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Sample records for pathway combinations predict

  1. Prediction of Effective Drug Combinations by Chemical Interaction, Protein Interaction and Target Enrichment of KEGG Pathways

    PubMed Central

    Chen, Lei; Zheng, Ming-Yue; Zhang, Jian; Feng, Kai-Yan; Cai, Yu-Dong

    2013-01-01

    Drug combinatorial therapy could be more effective in treating some complex diseases than single agents due to better efficacy and reduced side effects. Although some drug combinations are being used, their underlying molecular mechanisms are still poorly understood. Therefore, it is of great interest to deduce a novel drug combination by their molecular mechanisms in a robust and rigorous way. This paper attempts to predict effective drug combinations by a combined consideration of: (1) chemical interaction between drugs, (2) protein interactions between drugs' targets, and (3) target enrichment of KEGG pathways. A benchmark dataset was constructed, consisting of 121 confirmed effective combinations and 605 random combinations. Each drug combination was represented by 465 features derived from the aforementioned three properties. Some feature selection techniques, including Minimum Redundancy Maximum Relevance and Incremental Feature Selection, were adopted to extract the key features. Random forest model was built with its performance evaluated by 5-fold cross-validation. As a result, 55 key features providing the best prediction result were selected. These important features may help to gain insights into the mechanisms of drug combinations, and the proposed prediction model could become a useful tool for screening possible drug combinations. PMID:24083237

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

    NASA Astrophysics Data System (ADS)

    Huang, Lu; Jiang, Yuyang; Chen, Yuzong

    2017-01-01

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

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

    PubMed Central

    Huang, Lu; Jiang, Yuyang; Chen, Yuzong

    2017-01-01

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

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

    PubMed

    Huang, Lu; Jiang, Yuyang; Chen, Yuzong

    2017-01-19

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

  5. 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.

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2013-04-01

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

  8. 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.

  9. 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. Copyright © 2016 Elsevier Ltd. All rights reserved.

  10. Hormone signaling pathways under stress combinations.

    PubMed

    Suzuki, Nobuhiro

    2016-11-01

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

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

    PubMed

    Solomon, Brittany C; Jackson, Joshua J

    2014-06-01

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

  12. The University of Minnesota pathway prediction system: predicting metabolic logic.

    PubMed

    Ellis, Lynda B M; Gao, Junfeng; Fenner, Kathrin; Wackett, Lawrence P

    2008-07-01

    The University of Minnesota pathway prediction system (UM-PPS, http://umbbd.msi.umn.edu/predict/) recognizes functional groups in organic compounds that are potential targets of microbial catabolic reactions, and predicts transformations of these groups based on biotransformation rules. Rules are based on the University of Minnesota biocatalysis/biodegradation database (http://umbbd.msi.umn.edu/) and the scientific literature. As rules were added to the UM-PPS, more of them were triggered at each prediction step. The resulting combinatorial explosion is being addressed in four ways. Biodegradation experts give each rule an aerobic likelihood value of Very Likely, Likely, Neutral, Unlikely or Very Unlikely. Users now can choose whether they view all, or only the more aerobically likely, predicted transformations. Relative reasoning, allowing triggering of some rules to inhibit triggering of others, was implemented. Rules were initially assigned to individual chemical reactions. In selected cases, these have been replaced by super rules, which include two or more contiguous reactions that form a small pathway of their own. Rules are continually modified to improve the prediction accuracy; increasing rule stringency can improve predictions and reduce extraneous choices. The UM-PPS is freely available to all without registration. Its value to the scientific community, for academic, industrial and government use, is good and will only increase.

  13. Predictive mathematical models of cancer signalling pathways.

    PubMed

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

    2012-02-01

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

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

    PubMed Central

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

    2016-01-01

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

  15. Integrating Temporal Pattern Mining in Ischemic Stroke Prediction and Treatment Pathway Discovery for Atrial Fibrillation.

    PubMed

    Guo, Shijing; Li, Xiang; Liu, Haifeng; Zhang, Ping; Du, Xin; Xie, Guotong; Wang, Fei

    2017-01-01

    Atrial fibrillation (AF) is associated with an increased risk of acute ischemic stroke (AIS). Accurately predicting AIS and planning effective treatment pathways for AIS prevention are crucial for AF patients. Because of the temporality of patients' disease progressions, sequential disease and treatment patterns have the potential to improve risk prediction performance and contribute to effective treatment pathways. This paper integrates temporal pattern mining into the AF study of AIS prediction and treatment pathway discovery. We combine temporal pattern mining with feature selection to identify temporal risk factors that have predictive ability, and integrate temporal pattern mining with treatment efficacy analysis to discover temporal treatment patterns that are statistically effective. Results show that our approach has identified new potential temporal risk factors for AIS that can improve the prediction performance, and has discovered treatment pathway patterns that are statistically effective to prevent AIS for AF patients.

  16. Drug Inhibition Profile Prediction for NFκB Pathway in Multiple Myeloma

    PubMed Central

    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

  17. 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.

  18. Predicting combinations of left and right asymmetries.

    PubMed

    Annett, M

    2000-09-01

    This paper explains how combinations of asymmetries for pairs of laterality variables may be predicted. It shows that many pairs combine as expected by chance, plus the influence of the RS+ gene hypothesised by Annett (1978, 1985). These include: eye dominance with writing hand, with throwing hand, and with foot for kicking; nonright handedness with planum temporale asymmetry, with asymmetry of the parietal operculum, and the association between these two anatomical asymmetries. Handedness for writing and throwing, and hand and foot preferences are more strongly associated, suggesting the presence of an additional influence. The reliability of the present analyses was supported by replication, especially in findings for hand preferences for writing and throwing described by Gilbert and Wysocki (1992). The relative frequencies of discordant patterns of preference, LR (writing, throwing) versus RL, are a function of the frequencies of the two variables in the population. These differ between age groups and also with the classification of 'either' hand preferences. Patterns of preference for writing, throwing and eye-dominance (RRR, RRL etc) are related in an orderly manner to differences between the hands for peg moving time. The success of the present application of the RS theory strengthens the argument that individual differences for hand preference depend on a continuous distribution of asymmetry, not on the 'types' commonly assumed in the literature.

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

    PubMed

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

    2010-10-01

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

  20. 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...

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

    PubMed

    Conejeros, R; Vassiliadis, V S

    2000-05-05

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

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

    PubMed

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

    1999-12-01

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

  3. 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.

