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Sample records for pathway combinations predict

  1. Cytokine Combination Therapy Prediction for Bone Remodeling in Tissue Engineering Based on the Intracellular Signaling Pathway

    PubMed Central

    Sun, Xiaoqiang; Su, Jing; Bao, Jiguang; Peng, Tao; Zhang, Le; Zhang, Yuanyuan; Yang, Yunzhi; Zhou, Xiaobo

    2012-01-01

    The long-term performance of tissue-engineered bone grafts is determined by a dynamic balance between bone regeneration and resorption. We proposed using embedded cytokine slow-releasing hydrogels to tune this balance toward a desirable final bone density. In this study we established a systems biology model, and quantitatively explored the combinatorial effects of delivered cytokines from hydrogels on final bone density. We hypothesized that: 1) bone regeneration was driven by transcription factors Runx2 and Osterix, which responded to released cytokines, such as Wnt, BMP2, and TGFβ, drove the development of osteoblast lineage, and contributed to bone mass generation; and 2) the osteoclast lineage, on the other hand, governed the bone resorption, and communications between these two lineages determined the dynamics of bone remodeling. In our model, Intracellular signaling pathways were represented by ordinary differential equations, while the intercellular communications and cellular population dynamics were modeled by stochastic differential equations. Effects of synergistic cytokine combinations were evaluated by Loewe index and Bliss index. Simulation results revealed that the Wnt/BMP2 combinations released from hydrogels showed best control of bone regeneration and synergistic effects, and suggested optimal dose ratios of given cytokine combinations released from hydrogels to most efficiently control the long-term bone remodeling. We revealed the characteristics of cytokine combinations of Wnt/BMP2 which could be used to guide the design of in vivo bone scaffolds and the clinical treatment of some diseases such as osteoporosis. PMID:22910219

  2. 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. PMID:24416282

  3. 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

  4. Combinations of gene ontology and pathway characterize and predict prognosis genes for recurrence of gastric cancer after surgery.

    PubMed

    Fan, Haiyan; Guo, Zhanjun; Wang, Cuijv

    2015-09-01

    Gastric cancer (GC) is the second leading cause of death from cancer globally. The most common cause of GC is the infection of Helicobacter pylori, but ∼11% of cases are caused by genetic factors. However, recurrences occur in approximately one-third of stage II GC patients, even if they are treated with adjuvant chemotherapy or chemoradiotherapy. This is potentially due to expression variation of genes; some candidate prognostic genes were identified in patients with high-risk recurrences. The objective of this study was to develop an effective computational method for meaningfully interpreting these GC-related genes and accurately predicting novel prognostic genes for high-risk recurrence patients. We employed properties of genes (gene ontology [GO] and KEGG pathway information) as features to characterize GC-related genes. We obtained an optimal set of features for interpreting these genes. By applying the minimum redundancy maximum relevance algorithm, we predicted the GC-related genes. With the same approach, we further predicted the genes for the prognostic of high-risk recurrence. We obtained 1104 GO terms and KEGG pathways and 530 GO terms and KEGG pathways, respectively, that characterized GC-related genes and recurrence-related genes well. Finally, three novel prognostic genes were predicted to help supplement genetic markers of high-risk GC patients for recurrence after surgery. An in-depth text mining indicated that the results are quite consistent with previous knowledge. Survival analysis of patients confirmed the novel prognostic genes as markers. By analyzing the related genes, we developed a systematic method to interpret the possible underlying mechanism of GC. The novel prognostic genes facilitate the understanding and therapy of GC recurrences after surgery. PMID:26154702

  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. Predicting microbial nitrogen pathways from basic principles.

    PubMed

    van de Leemput, Ingrid A; Veraart, Annelies J; Dakos, Vasilis; de Klein, Jeroen J M; Strous, Marc; Scheffer, Marten

    2011-06-01

    Nitrogen compounds are transformed by a complicated network of competing geochemical processes or microbial pathways, each performed by a different ecological guild of microorganisms. Complete experimental unravelling of this network requires a prohibitive experimental effort. Here we present a simple model that predicts relative rates of hypothetical nitrogen pathways, based only on the stoichiometry and energy yield of the performed redox reaction, assuming competition for resources between alternative pathways. Simulating competing pathways in hypothetical freshwater and marine sediment situations, we surprisingly found that much of the variation observed in nature can simply be predicted from these basic principles. Investigating discrepancies between observations and predictions led to two important biochemical factors that may create barriers for the viability of pathways: enzymatic costs for long pathways and high ammonium activation energy. We hypothesize that some discrepancies can be explained by non-equilibrium dynamics. The model predicted a pathway that has not been discovered in nature yet: the dismutation of nitrite to the level of nitrate and dinitrogen gas. PMID:21429064

  7. Predicting pathway perturbations in Down syndrome.

    PubMed

    Gardiner, K

    2003-01-01

    Comparative annotation of human chromosome 21 genomic sequence with homologous regions of mouse chromosomes 16, 17 and 10 has identified 170 orthologous gene pairs. Functional annotation of these genes, based on literature reports and computationally-derived predictions, shows that a broad range of cellular processes are represented. A goal of Down syndrome research is to determine which of these processes are perturbed by overexpression of chromosome 21 genes, and which may, therefore, contribute to the cognitive deficits that characterize Down syndrome. Eleven chromosome 21 genes are annotated to interact with or be affected by components of the MAP Kinase pathway and eight are involved in Ca2+/calcineurin signaling. Both pathways are critical for normal neurological function, and consequently their perturbations are proposed as candidates for phenotypic relevance. We present evidence suggesting that the MAP Kinase pathway is perturbed in the Ts65Dn mouse model of Down syndrome at 4-6 months of age. Analysis is complicated by the observation that overexpression of chromosome 21 genes in trisomy may be affected by method of detection, organism, tissue or brain region, and/or developmental age. PMID:15068236

  8. [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

  9. Drug inhibition profile prediction for NFκB pathway in multiple myeloma.

    PubMed

    Peng, Huiming; Wen, Jianguo; Li, Hongwei; Chang, Jeff; Zhou, Xiaobo

    2011-01-01

    Nuclear factor κB (NFκB) activation plays a crucial role in anti-apoptotic responses in response to the apoptotic signaling during tumor necrosis factor (TNFα) stimulation in Multiple Myeloma (MM). Although several drugs have been found effective for the treatment of MM by mainly inhibiting NFκB pathway, there are not any quantitative or qualitative results of comparison assessment on inhibition effect between different drugs either used alone or in combinations. Computational modeling is becoming increasingly indispensable for applied biological research mainly because it can provide strong quantitative predicting power. In this study, a novel computational pathway modeling approach is employed to comparably assess the inhibition effects of specific drugs used alone or in combinations on the NFκB pathway in MM and to predict the potential synergistic drug combinations. PMID:21408099

  10. Predicting metabolic pathways by sub-network extraction.

    PubMed

    Faust, Karoline; van Helden, Jacques

    2012-01-01

    Various methods result in groups of functionally related genes obtained from genomes (operons, regulons, syntheny groups, and phylogenetic profiles), transcriptomes (co-expression groups) and proteomes (modules of interacting proteins). When such groups contain two or more enzyme-coding genes, graph analysis methods can be applied to extract a metabolic pathway that interconnects them. We describe here the way to use the Pathway extraction tool available on the NeAT Web server ( http://rsat.ulb.ac.be/neat/ ) to piece together the metabolic pathway from a group of associated, enzyme-coding genes. The tool identifies the reactions that can be catalyzed by the products of the query genes (seed reactions), and applies sub-graph extraction algorithms to extract from a metabolic network a sub-network that connects the seed reactions. This sub-network represents the predicted metabolic pathway. We describe here the pathway prediction process in a step-by-step way, give hints about the main parametric choices, and illustrate how this tool can be used to extract metabolic pathways from bacterial genomes, on the basis of two study cases: the isoleucine-valine operon in Escherichia coli and a predicted operon in Cupriavidus (Ralstonia) metallidurans. PMID:22144151

  11. 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.

  12. 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

  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. Performance of an Adipokine Pathway-Based Multilocus Genetic Risk Score for Prostate Cancer Risk Prediction

    PubMed Central

    Ribeiro, Ricardo J. T.; Monteiro, Cátia P. D.; Azevedo, Andreia S. M.; Cunha, Virgínia F. M.; Ramanakumar, Agnihotram V.; Fraga, Avelino M.; Pina, Francisco M.; Lopes, Carlos M. S.; Medeiros, Rui M.; Franco, Eduardo L.

    2012-01-01

    Few biomarkers are available to predict prostate cancer risk. Single nucleotide polymorphisms (SNPs) tend to have weak individual effects but, in combination, they have stronger predictive value. Adipokine pathways have been implicated in the pathogenesis. We used a candidate pathway approach to investigate 29 functional SNPs in key genes from relevant adipokine pathways in a sample of 1006 men eligible for prostate biopsy. We used stepwise multivariate logistic regression and bootstrapping to develop a multilocus genetic risk score by weighting each risk SNP empirically based on its association with disease. Seven common functional polymorphisms were associated with overall and high-grade prostate cancer (Gleason≥7), whereas three variants were associated with high metastatic-risk prostate cancer (PSA≥20 ng/mL and/or Gleason≥8). The addition of genetic variants to age and PSA improved the predictive accuracy for overall and high-grade prostate cancer, using either the area under the receiver-operating characteristics curves (P<0.02), the net reclassification improvement (P<0.001) and integrated discrimination improvement (P<0.001) measures. These results suggest that functional polymorphisms in adipokine pathways may act individually and cumulatively to affect risk and severity of prostate cancer, supporting the influence of adipokine pathways in the pathogenesis of prostate cancer. Use of such adipokine multilocus genetic risk score can enhance the predictive value of PSA and age in estimating absolute risk, which supports further evaluation of its clinical significance. PMID:22792137

  15. Pathway-based gene signatures predicting clinical outcome of lung adenocarcinoma

    PubMed Central

    Chang, Ya-Hsuan; Chen, Chung-Ming; Chen, Hsuan-Yu; Yang, Pan-Chyr

    2015-01-01

    Lung adenocarcinoma is often diagnosed at an advanced stage with poor prognosis. Patients with different clinical outcomes may have similar clinico-pathological characteristics. The results of previous studies for biomarkers for lung adenocarcinoma have generally been inconsistent and limited in clinical application. In this study, we used inverse-variance weighting to combine the hazard ratios for the four datasets and performed pathway analysis to identify prognosis-associated gene signatures. A total of 2,418 genes were found to be significantly associated with overall survival. Of these, a 21-gene signature in the HMGB1/RAGE signalling pathway and a 31-gene signature in the clathrin-coated vesicle cycle pathway were significantly associated with prognosis of lung adenocarcinoma across all four datasets (all p-values < 0.05, log-rank test). We combined the scores for the three pathways to derive a combined pathway-based risk (CPBR) score. Three pathway-based signatures and CPBR score also had more predictive power than single genes. Finally, the CPBR score was validated in two independent cohorts (GSE14814 and GSE13213 in the GEO database) and had significant adjusted hazard ratios 2.72 (p-value < 0.0001) and 1.71 (p-value < 0.0001), respectively. These results could provide a more complete picture of the lung cancer pathogenesis. PMID:26042604

  16. Pathway-based gene signatures predicting clinical outcome of lung adenocarcinoma.

    PubMed

    Chang, Ya-Hsuan; Chen, Chung-Ming; Chen, Hsuan-Yu; Yang, Pan-Chyr

    2015-01-01

    Lung adenocarcinoma is often diagnosed at an advanced stage with poor prognosis. Patients with different clinical outcomes may have similar clinico-pathological characteristics. The results of previous studies for biomarkers for lung adenocarcinoma have generally been inconsistent and limited in clinical application. In this study, we used inverse-variance weighting to combine the hazard ratios for the four datasets and performed pathway analysis to identify prognosis-associated gene signatures. A total of 2,418 genes were found to be significantly associated with overall survival. Of these, a 21-gene signature in the HMGB1/RAGE signalling pathway and a 31-gene signature in the clathrin-coated vesicle cycle pathway were significantly associated with prognosis of lung adenocarcinoma across all four datasets (all p-values < 0.05, log-rank test). We combined the scores for the three pathways to derive a combined pathway-based risk (CPBR) score. Three pathway-based signatures and CPBR score also had more predictive power than single genes. Finally, the CPBR score was validated in two independent cohorts (GSE14814 and GSE13213 in the GEO database) and had significant adjusted hazard ratios 2.72 (p-value < 0.0001) and 1.71 (p-value < 0.0001), respectively. These results could provide a more complete picture of the lung cancer pathogenesis. PMID:26042604

  17. 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.

  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. Combined biological tests for suicide prediction

    PubMed Central

    Coryell, William; Schlesser, Michael

    2007-01-01

    Disturbances in serotonin neuroregulation and in hypothalamic-pituitary-adrenal axis activity are both likely, and possibly independent, factors in the genesis of suicidal behavior. This analysis considers whether clinically accessible measures of these two disturbances have additive value in the estimation of risk for suicide. Seventy-four inpatients with RDC major or schizoaffective depressive disorders entered a prospective follow-up study from 1978–1981, underwent a dexamethasone suppression test (DST) and had fasting serum cholesterol levels available in the medical record. As reported earlier, patients who had had an abnormal DST result were significantly more likely to commit suicide during follow-up. Serum cholesterol concentrations did not differ by DST result and low cholesterol values were associated with subsequent suicide when age and sex were included as covariates. These results indicate that, with the use of age-appropriate thresholds, serum cholesterol concentrations may be combined with DST results to provide a clinically useful estimate of suicide risk. PMID:17289156

  20. 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

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

    NASA Astrophysics Data System (ADS)

    Kosek, Wieslaw

    2016-04-01

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

  2. Detection of gene pathways with predictive power for breast cancer prognosis

    PubMed Central

    2010-01-01

    Background Prognosis is of critical interest in breast cancer research. Biomedical studies suggest that genomic measurements may have independent predictive power for prognosis. Gene profiling studies have been conducted to search for predictive genomic measurements. Genes have the inherent pathway structure, where pathways are composed of multiple genes with coordinated functions. The goal of this study is to identify gene pathways with predictive power for breast cancer prognosis. Since our goal is fundamentally different from that of existing studies, a new pathway analysis method is proposed. Results The new method advances beyond existing alternatives along the following aspects. First, it can assess the predictive power of gene pathways, whereas existing methods tend to focus on model fitting accuracy only. Second, it can account for the joint effects of multiple genes in a pathway, whereas existing methods tend to focus on the marginal effects of genes. Third, it can accommodate multiple heterogeneous datasets, whereas existing methods analyze a single dataset only. We analyze four breast cancer prognosis studies and identify 97 pathways with significant predictive power for prognosis. Important pathways missed by alternative methods are identified. Conclusions The proposed method provides a useful alternative to existing pathway analysis methods. Identified pathways can provide further insights into breast cancer prognosis. PMID:20043860

  3. Computational prediction of human metabolic pathways from the complete human genome

    PubMed Central

    Romero, Pedro; Wagg, Jonathan; Green, Michelle L; Kaiser, Dale; Krummenacker, Markus; Karp, Peter D

    2005-01-01

    Background We present a computational pathway analysis of the human genome that assigns enzymes encoded therein to predicted metabolic pathways. Pathway assignments place genes in their larger biological context, and are a necessary first step toward quantitative modeling of metabolism. Results Our analysis assigns 2,709 human enzymes to 896 bioreactions; 622 of the enzymes are assigned roles in 135 predicted metabolic pathways. The predicted pathways closely match the known nutritional requirements of humans. This analysis identifies probable omissions in the human genome annotation in the form of 203 pathway holes (missing enzymes within the predicted pathways). We have identified putative genes to fill 25 of these holes. The predicted human metabolic map is described by a Pathway/Genome Database called HumanCyc, which is available at . We describe the generation of HumanCyc, and present an analysis of the human metabolic map. For example, we compare the predicted human metabolic pathway complement to the pathways of Escherichia coli and Arabidopsis thaliana and identify 35 pathways that are shared among all three organisms. Conclusions Our analysis elucidates a significant portion of the human metabolic map, and also indicates probable unidentified genes in the genome. HumanCyc provides a genome-based view of human nutrition that associates the essential dietary requirements of humans with a set of metabolic pathways whose existence is supported by the human genome. The database places many human genes in a pathway context, thereby facilitating analysis of gene expression, proteomics, and metabolomics datasets through a publicly available online tool called the Omics Viewer. PMID:15642094

  4. Quantitative phenotypic and pathway profiling guides rational drug combination strategies

    PubMed Central

    Dawson, John C.; Carragher, Neil O.

