt and p-ERK, as well as the promotion of p-p38 MAPK, were reversed by treatment with BMSCs combined with plumbagin. These effects suggest that treatment with BMSCs combined with plumbagin alleviates SCI through its effects on oxidative stress, inflammation, apoptotis and activation of the Nrf2 pathway. PMID:26936518

  1. 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

  2. Preexisting autoantibodies predict efficacy of oral insulin to cure autoimmune diabetes in combination with anti-CD3.

    PubMed

    Mamchak, Alusha A; Manenkova, Yulia; Leconet, Wilhem; Zheng, Yanan; Chan, Jason R; Stokes, Cynthia L; Shoda, Lisl K M; von Herrath, Matthias; Bresson, Damien

    2012-06-01

    We have previously developed a combination therapy (CT) using anti-CD3 monoclonal antibodies together with islet-(auto)antigen immunizations that can more efficiently reverse type 1 diabetes (T1D) than either entity alone. However, clinical translation of antigen-specific therapies in general is hampered by the lack of biomarkers that could be used to optimize the modalities of antigen delivery and to predict responders from nonresponders. To support the rapid identification of candidate biomarkers, we systematically evaluated multiple variables in a mathematical disease model. The in silico predictions were validated by subsequent laboratory data in NOD mice with T1D that received anti-CD3/oral insulin CT. Our study shows that higher anti-insulin autoantibody levels at diagnosis can distinguish responders and nonresponders among recipients of CT exquisitely well. In addition, early posttreatment changes in proinflammatory cytokines were indicative of long-term remission. Coadministration of oral insulin improved and prolonged the therapeutic efficacy of anti-CD3 therapy, and long-term protection was achieved by maintaining elevated insulin-specific regulatory T cell numbers that efficiently lowered diabetogenic effector memory T cells. Our validation of preexisting autoantibodies as biomarkers to distinguish future responders from nonresponders among recipients of oral insulin provides a compelling and mechanistic rationale to more rapidly translate anti-CD3/oral insulin CT for human T1D.

  3. 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.

  4. Identification of miRNA-Mediated Core Gene Module for Glioma Patient Prediction by Integrating High-Throughput miRNA, mRNA Expression and Pathway Structure

    PubMed Central

    Han, Junwei; Shang, Desi; Zhang, Yunpeng; Zhang, Wei; Yao, Qianlan; Han, Lei; Xu, Yanjun; Yan, Wei; Bao, Zhaoshi; You, Gan; Jiang, Tao; Kang, Chunsheng; Li, Xia

    2014-01-01

    The prognosis of glioma patients is usually poor, especially in patients with glioblastoma (World Health Organization (WHO) grade IV). The regulatory functions of microRNA (miRNA) on genes have important implications in glioma cell survival. However, there are not many studies that have investigated glioma survival by integrating miRNAs and genes while also considering pathway structure. In this study, we performed sample-matched miRNA and mRNA expression profilings to systematically analyze glioma patient survival. During this analytical process, we developed pathway-based random walk to identify a glioma core miRNA-gene module, simultaneously considering pathway structure information and multi-level involvement of miRNAs and genes. The core miRNA-gene module we identified was comprised of four apparent sub-modules; all four sub-modules displayed a significant correlation with patient survival in the testing set (P-values≤0.001). Notably, one sub-module that consisted of 6 miRNAs and 26 genes also correlated with survival time in the high-grade subgroup (WHO grade III and IV), P-value = 0.0062. Furthermore, the 26-gene expression signature from this sub-module had robust predictive power in four independent, publicly available glioma datasets. Our findings suggested that the expression signatures, which were identified by integration of miRNA and gene level, were closely associated with overall survival among the glioma patients with various grades. PMID:24809850

  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 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.

  7. 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.

  8. Prediction of CL-20 chemical degradation pathways, theoretical and experimental evidence for dependence on competing modes of reaction.

    PubMed

    Qasim, M; Fredrickson, H; Honea, P; Furey, J; Leszczynski, J; Okovytyy, S; Szecsody, J; 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 pi 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. PMID:16272046

  9. 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

  10. 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

  11. Sequence-dependent inhibition of human colon cancer cell growth and of prosurvival pathways by oxaliplatin in combination with ZD6474 (Zactima), an inhibitor of VEGFR and EGFR tyrosine kinases.

    PubMed

    Troiani, Teresa; Lockerbie, Owen; Morrow, Mark; Ciardiello, Fortunato; Eckhardt, S Gail

    2006-07-01

    To date, clinical studies combining the new generation of targeted therapies and chemotherapy have had mixed results. Preclinical studies can be used to identify potential antagonism/synergy between certain agents, with the potential to predict the most efficacious combinations for further investigation in the clinical setting. In this study, we investigated the sequence-dependent interactions of ZD6474 with oxaliplatin in two human colon cell lines in vitro. We evaluated the in vitro antitumor activity of ZD6474, an inhibitor of vascular endothelial growth factor receptor (VEGFR), epidermal growth factor receptor (EGFR) and RET tyrosine kinase activity, and oxaliplatin using three combination schedules: ZD6474 before oxaliplatin, oxaliplatin before ZD6474, and concurrent exposure. Cell proliferation studies showed that treatment with oxaliplatin followed by ZD6474 was highly synergistic, whereas the reverse sequence was clearly antagonistic as was concurrent exposure. Oxaliplatin induced a G(2)-M arrest, which was antagonized if the cells were previously or concurrently treated with ZD6474. ZD6474 enhanced oxaliplatin-induced apoptosis but only when added after oxaliplatin. The sequence-dependent antitumor effects appeared, in part, to be based on modulation of compensatory prosurvival pathways. Thus, expression of total and active phosphorylated EGFR, as well as AKT and extracellular signal-regulated kinase, was markedly increased by oxaliplatin. This increase was blocked by subsequent treatment with ZD6474. Furthermore, the synergistic sequence resulted in reduced expression of insulin-like growth factor-I receptor and a marked reduction in secretion of vascular endothelial growth factor protein. ZD6474 in combination with oxaliplatin has synergistic antiproliferative properties in human colorectal cancer cell lines in vitro when oxaliplatin is administered before ZD6474.

  12. 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

  13. Studies on metabolites and metabolic pathways of bulleyaconitine A in rat liver microsomes using LC-MS(n) combined with specific inhibitors.