  4. Improving neural networks prediction accuracy using particle swarm optimization combiner.

    PubMed

    Elragal, Hassan M

    2009-10-01

    This paper proposes a technique to improve Artificial Neural Network (ANN) prediction accuracy using Particle Swarm Optimization (PSO) combiner. A hybrid system consists of two stages with the first stage containing two ANNs. The first ANN predictor is a multi-layer feed-forward network trained with error back-propagation and the second predictor is a functional link network. These two predictors are combined in the second stage using PSO combiner in a linear and non-linear fashion. The proposed method is applied to problem of predicting daily natural gas consumption. The performance of ANN predictors and combination methods are tested on real data from four different gas utilities. The experimental results show that the proposed particle swarm optimization combiners results in more accurate prediction compared to using single ANN predictor. Prediction accuracy improvement of the proposed PSO combiners have been shown using hypothesis testing.

  5. 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.

  6. Combined MRI Might Help Predict Brain Damage in Boxers

    MedlinePlus

    ... fullstory_167571.html Combined MRI Might Help Predict Brain Damage in Boxers Pro fighters, like football players, ... 3, 2017 WEDNESDAY, Aug. 2, 2017 (HealthDay News) -- Brain injuries among pro football players are in the ...

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

    PubMed

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

    2017-02-16

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

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

    PubMed Central

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

    2017-01-01

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

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

    DTIC Science & Technology

    2011-01-01

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

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

    PubMed Central

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

    2017-01-01

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

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

    ERIC Educational Resources Information Center

    Schwartz, Robert B.

    2014-01-01

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

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

    PubMed

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

    2012-07-01

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

  13. 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. Copyright © 2016 The Authors. Published by Elsevier Ltd.. All rights reserved.

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

    DTIC Science & Technology

    2012-09-01

    TITLE: Predicting the Toxicity of Adjuvant Breast Cancer Drug Combination Therapy PRINCIPAL INVESTIGATOR: Susan F Hudachek, MS, PhD...toxicity of adjuvant breast cancer drug combination therapy 5a. CONTRACT NUMBER 5b. GRANT NUMBER W81XWH-09-1-0457 5c. PROGRAM ELEMENT NUMBER 6...breast cancer. However, co-administration of drugs , particularly agents that are substrates for or inhibitors of p-glycoprotein, can result in

  15. 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.

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

    DTIC Science & Technology

    2014-06-01

    critical metabolic pathways necessary for survival of liver kinase B1 (LKB1)-deficient non -small cell lung cancer (NSCLC) cell lines. We have conducted...removal/washout of phenformin. Given the dependence of LKB1-deficient tumor cells on glutamine metabolism, we are targeting a critical enzyme responsible...B1, non -small cell lung cancer, glutamine metabolism, biguanides 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT 18. NUMBER OF

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

    PubMed Central

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

    2010-01-01

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

  18. A Binary Classifier for Prediction of the Types of Metabolic Pathway of Chemicals.

    PubMed

    Fang, Yemin; Chen, Lei

    2017-01-01

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

  19. Prediction of a service demand using combined forecasting approach

    NASA Astrophysics Data System (ADS)

    Zhou, Ling

    2017-08-01

    Forecasting facilitates cutting down operational and management costs while ensuring service level for a logistics service provider. Our case study here is to investigate how to forecast short-term logistic demand for a LTL carrier. Combined approach depends on several forecasting methods simultaneously, instead of a single method. It can offset the weakness of a forecasting method with the strength of another, which could improve the precision performance of prediction. Main issues of combined forecast modeling are how to select methods for combination, and how to find out weight coefficients among methods. The principles of method selection include that each method should apply to the problem of forecasting itself, also methods should differ in categorical feature as much as possible. Based on these principles, exponential smoothing, ARIMA and Neural Network are chosen to form the combined approach. Besides, least square technique is employed to settle the optimal weight coefficients among forecasting methods. Simulation results show the advantage of combined approach over the three single methods. The work done in the paper helps manager to select prediction method in practice.

  20. A formal model for analyzing drug combination effects and its application in TNF-α-induced NFκB pathway

    PubMed Central

    2010-01-01

    Background Drug combination therapy is commonly used in clinical practice. Many methods including Bliss independence method have been proposed for drug combination design based on simulations models or experiments. Although Bliss independence method can help to solve the drug combination design problem when there are only a small number of combinations, as the number of combinations increases, it may not be scalable. Exploration of system structure becomes important to reduce the complexity of the design problem. Results In this paper, we deduced a mathematical model which can simplify the serial structure and parallel structure of biological pathway for synergy evaluation of drug combinations. We demonstrated in steady state the sign of the synergism assessment factor derivative of the original system can be predicted by the sign of its simplified system. In addition, we analyzed the influence of feedback structure on survival ratio of the serial structure. We provided a sufficient condition under which the combination effect could be maintained. Furthermore, we applied our method to find three synergistic drug combinations on tumor necrosis factor α-induced NFκB pathway and subsequently verified by the cell experiment. Conclusions We identified several structural properties underlying the Bliss independence criterion, and developed a systematic simplification framework for drug combiation desgin by combining simulation and system reaction network topology analysis. We hope that this work can provide insights to tackle the challenging problem of assessment of combinational drug therapy effect in a large scale signaling pathway. And hopefully in the future our method could be expanded to more general criteria. PMID:20416113

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

    PubMed

    Srisawat, Anantaporn; Kijsirikul, Boonserm

    2008-01-01

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

  2. Prediction of novel synthetic pathways for the production of desired chemicals.

    PubMed

    Cho, Ayoun; Yun, Hongseok; Park, Jin Hwan; Lee, Sang Yup; Park, Sunwon

    2010-03-28

    There have been several methods developed for the prediction of synthetic metabolic pathways leading to the production of desired chemicals. In these approaches, novel pathways were predicted based on chemical structure changes, enzymatic information, and/or reaction mechanisms, but the approaches generating a huge number of predicted results are difficult to be applied to real experiments. Also, some of these methods focus on specific pathways, and thus are limited to expansion to the whole metabolism. In the present study, we propose a system framework employing a retrosynthesis model with a prioritization scoring algorithm. This new strategy allows deducing the novel promising pathways for the synthesis of a desired chemical together with information on enzymes involved based on structural changes and reaction mechanisms present in the system database. The prioritization scoring algorithm employing Tanimoto coefficient and group contribution method allows examination of structurally qualified pathways to recognize which pathway is more appropriate. In addition, new concepts of binding site covalence, estimation of pathway distance and organism specificity were taken into account to identify the best synthetic pathway. Parameters of these factors can be evolutionarily optimized when a newly proven synthetic pathway is registered. As the proofs of concept, the novel synthetic pathways for the production of isobutanol, 3-hydroxypropionate, and butyryl-CoA were predicted. The prediction shows a high reliability, in which experimentally verified synthetic pathways were listed within the top 0.089% of the identified pathway candidates. It is expected that the system framework developed in this study would be useful for the in silico design of novel metabolic pathways to be employed for the efficient production of chemicals, fuels and materials.