    2014-01-01

    Advances in target-based drug discovery strategies have enabled drug discovery groups in academia and industry to become very effective at generating molecules that are potent and selective against single targets. However, it has become apparent from disappointing results in recent clinical trials that a major challenge to the development of successful targeted therapies for treating complex multifactorial diseases is overcoming heterogeneity in target mechanism among patients and inherent or acquired drug resistance. Consequently, reductionist target directed drug-discovery approaches are not appropriately tailored toward identifying and optimizing multi-targeted therapeutics or rational drug combinations for complex disease. In this article, we describe the application of emerging high-content phenotypic profiling and analysis tools to support robust evaluation of drug combination performance following dose-ratio matrix screening. We further describe how the incorporation of high-throughput reverse phase protein microarrays with phenotypic screening can provide rational drug combination hypotheses but also confirm the mechanism-of-action of novel drug combinations, to facilitate future preclinical and clinical development strategies. PMID:24904421

  5. Synthetic lethal screening with small molecule inhibitors provides a pathway to rational combination therapies for melanoma

    PubMed Central

    Roller, Devin; Axelrod, Mark; Capaldo, Brian; Jensen, Karin; Mackey, Aaron; Weber, Michael J; Gioeli, Daniel

    2012-01-01

    Recent data demonstrate that extracellular signals are transmitted through a network of proteins rather than hierarchical signaling pathways suggesting why inhibition of a single component of a canonical pathway is insufficient for the treatment of cancer. The biological outcome of signaling through a network is inherently more robust and resistant to inhibition of a single network component. In this study, we performed a functional chemical genetic screen to identify novel interactions between signaling inhibitors that would not be predicted based on our current understanding of signaling networks. We screened over 300 drug combinations in nine melanoma cell lines and have identified pairs of compounds that show synergistic cytotoxicity. The synergistic cytotoxicities identified did not correlate with the known RAS and BRAF mutational status of the melanoma cell lines. Among the most robust results was synergy between sorafenib, a multi-kinase inhibitor with activity against RAF, and diclofenac, a non-steroidal anti-inflammatory drug (NSAID). Drug substitution experiments using the NSAIDs celecoxib and ibuprofen or the MEK inhibitor PD325901 and the RAF inhibitor RAF265 suggest that inhibition of cyclooxygenase (COX) and MAP kinase signaling are targets for the synergistic cytotoxicity of sorafenib and diclofenac. Co-treatment with sorafenib and diclofenac interrupts a positive feedback signaling loop involving ERK, cPLA2, and COX. Genome-wide expression profiling demonstrates synergy-specific down-regulation of survival-related genes. This study has uncovered novel functional drug combinations and suggests that the underlying signaling networks that control responses to targeted agents can vary substantially depending on unexplored components of the cell genotype. PMID:22962324

  6. 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

  7. 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...

  8. A predictive theoretical model for electron tunneling pathways in proteins

    NASA Technical Reports Server (NTRS)

    Onuchic, Jose Nelson; Beratan, David N.

    1990-01-01

    A practical method is presented for calculating the dependence of electron transfer rates on details of the protein medium intervening between donor and acceptor. The method takes proper account of the relative energetics and mutual interactions of the donor, acceptor, and peptide groups. It also provides a quantitative search scheme for determining the important tunneling pathways (specific sequences of localized bonding and antibonding orbitals of the protein which dominate the donor-acceptor electronic coupling) in native and tailored proteins, a tool for designing new proteins with prescribed electron transfer rates, and a consistent description of observed electron transfer rates in existing redox labeled metalloproteins and small molecule model compounds.

  9. Combination of Entner-Doudoroff pathway with MEP increases isoprene production in engineered Escherichia coli.

    PubMed

    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

  10. Using activation status of signaling pathways as mechanism-based biomarkers to predict drug sensitivity

    PubMed Central

    Amadoz, Alicia; Sebastian-Leon, Patricia; Vidal, Enrique; Salavert, Francisco; Dopazo, Joaquin

    2015-01-01

    Many complex traits, as drug response, are associated with changes in biological pathways rather than being caused by single gene alterations. Here, a predictive framework is presented in which gene expression data are recoded into activity statuses of signal transduction circuits (sub-pathways within signaling pathways that connect receptor proteins to final effector proteins that trigger cell actions). Such activity values are used as features by a prediction algorithm which can efficiently predict a continuous variable such as the IC50 value. The main advantage of this prediction method is that the features selected by the predictor, the signaling circuits, are themselves rich-informative, mechanism-based biomarkers which provide insight into or drug molecular mechanisms of action (MoA). PMID:26678097

  11. 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.

  12. Improving Phenotypic Prediction by Combining Genetic and Epigenetic Associations.

    PubMed

    Shah, Sonia; Bonder, Marc J; Marioni, Riccardo E; Zhu, Zhihong; McRae, Allan F; Zhernakova, Alexandra; Harris, Sarah E; Liewald, Dave; Henders, Anjali K; Mendelson, Michael M; Liu, Chunyu; Joehanes, Roby; Liang, Liming; Levy, Daniel; Martin, Nicholas G; Starr, John M; Wijmenga, Cisca; Wray, Naomi R; Yang, Jian; Montgomery, Grant W; Franke, Lude; Deary, Ian J; Visscher, Peter M

    2015-07-01

    We tested whether DNA-methylation profiles account for inter-individual variation in body mass index (BMI) and height and whether they predict these phenotypes over and above genetic factors. Genetic predictors were derived from published summary results from the largest genome-wide association studies on BMI (n ∼ 350,000) and height (n ∼ 250,000) to date. We derived methylation predictors by estimating probe-trait effects in discovery samples and tested them in external samples. Methylation profiles associated with BMI in older individuals from the Lothian Birth Cohorts (LBCs, n = 1,366) explained 4.9% of the variation in BMI in Dutch adults from the LifeLines DEEP study (n = 750) but did not account for any BMI variation in adolescents from the Brisbane Systems Genetic Study (BSGS, n = 403). Methylation profiles based on the Dutch sample explained 4.9% and 3.6% of the variation in BMI in the LBCs and BSGS, respectively. Methylation profiles predicted BMI independently of genetic profiles in an additive manner: 7%, 8%, and 14% of variance of BMI in the LBCs were explained by the methylation predictor, the genetic predictor, and a model containing both, respectively. The corresponding percentages for LifeLines DEEP were 5%, 9%, and 13%, respectively, suggesting that the methylation profiles represent environmental effects. The differential effects of the BMI methylation profiles by age support previous observations of age modulation of genetic contributions. In contrast, methylation profiles accounted for almost no variation in height, consistent with a mainly genetic contribution to inter-individual variation. The BMI results suggest that combining genetic and epigenetic information might have greater utility for complex-trait prediction. PMID:26119815

  13. 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'…

  14. Inter-species pathway perturbation prediction via data-driven detection of functional homology

    PubMed Central

    Hafemeister, Christoph; Romero, Roberto; Bilal, Erhan; Meyer, Pablo; Norel, Raquel; Rhrissorrakrai, Kahn; Bonneau, Richard; Tarca, Adi L.

    2015-01-01

    Motivation: Experiments in animal models are often conducted to infer how humans will respond to stimuli by assuming that the same biological pathways will be affected in both organisms. The limitations of this assumption were tested in the IMPROVER Species Translation Challenge, where 52 stimuli were applied to both human and rat cells and perturbed pathways were identified. In the Inter-species Pathway Perturbation Prediction sub-challenge, multiple teams proposed methods to use rat transcription data from 26 stimuli to predict human gene set and pathway activity under the same perturbations. Submissions were evaluated using three performance metrics on data from the remaining 26 stimuli. Results: We present two approaches, ranked second in this challenge, that do not rely on sequence-based orthology between rat and human genes to translate pathway perturbation state but instead identify transcriptional response orthologs across a set of training conditions. The translation from rat to human accomplished by these so-called direct methods is not dependent on the particular analysis method used to identify perturbed gene sets. In contrast, machine learning-based methods require performing a pathway analysis initially and then mapping the pathway activity between organisms. Unlike most machine learning approaches, direct methods can be used to predict the activation of a human pathway for a new (test) stimuli, even when that pathway was never activated by a training stimuli. Availability: Gene expression data are available from ArrayExpress (accession E-MTAB-2091), while software implementations are available from http://bioinformaticsprb.med.wayne.edu?p=50 and http://goo.gl/hJny3h. Contact: christoph.hafemeister@nyu.edu or atarca@med.wayne.edu. Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25150249

  15. Uniform curation protocol of metazoan signaling pathways to predict novel signaling components.

    PubMed

    Pálfy, Máté; Farkas, Illés J; Vellai, Tibor; Korcsmáros, Tamás

    2013-01-01

    A relatively large number of signaling databases available today have strongly contributed to our understanding of signaling pathway properties. However, pathway comparisons both within and across databases are currently severely hampered by the large variety of data sources and the different levels of detail of their information content (on proteins and interactions). In this chapter, we present a protocol for a uniform curation method of signaling pathways, which intends to overcome this insufficiency. This uniformly curated database called SignaLink ( http://signalink.org ) allows us to systematically transfer pathway annotations between different species, based on orthology, and thereby to predict novel signaling pathway components. Thus, this method enables the compilation of a comprehensive signaling map of a given species and identification of new potential drug targets in humans. We strongly believe that the strict curation protocol we have established to compile a signaling pathway database can also be applied for the compilation of other (e.g., metabolic) databases. Similarly, the detailed guide to the orthology-based prediction of novel signaling components across species may also be utilized for predicting components of other biological processes. PMID:23715991

  16. Evidence supporting predicted metabolic pathways for Vibrio cholerae: gene expression data and clinical tests

    PubMed Central

    Shi, Jing; Romero, Pedro R.; Schoolnik, Gary K.; Spormann, Alfred M.; Karp, Peter D.

    2006-01-01

    Vibrio cholerae, the etiological agent of the diarrheal illness cholera, can kill an infected adult in 24 h. V.cholerae lives as an autochthonous microbe in estuaries, rivers and coastal waters. A better understanding of its metabolic pathways will assist the development of more effective treatments and will provide a deeper understanding of how this bacterium persists in natural aquatic habitats. Using the completed V.cholerae genome sequence and PathoLogic software, we created VchoCyc, a pathway-genome database that predicted 171 likely metabolic pathways in the bacterium. We report here experimental evidence supporting the computationally predicted pathways. The evidence comes from microarray gene expression studies of V.cholerae in the stools of three cholera patients [D. S. Merrell, S. M. Butler, F. Qadri, N. A. Dolganov, A. Alam, M. B. Cohen, S. B. Calderwood, G. K. Schoolnik and A. Camilli (2002) Nature, 417, 642–645.], from gene expression studies in minimal growth conditions and LB rich medium, and from clinical tests that identify V.cholerae. Expression data provide evidence supporting 92 (53%) of the 171 pathways. The clinical tests provide evidence supporting seven pathways, with six pathways supported by both methods. VchoCyc provides biologists with a useful tool for analyzing this organism's metabolic and genomic information, which could lead to potential insights into new anti-bacterial agents. VchoCyc is available in the BioCyc database collection (). PMID:16682451

  17. 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

  18. 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

  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. 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

  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. Combined inhibition of the EGFR/AKT pathways by a novel conjugate of quinazoline with isothiocyanate.

    PubMed

    Tarozzi, Andrea; Marchetti, Chiara; Nicolini, Benedetta; D'Amico, Massimo; Ticchi, Nicole; Pruccoli, Letizia; Tumiatti, Vincenzo; Simoni, Elena; Lodola, Alessio; Mor, Marco; Milelli, Andrea; Minarini, Anna

    2016-07-19

    Epidermal growth factor receptor inhibitors (EGFR-TKIs) represent a class of compounds widely used in anticancer therapy. An increasing number of studies reports on combination therapies in which the block of the EGFR-TK activity is associated with inhibition of its downstream pathways, as PI3K-Akt. Sulforaphane targets the PI3K-Akt pathway whose dysregulation is implicated in many functions of cancer cells. According to these considerations, a series of multitarget molecules have been designed by combining key structural features derived from an EGFR-TKI, PD168393, and the isothiocyanate sulforaphane. Among the obtained molecules 1-6, compound 6 emerges as a promising lead compound able to exert antiproliferative and proapoptotic effects in A431 epithelial cancer cell line by covalently binding to EGFR-TK, and reducing the phosphorylation of Akt without affecting the total Akt levels. PMID:27135370

  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. 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

  7. Nonstationary time series prediction combined with slow feature analysis

    NASA Astrophysics Data System (ADS)

    Wang, G.; Chen, X.

    2015-07-01

    Almost all climate time series have some degree of nonstationarity due to external driving forces perturbing the observed system. Therefore, these external driving forces should be taken into account when constructing the climate dynamics. This paper presents a new technique of obtaining the driving forces of a time series from the slow feature analysis (SFA) approach, and then introduces them into a predictive model to predict nonstationary time series. The basic theory of the technique is to consider the driving forces as state variables and to incorporate them into the predictive model. Experiments using a modified logistic time series and winter ozone data in Arosa, Switzerland, were conducted to test the model. The results showed improved prediction skills.

  8. 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. PMID:24325207

  9. Can three incongruence tests predict when data should be combined?

    PubMed

    Cunningham, C W

    1997-07-01

    Advocates of conditional combination have argued that testing for incongruence between data partitions is an important step in data exploration. Unless the partitions have had distinct histories, as in horizontal gene transfer, incongruence means that one or more data support the wrong phylogeny. This study examines the relationship between incongruence and phylogenetic accuracy using three tests of incongruence. These tests were applied to pairs of mitochondrial DNA data partitions from two well-corroborated vertebrate phylogenies. Of the three tests, the most useful was the incongruence length difference test (ILD, also called the partition homogeneity test). This test distinguished between cases in which combining the data generally improved phylogenetic accuracy (P > 0.01) and cases in which accuracy of the combined data suffered relative to the individual partitions (P < 0.001). In contrast, in several cases, the Templeton and Rodrigo tests detected highly significant incongruence (P < 0.001) even though combining the incongruent partitions actually increased phylogenetic accuracy. All three tests identified cases in which improving the reconstruction model would improve the phylogenetic accuracy of the individual partitions. PMID:9214746

  10. COMBINING HETEROGENOUS DATA FOR PREDICTION OF DISEASE RELATED AND PHARMACOGENES

    PubMed Central

    HUNTER, LAWRENCE E.; COHEN, K. BRETONNEL

    2014-01-01

    Identifying genetic variants that affect drug response or play a role in disease is an important task for clinicians and researchers. Before individual variants can be explored efficiently for effect on drug response or disease relationships, specific candidate genes must be identified. While many methods rank candidate genes through the use of sequence features and network topology, only a few exploit the information contained in the biomedical literature. In this work, we train and test a classifier on known pharmacogenes from PharmGKB and present a classifier that predicts pharmacogenes on a genome-wide scale using only Gene Ontology annotations and simple features mined from the biomedical literature. Performance of F=0.86, AUC=0.860 is achieved. The top 10 predicted genes are analyzed. Additionally, a set of enriched pharmacogenic Gene Ontology concepts is produced. PMID:24297559

  11. 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

  12. 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

  13. 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.