    PubMed

    Bi, Yunfeng; Zhuang, Xiaoyu; Zhu, Hongbin; Song, Fengrui; Liu, Zhiqiang; Liu, Shuying

    2015-07-01

    Bulleyaconitine A (BLA) from Aconitum bulleyanum plants is usually used as anti-inflammatory drug in some Asian countries. It has a variety of bioactivities, and at the same time some toxicities. Since the bioactivities and toxicities of BLA are closely related to its metabolism, the metabolites and the metabolic pathways of BLA in rat liver microsomes were investigated by HPLC-MS(n). In this research, the 12 metabolites of BLA were identified according to the results of HPLC-MS(n) data and the relevant literature. The results showed that there are multiple metabolites of BLA in rat liver microsomes, including demethylation, deacetylation, dehydrogenation deacetylation and hydroxylation. The major metabolic pathways of BLA in rat liver microsomes were clarified by HPLC-MS combined with specific inhibitors of CYP450 isoforms. As a result, CYP3A and 2C were found to be the principal CYP isoforms contributing to the metabolism of BLA. Moreover, CYP2D6 and 2E1 are also more important CYP isoforms for the metabolism of BLA. While CYP1A2 only affected the formation rate of M11, its effect on the metabolism of BLA is very small. PMID:25425064

  14. Murine Macrophages Secrete Interferon γ upon Combined Stimulation with Interleukin (IL)-12 and IL-18: A Novel Pathway of Autocrine Macrophage Activation

    PubMed Central

    Munder, Markus; Mallo, Moisés; Eichmann, Klaus; Modolell, Manuel

    1998-01-01

    Interferon (IFN)-γ, a key immunoregulatory cytokine, has been thought to be produced solely by activated T cells and natural killer cells. In this study, we show that murine bone marrow– derived macrophages (BMMΦ) secrete large amounts of IFN-γ upon appropriate stimulation. Although interleukin (IL)-12 and IL-18 alone induce low levels of IFN-γ mRNA transcripts, the combined stimulation of BMMΦ with both cytokines leads to the efficient production of IFN-γ protein. The macrophage-derived IFN-γ is biologically active as shown by induction of inducible nitric oxide synthase as well as upregulation of CD40 in macrophages. Our findings uncover a novel pathway of autocrine macrophage activation by demonstrating that the macrophage is not only a key cell type responding to IFN-γ but also a potent IFN-γ–producing cell. PMID:9625771

  15. Use of a combined cryo-EM and X-ray crystallography approach to reveal molecular details of bacterial pilus assembly by the chaperone/usher pathway

    SciTech Connect

    Li, H.; Thanassi, D. G.

    2009-06-01

    Many bacteria assemble hair-like fibers termed pili or fimbriae on their cell surface. These fibers mediate adhesion to various surfaces, including host cells, and play crucial roles in pathogenesis. Pili are polymers composed of thousands of individual subunit proteins. Understanding how these subunit proteins cross the bacterial envelope and correctly assemble at the cell surface is important not only for basic biology but also for the development of novel antimicrobial agents. The chaperone/usher pilus biogenesis pathway is one of the best-understood protein secretion systems, thanks largely to innovative efforts in biophysical techniques such as X-ray crystallography and cryo-electron microscopy. Such a combined approach holds promise for further elucidating remaining questions regarding the multi-step and highly dynamic pilus assembly process, as well as for studying other protein secretion and organelle biogenesis systems.

  16. Combined Transcriptomic and Proteomic Approach to Identify Toxicity Pathways in Early Life Stages of Japanese Medaka (Oryzias latipes) Exposed to 1,2,5,6-Tetrabromocyclooctane (TBCO).

    PubMed

    Sun, Jianxian; Tang, Song; Peng, Hui; Saunders, David M V; Doering, Jon A; Hecker, Markus; Jones, Paul D; Giesy, John P; Wiseman, Steve

    2016-07-19

    Currently, the novel brominated flame retardant 1,2,5,6-tetrabromocyclooctane (TBCO) is considered a potential replacement for hexabromocyclododecane (HBCD). Therefore, use of TBCO could increase in the near future. To assess potential toxicological risks to aquatic organisms, embryos of Japanese medaka (Oryzias latipes) were exposed to 10, 100, or 1000 μg/L TBCO from 2 h postfertilization until 1 day post-hatch. TBCO accumulated in embryos in the order of 0.43-1.3 × 10(4)-fold, and the rate constant of accumulation was 1.7-1.8 per day. The number of days to hatch and the hatching success of embryos exposed to the medium and the greatest concentrations of TBCO were impaired. Responses of the transcriptome (RNA-seq) and proteome were characterized in embryos exposed to 100 μg/L TBCO because this was the least concentration of TBCO that caused an effect on hatching. Consistent with effects on hatching, proteins whose abundances were reduced by exposure to TBCO were enriched in embryo development and hatching pathways. Also, on the basis of the responses of transcriptome and proteome, it was predicted that TBCO might impair vision and contraction of cardiac muscle, respectively, and these effects were confirmed by targeted bioassays. This study provided a comprehensive understanding of effects of TBCO on medaka at early life stages and illustrated the power of "omics" to explain and predict phenotypic responses to chemicals. PMID:27322799

  17. Predictors of remission in depression to individual and combined treatments (PReDICT): study protocol for a randomized controlled trial

    PubMed Central

    2012-01-01

    treatment, during which they receive a combination of CBT and antidepressant medication. Predictors of the primary outcome, remission, will be identified for overall and treatment-specific effects, and a statistical model incorporating multiple predictors will be developed to predict outcomes. Discussion The PReDICT study’s evaluation of biological, psychological, and clinical factors that may differentially impact treatment outcomes represents a sizeable step toward developing personalized treatments for MDD. Identified predictors should help guide the selection of initial treatments, and identify those patients most vulnerable to recurrence, who thus warrant maintenance or combination treatments to achieve and maintain wellness. Trial registration Clinicaltrials.gov Identifier: NCT00360399. Registered 02 AUG 2006. First patient randomized 09 FEB 2007. PMID:22776534

  18. Further increased production of free fatty acids by overexpressing a predicted transketolase gene of the pentose phosphate pathway in Aspergillus oryzae faaA disruptant.

    PubMed

    Tamano, Koichi; Miura, Ai

    2016-09-01

    Free fatty acids are useful as source materials for the production of biodiesel fuel and various chemicals such as pharmaceuticals and dietary supplements. Previously, we attained a 9.2-fold increase in free fatty acid productivity by disrupting a predicted acyl-CoA synthetase gene (faaA, AO090011000642) in Aspergillus oryzae. In this study, we achieved further increase in the productivity by overexpressing a predicted transketolase gene of the pentose phosphate pathway in the faaA disruptant. The A. oryzae genome is predicted to have three transketolase genes and overexpression of AO090023000345, one of the three genes, resulted in phenotypic change and further increase (corresponding to an increased production of 0.38 mmol/g dry cell weight) in free fatty acids at 1.4-fold compared to the faaA disruptant. Additionally, the biomass of hyphae increased at 1.2-fold by the overexpression. As a result, free fatty acid production yield per liter of liquid culture increased at 1.7-fold by the overexpression.

  19. 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.

  20. Combining Amplitude Spectrum Area with Previous Shock Information Using Neural Networks Improves Prediction Performance of Defibrillation Outcome for Subsequent Shocks in Out-Of-Hospital Cardiac Arrest Patients

    PubMed Central

    He, Mi; Lu, Yubao; Zhang, Lei; Zhang, Hehua; Gong, Yushun; Li, Yongqin

    2016-01-01

    Objective Quantitative ventricular fibrillation (VF) waveform analysis is a potentially powerful tool to optimize defibrillation. However, whether combining VF features with additional attributes that related to the previous shock could enhance the prediction performance for subsequent shocks is still uncertain. Methods A total of 528 defibrillation shocks from 199 patients experienced out-of-hospital cardiac arrest were analyzed in this study. VF waveform was quantified using amplitude spectrum area (AMSA) from defibrillator's ECG recordings prior to each shock. Combinations of AMSA with previous shock index (PSI) or/and change of AMSA (ΔAMSA) between successive shocks were exercised through a training dataset including 255shocks from 99patientswith neural networks. Performance of the combination methods were compared with AMSA based single feature prediction by area under receiver operating characteristic curve(AUC), sensitivity, positive predictive value (PPV), negative predictive value (NPV) and prediction accuracy (PA) through a validation dataset that was consisted of 273 shocks from 100patients. Results A total of61 (61.0%) patients required subsequent shocks (N = 173) in the validation dataset. Combining AMSA with PSI and ΔAMSA obtained highest AUC (0.904 vs. 0.819, p<0.001) among different combination approaches for subsequent shocks. Sensitivity (76.5% vs. 35.3%, p<0.001), NPV (90.2% vs. 76.9%, p = 0.007) and PA (86.1% vs. 74.0%, p = 0.005)were greatly improved compared with AMSA based single feature prediction with a threshold of 90% specificity. Conclusion In this retrospective study, combining AMSA with previous shock information using neural networks greatly improves prediction performance of defibrillation outcome for subsequent shocks. PMID:26863222

  1. 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.