  3. Prediction of novel synthetic pathways for the production of desired chemicals

    PubMed Central

    2010-01-01

    Background There have been several methods developed for the prediction of synthetic metabolic pathways leading to the production of desired chemicals. In these approaches, novel pathways were predicted based on chemical structure changes, enzymatic information, and/or reaction mechanisms, but the approaches generating a huge number of predicted results are difficult to be applied to real experiments. Also, some of these methods focus on specific pathways, and thus are limited to expansion to the whole metabolism. Results In the present study, we propose a system framework employing a retrosynthesis model with a prioritization scoring algorithm. This new strategy allows deducing the novel promising pathways for the synthesis of a desired chemical together with information on enzymes involved based on structural changes and reaction mechanisms present in the system database. The prioritization scoring algorithm employing Tanimoto coefficient and group contribution method allows examination of structurally qualified pathways to recognize which pathway is more appropriate. In addition, new concepts of binding site covalence, estimation of pathway distance and organism specificity were taken into account to identify the best synthetic pathway. Parameters of these factors can be evolutionarily optimized when a newly proven synthetic pathway is registered. As the proofs of concept, the novel synthetic pathways for the production of isobutanol, 3-hydroxypropionate, and butyryl-CoA were predicted. The prediction shows a high reliability, in which experimentally verified synthetic pathways were listed within the top 0.089% of the identified pathway candidates. Conclusions It is expected that the system framework developed in this study would be useful for the in silico design of novel metabolic pathways to be employed for the efficient production of chemicals, fuels and materials. PMID:20346180

  4. Quantitative network signal combinations downstream of TCR activation can predict IL-2 production response.

    PubMed

    Kemp, Melissa L; Wille, Lucia; Lewis, Christina L; Nicholson, Lindsay B; Lauffenburger, Douglas A

    2007-04-15

    Proximal signaling events activated by TCR-peptide/MHC (TCR-pMHC) binding have been the focus of intense ongoing study, but understanding how the consequent downstream signaling networks integrate to govern ultimate avidity-appropriate TCR-pMHC T cell responses remains a crucial next challenge. We hypothesized that a quantitative combination of key downstream network signals across multiple pathways must encode the information generated by TCR activation, providing the basis for a quantitative model capable of interpreting and predicting T cell functional responses. To this end, we measured 11 protein nodes across six downstream pathways, along five time points from 10 min to 4 h, in a 1B6 T cell hybridoma stimulated by a set of three myelin proteolipid protein 139-151 altered peptide ligands. A multivariate regression model generated from this data compendium successfully comprehends the various IL-2 production responses and moreover successfully predicts a priori the response to an additional peptide treatment, demonstrating that TCR binding information is quantitatively encoded in the downstream network. Individual node and/or time point measurements less effectively accounted for the IL-2 responses, indicating that signals must be integrated dynamically across multiple pathways to adequately represent the encoded TCR signaling information. Of further importance, the model also successfully predicted a priori direct experimental tests of the effects of individual and combined inhibitors of the MEK/ERK and PI3K/Akt pathways on this T cell response. Together, our findings show how multipathway network signals downstream of TCR activation quantitatively integrate to translate pMHC stimuli into functional cell responses.

  5. Combining Gene Signatures Improves Prediction of Breast Cancer Survival

    PubMed Central

    Zhao, Xi; Naume, Bjørn; Langerød, Anita; Frigessi, Arnoldo; Kristensen, Vessela N.; Børresen-Dale, Anne-Lise; Lingjærde, Ole Christian

    2011-01-01

    Background Several gene sets for prediction of breast cancer survival have been derived from whole-genome mRNA expression profiles. Here, we develop a statistical framework to explore whether combination of the information from such sets may improve prediction of recurrence and breast cancer specific death in early-stage breast cancers. Microarray data from two clinically similar cohorts of breast cancer patients are used as training (n = 123) and test set (n = 81), respectively. Gene sets from eleven previously published gene signatures are included in the study. Principal Findings To investigate the relationship between breast cancer survival and gene expression on a particular gene set, a Cox proportional hazards model is applied using partial likelihood regression with an L2 penalty to avoid overfitting and using cross-validation to determine the penalty weight. The fitted models are applied to an independent test set to obtain a predicted risk for each individual and each gene set. Hierarchical clustering of the test individuals on the basis of the vector of predicted risks results in two clusters with distinct clinical characteristics in terms of the distribution of molecular subtypes, ER, PR status, TP53 mutation status and histological grade category, and associated with significantly different survival probabilities (recurrence: p = 0.005; breast cancer death: p = 0.014). Finally, principal components analysis of the gene signatures is used to derive combined predictors used to fit a new Cox model. This model classifies test individuals into two risk groups with distinct survival characteristics (recurrence: p = 0.003; breast cancer death: p = 0.001). The latter classifier outperforms all the individual gene signatures, as well as Cox models based on traditional clinical parameters and the Adjuvant! Online for survival prediction. Conclusion Combining the predictive strength of multiple gene signatures improves prediction of breast

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

    PubMed

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

    2014-11-03

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

  7. 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

  8. Quantitative Adverse Outcome Pathways and Their Application to Predictive Toxicology

    EPA Science Inventory

    A quantitative adverse outcome pathway (qAOP) consists of one or more biologically based, computational models describing key event relationships linking a molecular initiating event (MIE) to an adverse outcome. A qAOP provides quantitative, dose–response, and time-course p...

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

    PubMed

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

    2014-07-01

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

  10. Addressing genetic tumor heterogeneity through computationally predictive combination therapy.

    PubMed

    Zhao, Boyang; Pritchard, Justin R; Lauffenburger, Douglas A; Hemann, Michael T

    2014-02-01

    Recent tumor sequencing data suggest an urgent need to develop a methodology to directly address intratumoral heterogeneity in the design of anticancer treatment regimens. We use RNA interference to model heterogeneous tumors, and demonstrate successful validation of computational predictions for how optimized drug combinations can yield superior effects on these tumors both in vitro and in vivo. Importantly, we discover here that for many such tumors knowledge of the predominant subpopulation is insufficient for determining the best drug combination. Surprisingly, in some cases, the optimal drug combination does not include drugs that would treat any particular subpopulation most effectively, challenging straightforward intuition. We confirm examples of such a case with survival studies in a murine preclinical lymphoma model. Altogether, our approach provides new insights about design principles for combination therapy in the context of intratumoral diversity, data that should inform the development of drug regimens superior for complex tumors. This study provides the first example of how combination drug regimens, using existing chemotherapies, can be rationally designed to maximize tumor cell death, while minimizing the outgrowth of clonal subpopulations. 2013 AACR

  11. 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...