  14. 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

  15. 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

  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. Combining De Ley-Doudoroff and methylerythritol phosphate pathways for enhanced isoprene biosynthesis from D-galactose.

    PubMed

    Ramos, Kristine Rose M; Valdehuesa, Kris Niño G; Liu, Huaiwei; Nisola, Grace M; Lee, Won-Keun; Chung, Wook-Jin

    2014-12-01

    An engineered Escherichia coli strain was developed for enhanced isoprene production using D-galactose as substrate. Isoprene is a valuable compound that can be biosynthetically produced from pyruvate and glyceraldehyde-3-phosphate (G3P) through the methylerythritol phosphate pathway (MEP). The Leloir and De Ley-Doudoroff (DD) pathways are known existing routes in E. coli that can supply the MEP precursors from D-galactose. The DD pathway was selected as it is capable of supplying equimolar amounts of pyruvate and G3P simultaneously. To exclusively direct D-galactose toward the DD pathway, an E. coli ΔgalK strain with blocked Leloir pathway was used as the host. To obtain a fully functional DD pathway, a dehydrogenase encoding gene (gld) was recruited from Pseudomonas syringae to catalyze D-galactose conversion to D-galactonate. Overexpressions of endogenous genes known as MEP bottlenecks, and a heterologous gene, were conducted to enhance and enable isoprene production, respectively. Growth test confirmed a functional DD pathway concomitant with equimolar generation of pyruvate and G3P, in contrast to the wild-type strain where G3P was limiting. Finally, the engineered strain with combined DD-MEP pathway exhibited the highest isoprene production. This suggests that the equimolar pyruvate and G3P pools resulted in a more efficient carbon flux toward isoprene production. This strategy provides a new platform for developing improved isoprenoid producing strains through the combined DD-MEP pathway. PMID:24928200

  18. 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

  19. 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

  20. 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.

  1. 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.

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

    NASA Astrophysics Data System (ADS)

    Vassiliadis, D.

    2001-09-01

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

  3. 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

  4. 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

  5. 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

  6. Systems Modeling of Anti-apoptotic Pathways in Prostate Cancer: Psychological Stress Triggers a Synergism Pattern Switch in Drug Combination Therapy

    PubMed Central

    Sun, Xiaoqiang; Bao, Jiguang; Nelson, Kyle C.; Li, King Chuen; Kulik, George; Zhou, Xiaobo

    2013-01-01

    Prostate cancer patients often have increased levels of psychological stress or anxiety, but the molecular mechanisms underlying the interaction between psychological stress and prostate cancer as well as therapy resistance have been rarely studied and remain poorly understood. Recent reports show that stress inhibits apoptosis in prostate cancer cells via epinephrine/beta2 adrenergic receptor/PKA/BAD pathway. In this study, we used experimental data on the signaling pathways that control BAD phosphorylation to build a dynamic network model of apoptosis regulation in prostate cancer cells. We then compared the predictive power of two different models with or without the role of Mcl-1, which justified the role of Mcl-1 stabilization in anti-apoptotic effects of emotional stress. Based on the selected model, we examined and quantitatively evaluated the induction of apoptosis by drug combination therapies. We predicted that the combination of PI3K inhibitor LY294002 and inhibition of BAD phosphorylation at S112 would produce the best synergistic effect among 8 interventions examined. Experimental validation confirmed the effectiveness of our predictive model. Moreover, we found that epinephrine signaling changes the synergism pattern and decreases efficacy of combination therapy. The molecular mechanisms responsible for therapeutic resistance and the switch in synergism were explored by analyzing a network model of signaling pathways affected by psychological stress. These results provide insights into the mechanisms of psychological stress signaling in therapy-resistant cancer, and indicate the potential benefit of reducing psychological stress in designing more effective therapies for prostate cancer patients. PMID:24339759

  7. Combining predictive capabilities of transcranial doppler with electrocardiogram to predict hemorrhagic shock.

    PubMed

    Najarian, Kayvan; Hakimzadeh, Roya; Ward, Kevin; Daneshvar, Kasra; Ji, Soo-Yeon

    2009-01-01

    Hemorrhagic shock (HS) potentially impacts the chance of survival in most traumatic injuries. Thus, it is highly desirable to maximize the survival rate in cases of blood loss by predicting the occurrence of hemorrhagic shock with biomedical signals. Since analyzing one physiological signal may not enough to accurately predict blood loss severity, two types of physiological signals - Electrocardiography (ECG) and Transcranial Doppler (TCD) - are used to discover the degree of severity. In this study, these degrees are classified as mild, moderate and severe, and also severe and non-severe. The data for this study were generated using the human simulated model of hemorrhage, which is called lower body negative pressure (LBNP). The analysis is done by applying discrete wavelet transformation (DWT). The wavelet-based features are defined using the detail and approximate coefficients and machine learning algorithms are used for classification. The objective of this study is to evaluate the improvement when analyzing ECG and TCD physiological signals together to classify the severity of blood loss. The results of this study show a prediction accuracy of 85.9% achieved by support vector machine in identifying severe/non-severe states. PMID:19965226

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

    ERIC Educational Resources Information Center

    Seto, Michael C.

    2005-01-01

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

  9. 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.

  10. 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.

  11. 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.

  12. 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. PMID:26895641

  13. 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

  14. 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

  15. Graphics processing units as tools to predict mechanisms of biological signaling pathway regulation

    NASA Astrophysics Data System (ADS)

    McCarter, Patrick; Elston, Timothy; Nagiek, Michal; Dohlman, Henrik

    2013-04-01

    Biochemical and genomic studies have revealed protein components of S. cerevisiae (yeast) signal transduction networks. These networks allow the transmission of extracellular signals to the cell nucleus through coordinated biochemical interactions, resulting in direct responses to specific external stimuli. The coordination and regulation mechanisms of proteins in these networks have not been fully characterized. Thus, in this work we develop systems of ordinary differential equations to characterize processes that regulate signaling pathways. We employ graphics processing units (GPUs) in high performance computing environments to search in parallel through substantially more comprehensive parameter sets than allowed by personal computers. As a result, we are able to parameterize larger models with experimental data, leading to an increase in our model prediction capabilities. Thus far these models have helped to identify specific mechanisms such as positive and negative feedback loops that control network protein activity. We ultimately believe that the use of GPUs in biochemical signal transduction pathway modeling will help to discern how regulation mechanisms allow cells to respond to multiple external stimuli.

  16. A signal transduction score flow algorithm for cyclic cellular pathway analysis, which combines transcriptome and ChIP-seq data.

    PubMed

    Isik, Zerrin; Ersahin, Tulin; Atalay, Volkan; Aykanat, Cevdet; Cetin-Atalay, Rengul

    2012-10-30

    Determination of cell signalling behaviour is crucial for understanding the physiological response to a specific stimulus or drug treatment. Current approaches for large-scale data analysis do not effectively incorporate critical topological information provided by the signalling network. We herein describe a novel model- and data-driven hybrid approach, or signal transduction score flow algorithm, which allows quantitative visualization of cyclic cell signalling pathways that lead to ultimate cell responses such as survival, migration or death. This score flow algorithm translates signalling pathways as a directed graph and maps experimental data, including negative and positive feedbacks, onto gene nodes as scores, which then computationally traverse the signalling pathway until a pre-defined biological target response is attained. Initially, experimental data-driven enrichment scores of the genes were computed in a pathway, then a heuristic approach was applied using the gene score partition as a solution for protein node stoichiometry during dynamic scoring of the pathway of interest. Incorporation of a score partition during the signal flow and cyclic feedback loops in the signalling pathway significantly improves the usefulness of this model, as compared to other approaches. Evaluation of the score flow algorithm using both transcriptome and ChIP-seq data-generated signalling pathways showed good correlation with expected cellular behaviour on both KEGG and manually generated pathways. Implementation of the algorithm as a Cytoscape plug-in allows interactive visualization and analysis of KEGG pathways as well as user-generated and curated Cytoscape pathways. Moreover, the algorithm accurately predicts gene-level and global impacts of single or multiple in silico gene knockouts. PMID:23042589

  17. 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

  18. 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

  19. Mechanism-independent method for predicting response to multidrug combinations in bacteria

    PubMed Central

    Wood, Kevin; Nishida, Satoshi; Sontag, Eduardo D.; Cluzel, Philippe

    2012-01-01

    Drugs are commonly used in combinations larger than two for treating bacterial infection. However, it is generally impossible to infer directly from the effects of individual drugs the net effect of a multidrug combination. Here we develop a mechanism-independent method for predicting the microbial growth response to combinations of more than two drugs. Performing experiments in both Gram-negative (Escherichia coli) and Gram-positive (Staphylococcus aureus) bacteria, we demonstrate that for a wide range of drugs, the bacterial responses to drug pairs are sufficient to infer the effects of larger drug combinations. To experimentally establish the broad applicability of the method, we use drug combinations comprising protein synthesis inhibitors (macrolides, aminoglycosides, tetracyclines, lincosamides, and chloramphenicol), DNA synthesis inhibitors (fluoroquinolones and quinolones), folic acid synthesis inhibitors (sulfonamides and diaminopyrimidines), cell wall synthesis inhibitors, polypeptide antibiotics, preservatives, and analgesics. Moreover, we show that the microbial responses to these drug combinations can be predicted using a simple formula that should be widely applicable in pharmacology. These findings offer a powerful, readily accessible method for the rational design of candidate therapies using combinations of more than two drugs. In addition, the accurate predictions of this framework raise the question of whether the multidrug response in bacteria obeys statistical, rather than chemical, laws for combinations larger than two. PMID:22773816

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

    NASA Astrophysics Data System (ADS)

    Krawczyk, C. M.; Kolditz, O.

    2013-12-01

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

  1. An ancient pathway combining carbon dioxide fixation with the generation and utilization of a sodium ion gradient for ATP synthesis.

    PubMed

    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

  2. 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

  3. 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

  4. 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

  5. Induction of ATM/ATR pathway combined with Vγ2Vδ2 T cells enhance cytotoxicity of ovarian cancer cells

    PubMed Central

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

    2014-01-01

    Summary Many ovarian cancer cells express stress-related molecule MICA/B on their surface that is recognized by Vγ2Vδ2 T cells through their NKG2D receptor, which is transmitted to downstream stress-signaling pathway. However, it is yet to be established how Vγ2Vδ2 T cells-mediated recognition of MICA/B signal is transmitted to downstream stress-related molecules. Identifying targeted molecules would be critical to develop a better therapy for ovarian cancer cells. It is well established that ATM/ATR signal transduction pathways, which is modulated by DNA damage, replication stress, and oxidative stress play central role in stress signaling pathway regulating cell cycle checkpoint and apoptosis. We investigated whether ATM/ATR and its down stream molecules affect Vγ2Vδ2 T cells-mediated cytotoxicity. Herein, we show that ATM/ATR pathway is modulated in ovarian cancer cells in presence of Vγ2Vδ2 T cells. Furthermore, downregulation of ATM pathway resulted downregulation of MICA, and reduced Vγ2Vδ2 T cells-mediated cytotoxicity. Alternately, stimulating ATM pathway enhanced expression of MICA, and sensitized ovarian cancer cells for cytotoxic lysis by Vγ2Vδ2 T cells. We further show that combining currently approved chemotherapeutic drugs, which induced ATM signal transduction, along with Vγ2Vδ2 T cells enhanced cytotoxicity of resistant ovarian cancer cells. These findings indicate that ATM/ATR pathway plays an important role in tumor recognition, and drugs promoting ATM signaling pathway might be considered as a combination therapy together with Vγ2Vδ2 T cells for effectively treating resistant ovarian cancer cells. PMID:24726882

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

    PubMed

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

    2016-06-01

    People who experience homelessness face many challenges and disadvantages that negatively impact health and well-being and form barriers to achieving stable housing. Further, people who are homeless often have limited social connections and support. Building on previous research that has shown the beneficial effect of group identification on health and well-being, the current study explores the relationship between two social identity processes - multiple group memberships and service identification - and well-being and positive housing outcomes. Measures were collected from 76 participants while they were residing in a homeless accommodation service (T1) and again 2-4 weeks after leaving the service (or 3 months after T1 if participants had not left the service). Mediation analyses revealed that multiple group memberships and service identification at T1 independently predicted well-being at T2 indirectly, via social support. Further, both social identity processes also indirectly predicted housing outcomes via social support. The implications of these findings are twofold. First, while belonging to multiple social groups may provide a pathway to gaining social support and well-being, group belonging may not necessarily be beneficial to achieve stable housing. Second, fostering identification with homeless services may be particularly important as a source of support that contributes to well-being. PMID:26333919

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

    NASA Astrophysics Data System (ADS)

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

    2007-12-01

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

  8. Enhanced apoptotic cancer cell killing after Foscan photodynamic therapy combined with fenretinide via de novo sphingolipid biosynthesis pathway.

    PubMed

    Boppana, Nithin B; DeLor, Jeremy S; Van Buren, Eric; Bielawska, Alicja; Bielawski, Jacek; Pierce, Jason S; Korbelik, Mladen; Separovic, Duska

    2016-06-01

    We and others have shown that stresses, including photodynamic therapy (PDT), can disrupt the de novo sphingolipid biosynthesis pathway, leading to changes in the levels of sphingolipids, and subsequently, modulation of cell death. The de novo sphingolipid biosynthesis pathway includes a ceramide synthase-dependent reaction, giving rise to dihydroceramide, which is then converted in a desaturase-dependent reaction to ceramide. In this study we tested the hypothesis that combining Foscan-mediated PDT with desaturase inhibitor fenretinide (HPR) enhances cancer cell killing. We discovered that by subjecting SCC19 cells, a human head and neck squamous cell carcinoma cell line, to PDT+HPR resulted in enhanced accumulation of C16-dihydroceramide, not ceramide. Concomitantly, mitochondrial depolarization was enhanced by the combined treatment. Enhanced activation of caspase-3 after PDT+HPR was inhibited by FB. Enhanced clonogenic cell death after the combination was sensitive to FB, as well as Bcl2- and caspase inhibitors. Treatment of mouse SCCVII squamous cell carcinoma tumors with PDT+HPR resulted in improved long-term tumor cures. Overall, our data showed that combining PDT with HPR enhanced apoptotic cancer cell killing and antitumor efficacy of PDT. The data suggest the involvement of the de novo sphingolipid biosynthesis pathway in enhanced apoptotic cell killing after PDT+HPR, and identify the combination as a novel more effective anticancer treatment than either treatment alone. PMID:27085050

  9. 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

  10. A Hadoop-Based Method to Predict Potential Effective Drug Combination

    PubMed Central

    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

  11. Vertical and Horizontal Convergences of Targeting Pathways in Combination Therapy with Baicalin and Jasminoidin for Cerebral Ischemia.