  2. Combination of selected biochemical markers and cervical length in the prediction of impending preterm delivery in symptomatic patients.

    PubMed

    Hadži-Legal, M; Markova, A Daneva; Stefanovic, M; Tanturovski, M

    2016-01-01

    The pathophysiology of preterm delivery (PTD) is complex and multifactorial.It occurs in 8-12% of all deliveries, and the rate of PTD has increased during the past years in spite of intensive efforts towards early detection and prompt treatment. Fifty-eight pregnant women were eligible to join the study if they attended the University Clinic for Gynecology and Obstetrics, Skopje and were admitted to Department of High Risk pregnancy Unit with symptoms of preterm labor (PTL) (symptoms of uterine activity judged by the assessing physician to be indicative of PTL) at 24.0 to 36.6 weeks gestation.Test specimens for fetal fibronectin (fFN), phosphorylated insulin like growth factor binding protein 1 (phIGFBP-1), IL-6, and IL-2R and measuring the cervical length via transvaginal ultrasound were performed for each patient. The best statistical model for predicting PTL in the present study was to use a combination of the phIGFBP-1 test, a positive fFN test, cervical length less than 21.5 mm, levels of IL-6 higher than 1,305 pg/ml in the cervico-vaginal fluid (CVF), and serum levels of C-reactive protein (CRP) higher than 6.1 mg/L which was excellent at identifying the patients that were to deliver within 14 days of admittance. PMID:27048042

  3. 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

  4. A combined theoretical and in vitro modeling approach for predicting the magnetic capture and retention of magnetic nanoparticles in vivo

    PubMed Central

    David, Allan E.; Cole, Adam J.; Chertok, Beata; Park, Yoon Shin; Yang, Victor C.

    2011-01-01

    Magnetic nanoparticles (MNP) continue to draw considerable attention as potential diagnostic and therapeutic tools in the fight against cancer. Although many interacting forces present themselves during magnetic targeting of MNP to tumors, most theoretical considerations of this process ignore all except for the magnetic and drag forces. Our validation of a simple in vitro model against in vivo data, and subsequent reproduction of the in vitro results with a theoretical model indicated that these two forces do indeed dominate the magnetic capture of MNP. However, because nanoparticles can be subject to aggregation, and large MNP experience an increased magnetic force, the effects of surface forces on MNP stability cannot be ignored. We accounted for the aggregating surface forces simply by measuring the size of MNP retained from flow by magnetic fields, and utilized this size in the mathematical model. This presumably accounted for all particle-particle interactions, including those between magnetic dipoles. Thus, our “corrected” mathematical model provided a reasonable estimate of not only fractional MNP retention, but also predicted the regions of accumulation in a simulated capillary. Furthermore, the model was also utilized to calculate the effects of MNP size and spatial location, relative to the magnet, on targeting of MNPs to tumors. This combination of an in vitro model with a theoretical model could potentially assist with parametric evaluations of magnetic targeting, and enable rapid enhancement and optimization of magnetic targeting methodologies. PMID:21295085

  5. Synergistic antitumor activity of vitamin D3 combined with metformin in human breast carcinoma MDA-MB-231 cells involves m-TOR related signaling pathways.

    PubMed

    Guo, Li-Shu; Li, Hong-Xia; Li, Chun-Yang; Zhang, Sheng-Yan; Chen, Jia; Wang, Qi-Long; Gao, Jing-Miao; Liang, Jia-Qi; Gao, Ming-Tang; Wu, Yong-Jie

    2015-02-01

    Metformin is usually used for the treatment of type 2 diabetes. Recently, many studies suggest that metformin and vitamin D have broad-spectrum antitumor activities. Our aim in this research was to study the effects of vitamin D3 combined with metformin on the apoptosis induction and its mechanisms in the human breast cancer cell line MDA-MB-231. Cell proliferation was measured by methylthiazol tetrazolium (MTT) assay. The morphology of cell apoptosis was observed after Hoechst 33342 staining. Here we show that vitamin D3 280 μg/ml or vitamin D3 300 μg/ml or vitamin D3 320 μg/ml seperately combined with metformin 15000 μg/ml exhibited synergistic effects on cell proliferation and apoptosis. The underlying anti-tumor mechanisms may involve m-TOR related pathways, which are related to activating expression of cleaved caspase-3, Bax and p-AMPK, as well as inhibiting expressions of p-Bcl-2, c-Myc, p-IGF-IR, p-mTOR, p-P70S6K, p-S6. PMID:25997252

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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.

  11. Predicting the aquatic stage sustainability of a restored backwater channel combining in-situ and airborne remotely sensed bathymetric models.

    NASA Astrophysics Data System (ADS)

    Jérôme, Lejot; Jérémie, Riquier; Hervé, Piégay

    2014-05-01

    As other large river floodplain worldwide, the floodplain of the Rhône has been deeply altered by human activities and infrastructures over the last centuries both in term of structure and functioning. An ambitious restoration plan of selected by-passed reaches has been implemented since 1999, in order to improve their ecological conditions. One of the main action aimed to increase the aquatic areas in floodplain channels (i.e. secondary channels, backwaters, …). In practice, fine and/or coarse alluvium were dredged, either locally or over the entire cut-off channel length. Sometimes the upstream or downstream alluvial plugs were also removed to reconnect the restored feature to the main channel. Such operation aims to restore forms and associated habitats of biotic communities, which are no more created or maintained by the river itself. In this context, assessing the sustainability of such restoration actions is a major issue. In this study, we focus on 1 of the 24 floodplain channels which have been restored along the Rhône River since 1999, the Malourdie channel (Chautagne reach, France). A monitoring of the geomorphologic evolution of the channel has been conducted during a decade to assess the aquatic stage sustainability of this former fully isolated channel, which has been restored as a backwater in 2004. Two main types of measures were performed: (a) water depth and fine sediment thickness were surveyed with an auger every 10 m along the channel centerline in average every year and a half allowing to establish an exponential decay model of terrestrialization rates through time; (b) three airborne campaigns (2006, 2007, 2012) by Ultra Aerial Vehicle (UAV) provided images from which bathymetry were inferred in combination with observed field measures. Coupling field and airborne models allows us to simulate different states of terrestrialization at the scale of the whole restore feature (e.g. 2020/2030/2050). Raw results indicate that terrestrialization