  12. 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...

  13. Combined inhibition of morphogen pathways demonstrates additive antifibrotic effects and improved tolerability.

    PubMed

    Distler, Alfiya; Lang, Veronika; Del Vecchio, Tina; Huang, Jingang; Zhang, Yun; Beyer, Christian; Lin, Neng-Yu; Palumbo-Zerr, Katrin; Distler, Oliver; Schett, Georg; Distler, Jörg Hw

    2014-06-01

    The morphogen pathways Hedgehog, Wnt and Notch are attractive targets for antifibrotic therapies in systemic sclerosis. Interference with stem cell regeneration, however, may complicate the use of morphogen pathway inhibitors. We therefore tested the hypothesis that combination therapies with low doses of Hedgehog, Wnt and Notch inhibitors maybe safe and effective for the treatment of fibrosis. Skin fibrosis was induced by bleomycin and by overexpression of a constitutively active TGF-β receptor type I. Adverse events were assessed by clinical monitoring, pathological evaluation and quantification of Lgr5-positive intestinal stem cells. Inhibition of Hedgehog, Wnt and Notch signalling dose-dependently ameliorated bleomycin-induced and active TGF-β receptor type I-induced fibrosis. Combination therapies with low doses of Hedgehog/Wnt inhibitors or Hedgehog/Notch inhibitors demonstrated additive antifibrotic effects in preventive as well as in therapeutic regimes. Combination therapies were well tolerated. In contrast with high dose monotherapies, combination therapies did not reduce the number of Lgr5 positive intestinal stem cells. Combined inhibition of morphogen pathways exerts additive antifibrotic effects. Combination therapies are well tolerated and, in contrast to high dose monotherapies, may not impair stem cell renewal. Combined targeting of morphogen pathways may thus help to overcome dose-limiting toxicity of Hedgehog, Wnt and Notch signalling.

  14. Co-expressed Pathways DataBase for Tomato: a database to predict pathways relevant to a query gene.

    PubMed

    Narise, Takafumi; Sakurai, Nozomu; Obayashi, Takeshi; Ohta, Hiroyuki; Shibata, Daisuke

    2017-06-05

    Gene co-expression, the similarity of gene expression profiles under various experimental conditions, has been used as an indicator of functional relationships between genes, and many co-expression databases have been developed for predicting gene functions. These databases usually provide users with a co-expression network and a list of strongly co-expressed genes for a query gene. Several of these databases also provide functional information on a set of strongly co-expressed genes (i.e., provide biological processes and pathways that are enriched in these strongly co-expressed genes), which is generally analyzed via over-representation analysis (ORA). A limitation of this approach may be that users can predict gene functions only based on the strongly co-expressed genes. In this study, we developed a new co-expression database that enables users to predict the function of tomato genes from the results of functional enrichment analyses of co-expressed genes while considering the genes that are not strongly co-expressed. To achieve this, we used the ORA approach with several thresholds to select co-expressed genes, and performed gene set enrichment analysis (GSEA) applied to a ranked list of genes ordered by the co-expression degree. We found that internal correlation in pathways affected the significance levels of the enrichment analyses. Therefore, we introduced a new measure for evaluating the relationship between the gene and pathway, termed the percentile (p)-score, which enables users to predict functionally relevant pathways without being affected by the internal correlation in pathways. In addition, we evaluated our approaches using receiver operating characteristic curves, which concluded that the p-score could improve the performance of the ORA. We developed a new database, named Co-expressed Pathways DataBase for Tomato, which is available at http://cox-path-db.kazusa.or.jp/tomato . The database allows users to predict pathways that are relevant to a

  15. 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.

  16. Region-based and pathway-based QTL mapping using a p-value combination method.

    PubMed

    Yang, Hsin-Chou; Chen, Chia-Wei

    2011-11-29

    Quantitative trait locus (QTL) mapping using deep DNA sequencing data is a challenging task. In this study we performed region-based and pathway-based QTL mappings using a p-value combination method to analyze the simulated quantitative traits Q1 and Q4 and the exome sequencing data. The aims were to evaluate the performance of the QTL mapping approaches that were used and to suggest plausible strategies for QTL mapping of DNA sequencing data. We conducted single-locus QTL mappings using a linear regression model with adjustments for age and smoking status, and we also conducted region-based and pathway-based QTL mappings using a truncated product method for combining p-values from the single-locus QTL mapping. To account for the features of rare variants and common single-nucleotide polymorphisms (SNPs), we considered independently rare-variant-only, common-SNP-only, and combined analyses. An analysis of 200 simulated replications showed that the three region-based methods reasonably controlled type I error, whereas the combined analysis yielded the greatest statistical power. Rare-variant-only, common-SNP-only, and combined analyses were also applied to pathway-based QTL mappings. We found that pathway-based QTL mappings had a power of approximately 100% when the significance of the vascular endothelial growth factor pathway was evaluated, but type I errors were slightly inflated. Our approach complements single-locus QTL mapping. An integrated approach using single-locus, combined region-based, and combined pathway-based analyses should yield promising results for QTL mapping of DNA sequencing data.

  17. Global dynamic optimization approach to predict activation in metabolic pathways.

    PubMed

    de Hijas-Liste, Gundián M; Klipp, Edda; Balsa-Canto, Eva; Banga, Julio R

    2014-01-06

    During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been successfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary

  18. Global dynamic optimization approach to predict activation in metabolic pathways

    PubMed Central

    2014-01-01

    Background During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been succesfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. Results In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. Conclusions The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to

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

    PubMed

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

    2014-06-01

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

  20. Toxicity prediction of binary combinations of cadmium, carbendazim and low dissolved oxygen on Daphnia magna.

    PubMed

    Ferreira, Abel L G; Loureiro, Susana; Soares, Amadeu M V M

    2008-08-11

    Environmental contamination is often characterised by a combination of stress factors of various sources (biological, physical and chemical). The predictability of their joint effects is an important stage in environmental risk assessment procedures. In this study, the two main conceptual models for mixture evaluation based on the effect of individual compounds, concentration addition (CA) and independent action (IA) and deviations to synergism/antagonism, "dose ratio" and "dose level" dependency were used. The single and combined effects of cadmium, carbendazim and low dissolved oxygen levels were assayed for life-cycle parameters (survival and feeding) of the water flea Daphnia magna Straus. The results of single exposures revealed an increase of acute and chronic toxicity as concentrations of cadmium and carbendazim increases. At low dissolved oxygen levels both survival and feeding parameters were significantly affected (P< or =0.05). In the acute mixture exposure of cadmium and carbendazim a "dose ratio" dependency was observed with a higher toxicity when cadmium was dominant whereas at high concentrations of carbendazim a lower effect on survival was observed. At chronic exposures an antagonistic deviation from IA model was observed for this mixture. The IA model showed to be adequate for toxicity prediction on acute exposure combinations with low DO levels where a synergistic behaviour was observed. However, at sublethal exposures IA and CA models failed by underestimation. Validation from toxicokinetic and toxicodynamic modelling studies should be made in the future as a way to understand toxicological pathways involved in complex mixture/combination exposures.