    PubMed

    Li, Bing; Yu, Yanan; Zhang, Yingying; Liu, Jun; Li, Haixia; Dang, Haixia; Guo, Shanshan; Wang, Liying; Wu, Hongli; Wang, Zhong; Wang, Yongyan

    2016-01-01

    Baicalin (BA) and jasminoidin (JA) exert an additive effect in the treatment of cerebral ischemia, but the underlying molecular mechanism is still unclear. One hundred mice with focal cerebral ischemia/re-perfusion injury were divided into 5 groups: BA, JA, combination therapy (BJ), sham and vehicle. The differentially expressed genes identified by microarray consisting of 374 cDNAs were uploaded into GeneGo MetaCore software for pathway analyses. Networks were constructed to visualize the interactions of the differentially expressed genes. Among the top ten pathways and processes, we found 5, 3, 2 overlapping pathways and 6, 4, 6 overlapping processes between the BA and JA, BA and BJ, JA and BJ groups, respectively; of which 1 pathway and 3 processes were shared by all the three groups. Six representative pathways and 3 processes were activated only in BJ, such as Gamma-secretase proteolytic targets,etc. These BJ representative targeting pathways showed both vertical (e.g. Cytoplasmic/mitochondrial transport of proapoptotic Bid Bmf and Bim) and horizontal (e.g. Endothelin-1/EDNRA signaling) convergences with those of the BA and JA groups based on the upstream and downstream relationship of cerebral ischemia network, which may help to reveal their additive mechanism in the treatment of cerebral ischemia. Network comparison identified important transcription factors that regulated some of the other BJ related genes, such as cMyb and NF-AT. Such a systemic approach based on multiple pathways and networks may provide a robust path to understand the complex pharmacological variations of combination therapies. PMID:26996167

  12. 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

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

    SciTech Connect

    Greitzer, Frank L.; Frincke, Deborah A.

    2010-09-01

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

  14. 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.

  15. A Synthetic Lethal Screen Identifies DNA Repair Pathways that Sensitize Cancer Cells to Combined ATR Inhibition and Cisplatin Treatments

    PubMed Central

    Mohni, Kareem N.; Thompson, Petria S.; Luzwick, Jessica W.; Glick, Gloria G.; Pendleton, Christopher S.; Lehmann, Brian D.; Pietenpol, Jennifer A.; Cortez, David

    2015-01-01

    The DNA damage response kinase ATR may be a useful cancer therapeutic target. ATR inhibition synergizes with loss of ERCC1, ATM, XRCC1 and DNA damaging chemotherapy agents. Clinical trials have begun using ATR inhibitors in combination with cisplatin. Here we report the first synthetic lethality screen with a combination treatment of an ATR inhibitor (ATRi) and cisplatin. Combination treatment with ATRi/cisplatin is synthetically lethal with loss of the TLS polymerase ζ and 53BP1. Other DNA repair pathways including homologous recombination and mismatch repair do not exhibit synthetic lethal interactions with ATRi/cisplatin, even though loss of some of these repair pathways sensitizes cells to cisplatin as a single-agent. We also report that ATRi strongly synergizes with PARP inhibition, even in homologous recombination-proficient backgrounds. Lastly, ATR inhibitors were able to resensitize cisplatin-resistant cell lines to cisplatin. These data provide a comprehensive analysis of DNA repair pathways that exhibit synthetic lethality with ATR inhibitors when combined with cisplatin chemotherapy, and will help guide patient selection strategies as ATR inhibitors progress into the cancer clinic. PMID:25965342

  16. 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

  17. 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

  18. 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...

  19. 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.

  20. 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),…

  1. 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…

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

    PubMed

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

    2014-01-01

    Combined sewer overflows (CSOs) represent a common feature in combined urban drainage systems and are used to discharge excess water to the environment during heavy storms. To better understand the performance of CSOs, the UK water industry has installed a large number of monitoring systems that provide data for these assets. This paper presents research into the prediction of the hydraulic performance of CSOs using artificial neural networks (ANN) as an alternative to hydraulic models. Previous work has explored using an ANN model for the prediction of chamber depth using time series for depth and rain gauge data. Rainfall intensity data that can be provided by rainfall radar devices can be used to improve on this approach. Results are presented using real data from a CSO for a catchment in the North of England, UK. An ANN model trained with the pseudo-inverse rule was shown to be capable of predicting CSO depth with less than 5% error for predictions more than 1 hour ahead for unseen data. Such predictive approaches are important to the future management of combined sewer systems. PMID:24647201

  3. Assessment of prediction skill of intraseasonal variation from dynamical, statistical, and combined models

    NASA Astrophysics Data System (ADS)

    Kim, H.; Kang, I.; Webster, P. J.

    2008-12-01

    We consider intraseasonal variation (ISV) prediction by statistical and dynamical models. For the fair comparison, the real-time multivariate Madden-Julian Oscillation (MJO) (RMM) index for the boreal winter is used as a predictand. The statistical prediction results are compared by reassessing the multi linear regression (MLR), wavelet, and singular spectrum analysis (SSA) model. The correlation score for RMM1 (RMM2) falls away to 0.5 between 16-17 (15-16) days for MLR, 7-8 (9-10) days for wavelet, and 8-9 (9-10) days for SSA model. As both wavelet and SSA model have a discontinuity at the boundary of data, the skill of the real-time forecast shows a steep decrease at the beginning of the forecasts. To examine the skill of dynamical prediction, serial integration is performed with Seoul National University AGCM and CGCM over the entire boreal winter period. The ocean-atmosphere coupling acts to improve the simulation ability of MJO variability, the eastward propagation, and the phase relationship between convection and SST. The skill score of RMM1 (RMM2) falls out to 0.5 at 18-19 (22-23), 15-16 (17-18), and 16-17 (15-16) for CGCM, AGCM, and MLR. This result demonstrates that dynamical prediction does not lag statistical prediction in skill and is even better when ocean-atmosphere coupling is included. The dependency of prediction skill on the initial phase and amplitude of the MJO is investigated. The score is better when the MJO is initialized during an active period than during a quiescent period for both systems. Based on different characteristics of prediction skill for each phase and amplitude and for individual models, predictions are combined using available information extracted using the better of the two predictions. By simple selection, the prediction skill is clearly improved in strong MJO cases. Using another combination process based on Bayesian concepts, two independent predictions are combined by minimizing the forecast error that is known from

  4. 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

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

    PubMed Central

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

    2015-01-01

    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. PMID:26713437

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

    PubMed Central

    Nikoloudis, Dimitris; Pitts, Jim E.

    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. PMID:25071985

  7. 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. PMID:25071985

  8. Quantum chemical prediction of redox reactivity and degradation pathways for aqueous phase contaminants: an example with HMPA.

    PubMed

    Blotevogel, Jens; Borch, Thomas; Desyaterik, Yury; Mayeno, Arthur N; Sale, Tom C

    2010-08-01

    Models used to predict the fate of aqueous phase contaminants are often limited by their inability to address the widely varying redox conditions in natural and engineered systems. Here, we present a novel approach based on quantum chemical calculations that identifies the thermodynamic conditions necessary for redox-promoted degradation and predicts potential degradation pathways. Hexamethylphosphoramide (HMPA), a widely used solvent and potential groundwater contaminant, is used as a test case. Its oxidation is estimated to require at least iron-reducing conditions at low to neutral pH and nitrate-reducing conditions at high pH. Furthermore, the transformation of HMPA by permanganate is predicted to proceed through sequential N-demethylation. Experimental validation based on LC/TOF-MS analysis confirms the predicted pathways of HMPA oxidation by permanganate to phosphoramide via the formation of less methylated as well as singly and multiply oxygenated reaction intermediates. Pathways predicted to be thermodynamically or kinetically unfavorable are similarly absent in the experimental studies. Our newly developed methodology will enable scientists and engineers to estimate the favorability of contaminant degradation at a specific field site, suitable approaches to enhance degradation, and the persistence of a contaminant and its reaction intermediates. PMID:20608732

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

    NASA Astrophysics Data System (ADS)

    Greitzer, Frank L.; Frincke, Deborah A.

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

  10. 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. PMID:26103634

  11. 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

  12. 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

  13. 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

  14. 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

  15. The Relationship between Vocational Interests, Self-Efficacy, and Achievement in the Prediction of Educational Pathways

    ERIC Educational Resources Information Center

    Patrick, Lyn; Care, Esther; Ainley, Mary

    2011-01-01

    The influence of vocational interest, self-efficacy beliefs, and academic achievement on choice of educational pathway is described for a cohort of Australian students. Participants were 189 students aged 14-15 years, who were considering either academic or applied learning pathways and subject choices for the final 3 years of secondary school.…

  16. 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.

  17. [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

  18. Combined CFD/Population Balance Model for Gas Hydrate Particle Size Prediction in Turbulent Pipeline Flow

    NASA Astrophysics Data System (ADS)

    Balakin, Boris V.; Hoffmann, Alex C.; Kosinski, Pawel; Istomin, Vladimir A.; Chuvilin, Evgeny M.

    2010-09-01

    A combined computational fluid dynamics/population balance model (CFD-PBM) is developed for gas hydrate particle size prediction in turbulent pipeline flow. The model is based on a one-moment population balance technique, which is coupled with flow field parameters computed using commercial CFD software. The model is calibrated with a five-moment, off-line population balance model and validated with experimental data produced in a low-pressure multiphase flow loop.

  19. Combination of Hansen Robotic system with cryocatheter in a challenging parahisian accessory pathway ablation

    PubMed Central

    Rodríguez-Mañero, Moisés; Schurmann, Paul; Valderrábano, Miguel

    2016-01-01

    A perceived distinctive feature of cryoablation is the stability (cryoadherence) of the catheter tip during cold temperatures at the desired location, even during tachycardia. We report the case report of a young patient with a parahisian accessory pathway where stability of the ablation catheter was not achieved despite using the cryocatheter with a steerable sheath. Ultimately, stability at the desired location was achieved robotically by means of Hansen system (Hansen Medical, Mountain View, CA, USA).

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

    PubMed Central

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

    2015-01-01

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

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

    PubMed

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

    2013-10-01

    A heat transfer model was used to simulate the temperature in 3 dimensions inside the meat. This model was combined with a first-order kinetic models to predict cooking losses. Identification of the parameters of the kinetic models and first validations were performed in a water bath. Afterwards, the performance of the combined model was determined in a fan-assisted oven under different air/steam conditions. Accurate knowledge of the heat transfer coefficient values and consideration of the retraction of the meat pieces are needed for the prediction of meat temperature. This is important since the temperature at the center of the product is often used to determine the cooking time. The combined model was also able to predict cooking losses from meat pieces of different sizes and subjected to different air/steam conditions. It was found that under the studied conditions, most of the water loss comes from the juice expelled by protein denaturation and contraction and not from evaporation. PMID:23747627

  2. 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.

  3. 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

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

    PubMed

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

    2015-12-01

    The Wnt-inhibitor dickkopf-1 (DKK-1) promotes cancer-induced osteolytic bone lesions by direct inhibition of osteoblast differentiation and indirect activation of osteoclasts. DKK-1 is highly expressed in human breast cancer cells and can be suppressed by inhibitors of the mevalonate pathway such as statins and amino-bisphosphonates. However, supraphysiological concentrations are required to suppress DKK-1. We show that a sequential mevalonate pathway blockade using statins and amino-bisphosphonates suppresses DKK-1 more significantly than the individual agents alone. Thus, the reduction of the DKK-1 expression and secretion in the human osteotropic tumor cell lines MDA-MB-231, MDA-MET, and MDA-BONE by zoledronic acid was potentiated by the combination with low concentrations of statins (atorvastatin, simvastatin, and rosuvastatin) by up to 75% (p < 0.05). The specific rescue of prenylation using farnesyl pyrophosphate or geranylgeranyl pyrophosphate revealed that these effects were mediated by suppressed geranylgeranylation rather than by suppressed farnesylation. Moreover, combining low concentrations of statins (1 µM atorvastatin or 0.25 µM simvastatin) and zoledronic acid at low concentrations resulted in an at least 50% reversal of breast cancer-derived DKK-1-mediated inhibition of osteogenic markers in C2C12 cells (p < 0.05). Finally, the intratumoral injection of atorvastatin and zoledronic acid in as subcutaneous MDA-MB-231 mouse model reduced the serum level of human DKK-1 by 25% compared to untreated mice. Hence our study reveals that a sequential mevalonate pathway blockade allows for the combined use of low concentration of statins and amino-bisphosphonates. This combination still significantly suppresses breast cancer-derived DKK-1 to levels where it can no longer inhibit Wnt-mediated osteoblast differentiation. PMID:26515701

  5. An adenylyl cyclase signaling pathway predicts direct dopaminergic input to vestibular hair cells.

    PubMed

    Drescher, M J; Cho, W J; Folbe, A J; Selvakumar, D; Kewson, D T; Abu-Hamdan, M D; Oh, C K; Ramakrishnan, N A; Hatfield, J S; Khan, K M; Anne, S; Harpool, E C; Drescher, D G

    2010-12-29

    Adenylyl cyclase (AC) signaling pathways have been identified in a model hair cell preparation from the trout saccule, for which the hair cell is the only intact cell type. The use of degenerate primers targeting cDNA sequence conserved across AC isoforms, and reverse transcription-polymerase chain reaction (RT-PCR), coupled with cloning of amplification products, indicated expression of AC9, AC7 and AC5/6, with cloning efficiencies of 11:5:2. AC9 and AC5/6 are inhibited by Ca(2+), the former in conjunction with calcineurin, and message for calcineurin has also been identified in the trout saccular hair cell layer. AC7 is independent of Ca(2+). Given the lack of detection of calcium/calmodulin-activated isoforms previously suggested to mediate AC activation in the absence of Gαs in mammalian cochlear hair cells, the issue of hair-cell Gαs mRNA expression was re-examined in the teleost vestibular hair cell model. Two full-length coding sequences were obtained for Gαs/olf in the vestibular type II-like hair cells of the trout saccule. Two messages for Gαi have also been detected in the hair cell layer, one with homology to Gαi1 and the second with homology to Gαi3 of higher vertebrates. Both Gαs/olf protein and Gαi1/Gαi3 protein were immunolocalized to stereocilia and to the base of the hair cell, the latter consistent with sites of efferent input. Although a signaling event coupling to Gαs/olf and Gαi1/Gαi3 in the stereocilia is currently unknown, signaling with Gαs/olf, Gαi3, and AC5/6 at the base of the hair cell would be consistent with transduction pathways activated by dopaminergic efferent input. mRNA for dopamine receptors D1A4 and five forms of dopamine D2 were found to be expressed in the teleost saccular hair cell layer, representing information on vestibular hair cell expression not directly available for higher vertebrates. Dopamine D1A receptor would couple to Gαolf and activation of AC5/6. Co-expression with dopamine D2 receptor, which

  6. Prediction of calcium-binding sites by combining loop-modeling with machine learning

    PubMed Central

    2009-01-01

    Background Protein ligand-binding sites in the apo state exhibit structural flexibility. This flexibility often frustrates methods for structure-based recognition of these sites because it leads to the absence of electron density for these critical regions, particularly when they are in surface loops. Methods for recognizing functional sites in these missing loops would be useful for recovering additional functional information. Results We report a hybrid approach for recognizing calcium-binding sites in disordered regions. Our approach combines loop modeling with a machine learning method (FEATURE) for structure-based site recognition. For validation, we compared the performance of our method on known calcium-binding sites for which there are both holo and apo structures. When loops in the apo structures are rebuilt using modeling methods, FEATURE identifies 14 out of 20 crystallographically proven calcium-binding sites. It only recognizes 7 out of 20 calcium-binding sites in the initial apo crystal structures. We applied our method to unstructured loops in proteins from SCOP families known to bind calcium in order to discover potential cryptic calcium binding sites. We built 2745 missing loops and evaluated them for potential calcium binding. We made 102 predictions of calcium-binding sites. Ten predictions are consistent with independent experimental verifications. We found indirect experimental evidence for 14 other predictions. The remaining 78 predictions are novel predictions, some with intriguing potential biological significance. In particular, we see an enrichment of beta-sheet folds with predicted calcium binding sites in the connecting loops on the surface that may be important for calcium-mediated function switches. Conclusion Protein crystal structures are a potentially rich source of functional information. When loops are missing in these structures, we may be losing important information about binding sites and active sites. We have shown that

  7. 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.