  12. Prediction of hot spot residues at protein-protein interfaces by combining machine learning and energy-based methods

    PubMed Central

    Lise, Stefano; Archambeau, Cedric; Pontil, Massimiliano; Jones, David T

    2009-01-01

    Background Alanine scanning mutagenesis is a powerful experimental methodology for investigating the structural and energetic characteristics of protein complexes. Individual amino-acids are systematically mutated to alanine and changes in free energy of binding (ΔΔG) measured. Several experiments have shown that protein-protein interactions are critically dependent on just a few residues ("hot spots") at the interface. Hot spots make a dominant contribution to the free energy of binding and if mutated they can disrupt the interaction. As mutagenesis studies require significant experimental efforts, there is a need for accurate and reliable computational methods. Such methods would also add to our understanding of the determinants of affinity and specificity in protein-protein recognition. Results We present a novel computational strategy to identify hot spot residues, given the structure of a complex. We consider the basic energetic terms that contribute to hot spot interactions, i.e. van der Waals potentials, solvation energy, hydrogen bonds and Coulomb electrostatics. We treat them as input features and use machine learning algorithms such as Support Vector Machines and Gaussian Processes to optimally combine and integrate them, based on a set of training examples of alanine mutations. We show that our approach is effective in predicting hot spots and it compares favourably to other available methods. In particular we find the best performances using Transductive Support Vector Machines, a semi-supervised learning scheme. When hot spots are defined as those residues for which ΔΔG ≥ 2 kcal/mol, our method achieves a precision and a recall respectively of 56% and 65%. Conclusion We have developed an hybrid scheme in which energy terms are used as input features of machine learning models. This strategy combines the strengths of machine learning and energy-based methods. Although so far these two types of approaches have mainly been applied separately to

  13. Usefulness of a Combination of Interatrial Block and a High CHADS2 Score to Predict New Onset Atrial Fibrillation.

    PubMed

    Wu, Jin-Tao; Wang, Shan-Ling; Chu, Ying-Jie; Long, De-Yong; Dong, Jian-Zeng; Fan, Xian-Wei; Yang, Hai-Tao; Duan, Hong-Yan; Yan, Li-Jie; Qian, Peng; Yang, Chao-Kuan

    2016-09-28

    Interatrial block (IAB) is associated with an increased risk of atrial fibrillation (AF). The aim of this retrospective study was to investigate the association of a combination of IAB and the CHADS2 score, an AF-related risk score for ischemic stroke, with new onset AF in patients in sinus rhythm. A total of 1,571 patients (803 males, 768 females; mean age: 58 ± 16 years) were included in this study. IAB was defined as a P-wave duration > 120 ms in the 12-lead electrocardiogram, and a high CHADS2 score as ≥ 2 points. During the mean follow-up period of 4.8 ± 0.7 years, new onset AF occurred in 122 patients (16.1 per 1,000 patient-years). The incidence of new onset AF was 4.0 per 1,000 patient-years in patients with no IAB and a low CHADS2 score, and 44.0 per 1,000 patient-years in patients with IAB and a high CHADS2 score. In multivariate Cox regression analysis, the hazard ratio for IAB and a high CHADS2 score compared with no IAB and a low CHADS2 score was 12.18 (95% confidence interval: 6.22-23.87, P < 0.001), after adjustment for age, sex, coronary artery disease, valvular heart disease, smoking, medications, and echocardiographic parameters. In conclusion, IAB and a high CHADS2 score independently and synergistically predict new onset AF in patients in sinus rhythm, indicating an approximately 12-fold higher risk in patients with both IAB and a high CHADS2 score. Patients meeting these criteria should have more aggressive early intervention to prevent AF. PMID:27593538

  14. Usefulness of a Combination of Interatrial Block and a High CHADS2 Score to Predict New Onset Atrial Fibrillation.

    PubMed

    Wu, Jin-Tao; Wang, Shan-Ling; Chu, Ying-Jie; Long, De-Yong; Dong, Jian-Zeng; Fan, Xian-Wei; Yang, Hai-Tao; Duan, Hong-Yan; Yan, Li-Jie; Qian, Peng; Yang, Chao-Kuan

    2016-09-28

    Interatrial block (IAB) is associated with an increased risk of atrial fibrillation (AF). The aim of this retrospective study was to investigate the association of a combination of IAB and the CHADS2 score, an AF-related risk score for ischemic stroke, with new onset AF in patients in sinus rhythm. A total of 1,571 patients (803 males, 768 females; mean age: 58 ± 16 years) were included in this study. IAB was defined as a P-wave duration > 120 ms in the 12-lead electrocardiogram, and a high CHADS2 score as ≥ 2 points. During the mean follow-up period of 4.8 ± 0.7 years, new onset AF occurred in 122 patients (16.1 per 1,000 patient-years). The incidence of new onset AF was 4.0 per 1,000 patient-years in patients with no IAB and a low CHADS2 score, and 44.0 per 1,000 patient-years in patients with IAB and a high CHADS2 score. In multivariate Cox regression analysis, the hazard ratio for IAB and a high CHADS2 score compared with no IAB and a low CHADS2 score was 12.18 (95% confidence interval: 6.22-23.87, P < 0.001), after adjustment for age, sex, coronary artery disease, valvular heart disease, smoking, medications, and echocardiographic parameters. In conclusion, IAB and a high CHADS2 score independently and synergistically predict new onset AF in patients in sinus rhythm, indicating an approximately 12-fold higher risk in patients with both IAB and a high CHADS2 score. Patients meeting these criteria should have more aggressive early intervention to prevent AF.

  15. 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

  16. Prediction of hot regions in protein-protein interaction by combining density-based incremental clustering with feature-based classification.

    PubMed

    Hu, Jing; Zhang, Xiaolong; Liu, Xiaoming; Tang, Jinshan

    2015-06-01

    Discovering hot regions in protein-protein interaction is important for drug and protein design, while experimental identification of hot regions is a time-consuming and labor-intensive effort; thus, the development of predictive models can be very helpful. In hot region prediction research, some models are based on structure information, and others are based on a protein interaction network. However, the prediction accuracy of these methods can still be improved. In this paper, a new method is proposed for hot region prediction, which combines density-based incremental clustering with feature-based classification. The method uses density-based incremental clustering to obtain rough hot regions, and uses feature-based classification to remove the non-hot spot residues from the rough hot regions. Experimental results show that the proposed method significantly improves the prediction performance of hot regions.

  17. 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.

  18. 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 ...

  19. Flower Bud Transcriptome Analysis of Sapium sebiferum (Linn.) Roxb. and Primary Investigation of Drought Induced Flowering: Pathway Construction and G-Quadruplex Prediction Based on Transcriptome

    PubMed Central

    Hou, Jinyan; Mao, Yingji; Liu, Wenbo; Shen, Yangcheng; Wu, Lifang

    2015-01-01

    Sapium sebiferum (Linn.) Roxb. (Chinese Tallow Tree) is a perennial woody tree and its seeds are rich in oil which hold great potential for biodiesel production. Despite a traditional woody oil plant, our understanding on S. sebiferum genetics and molecular biology remains scant. In this study, the first comprehensive transcriptome of S. sebiferum flower has been generated by sequencing and de novo assembly. A total of 149,342 unigenes were generated from raw reads, of which 24,289 unigenes were successfully matched to public database. A total of 61 MADS box genes and putative pathways involved in S. sebiferum flower development have been identified. Abiotic stress response network was also constructed in this work, where 2,686 unigenes are involved in the pathway. As for lipid biosynthesis, 161 unigenes have been identified in fatty acid (FA) and triacylglycerol (TAG) biosynthesis. Besides, the G-Quadruplexes in RNA of S. sebiferum also have been predicted. An interesting finding is that the stress-induced flowering was observed in S. sebiferum for the first time. According to the results of semi-quantitative PCR, expression tendencies of flowering-related genes, GA1, AP2 and CRY2, accorded with stress-related genes, such as GRX50435 and PRXⅡ39562. This transcriptome provides functional genomic information for further research of S. sebiferum, especially for the genetic engineering to shorten the juvenile period and improve yield by regulating flower development. It also offers a useful database for the research of other Euphorbiaceae family plants. PMID:25738565

  20. A Combined Pharmacokinetic and Radiologic Assessment of Dynamic Contrast-Enhanced Magnetic Resonance Imaging Predicts Response to Chemoradiation in Locally Advanced Cervical Cancer

    SciTech Connect

    Semple, Scott Harry, Vanessa N. MRCOG.; Parkin, David E.; Gilbert, Fiona J.