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

    PubMed Central

    Newfeld, Stuart J.

    2012-01-01

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

  2. 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.

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

    PubMed

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

    2011-08-01

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

  4. 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

    2015-07-02

    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. Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.

  5. Mathematical modeling of the insulin signal transduction pathway for prediction of insulin sensitivity from expression data.

    PubMed

    Ho, Clark K; Rahib, Lola; Liao, James C; Sriram, Ganesh; Dipple, Katrina M

    2015-01-01

    Mathematical models of biological pathways facilitate a systems biology approach to medicine. However, these models need to be updated to reflect the latest available knowledge of the underlying pathways. We developed a mathematical model of the insulin signal transduction pathway by expanding the last major previously reported model and incorporating pathway components elucidated since the original model was reported. Furthermore, we show that inputting gene expression data of key components of the insulin signal transduction pathway leads to sensible predictions of glucose clearance rates in agreement with reported clinical measurements. In one set of simulations, our model predicted that glycerol kinase knockout mice have reduced GLUT4 translocation, and consequently, reduced glucose uptake. Additionally, a comparison of our extended model with the original model showed that the added pathway components improve simulations of glucose clearance rates. We anticipate this expanded model to be a useful tool for predicting insulin sensitivity in mammalian tissues with altered expression protein phosphorylation or mRNA levels of insulin signal transduction pathway components. Copyright © 2014 Elsevier Inc. All rights reserved.

  6. A data mining approach for identifying pathway-gene biomarkers for predicting clinical outcome: A case study of erlotinib and sorafenib

    PubMed Central

    2017-01-01

    A novel data mining procedure is proposed for identifying potential pathway-gene biomarkers from preclinical drug sensitivity data for predicting clinical responses to erlotinib or sorafenib. The analysis applies linear ridge regression modeling to generate a small (N~1000) set of baseline gene expressions that jointly yield quality predictions of preclinical drug sensitivity data and clinical responses. Standard clustering of the pathway-gene combinations from gene set enrichment analysis of this initial gene set, according to their shared appearance in molecular function pathways, yields a reduced (N~300) set of potential pathway-gene biomarkers. A modified method for quantifying pathway fitness is used to determine smaller numbers of over and under expressed genes that correspond with favorable and unfavorable clinical responses. Detailed literature-based evidence is provided in support of the roles of these under and over expressed genes in compound efficacy. RandomForest analysis of potential pathway-gene biomarkers finds average treatment prediction errors of 10% and 22%, respectively, for patients receiving erlotinib or sorafenib that had a favorable clinical response. Higher errors were found for both compounds when predicting an unfavorable clinical response. Collectively these results suggest complementary roles for biomarker genes and biomarker pathways when predicting clinical responses from preclinical data. PMID:28792525

  7. Nonparametric predictive inference for combining diagnostic tests with parametric copula

    NASA Astrophysics Data System (ADS)

    Muhammad, Noryanti; Coolen, F. P. A.; Coolen-Maturi, T.

    2017-09-01

    Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine and health care. The Receiver Operating Characteristic (ROC) curve is a popular statistical tool for describing the performance of diagnostic tests. The area under the ROC curve (AUC) is often used as a measure of the overall performance of the diagnostic test. In this paper, we interest in developing strategies for combining test results in order to increase the diagnostic accuracy. We introduce nonparametric predictive inference (NPI) for combining two diagnostic test results with considering dependence structure using parametric copula. NPI is a frequentist statistical framework for inference on a future observation based on past data observations. NPI uses lower and upper probabilities to quantify uncertainty and is based on only a few modelling assumptions. While copula is a well-known statistical concept for modelling dependence of random variables. A copula is a joint distribution function whose marginals are all uniformly distributed and it can be used to model the dependence separately from the marginal distributions. In this research, we estimate the copula density using a parametric method which is maximum likelihood estimator (MLE). We investigate the performance of this proposed method via data sets from the literature and discuss results to show how our method performs for different family of copulas. Finally, we briefly outline related challenges and opportunities for future research.

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

    PubMed Central

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

    2010-01-01

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

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

    PubMed

    Chen, Lei; Chu, Chen; Feng, Kaiyan

    2016-01-01

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

  10. Combination of two pathways involved in raltegravir resistance confers dolutegravir resistance.

    PubMed

    Malet, Isabelle; Thierry, Eloise; Wirden, Marc; Lebourgeois, Samuel; Subra, Frédéric; Katlama, Christine; Deprez, Eric; Calvez, Vincent; Marcelin, Anne-Geneviève; Delelis, Olivier

    2015-10-01

    HIV-1 integration can be efficiently inhibited by strand-transfer inhibitors such as raltegravir, elvitegravir or dolutegravir. Three pathways conferring raltegravir/elvitegravir cross-resistance (involving integrase residues Q148, N155 and Y143) were identified. Dolutegravir, belonging to the second generation of strand-transfer compounds, inhibits the Y143 and N155 pathways, but is less efficient at inhibiting the Q148 pathway. The aim of this study was to characterize the combination of two pathways involved in raltegravir resistance described in one patient failing a dolutegravir regimen for their propensity to confer dolutegravir resistance. In this study, a patient first failing a regimen including raltegravir was treated with dolutegravir and showed an increase in viruses carrying a combination of two pathways (N155 and Q148). Impacts of these mutations on integrase activity and resistance to strand-transfer inhibitors were characterized using both in vitro and virological assays. Our data showed that the combination of N155H, G140S and Q148H mutations led to strong resistance to dolutegravir. Combination of N155H, G140S and Q148H mutations originating from two distinct resistance pathways to raltegravir or elvitegravir led to a high level of dolutegravir resistance. Due to its high genetic barrier of resistance, it would be reasonable to use dolutegravir in first-line therapy before emergence of raltegravir or elvitegravir resistance. © The Author 2015. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