  8. In silico prediction of major drug clearance pathways by support vector machines with feature-selected descriptors.

    PubMed

    Toshimoto, Kouta; Wakayama, Naomi; Kusama, Makiko; Maeda, Kazuya; Sugiyama, Yuichi; Akiyama, Yutaka

    2014-11-01

    We have previously established an in silico classification method ("CPathPred") to predict the major clearance pathways of drugs based on an empirical decision with only four physicochemical descriptors-charge, molecular weight, octanol-water distribution coefficient, and protein unbound fraction in plasma-using a rectangular method. In this study, we attempted to improve the prediction performance of the method by introducing a support vector machine (SVM) and increasing the number of descriptors. The data set consisted of 141 approved drugs whose major clearance pathways were classified into metabolism by CYP3A4, CYP2C9, or CYP2D6; organic anion transporting polypeptide-mediated hepatic uptake; or renal excretion. With the same four default descriptors as used in CPathPred, the SVM-based predictor (named "default descriptor SVM") resulted in higher prediction performance compared with a rectangular-based predictor judged by 10-fold cross-validation. Two SVM-based predictors were also established by adding some descriptors as follows: 1) 881 descriptors predicted in silico from the chemical structures of drugs in addition to 4 default descriptors ("885 descriptor SVM"); and 2) selected descriptors extracted by a feature selection based on a greedy algorithm with default descriptors ("feature selection SVM"). The prediction accuracies of the rectangular-based predictor, default descriptor SVM, 885 descriptor SVM, and feature selection SVM were 0.49, 0.60, 0.72, and 0.91, respectively, and the overall precision values for these four methods were 0.72, 0.77, 0.86, and 0.98, respectively. In conclusion, we successfully constructed SVM-based predictors with limited numbers of descriptors to classify the major clearance pathways of drugs in humans with high prediction performance. PMID:25128502

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

    PubMed

    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

  10. Germline polymorphisms in genes involved in the IGF1-pathway predict efficacy of cetuximab in wild-type KRAS mCRC patients

    PubMed Central

    Winder, Thomas; Zhang, Wu; Yang, Dongyun; Ning, Yan; Bohanes, Pierre; Gerger, Armin; Wilson, Peter M.; Pohl, Alexandra; Mauro, David J.; Langer, Christiane; Rowinsky, Eric K.; Lenz, Heinz-Josef

    2010-01-01

    Purpose The insulin-like growth factor 1 (IGF1) signalling pathway is an important growth-regulatory pathway, which plays a crucial role in colorectal cancer (CRC) proliferation, differentiation, migration, angiogenesis, and apoptosis. Previous studies showed that hyperactivation of the IGF1-Receptor (IGF1R) may result in resistance to anti-EGFR targeted treatment. We tested whether germline variations within the IGF1-pathway are associated with clinical outcome in wild-type KRAS (wt KRAS) drug-refractory metastatic CRC (mCRC) patients who were treated with cetuximab monotherapy (IMC-0144). Experimental Design Formalin-fixed paraffin-embedded (FFPE) tissue samples of 130 drug-refractory mCRC patients enrolled in IMC-0144, a phase II clinical trial of cetuximab monotherapy, were analyzed. gDNA was extracted from dissected FFPE tumor tissue and KRAS mutation status and six potentially functional IGF1 and IGF1R polymorphisms were analyzed using direct DNA-sequencing or PCR-RFLP. Tumor response analysis was based on recursive partitioning and survival analyses were based on univariate and multivariate hazard regression models. Results In univariate and multivariate analyses five IGF-pathway SNPs were significantly associated with progression-free-survival (PFS) and/or overall survival (OS). In multivariate combined risk allele analysis the additive model for PFS and OS was significantly associated with the number of risk alleles in wt KRAS patients (p=0.001 and p=0.02, respectively). In addition, wt KRAS patients harbouring IGF1 rs2946834 A/A genotype had a 50 % ORR compared to 0% for A/G genotype. Conclusions These results indicate that IGF1-pathway polymorphisms are potential predictive/prognostic molecular markers for cetuximab efficacy in wt KRAS mCRC patients. Prospective biomarker embedded clinical trials are warranted to validate our findings. PMID:20935157

  11. Abrupt CO2 experiments as tools for predicting and understanding CMIP5 representative concentration pathway projections

    NASA Astrophysics Data System (ADS)

    Good, Peter; Gregory, Jonathan M.; Lowe, Jason A.; Andrews, Timothy

    2013-02-01

    A fast simple climate modelling approach is developed for predicting and helping to understand general circulation model (GCM) simulations. We show that the simple model reproduces the GCM results accurately, for global mean surface air temperature change and global-mean heat uptake projections from 9 GCMs in the fifth coupled model inter-comparison project (CMIP5). This implies that understanding gained from idealised CO2 step experiments is applicable to policy-relevant scenario projections. Our approach is conceptually simple. It works by using the climate response to a CO2 step change taken directly from a GCM experiment. With radiative forcing from non-CO2 constituents obtained by adapting the Forster and Taylor method, we use our method to estimate results for CMIP5 representative concentration pathway (RCP) experiments for cases not run by the GCMs. We estimate differences between pairs of RCPs rather than RCP anomalies relative to the pre-industrial state. This gives better results because it makes greater use of available GCM projections. The GCMs exhibit differences in radiative forcing, which we incorporate in the simple model. We analyse the thus-completed ensemble of RCP projections. The ensemble mean changes between 1986-2005 and 2080-2099 for global temperature (heat uptake) are, for RCP8.5: 3.8 K (2.3 × 1024 J); for RCP6.0: 2.3 K (1.6 × 1024 J); for RCP4.5: 2.0 K (1.6 × 1024 J); for RCP2.6: 1.1 K (1.3 × 1024 J). The relative spread (standard deviation/ensemble mean) for these scenarios is around 0.2 and 0.15 for temperature and heat uptake respectively. We quantify the relative effect of mitigation action, through reduced emissions, via the time-dependent ratios (change in RCPx)/(change in RCP8.5), using changes with respect to pre-industrial conditions. We find that the effects of mitigation on global-mean temperature change and heat uptake are very similar across these different GCMs.

  12. An Efficient Computational Model to Predict Protonation at the Amide Nitrogen and Reactivity along the C–N Rotational Pathway

    PubMed Central

    Szostak, Roman; Aubé, Jeffrey

    2015-01-01

    N-protonation of amides is critical in numerous biological processes, including amide bonds proteolysis and protein folding, as well as in organic synthesis as a method to activate amide bonds towards unconventional reactivity. A computational model enabling prediction of protonation at the amide bond nitrogen atom along the C–N rotational pathway is reported. Notably, this study provides a blueprint for the rational design and application of amides with a controlled degree of rotation in synthetic chemistry and biology. PMID:25766378

  13. 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...

  14. 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

  15. Hierarchical porous polycaprolactone microspheres generated via a simple pathway combining nanoprecipitation and hydrolysis.

    PubMed

    Fan, Hailong; Jin, Zhaoxia

    2015-10-21

    We demonstrated a one-pot, soap-free fabrication of porous polycaprolactone microspheres by combining nanoprecipitation and hydrolysis. The obtained porous polycaprolactone microspheres show great advantages for application in drug delivery. PMID:26324843

  16. 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.

  17. Event-based prediction of stream turbidity using a combined cluster analysis and classification tree approach

    NASA Astrophysics Data System (ADS)

    Mather, Amanda L.; Johnson, Richard L.

    2015-11-01

    Stream turbidity typically increases during streamflow events; however, similar event hydrographs can produce markedly different event turbidity behaviors because many factors influence turbidity in addition to streamflow, including antecedent moisture conditions, season, and supply of turbidity-causing materials. Modeling of sub-hourly turbidity as a function of streamflow shows that event model parameters vary on an event-by-event basis. Here we examine the extent to which stream turbidity can be predicted through the prediction of event model parameters. Using three mid-sized streams from the Mid-Atlantic region of the U.S., we show the model parameter set for each event can be predicted based on the event characteristics (e.g., hydrologic, meteorologic and antecedent moisture conditions) using a combined cluster analysis and classification tree approach. The results suggest that the ratio of beginning event discharge to peak event discharge (an estimate of the event baseflow index), as well as catchment antecedent moisture, are important factors in the prediction of event turbidity. Indicators of antecedent moisture, particularly those derived from antecedent discharge, account for the majority of the splitting nodes in the classification trees for all three streams. For this study, prediction of turbidity during streamflow events is based upon observed data (e.g., measured streamflow, precipitation and air temperature). However, the results also suggest that the methods presented here can, in future work, be used in conjunction with forecasts of streamflow, precipitation and air temperature to forecast stream turbidity.

  18. Ant Colony Optimization Algorithm for Interpretable Bayesian Classifiers Combination: Application to Medical Predictions

    PubMed Central

    Bouktif, Salah; Hanna, Eileen Marie; Zaki, Nazar; Khousa, Eman Abu

    2014-01-01

    Prediction and classification techniques have been well studied by machine learning researchers and developed for several real-word problems. However, the level of acceptance and success of prediction models are still below expectation due to some difficulties such as the low performance of prediction models when they are applied in different environments. Such a problem has been addressed by many researchers, mainly from the machine learning community. A second problem, principally raised by model users in different communities, such as managers, economists, engineers, biologists, and medical practitioners, etc., is the prediction models’ interpretability. The latter is the ability of a model to explain its predictions and exhibit the causality relationships between the inputs and the outputs. In the case of classification, a successful way to alleviate the low performance is to use ensemble classiers. It is an intuitive strategy to activate collaboration between different classifiers towards a better performance than individual classier. Unfortunately, ensemble classifiers method do not take into account the interpretability of the final classification outcome. It even worsens the original interpretability of the individual classifiers. In this paper we propose a novel implementation of classifiers combination approach that does not only promote the overall performance but also preserves the interpretability of the resulting model. We propose a solution based on Ant Colony Optimization and tailored for the case of Bayesian classifiers. We validate our proposed solution with case studies from medical domain namely, heart disease and Cardiotography-based predictions, problems where interpretability is critical to make appropriate clinical decisions. Availability The datasets, Prediction Models and software tool together with supplementary materials are available at http://faculty.uaeu.ac.ae/salahb/ACO4BC.htm. PMID:24498276

  19. 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

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

    PubMed

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

    2014-06-30

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

  1. 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.

  2. 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

  3. Combining Statistical and Ensemble Streamflow Predictions to Cope with Consensus Forecast

    NASA Astrophysics Data System (ADS)

    Mirfenderesgi, G.; Najafi, M.; Moradkhani, H.

    2012-12-01

    Monthly and seasonal water supply outlooks are used for water resource planning and management including the industrial and agriculture water allocation as well as reservoir operations. Currently consensus forecasts are jointly issued by the operational agencies in the Western US based on statistical regression equations and ensemble streamflow predictions. However, an objective method is needed to combine the forecasts from these methods. In this study monthly and seasonal streamflow predictions are generated from various hydrologic and statistical simulations including: Variable Infiltration Capacity (VIC), Sacramento Soil Moisture Accounting Model (SAC-SMA), Precipitation Runoff Modeling System (PRMS), Conceptual Hydrologic MODel (HYMOD), and Principal and Independent Component Regression (PCR and ICR), etc. The results are optimally combined by several objective multi-modeling methods. The increase in forecast accuracy is assessed in comparison with the available best and worst prediction. The precision of each multi-model method is also estimated. The study is performed over the Lake Granby, located in the headwaters of the Colorado River Basin. Overall the results show improvements in both monthly and seasonal forecasts as compared with single model simulations.

  4. 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

  5. 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

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

    PubMed

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

    2016-05-01

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

  7. Sensitivity Analysis of Intracellular Signaling Pathway Kinetics Predicts Targets for Stem Cell Fate Control

    PubMed Central

    Mahdavi, Alborz; Davey, Ryan E; Bhola, Patrick; Yin, Ting; Zandstra, Peter W

    2007-01-01

    Directing stem cell fate requires knowledge of how signaling networks integrate temporally and spatially segregated stimuli. We developed and validated a computational model of signal transducer and activator of transcription-3 (Stat3) pathway kinetics, a signaling network involved in embryonic stem cell (ESC) self-renewal. Our analysis identified novel pathway responses; for example, overexpression of the receptor glycoprotein-130 results in reduced pathway activation and increased ESC differentiation. We used a systematic in silico screen to identify novel targets and protein interactions involved in Stat3 activation. Our analysis demonstrates that signaling activation and desensitization (the inability to respond to ligand restimulation) is regulated by balancing the activation state of a distributed set of parameters including nuclear export of Stat3, nuclear phosphatase activity, inhibition by suppressor of cytokine signaling, and receptor trafficking. This knowledge was used to devise a temporally modulated ligand delivery strategy that maximizes signaling activation and leads to enhanced ESC self-renewal. PMID:17616983

  8. CTLA-4 and PD-1 Pathway Blockade: Combinations in the Clinic

    PubMed Central

    Callahan, Margaret K.; Postow, Michael A.; Wolchok, Jedd D.

    2014-01-01

    Checkpoint blocking antibodies targeting regulatory molecules on T cells such as CTLA-4 and PD-1 have reinvigorated the field of cancer immunotherapy. These agents have demonstrated clinical activity across a variety of tumor types. Now that safety and clinical activity has been demonstrated in the monotherapy setting, the field is moving in the direction of testing novel combinations. PMID:25642417

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

    Technology Transfer Automated Retrieval System (TEKTRAN)

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

  10. 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

  11. Combining Structure and Sequence Information Allows Automated Prediction of Substrate Specificities within Enzyme Families

    PubMed Central

    Röttig, Marc; Rausch, Christian; Kohlbacher, Oliver

    2010-01-01

    An important aspect of the functional annotation of enzymes is not only the type of reaction catalysed by an enzyme, but also the substrate specificity, which can vary widely within the same family. In many cases, prediction of family membership and even substrate specificity is possible from enzyme sequence alone, using a nearest neighbour classification rule. However, the combination of structural information and sequence information can improve the interpretability and accuracy of predictive models. The method presented here, Active Site Classification (ASC), automatically extracts the residues lining the active site from one representative three-dimensional structure and the corresponding residues from sequences of other members of the family. From a set of representatives with known substrate specificity, a Support Vector Machine (SVM) can then learn a model of substrate specificity. Applied to a sequence of unknown specificity, the SVM can then predict the most likely substrate. The models can also be analysed to reveal the underlying structural reasons determining substrate specificities and thus yield valuable insights into mechanisms of enzyme specificity. We illustrate the high prediction accuracy achieved on two benchmark data sets and the structural insights gained from ASC by a detailed analysis of the family of decarboxylating dehydrogenases. The ASC web service is available at http://asc.informatik.uni-tuebingen.de/. PMID:20072606

  12. 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

  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. Identifying combinations of risk and protective factors predicting physical activity change in high school students.