    2009-10-01

    Purpose: To investigate the combination of pharmacokinetic and radiologic assessment of dynamic contrast-enhanced magnetic resonance imaging (MRI) as an early response indicator in women receiving chemoradiation for advanced cervical cancer. Methods and Materials: Twenty women with locally advanced cervical cancer were included in a prospective cohort study. Dynamic contrast-enhanced MRI was carried out before chemoradiation, after 2 weeks of therapy, and at the conclusion of therapy using a 1.5-T MRI scanner. Radiologic assessment of uptake parameters was obtained from resultant intensity curves. Pharmacokinetic analysis using a multicompartment model was also performed. General linear modeling was used to combine radiologic and pharmacokinetic parameters and correlated with eventual response as determined by change in MRI tumor size and conventional clinical response. A subgroup of 11 women underwent repeat pretherapy MRI to test pharmacokinetic reproducibility. Results: Pretherapy radiologic parameters and pharmacokinetic K{sup trans} correlated with response (p < 0.01). General linear modeling demonstrated that a combination of radiologic and pharmacokinetic assessments before therapy was able to predict more than 88% of variance of response. Reproducibility of pharmacokinetic modeling was confirmed. Conclusions: A combination of radiologic assessment with pharmacokinetic modeling applied to dynamic MRI before the start of chemoradiation improves the predictive power of either by more than 20%. The potential improvements in therapy response prediction using this type of combined analysis of dynamic contrast-enhanced MRI may aid in the development of more individualized, effective therapy regimens for this patient group.

  1. 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

  2. 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…

  3. Class I T-cell epitope prediction: improvements using a combination of proteasome cleavage, TAP affinity, and MHC binding.

    PubMed

    Doytchinova, Irini A; Flower, Darren R

    2006-05-01

    Cleavage by the proteasome is responsible for generating the C terminus of T-cell epitopes. Modeling the process of proteasome cleavage as part of a multi-step algorithm for T-cell epitope prediction will reduce the number of non-binders and increase the overall accuracy of the predictive algorithm. Quantitative matrix-based models for prediction of the proteasome cleavage sites in a protein were developed using a training set of 489 naturally processed T-cell epitopes (nonamer peptides) associated with HLA-A and HLA-B molecules. The models were validated using an external test set of 227 T-cell epitopes. The performance of the models was good, identifying 76% of the C-termini correctly. The best model of proteasome cleavage was incorporated as the first step in a three-step algorithm for T-cell epitope prediction, where subsequent steps predicted TAP affinity and MHC binding using previously derived models. PMID:16524630

  4. 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. PMID:26212953

  5. 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.

  6. Genetic variants in DNA repair pathways and risk of upper aerodigestive tract cancers: combined analysis of data from two genome-wide association studies in European populations.

    PubMed

    Babron, Marie-Claude; Kazma, Rémi; Gaborieau, Valérie; McKay, James; Brennan, Paul; Sarasin, Alain; Benhamou, Simone

    2014-07-01

    DNA repair pathways are good candidates for upper aerodigestive tract cancer susceptibility because of their critical role in maintaining genome integrity. We have selected 13 pathways involved in DNA repair representing 212 autosomal genes. To assess the role of these pathways and their associated genes, two European data sets from the International Head and Neck Cancer Epidemiology consortium were pooled, totaling 1954 cases and 3121 controls, with documented demographic, lifetime alcohol and tobacco consumption information. We applied an innovative approach that tests single nucleotide polymorphism (SNP)-sets within DNA repair pathways and then within genes belonging to the significant pathways. We showed an association between the polymerase pathway and oral cavity/pharynx cancers (P-corrected = 4.45 × 10(-) (2)), explained entirely by the association with one SNP, rs1494961 (P = 2.65 × 10(-) (4)), a missense mutation V306I in the second exon of HELQ gene. We also found an association between the cell cycle regulation pathway and esophagus cancer (P-corrected = 1.48 × 10(-) (2)), explained by three SNPs located within or near CSNK1E gene: rs1534891 (P = 1.27 × 10(-) (4)), rs7289981 (P = 3.37 × 10(-) (3)) and rs13054361 (P = 4.09 × 10(-) (3)). As a first attempt to investigate pathway-level associations, our results suggest a role of specific DNA repair genes/pathways in specific upper aerodigestive tract cancer sites. PMID:24658182

  7. Predicting Prostate Biopsy Results Using a Panel of Plasma and Urine Biomarkers Combined in a Scoring System

    PubMed Central

    Albitar, Maher; Ma, Wanlong; Lund, Lars; Albitar, Ferras; Diep, Kevin; Fritsche, Herbert A.; Shore, Neal

    2016-01-01

    Background: Determining the need for prostate biopsy is frequently difficult and more objective criteria are needed to predict the presence of high grade prostate cancer (PCa). To reduce the rate of unnecessary biopsies, we explored the potential of using biomarkers in urine and plasma to develop a scoring system to predict prostate biopsy results and the presence of high grade PCa. Methods: Urine and plasma specimens were collected from 319 patients recommended for prostate biopsies. We measured the gene expression levels of UAP1, PDLIM5, IMPDH2, HSPD1, PCA3, PSA, TMPRSS2, ERG, GAPDH, B2M, AR, and PTEN in plasma and urine. Patient age, serum prostate-specific antigen (sPSA) level, and biomarkers data were used to develop two independent algorithms, one for predicting the presence of PCa and the other for predicting high-grade PCa (Gleason score [GS] ≥7). Results: Using training and validation data sets, a model for predicting the outcome of PCa biopsy was developed with an area under receiver operating characteristic curve (AUROC) of 0.87. The positive and negative predictive values (PPV and NPV) were 87% and 63%, respectively. We then developed a second algorithm to identify patients with high-grade PCa (GS ≥7). This algorithm's AUROC was 0.80, and had a PPV and NPV of 56% and 77%, respectively. Patients who demonstrated concordant results using both algorithms showed a sensitivity of 84% and specificity of 93% for predicting high-grade aggressive PCa. Thus, the use of both algorithms resulted in a PPV of 90% and NPV of 89% for predicting high-grade PCa with toleration of some low-grade PCa (GS <7) being detected. Conclusions: This model of a biomarker panel with algorithmic interpretation can be used as a “liquid biopsy” to reduce the need for unnecessary tissue biopsies, and help to guide appropriate treatment decisions. PMID:26918043

  8. Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: II. A focus on growth impairment in fish.