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

    PubMed Central

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

    2007-01-01

    With the integration of the KEGG and Predictome databases as well as two search engines for coexpressed genes/proteins using data sets obtained from the Stanford Microarray Database (SMD) and Gene Expression Omnibus (GEO) database, VisANT 3.0 supports exploratory pathway analysis, which includes multi-scale visualization of multiple pathways, editing and annotating pathways using a KEGG compatible visual notation and visualization of expression data in the context of pathways. Expression levels are represented either by color intensity or by nodes with an embedded expression profile. Multiple experiments can be navigated or animated. Known KEGG pathways can be enriched by querying either coexpressed components of known pathway members or proteins with known physical interactions. Predicted pathways for genes/proteins with unknown functions can be inferred from coexpression or physical interaction data. Pathways produced in VisANT can be saved as computer-readable XML format (VisML), graphic images or high-resolution Scalable Vector Graphics (SVG). Pathways in the format of VisML can be securely shared within an interested group or published online using a simple Web link. VisANT is freely available at http://visant.bu.edu. PMID:17586824

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

    PubMed

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

    2007-07-01

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

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

    PubMed

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

    2015-02-15

    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. 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. 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. christoph.hafemeister@nyu.edu or atarca@med.wayne.edu. Supplementary data are available at Bioinformatics online. Published by Oxford University Press 2014. This work is written by US Government employees and is in the public domain in the US.

  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.

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

    PubMed

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

    2014-09-01

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

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

    PubMed

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

    2012-01-01

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

  18. Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways.

    PubMed

    Chen, Lei; Zhang, Yu-Hang; Wang, ShaoPeng; Zhang, YunHua; Huang, Tao; Cai, Yu-Dong

    2017-01-01

    Identifying essential genes in a given organism is important for research on their fundamental roles in organism survival. Furthermore, if possible, uncovering the links between core functions or pathways with these essential genes will further help us obtain deep insight into the key roles of these genes. In this study, we investigated the essential and non-essential genes reported in a previous study and extracted gene ontology (GO) terms and biological pathways that are important for the determination of essential genes. Through the enrichment theory of GO and KEGG pathways, we encoded each essential/non-essential gene into a vector in which each component represented the relationship between the gene and one GO term or KEGG pathway. To analyze these relationships, the maximum relevance minimum redundancy (mRMR) was adopted. Then, the incremental feature selection (IFS) and support vector machine (SVM) were employed to extract important GO terms and KEGG pathways. A prediction model was built simultaneously using the extracted GO terms and KEGG pathways, which yielded nearly perfect performance, with a Matthews correlation coefficient of 0.951, for distinguishing essential and non-essential genes. To fully investigate the key factors influencing the fundamental roles of essential genes, the 21 most important GO terms and three KEGG pathways were analyzed in detail. In addition, several genes was provided in this study, which were predicted to be essential genes by our prediction model. We suggest that this study provides more functional and pathway information on the essential genes and provides a new way to investigate related problems.

  19. Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways

    PubMed Central

    Wang, ShaoPeng; Zhang, YunHua; Huang, Tao

    2017-01-01

    Identifying essential genes in a given organism is important for research on their fundamental roles in organism survival. Furthermore, if possible, uncovering the links between core functions or pathways with these essential genes will further help us obtain deep insight into the key roles of these genes. In this study, we investigated the essential and non-essential genes reported in a previous study and extracted gene ontology (GO) terms and biological pathways that are important for the determination of essential genes. Through the enrichment theory of GO and KEGG pathways, we encoded each essential/non-essential gene into a vector in which each component represented the relationship between the gene and one GO term or KEGG pathway. To analyze these relationships, the maximum relevance minimum redundancy (mRMR) was adopted. Then, the incremental feature selection (IFS) and support vector machine (SVM) were employed to extract important GO terms and KEGG pathways. A prediction model was built simultaneously using the extracted GO terms and KEGG pathways, which yielded nearly perfect performance, with a Matthews correlation coefficient of 0.951, for distinguishing essential and non-essential genes. To fully investigate the key factors influencing the fundamental roles of essential genes, the 21 most important GO terms and three KEGG pathways were analyzed in detail. In addition, several genes was provided in this study, which were predicted to be essential genes by our prediction model. We suggest that this study provides more functional and pathway information on the essential genes and provides a new way to investigate related problems. PMID:28873455

  20. 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.

  1. 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

  2. When defense pathways collide. The response of Arabidopsis to a combination of drought and heat stress.

    PubMed

    Rizhsky, Ludmila; Liang, Hongjian; Shuman, Joel; Shulaev, Vladimir; Davletova, Sholpan; Mittler, Ron

    2004-04-01

    Within their natural habitat, plants are subjected to a combination of abiotic conditions that include stresses such as drought and heat. Drought and heat stress have been extensively studied; however, little is known about how their combination impacts plants. The response of Arabidopsis plants to a combination of drought and heat stress was found to be distinct from that of plants subjected to drought or heat stress. Transcriptome analysis of Arabidopsis plants subjected to a combination of drought and heat stress revealed a new pattern of defense response in plants that includes a partial combination of two multigene defense pathways (i.e. drought and heat stress), as well as 454 transcripts that are specifically expressed in plants during a combination of drought and heat stress. Metabolic profiling of plants subjected to drought, heat stress, or a combination of drought and heat stress revealed that plants subject to a combination of drought and heat stress accumulated sucrose and other sugars such as maltose and glucose. In contrast, Pro that accumulated in plants subjected to drought did not accumulate in plants during a combination of drought and heat stress. Heat stress was found to ameliorate the toxicity of Pro to cells, suggesting that during a combination of drought and heat stress sucrose replaces Pro in plants as the major osmoprotectant. Our results highlight the plasticity of the plant genome and demonstrate its ability to respond to complex environmental conditions that occur in the field.