    PubMed

    Dunton, Genevieve Fridlund; Atienza, Audie A; Tscherne, James; Rodriguez, Daniel

    2011-02-01

    Research sought to identify combinations of risk and protective factors predicting change in physical activity (PA) over one year in high school students. Adolescents (N = 344; M = 15.7 years) participated in a longitudinal study with assessment of demographics, substance use/smoking exposure, height and weight, psychological factors, and PA in 10th and 11th grade. PA participation in 11th grade was greatest for adolescents who engaged in PA and had high sports competence (78%), and least for adolescents who did not engage in or enjoy PA (13%) in 10th grade. Identifying adolescent subgroups at risk for decreasing PA can inform the development of tailored interventions. PMID:21467595

  15. 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.

  16. 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.

  17. Genetic variants in the Hippo pathway predict biochemical recurrence after radical prostatectomy for localized prostate cancer.

    PubMed

    Huang, Chao-Yuan; Huang, Shu-Pin; Lin, Victor C; Yu, Chia-Cheng; Chang, Ta-Yuan; Juang, Shin-Hun; Bao, Bo-Ying

    2015-01-01

    While localized prostate cancer is potentially curative, many patients still show biochemical recurrence (BCR) after curative treatments such as radical prostatectomy (RP). The Hippo pathway has recently been shown to be an evolutionarily conserved regulator of tissue growth, and its perturbation can trigger tumorigenesis. We hypothesize that genetic variants of the Hippo pathway may influence clinical outcomes in localized prostate cancer patients. We genotyped 53 tagging single-nucleotide polymorphisms (SNPs) from seven core Hippo pathway genes in 246 localized prostate cancer patients treated with RP. Kaplan-Meier analysis and Cox proportional hazard models were utilized to identify significant SNPs that correlated with BCR. For replication, five associated SNPs were genotyped in an independent cohort of 212 patients. After adjusting for known clinicopathologic factors, the association between STK3 rs7827435 and BCR (P = 0.018) was replicated in the second stage (P = 0.026; Pcombined = 0.001). Additional integrated in silico analysis provided evidence that rs7827435 affects STK3 expression, which in turn is significantly correlated with tumor aggressiveness and patient prognosis. In conclusion, genetic variants of the Hippo pathway contribute to the variable outcomes of prostate cancer, and the discovery of these biomarkers provides a molecular approach for prognostic risk assessment. PMID:25707771

  18. Combined Use of the Canine Adenovirus-2 and DREADD-Technology to Activate Specific Neural Pathways In Vivo

    PubMed Central

    Boender, Arjen J.; de Jong, Johannes W.; Boekhoudt, Linde; Luijendijk, Mieneke C. M.; van der Plasse, Geoffrey; Adan, Roger A. H.

    2014-01-01

    We here describe a technique to transiently activate specific neural pathways in vivo. It comprises the combined use of a CRE-recombinase expressing canine adenovirus-2 (CAV-2) and an adeno-associated virus (AAV-hSyn-DIO-hM3D(Gq)-mCherry) that contains the floxed inverted sequence of the designer receptor exclusively activated by designer drugs (DREADD) hM3D(Gq)-mCherry. CAV-2 retrogradely infects projection neurons, which allowed us to specifically express hM3D(Gq)-mCherry in neurons that project from the ventral tegmental area (VTA) to the nucleus accumbens (Acb), the majority of which were dopaminergic. Activation of hM3D(Gq)-mCherry by intraperitoneal (i.p.) injections of clozapine-N-oxide (CNO) leads to increases in neuronal activity, which enabled us to specifically activate VTA to Acb projection neurons. The VTA to Acb pathway is part of the mesolimbic dopamine system and has been implicated in behavioral activation and the exertion of effort. Injections of all doses of CNO led to increases in progressive ratio (PR) performance. The effect of the lowest dose of CNO was suppressed by administration of a DRD1-antagonist, suggesting that CNO-induced increases in PR-performance are at least in part mediated by DRD1-signaling. We hereby validate the combined use of CAV-2 and DREADD-technology to activate specific neural pathways and determine consequent changes in behaviorally relevant paradigms. PMID:24736748

  19. Combination of artificial neural-network forecasters for prediction of natural gas consumption.

    PubMed

    Khotanzad, A; Elragal, H; Lu, T L

    2000-01-01

    The focus of this paper is on combination of artificial neural-network (ANN) forecasters with application to the prediction of daily natural gas consumption needed by gas utilities. ANN forecasters can model the complex relationship between weather parameters and previous gas consumption with the future consumption. A two-stage system is proposed with the first stage containing two ANN forecasters, a multilayer feedforward ANN and a functional link ANN. These forecasters are initially trained with the error backpropagation algorithm, but an adaptive strategy is employed to adjust their weights during on-line forecasting. The second stage consists of a combination module to mix the two individual forecasts produced in the first stage. Eight different combination algorithms are examined, they are based on: averaging, recursive least squares, fuzzy logic, feedforward ANN, functional link ANN, temperature space approach, Karmarkar's linear programming algorithm and adaptive mixture of local experts (modular neural networks). The performance is tested on real data from six different gas utilities. The results indicate that combination strategies based on a single ANN outperform the other approaches. PMID:18249775

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

    PubMed

    Pitcher, David; Duchaine, Bradley; Walsh, Vincent

    2014-09-01

    Faces contain structural information, for identifying individuals, as well as changeable information, which can convey emotion and direct attention. Neuroimaging studies reveal brain regions that exhibit preferential responses to invariant [1, 2] or changeable [3-5] facial aspects but the functional connections between these regions are unknown. We addressed this issue by causally disrupting two face-selective regions with thetaburst transcranial magnetic stimulation (TBS) and measuring the effects of this disruption in local and remote face-selective regions with functional magnetic resonance imaging (fMRI). Participants were scanned, over two sessions, while viewing dynamic or static faces and objects. During these sessions, TBS was delivered over the right occipital face area (rOFA) or right posterior superior temporal sulcus (rpSTS). Disruption of the rOFA reduced the neural response to both static and dynamic faces in the downstream face-selective region in the fusiform gyrus. In contrast, the response to dynamic and static faces was doubly dissociated in the rpSTS. Namely, disruption of the rOFA reduced the response to static but not dynamic faces, while disruption of the rpSTS itself reduced the response to dynamic but not static faces. These results suggest that dynamic and static facial aspects are processed via dissociable cortical pathways that begin in early visual cortex, a conclusion inconsistent with current models of face perception [6-9]. PMID:25131678

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

    PubMed

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

    2016-05-19

    The solubility of organic semiconductors in environmentally benign solvents is an important prerequisite for the widespread adoption of organic electronic appliances. Solubility can be determined by considering the cohesive forces in a liquid via Hansen solubility parameters (HSP). We report a numerical approach to determine the HSP of fullerenes using a mathematical tool based on artificial neural networks (ANN). ANN transforms the molecular surface charge density distribution (σ-profile) as determined by density functional theory (DFT) calculations within the framework of a continuum solvation model into solubility parameters. We validate our model with experimentally determined HSP of the fullerenes C60, PC61BM, bisPC61BM, ICMA, ICBA, and PC71BM and through comparison with previously reported molecular dynamics calculations. Most excitingly, the ANN is able to correctly predict the dispersive contributions to the solubility parameters of the fullerenes although no explicit information on the van der Waals forces is present in the σ-profile. The presented theoretical DFT calculation in combination with the ANN mathematical tool can be easily extended to other π-conjugated, electronic material classes and offers a fast and reliable toolbox for future pathways that may include the design of green ink formulations for solution-processed optoelectronic devices. PMID:27070101

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

    PubMed Central

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

    2015-01-01

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

  3. Optimal Multitrial Prediction Combination and Subject-Specific Adaptation for Minimal Training Brain Switch Designs.

    PubMed

    Spyrou, Loukianos; Blokland, Yvonne; Farquhar, Jason; Bruhn, Jorgen

    2016-06-01

    Brain-Computer Interface (BCI) systems are traditionally designed by taking into account user-specific data to enable practical use. More recently, subject independent (SI) classification algorithms have been developed which bypass the subject specific adaptation and enable rapid use of the system. A brain switch is a particular BCI system where the system is required to distinguish from two separate mental tasks corresponding to the on-off commands of a switch. Such applications require a low false positive rate (FPR) while having an acceptable response time (RT) until the switch is activated. In this work, we develop a methodology that produces optimal brain switch behavior through subject specific (SS) adaptation of: a) a multitrial prediction combination model and b) an SI classification model. We propose a statistical model of combining classifier predictions that enables optimal FPR calibration through a short calibration session. We trained an SI classifier on a training synchronous dataset and tested our method on separate holdout synchronous and asynchronous brain switch experiments. Although our SI model obtained similar performance between training and holdout datasets, 86% and 85% for the synchronous and 69% and 66% for the asynchronous the between subject FPR and TPR variability was high (up to 62%). The short calibration session was then employed to alleviate that problem and provide decision thresholds that achieve when possible a target FPR=1% with good accuracy for both datasets. PMID:26529768

  4. 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

  5. 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

  6. 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

  7. 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.

  8. 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

  9. Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer

    PubMed Central

    Sun, Yi; Sheng, Zhen; Ma, Chao; Tang, Kailin; Zhu, Ruixin; Wu, Zhuanbin; Shen, Ruling; Feng, Jun; Wu, Dingfeng; Huang, Danyi; Huang, Dandan; Fei, Jian; Liu, Qi; Cao, Zhiwei

    2015-01-01

    The identification of synergistic chemotherapeutic agents from a large pool of candidates is highly challenging. Here, we present a Ranking-system of Anti-Cancer Synergy (RACS) that combines features of targeting networks and transcriptomic profiles, and validate it on three types of cancer. Using data on human β-cell lymphoma from the Dialogue for Reverse Engineering Assessments and Methods consortium we show a probability concordance of 0.78 compared with 0.61 obtained with the previous best algorithm. We confirm 63.6% of our breast cancer predictions through experiment and literature, including four strong synergistic pairs. Further in vivo screening in a zebrafish MCF7 xenograft model confirms one prediction with strong synergy and low toxicity. Validation using A549 lung cancer cells shows similar results. Thus, RACS can significantly improve drug synergy prediction and markedly reduce the experimental prescreening of existing drugs for repurposing to cancer treatment, although the molecular mechanism underlying particular interactions remains unknown. PMID:26412466

  10. 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. PMID:27034651

  11. 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

  12. 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. ...

  13. Prediction of resistance development against drug combinations by collateral responses to component drugs

    PubMed Central

    Munck, Christian; Gumpert, Heidi K.; Nilsson Wallin, Annika I.; Wang, Harris H.; Sommer, Morten O. A.

    2015-01-01

    Resistance arises quickly during chemotherapeutic selection and is particularly problematic during long-term treatment regimens such as those for tuberculosis, HIV infections, or cancer. Although drug combination therapy reduces the evolution of drug resistance, drug pairs vary in their ability to do so. Thus, predictive models are needed to rationally design resistance-limiting therapeutic regimens. Using adaptive evolution, we studied the resistance response of the common pathogen Escherichia coli to 5 different single antibiotics and all 10 different antibiotic drug pairs. By analyzing the genomes of all evolved E. coli lineages, we identified the mutational events that drive the differences in drug resistance levels and found that the degree of resistance development against drug combinations can be understood in terms of collateral sensitivity and resistance that occurred during adaptation to the component drugs. Then, using engineered E. coli strains, we confirmed that drug resistance mutations that imposed collateral sensitivity were suppressed in a drug pair growth environment. These results provide a framework for rationally selecting drug combinations that limit resistance evolution. PMID:25391482

  14. Prediction of resistance development against drug combinations by collateral responses to component drugs.

    PubMed

    Munck, Christian; Gumpert, Heidi K; Wallin, Annika I Nilsson; Wang, Harris H; Sommer, Morten O A

    2014-11-12

    Resistance arises quickly during chemotherapeutic selection and is particularly problematic during long-term treatment regimens such as those for tuberculosis, HIV infections, or cancer. Although drug combination therapy reduces the evolution of drug resistance, drug pairs vary in their ability to do so. Thus, predictive models are needed to rationally design resistance-limiting therapeutic regimens. Using adaptive evolution, we studied the resistance response of the common pathogen Escherichia coli to 5 different single antibiotics and all 10 different antibiotic drug pairs. By analyzing the genomes of all evolved E. coli lineages, we identified the mutational events that drive the differences in drug resistance levels and found that the degree of resistance development against drug combinations can be understood in terms of collateral sensitivity and resistance that occurred during adaptation to the component drugs. Then, using engineered E. coli strains, we confirmed that drug resistance mutations that imposed collateral sensitivity were suppressed in a drug pair growth environment. These results provide a framework for rationally selecting drug combinations that limit resistance evolution. PMID:25391482

  15. 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.

  16. Applying a novel combination of techniques to develop a predictive model for diabetes complications.

    PubMed

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

    2015-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  18. 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.

  19. 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

  20. DT-Web: a web-based application for drug-target interaction and drug combination prediction through domain-tuned network-based inference

    PubMed Central

    2015-01-01

    Background The identification of drug-target interactions (DTI) is a costly and time-consuming step in drug discovery and design. Computational methods capable of predicting reliable DTI play an important role in the field. Algorithms may aim to design new therapies based on a single approved drug or a combination of them. Recently, recommendation methods relying on network-based inference in connection with knowledge coming from the specific domain have been proposed. Description Here we propose a web-based interface to the DT-Hybrid algorithm, which applies a recommendation technique based on bipartite network projection implementing resources transfer within the network. This technique combined with domain-specific knowledge expressing drugs and targets similarity is used to compute recommendations for each drug. Our web interface allows the users: (i) to browse all the predictions inferred by the algorithm; (ii) to upload their custom data on which they wish to obtain a prediction through a DT-Hybrid based pipeline; (iii) to help in the early stages of drug combinations, repositioning, substitution, or resistance studies by finding drugs that can act simultaneously on multiple targets in a multi-pathway environment. Our system is periodically synchronized with DrugBank and updated accordingly. The website is free, open to all users, and available at http://alpha.dmi.unict.it/dtweb/. Conclusions Our web interface allows users to search and visualize information on drugs and targets eventually providing their own data to compute a list of predictions. The user can visualize information about the characteristics of each drug, a list of predicted and validated targets, associated enzymes and transporters. A table containing key information and GO classification allows the users to perform their own analysis on our data. A special interface for data submission allows the execution of a pipeline, based on DT-Hybrid, predicting new targets with the corresponding p

  1. Postprandial regulation of hepatic microRNAs predicted to target the insulin pathway in rainbow trout.

    PubMed

    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

  2. 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

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

    PubMed

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

    2015-11-01

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

  4. 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.

  5. 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

  6. A preliminary prediction of the (ν2 + ν4) combination band of 34SF6

    NASA Astrophysics Data System (ADS)

    Meskine, M.; Ouardi, O.; Kaarour, A.