    PubMed

    Groh, Ksenia J; Carvalho, Raquel N; Chipman, James K; Denslow, Nancy D; Halder, Marlies; Murphy, Cheryl A; Roelofs, Dick; Rolaki, Alexandra; Schirmer, Kristin; Watanabe, Karen H

    2015-02-01

    Adverse outcome pathways (AOPs) organize knowledge on the progression of toxicity through levels of biological organization. By determining the linkages between toxicity events at different levels, AOPs lay the foundation for mechanism-based alternative testing approaches to hazard assessment. Here, we focus on growth impairment in fish to illustrate the initial stages in the process of AOP development for chronic toxicity outcomes. Growth is an apical endpoint commonly assessed in chronic toxicity tests for which a replacement is desirable. Based on several criteria, we identified reduction in food intake to be a suitable key event for initiation of middle-out AOP development. To start exploring the upstream and downstream links of this key event, we developed three AOP case studies, for pyrethroids, selective serotonin reuptake inhibitors (SSRIs) and cadmium. Our analysis showed that the effect of pyrethroids and SSRIs on food intake is strongly linked to growth impairment, while cadmium causes a reduction in growth due to increased metabolic demands rather than changes in food intake. Locomotion impairment by pyrethroids is strongly linked to their effects on food intake and growth, while for SSRIs their direct influence on appetite may play a more important role. We further discuss which alternative tests could be used to inform on the predictive key events identified in the case studies. In conclusion, our work demonstrates how the AOP concept can be used in practice to assess critically the knowledge available for specific chronic toxicity cases and to identify existing knowledge gaps and potential alternative tests. PMID:25456049

  9. Development and application of the adverse outcome pathway framework for understanding and predicting chronic toxicity: II. A focus on growth impairment in fish.

    PubMed

    Groh, Ksenia J; Carvalho, Raquel N; Chipman, James K; Denslow, Nancy D; Halder, Marlies; Murphy, Cheryl A; Roelofs, Dick; Rolaki, Alexandra; Schirmer, Kristin; Watanabe, Karen H

    2015-02-01

    Adverse outcome pathways (AOPs) organize knowledge on the progression of toxicity through levels of biological organization. By determining the linkages between toxicity events at different levels, AOPs lay the foundation for mechanism-based alternative testing approaches to hazard assessment. Here, we focus on growth impairment in fish to illustrate the initial stages in the process of AOP development for chronic toxicity outcomes. Growth is an apical endpoint commonly assessed in chronic toxicity tests for which a replacement is desirable. Based on several criteria, we identified reduction in food intake to be a suitable key event for initiation of middle-out AOP development. To start exploring the upstream and downstream links of this key event, we developed three AOP case studies, for pyrethroids, selective serotonin reuptake inhibitors (SSRIs) and cadmium. Our analysis showed that the effect of pyrethroids and SSRIs on food intake is strongly linked to growth impairment, while cadmium causes a reduction in growth due to increased metabolic demands rather than changes in food intake. Locomotion impairment by pyrethroids is strongly linked to their effects on food intake and growth, while for SSRIs their direct influence on appetite may play a more important role. We further discuss which alternative tests could be used to inform on the predictive key events identified in the case studies. In conclusion, our work demonstrates how the AOP concept can be used in practice to assess critically the knowledge available for specific chronic toxicity cases and to identify existing knowledge gaps and potential alternative tests.

  10. Ghrelin accelerates wound healing through GHS-R1a-mediated MAPK-NF-κB/GR signaling pathways in combined radiation and burn injury in rats

    PubMed Central

    Liu, Cong; Huang, Jiawei; Li, Hong; Yang, Zhangyou; Zeng, Yiping; Liu, Jing; Hao, Yuhui; Li, Rong

    2016-01-01

    The therapeutic effect of ghrelin on wound healing was assessed using a rat model of combined radiation and burn injury (CRBI). Rat ghrelin, anti-rat tumor necrosis factor (TNF) α polyclonal antibody (PcAb), or selective antagonists of p38 mitogen-activated protein kinase (MAPK), c-Jun N-terminal kinase (JNK), and growth hormone secretagogue receptor (GHS-R) 1a (SB203580, SP600125, and [D-Lys3]-GHRP-6, respectively), were administered for seven consecutive days. Levels of various signaling molecules were assessed in isolated rat peritoneal macrophages. The results showed that serum ghrelin levels and levels of macrophage glucocorticoid receptor (GR) decreased, while phosphorylation of p38MAPK, JNK, and p65 nuclear factor (NF) κB increased. Ghrelin inhibited the serum induction of proinflammatory mediators, especially TNF-α, and promoted wound healing in a dose-dependent manner. Ghrelin treatment decreased phosphorylation of p38MAPK, JNK, and p65NF-κB, and increased GR levels in the presence of GHS-R1a. SB203580 or co-administration of SB203580 and SP600125 decreased TNF-α level, which may have contributed to the inactivation of p65NF-κB and increase in GR expression, as confirmed by western blotting. In conclusion, ghrelin enhances wound recovery in CRBI rats, possibly by decreasing the induction of TNF-α or other proinflammatory mediators that are involved in the regulation of GHS-R1a-mediated MAPK-NF-κB/GR signaling pathways. PMID:27271793

  11. The Value of Combining Blood Culture and SeptiFast Data for Predicting Complicated Bloodstream Infections Caused by Gram-Positive Bacteria or Candida Species

    PubMed Central

    Marín, Mercedes; Kestler, Martha; Alcalá, Luis; Rodriguez-Créixems, Marta; Bouza, Emilio

    2013-01-01

    Management of complicated bloodstream infections requires more aggressive treatment than uncomplicated bloodstream infections. We assessed the value of follow-up blood culture in bloodstream infections caused by Staphylococcus aureus, Enterococcus spp., Streptococcus spp., and Candida spp. and studied the value of persistence of DNA in blood (using SeptiFast) for predicting complicated bloodstream infections. Patients with bloodstream infections caused by these microorganisms were enrolled prospectively. After the first positive blood culture, samples were obtained every third day to perform blood culture and SeptiFast analyses simultaneously. Patients were followed to detect complicated bloodstream infection. The study sample comprised 119 patients. One-third of the patients developed complicated bloodstream infections. The values of persistently positive tests to predict complicated bloodstream infections were as follows: SeptiFast positive samples (sensitivity, 56%; specificity, 79.5%; positive predictive value, 54%; negative predictive value, 80.5%; accuracy, 72.3%) and positive blood cultures (sensitivity, 30.5%; specificity, 92.8%; positive predictive value, 64%; negative predictive value, 75.5%; accuracy, 73.9%). Multivariate analysis showed that patients with a positive SeptiFast result between days 3 and 7 had an almost 8-fold-higher risk of developing a complicated bloodstream infection. In S. aureus, the combination of both techniques to exclude endovascular complications was significantly better than the use of blood culture alone. We obtained a score with variables selected by the multivariate model. With a cutoff of 7, the negative predictive value for complicated bloodstream infection was 96.6%. Patients with a positive SeptiFast result between days 3 and 7 after a positive blood culture have an almost 8-fold-higher risk of developing complicated bloodstream infections. A score combining clinical data with the SeptiFast result may improve the

  12. BiPPred: Combined sequence- and structure-based prediction of peptide binding to the Hsp70 chaperone BiP.

    PubMed

    Schneider, Markus; Rosam, Mathias; Glaser, Manuel; Patronov, Atanas; Shah, Harpreet; Back, Katrin Christiane; Daake, Marina Angelika; Buchner, Johannes; Antes, Iris