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

    PubMed

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

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

  4. The use of Gene Ontology terms and KEGG pathways for analysis and prediction of oncogenes.

    PubMed

    Xing, Zhihao; Chu, Chen; Chen, Lei; Kong, Xiangyin

    2016-11-01

    Oncogenes are a type of genes that have the potential to cause cancer. Most normal cells undergo programmed cell death, namely apoptosis, but activated oncogenes can help cells avoid apoptosis and survive. Thus, studying oncogenes is helpful for obtaining a good understanding of the formation and development of various types of cancers. In this study, we proposed a computational method, called OPM, for investigating oncogenes from the view of Gene Ontology (GO) and biological pathways. All investigated genes, including validated oncogenes retrieved from some public databases and other genes that have not been reported to be oncogenes thus far, were encoded into numeric vectors according to the enrichment theory of GO terms and KEGG pathways. Some popular feature selection methods, minimum redundancy maximum relevance and incremental feature selection, and an advanced machine learning algorithm, random forest, were adopted to analyze the numeric vectors to extract key GO terms and KEGG pathways. Along with the oncogenes, GO terms and KEGG pathways were discussed in terms of their relevance in this study. Some important GO terms and KEGG pathways were extracted using feature selection methods and were confirmed to be highly related to oncogenes. Additionally, the importance of these terms and pathways in predicting oncogenes was further demonstrated by finding new putative oncogenes based on them. This study investigated oncogenes based on GO terms and KEGG pathways. Some important GO terms and KEGG pathways were confirmed to be highly related to oncogenes. We hope that these GO terms and KEGG pathways can provide new insight for the study of oncogenes, particularly for building more effective prediction models to identify novel oncogenes. The program is available upon request. We hope that the new findings listed in this study may provide a new insight for the investigation of oncogenes. This article is part of a Special Issue entitled "System Genetics" Guest Editor

  5. 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

  6. 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

  7. Hidden Hearing Loss and Computational Models of the Auditory Pathway: Predicting Speech Intelligibility Decline

    DTIC Science & Technology

    2016-11-28

    Title: Hidden Hearing Loss and Computational Models of the Auditory Pathway: Predicting Speech Intelligibility Decline Christopher J. Smalt...clinical hearing thresholds is difficulty in understanding speech in noise. Recent animal studies have shown that noise exposure causes selective loss ...to utilize computational models of the auditory periphery and auditory cortex to study the effect of low spontaneous rate ANF loss on the cortical

  8. Identification of miRNAs in Bovine Endometrium through RNAseq and Prediction of Regulated Pathways.

    PubMed

    Palma-Vera, S E; Sharbati, S; Einspanier, R

    2015-10-01

    Detection of miRNAs in reproductive tissues is a key step to understand their role in fertility. We hypothesize that miRNAs must be involved in pathways controlling endometrial physiology and defense against pathogens. In this study, we aimed to characterize miRNAs present in bovine endometrium and to predict regulated pathways. Cytobrush endometrial samples from four cows were collected at oestrous cycle days 1-5, 6-12, 13-18 and 19-21. RNA was extracted and sequenced using Ion Torrent (®) technology. After mapping of the reads to miRNA stem loops, rRNAs and tRNAs, data were normalized and analysed using DESeq2. Targets and pathways were predicted with miRmap and KEGG, respectively. Validation of miRNAs in tissue was done by RT-qPCR (miR-Q). A total of 221 identities were common among groups, accumulating more than 99% of miRNA expression. MiRNAs were predicted to regulate MAPK signalling pathway, lysosome and extracellular matrix (ECM)-receptor interaction. Eight miRNAs were validated by miR-Q, showing that let-7a-5p and let-7b were regulated across the oestrous cycle. This study demonstrated a high similarity in miRNA expression profile across the oestrous cycles in bovine endometrium. These miRNAs were predicted to regulate pathways involved in cell proliferation, differentiation, transport and catabolism. The number of pathways shared by different miRNAs indicates the broad range of regulation these molecules exhibit in the endometrium. © 2015 Blackwell Verlag GmbH.

  9. 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.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed Central

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

    2011-01-01

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

  12. Incorporating pathway information into boosting estimation of high-dimensional risk prediction models.

    PubMed

    Binder, Harald; Schumacher, Martin

    2009-01-13

    There are several techniques for fitting risk prediction models to high-dimensional data, arising from microarrays. However, the biological knowledge about relations between genes is only rarely taken into account. One recent approach incorporates pathway information, available, e.g., from the KEGG database, by augmenting the penalty term in Lasso estimation for continuous response models. As an alternative, we extend componentwise likelihood-based boosting techniques for incorporating pathway information into a larger number of model classes, such as generalized linear models and the Cox proportional hazards model for time-to-event data. In contrast to Lasso-like approaches, no further assumptions for explicitly specifying the penalty structure are needed, as pathway information is incorporated by adapting the penalties for single microarray features in the course of the boosting steps. This is shown to result in improved prediction performance when the coefficients of connected genes have opposite sign. The properties of the fitted models resulting from this approach are then investigated in two application examples with microarray survival data. The proposed approach results not only in improved prediction performance but also in structurally different model fits. Incorporating pathway information in the suggested way is therefore seen to be beneficial in several ways.

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

    PubMed

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

    2013-01-01

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

  14. 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.

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed Central

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

    2014-01-01

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

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

    PubMed Central

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

    2014-01-01

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

  18. 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. © 2013 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed

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

    2014-08-14

    Coordinated enzymatic reactions regulate blood clot generation. To explore the contributions of various coagulation enzymes in this process, we utilized a panel of aptamers against factors VIIa, IXa, Xa, and prothrombin. Each aptamer dose-dependently inhibited clot formation, yet none was able to completely impede this process in highly procoagulant settings. However, several combinations of two aptamers synergistically impaired clot formation. One extremely potent aptamer combination was able to maintain human blood fluidity even during extracorporeal circulation, a highly procoagulant setting encountered during cardiopulmonary bypass surgery. Moreover, this aptamer cocktail could be rapidly reversed with antidotes to restore normal hemostasis, indicating that even highly potent aptamer combinations can be rapidly controlled. These studies highlight the potential utility of using sets of aptamers to probe the functions of proteins in molecular pathways for research and therapeutic ends. Copyright © 2014 Elsevier Ltd. All rights reserved.

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

    PubMed Central

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

    2014-01-01

    SUMMARY Coordinated enzymatic reactions regulate blood clot generation. To explore the contributions of various coagulation enzymes in this process, we utilized a panel of aptamers against factors VIIa, IXa, Xa, and prothrombin. Each aptamer dose-dependently inhibited clot formation, yet none was able to completely impede this process in highly procoagulant settings. However several combinations of two aptamers synergistically impaired clot formation. One extremely potent aptamer combination was able to maintain human blood fluidity even during extracorporeal circulation, a highly procoagulant setting encountered during cardiopulmonary bypass surgery. Moreover, this aptamer cocktail could be rapidly reversed with antidotes to restore normal hemostasis, indicating that even highly potent aptamer combinations can be rapidly controlled. These studies highlight the potential utility of using sets of aptamers to probe the functions of proteins in molecular pathways for research and therapeutic ends. PMID:25065530