    2015-03-01

    Sulfur hexafluoride appears to be a species of growing importance in the field of atmospheric physics and chemistry as a pollutant that can contribute to the greenhouse effect. The infrared spectroscopy of SF6 is essential for quantitative measurements in the atmosphere, is still insufficiently known. In this work, we present a new contribution to this topic concerning (ν2+ν4) combination band. The prediction of this band is based on the tensorial formalism and vibrational extrapolation methods developed in Dijon. The effective Hamiltonian contains contribution from the ground state level GS, ν2 and ν4 witch are known from previous studies. The analysis has been performed using nonlinear least squares fit procedure included in the XTDS program to Jup=95.

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

    DOEpatents

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

    2013-04-09

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

  8. Multicellular compartmentation of catharanthus roseus alkaloid biosynthesis predicts intercellular translocation of a pathway intermediate

    PubMed Central

    St-Pierre, B; Vazquez-Flota, FA; De Luca V

    1999-01-01

    In situ RNA hybridization and immunocytochemistry were used to establish the cellular distribution of monoterpenoid indole alkaloid biosynthesis in Madagascar periwinkle (Catharanthus roseus). Tryptophan decarboxylase (TDC) and strictosidine synthase (STR1), which are involved in the biosynthesis of the central intermediate strictosidine, and desacetoxyvindoline 4-hydroxylase (D4H) and deacetylvindoline 4-O-acetyltransferase (DAT), which are involved in the terminal steps of vindoline biosynthesis, were localized. tdc and str1 mRNAs were present in the epidermis of stems, leaves, and flower buds, whereas they appeared in most protoderm and cortical cells around the apical meristem of root tips. In marked contrast, d4h and dat mRNAs were associated with the laticifer and idioblast cells of leaves, stems, and flower buds. Immunocytochemical localization for TDC, D4H, and DAT proteins confirmed the differential localization of early and late stages of vindoline biosynthesis. Therefore, we concluded that the elaboration of the major leaf alkaloids involves the participation of at least two cell types and requires the intercellular translocation of a pathway intermediate. A basipetal gradient of expression in maturing leaves also was shown for all four genes by in situ RNA hybridization studies and by complementary studies with dissected leaves, suggesting that expression of the vindoline pathway occurs transiently during early leaf development. These results partially explain why attempts to produce vindoline by cell culture technology have failed. PMID:10330473

  9. 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

  10. White matter anisotropy in the ventral language pathway predicts sound-to-word learning success.

    PubMed

    Wong, Francis C K; Chandrasekaran, Bharath; Garibaldi, Kyla; Wong, Patrick C M

    2011-06-15

    According to the dual stream model of auditory language processing, the dorsal stream is responsible for mapping sound to articulation and the ventral stream plays the role of mapping sound to meaning. Most researchers agree that the arcuate fasciculus (AF) is the neuroanatomical correlate of the dorsal steam; however, less is known about what constitutes the ventral one. Nevertheless, two hypotheses exist: one suggests that the segment of the AF that terminates in middle temporal gyrus corresponds to the ventral stream, and the other suggests that it is the extreme capsule that underlies this sound-to-meaning pathway. The goal of this study was to evaluate these two competing hypotheses. We trained participants with a sound-to-word learning paradigm in which they learned to use a foreign phonetic contrast for signaling word meaning. Using diffusion tensor imaging, a brain-imaging tool to investigate white matter connectivity in humans, we found that fractional anisotropy in the left parietal-temporal region positively correlated with the performance in sound-to-word learning. In addition, fiber tracking revealed a ventral pathway, composed of the extreme capsule and the inferior longitudinal fasciculus, that mediated auditory comprehension. Our findings provide converging evidence supporting the importance of the ventral steam, an extreme capsule system, in the frontal-temporal language network. Implications for current models of speech processing are also discussed. PMID:21677162

  11. 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

  12. Mutations in the phosphatidylinositol-3-kinase pathway predict for antitumor activity of the inhibitor PX-866 while oncogenic Ras is a dominant predictor for resistance

    PubMed Central

    Ihle, NathanT.; Lemos, Robert; Wipf, Peter; Yacoub, Adly; Mitchell, Clint; Siwak, Doris; Mills, Gordon B.; Dent, Paul; Kirkpatrick, D Lynn.; Powis, Garth

    2008-01-01

    The novel phosphatidylinositol-3-kinase (PI-3-kinase) inhibitor PX-866 was tested against 13 experimental human tumor xenografts derived from cell lines of various tissue origins. Mutant PI-3-kinase (PIK3CA) and loss of PTEN activity were sufficient but not necessary as predictors of sensitivity to the antitumor activity of the PI-3-K inhibitor PX-866 in the presence of wild type Ras, while mutant oncogenic Ras was a dominant determinant of resistance, even in tumors with coexisting mutations in PIK3CA. The level of activation of PI-3-kinase signaling measured by tumor phospho-Ser473-Akt was insufficient to predict in vivo antitumor response to PX-866. Reverse phase protein array (RPPA) revealed that the Ras dependent down stream targets c-Myc and cyclin B were elevated in cell lines resistant to PX-866 in vivo. Studies using an H-Ras construct to constitutively and preferentially activate the three best defined downstream targets of Ras, namely Raf, RalGDS, and PI-3-kinase, showed that mutant Ras mediates resistance through its ability to utilize multiple pathways for tumorigenesis. The identification of Ras and downstream signaling pathways driving resistance to PI-3-kinase inhibition may serve as an important guide for patient selection as inhibitors enter clinical trials, and for the development of rational combinations with other molecularly targeted agents. PMID:19117997

  13. Pyrotechnic shock response predictions combining statistical energy analysis and local random phase reconstruction.

    PubMed

    Bodin, E; Brévart, B; Wagstaff, P; Borello, G

    2002-07-01

    Numerous pyrotechnic devices are used on satellites to separate structural subsystems, deploy appendages, and activate on-board operating subsystems. The firing of these pyrotechnic mechanisms leads to severe impulsive loads which could sometimes lead to failures in electronic systems. The objective of the present investigation is to assess the relevance of a method combining deterministic calculations and statistical energy analysis to predict the time overall shock environment of electronic equipment components. The methods are applied to the low- and high-frequency ranges, respectively, which may be defined using a modal parameter based on the effective transmissibility. Initially, in order to address the problem of the low-frequency content of the mechanical shock pulse, the linear dynamic response of the equipment was calculated using direct time integration of a finite element model of the structure. The inputs in the form of the accelerations measured in all three directions at each of the four bolted interfaces were injected into the model. The high-frequency content of the shock response is taken into account by considering the intrinsic dynamic filtering of the equipment. This frequency filter magnitude is extrapolated from the transfer function given by statistical energy analysis between the different imposed accelerations and the response accelerations. Their associated phases are synthesized by considering pseudo-modal phase variations around the group velocity of the structural flexural waves. Combining the effects of the high-frequency filter outputs and the low-frequency finite element calculations yields good predictions of the equipment shock time response over the whole frequency range of interest. PMID:12141340

  14. Pyrotechnic shock response predictions combining statistical energy analysis and local random phase reconstruction

    NASA Astrophysics Data System (ADS)

    Bodin, E.; Brevart, B.; Wagstaff, P.; Borello, G.

    2002-07-01

    Numerous pyrotechnic devices are used on satellites to separate structural subsystems, deploy appendages, and activate on-board operating subsystems. The firing of these pyrotechnic mechanisms leads to severe impulsive loads which could sometimes lead to failures in electronic systems. The objective of the present investigation is to assess the relevance of a method combining deterministic calculations and statistical energy analysis to predict the time overall shock environment of electronic equipment components. The methods are applied to the low- and high-frequency ranges, respectively, which may be defined using a modal parameter based on the effective transmissibility. Initially, in order to address the problem of the low-frequency content of the mechanical shock pulse, the linear dynamic response of the equipment was calculated using direct time integration of a finite element model of the structure. The inputs in the form of the accelerations measured in all three directions at each of the four bolted interfaces were injected into the model. The high-frequency content of the shock response is taken into account by considering the intrinsic dynamic filtering of the equipment. This frequency filter magnitude is extrapolated from the transfer function given by statistical energy analysis between the different imposed accelerations and the response accelerations. Their associated phases are synthesized by considering pseudo-modal phase variations around the group velocity of the structural flexural waves. Combining the effects of the high-frequency filter outputs and the low-frequency finite element calculations yields good predictions of the equipment shock time response over the whole frequency range of interest. copyright 2002 Acoustical Society of America.

  15. 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

  16. Microstructural properties of premotor pathways predict visuomotor performance in chronic stroke.

    PubMed

    Archer, Derek B; Misra, Gaurav; Patten, Carolynn; Coombes, Stephen A

    2016-06-01

    Microstructural properties of the corticospinal tract (CST) descending from the motor cortex predict strength and motor skill in the chronic phase after stroke. Much less is known about the relation between brain microstructure and visuomotor processing after stroke. In this study, individual's poststroke and age-matched controls performed a unimanual force task separately with each hand at three levels of visual gain. We collected diffusion MRI data and used probabilistic tractography algorithms to identify the primary and premotor CSTs. Fractional anisotropy (FA) within each tract was used to predict changes in force variability across different levels of visual gain. Our observations revealed that individuals poststroke reduced force variability with an increase in visual gain, performed the force task with greater variability as compared with controls across all gain levels, and had lower FA in the primary motor and premotor CSTs. Our results also demonstrated that the CST descending from the premotor cortex, rather than the primary motor cortex, best predicted force variability. Together, these findings demonstrate that the microstructural properties of the premotor CST predict visual gain-related changes in force variability in individuals poststroke. Hum Brain Mapp 37:2039-2054, 2016. © 2016 Wiley Periodicals, Inc. PMID:26920656

  17. 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.

  18. Modeling leucine's metabolic pathway and knockout prediction improving the production of surfactin, a biosurfactant from Bacillus subtilis.

    PubMed

    Coutte, François; Niehren, Joachim; Dhali, Debarun; John, Mathias; Versari, Cristian; Jacques, Philippe

    2015-08-01

    A Bacillus subtilis mutant strain overexpressing surfactin biosynthetic genes was previously constructed. In order to further increase the production of this biosurfactant, our hypothesis is that the surfactin precursors, especially leucine, must be overproduced. We present a three step approach for leucine overproduction directed by methods from computational biology. Firstly, we develop a new algorithm for gene knockout prediction based on abstract interpretation, which applies to a recent modeling language for reaction networks with partial kinetic information. Secondly, we model the leucine metabolic pathway as a reaction network in this language, and apply the knockout prediction algorithm with the target of leucine overproduction. Out of the 21 reactions corresponding to potential gene knockouts, the prediction algorithm selects 12 reactions. Six knockouts were introduced in B. subtilis 168 derivatives strains to verify their effects on surfactin production. For all generated mutants, the specific surfactin production is increased from 1.6- to 20.9-fold during the exponential growth phase, depending on the medium composition. These results show the effectiveness of the knockout prediction approach based on formal models for metabolic reaction networks with partial kinetic information, and confirms our hypothesis that precursors supply is one of the main parameters to optimize surfactin overproduction. PMID:26220295

  19. 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

  20. 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

  1. 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.

  2. 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

  3. 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

  4. Striking similarities in diverse telomerase proteins revealed by combining structure prediction and machine learning approaches.

    PubMed

    Lee, Jae-Hyung; Hamilton, Michael; Gleeson, Colin; Caragea, Cornelia; Zaback, Peter; Sander, Jeffry D; Li, Xue; Wu, Feihong; Terribilini, Michael; Honavar, Vasant; Dobbs, Drena

    2008-01-01

    Telomerase is a ribonucleoprotein enzyme that adds telomeric DNA repeat sequences to the ends of linear chromosomes. The enzyme plays pivotal roles in cellular senescence and aging, and because it provides a telomere maintenance mechanism for approximately 90% of human cancers, it is a promising target for cancer therapy. Despite its importance, a high-resolution structure of the telomerase enzyme has been elusive, although a crystal structure of an N-terminal domain (TEN) of the telomerase reverse transcriptase subunit (TERT) from Tetrahymena has been reported. In this study, we used a comparative strategy, in which sequence-based machine learning approaches were integrated with computational structural modeling, to explore the potential conservation of structural and functional features of TERT in phylogenetically diverse species. We generated structural models of the N-terminal domains from human and yeast TERT using a combination of threading and homology modeling with the Tetrahymena TEN structure as a template. Comparative analysis of predicted and experimentally verified DNA and RNA binding residues, in the context of these structures, revealed significant similarities in nucleic acid binding surfaces of Tetrahymena and human TEN domains. In addition, the combined evidence from machine learning and structural modeling identified several specific amino acids that are likely to play a role in binding DNA or RNA, but for which no experimental evidence is currently available. PMID:18229711

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

    PubMed

    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. PMID:2228607

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

    NASA Astrophysics Data System (ADS)

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

    2015-04-01

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

  7. 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. PMID:24356797

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

    PubMed Central

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

    2014-01-01

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

  9. 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

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

    PubMed

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

    2016-02-01

    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 AKI 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

  11. Aberrant DNA Damage Response Pathways May Predict the Outcome of Platinum Chemotherapy in Ovarian Cancer

    PubMed Central

    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

  12. 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

  13. 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

  14. Heart Rate Variability Predicts Levels of Inflammatory Markers: Evidence for the Vagal Anti-Inflammatory Pathway

    PubMed Central

    Cooper, Timothy M.; McKinley, Paula S.; Seeman, Teresa E.; Choo, Tse-Hwei; Lee, Seonjoo; Sloan, Richard P.

    2015-01-01

    Evidence from numerous animal models shows that vagal activity regulates inflammatory responses by decreasing cytokine release. Heart rate variability (HRV) is a reliable index of cardiac vagal regulation and should be inversely related to levels of inflammatory markers. Inflammation is also regulated by sympathetic inputs, but only one previous paper controlled for this. In a larger and more representative sample, we sought to replicate those results and examine potential sex differences in the relationship between HRV and inflammatory markers. Using data from the MIDUS II study, we analyzed the relationship between 6 inflammatory markers and both HF-HRV and LF-HRV. After controlling for sympathetic effects measured by urinary norepinephrine as well as a host of other factors, LF-HRV was found to be inversely associated with fibrinogen, CRP and IL-6, while HF-HRV was inversely associated with fibrinogen and CRP. We did not observe consistent sex differences. These results support the existence of the vagal anti-inflammatory pathway and suggest that it has similar effects in men and women. PMID:25541185

  15. 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

  16. 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

  17. 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. PMID:26434873

  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. Combining Clinical, Pathology, and Gene Expression Data to Predict Recurrence of Hepatocellular Carcinoma

    PubMed Central

    Villanueva, Augusto; Hoshida, Yujin; Battiston, Carlo; Tovar, Victoria; Sia, Daniela; Alsinet, Clara; Cornella, Helena; Liberzon, Arthur; Kobayashi, Masahiro; Kumada, Hiromitsu; Thung, Swan N.; Bruix, Jordi; Newell, Philippa; April, Craig; Fan, Jian-Bing; Roayaie, Sasan; Mazzaferro, Vincenzo; Schwartz, Myron E.; Llovet, Josep M.