    2016-10-01

    Substrate binding to Hsp70 chaperones is involved in many biological processes, and the identification of potential substrates is important for a comprehensive understanding of these events. We present a multi-scale pipeline for an accurate, yet efficient prediction of peptides binding to the Hsp70 chaperone BiP by combining sequence-based prediction with molecular docking and MMPBSA calculations. First, we measured the binding of 15mer peptides from known substrate proteins of BiP by peptide array (PA) experiments and performed an accuracy assessment of the PA data by fluorescence anisotropy studies. Several sequence-based prediction models were fitted using this and other peptide binding data. A structure-based position-specific scoring matrix (SB-PSSM) derived solely from structural modeling data forms the core of all models. The matrix elements are based on a combination of binding energy estimations, molecular dynamics simulations, and analysis of the BiP binding site, which led to new insights into the peptide binding specificities of the chaperone. Using this SB-PSSM, peptide binders could be predicted with high selectivity even without training of the model on experimental data. Additional training further increased the prediction accuracies. Subsequent molecular docking (DynaDock) and MMGBSA/MMPBSA-based binding affinity estimations for predicted binders allowed the identification of the correct binding mode of the peptides as well as the calculation of nearly quantitative binding affinities. The general concept behind the developed multi-scale pipeline can readily be applied to other protein-peptide complexes with linearly bound peptides, for which sufficient experimental binding data for the training of classical sequence-based prediction models is not available. Proteins 2016; 84:1390-1407. © 2016 Wiley Periodicals, Inc.

  13. Methods for Selection of Cancer Patients and Predicting Efficacy of Combination Therapy | NCI Technology Transfer Center | TTC

    Cancer.gov

    The Lung Cancer Biomarkers Group of the National Cancer Institute (NCI) seeks parties interested in collaborative research to further co-develop methods for selecting cancer patients for combination therapy.

  14. An Integrated Hydrologic Bayesian Multi-Model Combination Framework: Confronting Input, parameter and model structural uncertainty in Hydrologic Prediction

    SciTech Connect

    Ajami, N K; Duan, Q; Sorooshian, S

    2006-05-05

    This paper presents a new technique--Integrated Bayesian Uncertainty Estimator (IBUNE) to account for the major uncertainties of hydrologic rainfall-runoff predictions explicitly. The uncertainties from the input (forcing) data--mainly the precipitation observations and from the model parameters are reduced through a Monte Carlo Markov Chain (MCMC) scheme named Shuffled Complex Evolution Metropolis (SCEM) algorithm which has been extended to include a precipitation error model. Afterwards, the Bayesian Model Averaging (BMA) scheme is employed to further improve the prediction skill and uncertainty estimation using multiple model output. A series of case studies using three rainfall-runoff models to predict the streamflow in the Leaf River basin, Mississippi are used to examine the necessity and usefulness of this technique. The results suggests that ignoring either input forcings error or model structural uncertainty will lead to unrealistic model simulations and their associated uncertainty bounds which does not consistently capture and represent the real-world behavior of the watershed.

  15. In Silico Modeling for the Prediction of Dose and Pathway-Related Adverse Effects in Humans From In Vitro Repeated-Dose Studies.

    PubMed

    Klein, Sebastian; Maggioni, Silvia; Bucher, Joachim; Mueller, Daniel; Niklas, Jens; Shevchenko, Valery; Mauch, Klaus; Heinzle, Elmar; Noor, Fozia

    2016-01-01

    Long-term repeated-dose toxicity is mainly assessed in animals despite poor concordance of animal data with human toxicity. Nowadays advanced human in vitro systems, eg, metabolically competent HepaRG cells, are used for toxicity screening. Extrapolation of in vitro toxicity to in vivo effects is possible by reverse dosimetry using pharmacokinetic modeling. We assessed long-term repeated-dose toxicity of bosentan and valproic acid (VPA) in HepaRG cells under serum-free conditions. Upon 28-day exposure, the EC50 values for bosentan and VPA decreased by 21- and 33-fold, respectively. Using EC(10) as lowest threshold of toxicity in vitro, we estimated the oral equivalent doses for both test compounds using a simplified pharmacokinetic model for the extrapolation of in vitro toxicity to in vivo effect. The model predicts that bosentan is safe at the considered dose under the assumed conditions upon 4 weeks exposure. For VPA, hepatotoxicity is predicted for 4% and 47% of the virtual population at the maximum recommended daily dose after 3 and 4 weeks of exposure, respectively. We also investigated the changes in the central carbon metabolism of HepaRG cells exposed to orally bioavailable concentrations of both drugs. These concentrations are below the 28-day EC(10) and induce significant changes especially in glucose metabolism and urea production. These metabolic changes may have a pronounced impact in susceptible patients such as those with compromised liver function and urea cycle deficiency leading to idiosyncratic toxicity. We show that the combination of modeling based on in vitro repeated-dose data and metabolic changes allows the prediction of human relevant in vivo toxicity with mechanistic insights. PMID:26420750

  16. Predictions in the face of clinical reality: HistoCheck versus high-risk HLA allele mismatch combinations responsible for severe acute graft-versus-host disease.

    PubMed

    Askar, Medhat; Sobecks, Ronald; Morishima, Yasuo; Kawase, Takakazu; Nowacki, Amy; Makishima, Hideki; Maciejewski, Jaroslaw

    2011-09-01

    HLA polymorphism remains a major hurdle for hematopoietic stem cell transplantation (HSCT). In 2004, Elsner et al. proposed the HistoCheck Web-based tool to estimate the allogeneic potential between HLA-mismatched stem cell donor/recipient pairs expressed as a sequence similarity matching (SSM). SSM is based on the structure of HLA molecules and the functional similarity of amino acids. According to this algorithm, a high SSM score represents high dissimilarity between MHC molecules, resulting in a potentially more deleterious impact on stem cell transplant outcomes. We investigated the potential of SSM to predict high-risk HLA allele mismatch combinations responsible for severe acute graft-versus-host disease (aGVHD grades III and IV) published by Kawase et al., by comparing SSM in low- and high-risk combinations. SSM was calculated for allele mismatch combinations using the HistoCheck tool available on the Web (www.histocheck.org). We compared ranges and means of SSM among high-risk (15 combinations observed in 722 donor/recipient pairs) versus low-risk allele combinations (94 combinations in 3490 pairs). Simulation scenarios were created where the recipient's HLA allele was involved in multiple allele mismatch combinations with at least 1 high-risk and 1 low-risk mismatch combination. SSM values were then compared. The mean SSM for high- versus low-risk combinations were 2.39 and 2.90 at A, 1.06 and 2.53 at B, 16.60 and 14.99 at C, 4.02 and 3.81 at DRB1, and 7.47 and 6.94 at DPB1 loci, respectively. In simulation scenarios, no predictable SSM association with high- or low-risk combinations could be distinguished. No DQB1 combinations met the statistical criteria for our study. In conclusion, our analysis demonstrates that mean SSM scores were not significantly different, and SSM distributions were overlapping among high- and low-risk allele combinations within loci HLA-A, B, C, DRB1, and DPB1. This analysis does not support selecting donors for HSCT recipients

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

    PubMed

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

    2014-01-01

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

  18. Prediction of in vivo developmental toxicity by combination of Hand1-Luc embryonic stem cell test and metabolic stability test with clarification of metabolically inapplicable candidates.

    PubMed

    Nagahori, Hirohisa; Suzuki, Noriyuki; Le Coz, Florian; Omori, Takashi; Saito, Koichi

    2016-09-30

    Hand1-Luc Embryonic Stem Cell Test (Hand1-Luc EST) is a promising alternative method for evaluation of developmental toxicity. However, the problems of predictivity have remained due to appropriateness of the solubility, metabolic system, and prediction model. Therefore, we assessed the usefulness of rat liver S9 metabolic stability test using LC-MS/MS to develop new prediction model. A total of 71 chemicals were analyzed by measuring cytotoxicity and differentiation toxicity, and highly reproducible (CV=20%) results were obtained. The first prediction model was developed by discriminant analysis performed on a full dataset using Hand1-Luc EST, and 66.2% of the chemicals were correctly classified by the cross-validated classification. A second model was developed with additional descriptors obtained from the metabolic stability test to calculate hepatic availability, and an accuracy of 83.3% was obtained with applicability domain of 50.7% (=36/71) after exclusion of 22 metabolically inapplicable candidates, which potentially have a metabolic activation property. A step-wise prediction scheme with combination of Hand1-Luc EST and metabolic stability test was therefore proposed. The current results provide a promising in vitro test method for accurately predicting in vivo developmental toxicity.

  19. Combining Genomic and Genealogical Information in a Reproducing Kernel Hilbert Spaces Regression Model for Genome-Enabled Predictions in Dairy Cattle

    PubMed Central

    Rodríguez-Ramilo, Silvia Teresa; García-Cortés, Luis Alberto; González-Recio, Óscar

    2014-01-01

    Genome-enhanced genotypic evaluations are becoming popular in several livestock species. For this purpose, the combination of the pedigree-based relationship matrix with a genomic similarities matrix between individuals is a common approach. However, the weight placed on each matrix has been so far established with ad hoc procedures, without formal estimation thereof. In addition, when using marker- and pedigree-based relationship matrices together, the resulting combined relationship matrix needs to be adjusted to the same scale in reference to the base population. This study proposes a semi-parametric Bayesian method for combining marker- and pedigree-based information on genome-enabled predictions. A kernel matrix from a reproducing kernel Hilbert spaces regression model was used to combine genomic and genealogical information in a semi-parametric scenario, avoiding inversion and adjustment complications. In addition, the weights on marker- versus pedigree-based information were inferred from a Bayesian model with Markov chain Monte Carlo. The proposed method was assessed involving a large number of SNPs and a large reference population. Five phenotypes, including production and type traits of dairy cattle were evaluated. The reliability of the genome-based predictions was assessed using the correlation, regression coefficient and mean squared error between the predicted and observed values. The results indicated that when a larger weight was given to the pedigree-based relationship matrix the correlation coefficient was lower than in situations where more weight was given to genomic information. Importantly, the posterior means of the inferred weight were near the maximum of 1. The behavior of the regression coefficient and the mean squared error was similar to the performance of the correlation, that is, more weight to the genomic information provided a regression coefficient closer to one and a smaller mean squared error. Our results also indicated a greater

  20. Development and Validation of a Gene-Based Model for Outcome Prediction in Germ Cell Tumors Using a Combined Genomic and Expression Profiling Approach.

    PubMed

    Korkola, James E; Heck, Sandy; Olshen, Adam B; Feldman, Darren R; Reuter, Victor E; Houldsworth, Jane; Bosl, George J; Chaganti, R S K

    2015-01-01

    Germ Cell Tumors (GCT) have a high cure rate, but we currently lack the ability to accurately identify the small subset of patients who will die from their disease. We used a combined genomic and expression profiling approach to identify genomic regions and underlying genes that are predictive of outcome in GCT patients. We performed array-based comparative genomic hybridization (CGH) on 53 non-seminomatous GCTs (NSGCTs) treated with cisplatin based chemotherapy and defined altered genomic regions using Circular Binary Segmentation. We identified 14 regions associated with two year disease-free survival (2yDFS) and 16 regions associated with five year disease-specific survival (5yDSS). From corresponding expression data, we identified 101 probe sets that showed significant changes in expression. We built several models based on these differentially expressed genes, then tested them in an independent validation set of 54 NSGCTs. These predictive models correctly classified outcome in 64-79.6% of patients in the validation set, depending on the endpoint utilized. Survival analysis demonstrated a significant separation of patients with good versus poor predicted outcome when using a combined gene set model. Multivariate analysis using clinical risk classification with the combined gene model indicated that they were independent prognostic markers. This novel set of predictive genes from altered genomic regions is almost entirely independent of our previously identified set of predictive genes for patients with NSGCTs. These genes may aid in the identification of the small subset of patients who are at high risk of poor outcome. PMID:26624623

  1. Using Weighted Sparse Representation Model Combined with Discrete Cosine Transformation to Predict Protein-Protein Interactions from Protein Sequence

    PubMed Central

    Huang, Yu-An; You, Zhu-Hong; Gao, Xin; Wong, Leon; Wang, Lirong

    2015-01-01

    Increasing demand for the knowledge about protein-protein interactions (PPIs) is promoting the development of methods for predicting protein interaction network. Although high-throughput technologies have generated considerable PPIs data for various organisms, it has inevitable drawbacks such as high cost, time consumption, and inherently high false positive rate. For this reason, computational methods are drawing more and more attention for predicting PPIs. In this study, we report a computational method for predicting PPIs using the information of protein sequences. The main improvements come from adopting a novel protein sequence representation by using discrete cosine transform (DCT) on substitution matrix representation (SMR) and from using weighted sparse representation based classifier (WSRC). When performing on the PPIs dataset of Yeast, Human, and H. pylori, we got excellent results with average accuracies as high as 96.28%, 96.30%, and 86.74%, respectively, significantly better than previous methods. Promising results obtained have proven that the proposed method is feasible, robust, and powerful. To further evaluate the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier. Extensive experiments were also performed in which we used Yeast PPIs samples as training set to predict PPIs of other five species datasets. PMID:26634213

  2. An elementary psychophysical model to predict ride comfort in the combined stress of multiple degrees of freedom

    NASA Technical Reports Server (NTRS)

    Stone, R. W., Jr.

    1975-01-01

    The quality of airplane rides probably will become increasingly important to passengers, particularly in terminal area operations and on short haul trips. The development of models to predict ride comfort is considered. An elementary model concept is presented herein and compared with subjective ride comfort response ratings measured on actual scheduled airline flights and simulated flights.

  3. Prediction of small-molecule binding to cytochrome P450 3A4: flexible docking combined with multidimensional QSAR.

    PubMed

    Lill, Markus A; Dobler, Max; Vedani, Angelo

    2006-01-01

    The inhibition of cytochrome P450 3A4 (CYP3A4) by small molecules is a major mechanism associated with undesired drug-drug interactions, which are responsible for a substantial number of late-stage failures in the pharmaceutical drug-development process. For a quantitative prediction of associated pharmacokinetic parameters, a computational model was developed that allows prediction of the inhibitory potential of 48 structurally diverse molecules. Based on the experimental structure of CYP3A4, possible binding modes were first sampled by using automated docking (Yeti software) taking protein flexibility into account. The results are consistent with both X-ray crystallographic data and data from metabolic studies. Next, an ensemble of energetically favorable orientations was composed into a 4D dataset for use as input for a multidimensional QSAR technique (Raptor software). A