    2011-01-01

    Background & Aims In approximately 70% of patients with hepatocellular carcinoma (HCC) treated by resection or ablation, disease recurs within 5 years. Although gene expression signatures have been associated with outcome, there is no method to predict recurrence based on combined clinical, pathology, and genomic data (from tumor and cirrhotic tissue). We evaluated gene expression signatures associated with outcome in a large cohort of patients with early-stage (BCLC 0/A), single-nodule HCC and heterogeneity of signatures within tumor tissues. Methods We assessed 287 HCC patients undergoing resection and tested genome-wide expression platforms using tumor (n=287) and adjacent non-tumor, cirrhotic tissue (n=226). We evaluated gene expression signatures with reported prognostic ability generated from tumor or cirrhotic tissue in 18 and 4 reports, respectively. In 15 additional patients, we profiled samples from the center and periphery of the tumor, to determine stability of signatures. Data analysis included Cox modeling and random survival forests to identify independent predictors of tumor recurrence. Results Gene expression signatures that were associated with aggressive HCC were clustered, as well as those associated with tumors of progenitor cell origin and those from non-tumor, adjacent, cirrhotic tissues. On multivariate analysis, the tumor-associated signature “G3-proliferation” (hazard ratio [HR]=1.75, P=0.003) and an adjacent “poor-survival” signature (HR=1.74, P=0.004) were independent predictors of HCC recurrence, along with satellites (HR=1.66, P=0.04). Samples from different sites in the same tumor nodule were reproducibly classified. Conclusions We developed a composite prognostic model for HCC recurrence, based on gene expression patterns in tumor and adjacent tissues. These signatures predict early and overall recurrence in patients with HCC, and complement findings from clinical and pathology analyses. PMID:21320499

  20. 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

  1. 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.

  2. FMNL2/FMNL3 formins are linked with oncogenic pathways and predict melanoma outcome

    PubMed Central

    Heuser, Vanina D; Koskivuo, Ilkka; Koivisto, Mari; Carpén, Olli

    2016-01-01

    Abstract While most early (stage I‐II) melanomas are cured by surgery, recurrence is not uncommon. Prognostication by current clinicopathological parameters does not provide sufficient means for identifying patients who are at risk of developing metastases and in need of adjuvant therapy. Actin‐regulating formins may account for invasive properties of cancer cells, including melanoma. Here, we studied formin‐like protein 2 and 3 (FMNL2 and FMNL3) in melanoma by analysing their role in the invasive properties of melanoma cells and by evaluating whether FMNL2 expression is associated with melanoma outcome. Immunohistochemical characterization of FMNL2 in a cohort of 175 primary cutaneous stage I‐II melanomas indicated that high FMNL2 reactivity correlates with poor outcome as evaluated by recurrence free survival (p < 0.0001) or disease specific survival (p < 0.0001). In multivariate analysis, Breslow's thickness (p < 0.05) and FMNL2 expression (p < 0.001) remained as independent prognostic factors. Cellular studies revealed that FMNL2 is a component of filopodia in many melanoma cell lines. Inhibition of either FMNL2 or the closely related FMNL3 affected the maintenance of melanoma cell morphology and reduced migration. Finally, inhibition of the BRAF, PI3K and MAPK oncogenic pathways markedly reduced expression of both FMNL2 and FMNL3 in melanoma cells. The results suggest a major role for FMNL2/FMNL3 formins in melanoma biology and raise the possibility that the novel targeted melanoma drugs may interfere with the cellular properties regulated by these formins.

  3. 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

  4. 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

  5. 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.

  6. A Genetic Risk Score Combining Ten Psoriasis Risk Loci Improves Disease Prediction

    PubMed Central

    Chen, Haoyan; Poon, Annie; Yeung, Celestine; Helms, Cynthia; Pons, Jennifer; Bowcock, Anne M.; Kwok, Pui-Yan; Liao, Wilson

    2011-01-01

    Psoriasis is a chronic, immune-mediated skin disease affecting 2–3% of Caucasians. Recent genetic association studies have identified multiple psoriasis risk loci; however, most of these loci contribute only modestly to disease risk. In this study, we investigated whether a genetic risk score (GRS) combining multiple loci could improve psoriasis prediction. Two approaches were used: a simple risk alleles count (cGRS) and a weighted (wGRS) approach. Ten psoriasis risk SNPs were genotyped in 2815 case-control samples and 858 family samples. We found that the total number of risk alleles in the cases was significantly higher than in controls, mean 13.16 (SD 1.7) versus 12.09 (SD 1.8), p = 4.577×10−40. The wGRS captured considerably more risk than any SNP considered alone, with a psoriasis OR for high-low wGRS quartiles of 10.55 (95% CI 7.63–14.57), p = 2.010×10−65. To compare the discriminatory ability of the GRS models, receiver operating characteristic curves were used to calculate the area under the curve (AUC). The AUC for wGRS was significantly greater than for cGRS (72.0% versus 66.5%, p = 2.13×10−8). Additionally, the AUC for HLA-C alone (rs10484554) was equivalent to the AUC for all nine other risk loci combined (66.2% versus 63.8%, p = 0.18), highlighting the dominance of HLA-C as a risk locus. Logistic regression revealed that the wGRS was significantly associated with two subphenotypes of psoriasis, age of onset (p = 4.91×10−6) and family history (p = 0.020). Using a liability threshold model, we estimated that the 10 risk loci account for only11.6% of the genetic variance in psoriasis. In summary, we found that a GRS combining 10 psoriasis risk loci captured significantly more risk than any individual SNP and was associated with early onset of disease and a positive family history. Notably, only a small fraction of psoriasis heritability is captured by the common risk variants identified to date. PMID:21559375

  7. 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

  8. Prediction of the PPARα agonism of fibrates by combined MM-docking approaches.

    PubMed

    Lannutti, Fabio; Marrone, Alessandro; Re, Nazzareno

    2011-04-01

    Fibrates are peroxisome proliferator-activated alpha receptor (PPARα) activators derived from fibric acid and are the most clinically used therapeutics in the treatment of hypertriglyceridemia. Long standing studies on these drugs have accumulated a large body of experimental data about their biological activity and, more recently, on the molecular mechanism mediating their PPARα agonism. An immense interest for the discovery of new fibrates with improved potency and PPARα selectivity has stimulated many investigations toward a deeper understanding of structure-activity relationships controlling their activity. The present study aimed at investigating the binding properties of a set of 23 fibrates, characterized by similar carboxylic heads but differing in the size and orientation of the hydrophobic portion, using computational approaches. We combined standard docking and molecular mechanics approaches to better describe the adaptation of the protein target to the bound ligand. The agonist potencies were then regressed against the calculated binding energies to elaborate predictive model equations. The obtained models were characterized by good performances realizing a fair trade-off between accuracy and computational costs. The best model was obtained with a regression procedure allowing automatic generation of a training subset from the whole set of trials and filtering out outliers, thus highlighting the importance of regression strategies. PMID:21414824

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

    PubMed

    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

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

    NASA Astrophysics Data System (ADS)

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

    2008-06-01

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

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

    PubMed

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

    2008-06-28

    We present a combined theoretical approach based on analyzing molecular dynamics trajectories (at the nanosecond scale) generated by use of classical polarizable force fields and on quantum calculations to compute averaged hyperfine coupling constants. That method is used to estimate the constant of a prototypical nitroxide: the dimethylnitroxide. The molecule is embedded during the simulations in a cubic box containing about 500 water molecules and the molecular dynamics is generated using periodic conditions. Once the trajectories are achieved, the nitroxide and its first hydration shell molecules are extracted, and the coupling constants are computed by considering the latter aggregates by means of quantum computations. However, all the water molecules of the bulk are also accounted for during those computations by means of the electrostatic potential fitted method. Our results exhibit that in order to predict accurate and reliable coupling constants, one needs to describe carefully the out-of-plane motion of the nitroxide nitrogen and to sample trajectories with a time interval of 400 fs at least to generate an uncorrelated large set of nitroxide structures. Compared to Car-Parrinello molecular dynamics techniques, our approach can be used readily to compute hyperfine coupling constants of large systems, such as nitroxides of great size interacting with macromolecules such as proteins or polymers. PMID:18601346

  12. 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

  13. Structure predicts function: combining non-invasive electrophysiology with in-vivo histology.

    PubMed

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

    2015-03-01

    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 800μm 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

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

    PubMed

    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. PMID:27219707

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

    PubMed Central

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

    2010-01-01

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

  16. 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/. PMID:26671809

  17. Biosynthesis of poly(3-hydroxybutyrate-co-3-hydroxyvalerate) from glucose with elevated 3-hydroxyvalerate fraction via combined citramalate and threonine pathway in Escherichia coli.

    PubMed

    Wang, Qian; Liu, Xiaolong; Qi, Qingsheng

    2014-05-01

    The biosynthesis of polyhydroxyalkanoate copolymers in Escherichia coli from unrelated carbon sources becomes attractive nowadays. We previously developed a poly(hydroxybutyrate-co-hydroxyvalerte) (PHBV) biosynthetic pathway from an unrelated carbon source via threonine metabolic route in E. coli (Chen et al., Appl Environ Microbiol 77:4886-4893, 2011). In our study, a citramalate pathway was introduced in recombinant E. coli by cloning a cimA gene from Leptospira interrogans. By blocking the pyruvate and the propionyl-CoA catabolism and replacing the β-ketothiolase gene, the PHBV with 11.5 mol% 3HV fraction was synthesized. Further, the combination of citramalate pathway with the threonine biosynthesis pathway improved the 3HV fraction in PHBV copolymer to 25.4 mol% in recombinant E. coli. PMID:24425304

  18. 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.

  19. Predicting the outcome of modified tongue base suspension combined with uvulopalatopharyngoplasty.

    PubMed

    Turhan, Murat; Bostanci, Asli; Bozkurt, Selen

    2015-11-01

    The purpose of this study was to investigate the relationship between various polysomnographic variables and the success of modified tongue base suspension combined with uvulopharyngopalatoplasty in patients with severe obstructive sleep apnea (OSA). A total of 90 patients who had apnea hypopnea index (AHI) >30 and had both oropharyngeal and hypopharyngeal obstruction were included in this prospective case series with planned data collection. All patients were assessed preoperatively and at the sixth postoperative month by polysomnography. The surgery was considered to be successful when a ≥50% reduction in the mean AHI to a final AHI of <20/h was obtained. Multiple logistic regression analyses were performed to determine the impact of variables on the surgical success. A total of 67 patients (74.4%) met the surgical success criteria. The univariate analysis revealed a relationship between success and AHI (P = 0.001), obstructive apnea duration (P = 0.001), sleep time with oxygen saturation below 90% (ST90) (P = 0.004), minimum O2 saturation (P = 0.0001), mean O2 saturation (P = 0.011), mean O2 desaturation (P = 0.0001), and oxygen desaturation index (P = 0.001). However, ST90 [OR (95% CI) = 1.40 (1.04-1.89), P = 0.023] was the only independent parameter predicting the surgical success in multivariate analysis. The ROC analysis revealed that the ST90 value of ≤36 min was the best cutoff value with 96.97% sensitivity (CI 89.5-99.6), 95.83% specificity (CI 78.9-99.9), 98.5% PPV (CI 91.6-100.0), and 92.0% NPV (CI 74.0-99.0). The stratification of patients with severe OSA according to the ST90 may allow better identification of patients in whom surgical success is probable. PMID:25262194

  20. 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

  1. 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...

  2. 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...

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

    PubMed

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

    2015-01-01

    Predictive coding appears to be one of the fundamental working principles of brain processing. Amongst other aspects, brains often predict the sensory consequences of their own actions. Predictive coding resembles Kalman filtering, where incoming sensory information is filtered to produce prediction errors for subsequent adaptation and learning. However, to generate prediction errors given motor commands, a suitable temporal forward model is required to generate predictions. While in engineering applications, it is usually assumed that this forward model is known, the brain has to learn it. When filtering sensory input and learning from the residual signal in parallel, a fundamental problem arises: the system can enter a delusional loop when filtering the sensory information using an overly trusted forward model. In this case, learning stalls before accurate convergence because uncertainty about the forward model is not properly accommodated. We present a Bayes-optimal solution to this generic and pernicious problem for the case of linear forward models, which we call Predictive Inference and Adaptive Filtering (PIAF). PIAF filters incoming sensory information and learns the forward model simultaneously. We show that PIAF is formally related to Kalman filtering and to the Recursive Least Squares linear approximation method, but combines these procedures in a Bayes optimal fashion. Numerical evaluations confirm that the delusional loop is precluded and that the learning of the forward model is more than 10-times faster when compared to a naive combination of Kalman filtering and Recursive Least Squares. PMID:25983690

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

    PubMed

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

    2016-01-01

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

  5. Can thymidine phosphorylase be a predictive marker for gemcitabine and doxifluridine combination chemotherapy in cholangiocarcinoma?: case series.

    PubMed

    Kang, Myoung Hee; Lee, Won Sup; Go, Se-Il; Kim, Moon Jin; Lee, Un Seok; Choi, Hye Jung; Kim, Dong Chul; Lee, Jeong-Hee; Kim, Hoon-Gu; Bae, Kyung Soo; Cho, Jae Min

    2014-12-01

    Unresectable cholangiocarcinoma is poorly responded to chemotherapy, especially for the case refractory to gemcitabine and cisplatin. Here, we tested whether high expression of thymidine phosphorylase (TP) can be a predictive biomarker for the indicator for gemcitabine and doxifluridine combination chemotherapy in the cholangiocarcinoma refractory to gemcitabine and cisplatin. Immunohistochemical staining for TP was performed with a biopsy specimen. We accepted the result as positive when more than 10% of cancer cells were stained with moderate intensity. Here, we report 2 cases of TP-positive cholangiocarcinoma well controlled with gemcitabine and doxifluridine combination chemotherapy, which had been refractory to the first line treatment with gemcitabine and cisplatin combination chemotherapy. PMID:25526478

  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 sequence-based prediction methods and circular dichroism and infrared spectroscopic data to improve protein secondary structure determinations

    PubMed Central

    Lees, Jonathan G; Janes, Robert W

    2008-01-01

    Background A number of sequence-based methods exist for protein secondary structure prediction. Protein secondary structures can also be determined experimentally from circular dichroism, and infrared spectroscopic data using empirical analysis methods. It has been proposed that comparable accuracy can be obtained from sequence-based predictions as from these biophysical measurements. Here we have examined the secondary structure determination accuracies of sequence prediction methods with the empirically determined values from the spectroscopic data on datasets of proteins for which both crystal structures and spectroscopic data are available. Results In this study we show that the sequence prediction methods have accuracies nearly comparable to those of spectroscopic methods. However, we also demonstrate that combining the spectroscopic and sequences techniques produces significant overall improvements in secondary structure determinations. In addition, combining the extra information content available from synchrotron radiation circular dichroism data with sequence methods also shows improvements. Conclusion Combining sequence prediction with experimentally determined spectroscopic methods for protein secondary structure content significantly enhances the accuracy of the overall results obtained. PMID:18197968

  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. Multi-criterial coding sequence prediction. Combination of GeneMark with two novel, coding-character specific quantities.

    PubMed

    Almirantis, Yannis; Nikolaou, Christoforos

    2005-10-01

    This work applies two recently formulated quantities, strongly correlated with the coding character of a sequence, as an additional "module" on GeneMark, in a three-criterial method. The difference in the statistical approaches implicated by the methods combined here, is expected to contribute to an efficient assignment of functionality to unannotated genomic sequences. The developed combined algorithm is used to fractionalize a collection of GeneMark-predicted exons into sub-collections of different expectation to be coding. A further modification of the algorithm allows for the assignment of an improved estimation of the probability to be coding, to GeneMark-predicted exons. This is on the basis of a suitable training set of GeneMark-predicted exons of known functionality. PMID:15809100