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

    PubMed

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

    2013-11-01

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

  3. 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

  4. Effect of curcumin on aged Drosophila melanogaster: a pathway prediction analysis.

    PubMed

    Zhang, Zhi-guo; Niu, Xu-yan; Lu, Ai-ping; Xiao, Gary Guishan

    2015-02-01

    To re-analyze the data published in order to explore plausible biological pathways that can be used to explain the anti-aging effect of curcumin. Microarray data generated from other study aiming to investigate effect of curcumin on extending lifespan of Drosophila melanogaster were further used for pathway prediction analysis. The differentially expressed genes were identified by using GeneSpring GX with a criterion of 3.0-fold change. Two Cytoscape plugins including BisoGenet and molecular complex detection (MCODE) were used to establish the protein-protein interaction (PPI) network based upon differential genes in order to detect highly connected regions. The function annotation clustering tool of Database for Annotation, Visualization and Integrated Discovery (DAVID) was used for pathway analysis. A total of 87 genes expressed differentially in D. melanogaster melanogaster treated with curcumin were identified, among which 50 were up-regulated significantly and 37 were remarkably down-regulated in D. melanogaster melanogaster treated with curcumin. Based upon these differential genes, PPI network was constructed with 1,082 nodes and 2,412 edges. Five highly connected regions in PPI networks were detected by MCODE algorithm, suggesting anti-aging effect of curcumin may be underlined through five different pathways including Notch signaling pathway, basal transcription factors, cell cycle regulation, ribosome, Wnt signaling pathway, and p53 pathway. Genes and their associated pathways in D. melanogaster melanogaster treated with anti-aging agent curcumin were identified using PPI network and MCODE algorithm, suggesting that curcumin may be developed as an alternative therapeutic medicine for treating aging-associated diseases.

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

    PubMed

    Bernstein, Knut; Andersen, Ulrich

    2008-01-01

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

  6. Systems modeling of anti-apoptotic pathways in prostate cancer: psychological stress triggers a synergism pattern switch in drug combination therapy.

    PubMed

    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.

  7. Lycopene overproduction in Saccharomyces cerevisiae through combining pathway engineering with host engineering.

    PubMed

    Chen, Yan; Xiao, Wenhai; Wang, Ying; Liu, Hong; Li, Xia; Yuan, Yingjin

    2016-06-21

    Microbial production of lycopene, a commercially and medically important compound, has received increasing concern in recent years. Saccharomyces cerevisiae is regarded as a safer host for lycopene production than Escherichia coli. However, to date, the lycopene yield (mg/g DCW) in S. cerevisiae was lower than that in E. coli and did not facilitate downstream extraction process, which might be attributed to the incompatibility between host cell and heterologous pathway. Therefore, to achieve lycopene overproduction in S. cerevisiae, both host cell and heterologous pathway should be delicately engineered. In this study, lycopene biosynthesis pathway was constructed by integration of CrtE, CrtB and CrtI in S. cerevisiae CEN.PK2. When YPL062W, a distant genetic locus, was deleted, little acetate was accumulated and approximately 100 % increase in cytosolic acetyl-CoA pool was achieved relative to that in parental strain. Through screening CrtE, CrtB and CrtI from diverse species, an optimal carotenogenic enzyme combination was obtained, and CrtI from Blakeslea trispora (BtCrtI) was found to have excellent performance on lycopene production as well as lycopene proportion in carotenoid. Then, the expression level of BtCrtI was fine-tuned and the effect of cell mating types was also evaluated. Finally, potential distant genetic targets (YJL064W, ROX1, and DOS2) were deleted and a stress-responsive transcription factor INO2 was also up-regulated. Through the above modifications between host cell and carotenogenic pathway, lycopene yield was increased by approximately 22-fold (from 2.43 to 54.63 mg/g DCW). Eventually, in fed-batch fermentation, lycopene production reached 55.56 mg/g DCW, which is the highest reported yield in yeasts. Saccharomyces cerevisiae was engineered to produce lycopene in this study. Through combining host engineering (distant genetic loci and cell mating types) with pathway engineering (enzyme screening and gene fine-tuning), lycopene yield was

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  10. Combination of biomarkers to predict mortality in elderly patients with myocardial infarction.

    PubMed

    Olivieri, Fabiola; Spazzafumo, Liana; Antonicelli, Roberto; Marchegiani, Francesca; Cardelli, Maurizio; Sirolla, Cristina; Galeazzi, Roberta; Giovagnetti, Simona; Mocchegiani, Eugenio; Franceschi, Claudio

    2008-04-01

    The elderly subjects affected by Acute Myocardial Infarction (AMI) have the highest risk of mortality. Our study was designed to improve the capability of mortality risk stratification in elderly AMI patients through the concurrent evaluations of different biomarkers, including genetic markers. One-year follow-up study was performed in 250 elderly AMI patients. The combination of high total Homocysteine (tHcy), low folate and vitamin B12 plasma levels (18.0+/-9.0 micromol/l; 4.4+/-1.2 ng/ml; 404.2+/-287.5 pg/ml, respectively) and elevated CRP plasma levels (> or =6 mg/dl) identify the highest-risk pathway of heart mortality (RR=4.20, IC 95% 1.62-10.89, P<0.002) with respect to the combination of low total tHcy, high folate and vitamin B12 plasma levels (12.4+/-5.2 micromol/l; 8.9+/-2.5 ng/ml; 546.9+/-379.8 pg/ml, respectively) and low CRP plasma levels (<6 mg/dl). In elderly AMI patients the concomitant elevation of CRP and tHcy, associated with folate and vitamin B12 low levels, could be considered a significant predictive heart mortality risk factor.

  11. Combination of extrinsic and intrinsic pathways significantly accelerates axial vascularization of bioartificial tissues.

    PubMed

    Arkudas, Andreas; Pryymachuk, Galyna; Beier, Justus P; Weigel, Linda; Körner, Carolin; Singer, Robert F; Bleiziffer, Oliver; Polykandriotis, Elias; Horch, Raymund E; Kneser, Ulrich

    2012-01-01

    In this study, the authors present a modification of the arteriovenous loop model that combines extrinsic and intrinsic vascularization modes to enhance vascularization of bioartificial matrices. An arteriovenous loop was created in the medial thighs of 24 rats. The loop was placed in a newly developed titanium chamber, which was fabricated with an electron beam melting facility, and was embedded in a hydroxyapatite/β-tricalcium phosphate/fibrin matrix. At the explantation time points (2, 4, 6, and 8 weeks), constructs were perfused by differently colored dyes to determine the amount of tissue vascularized by either the intrinsic or the extrinsic vascular pathway. Specimens were investigated by means of micro-computed tomography and histologic and morphometric analysis. Although there was an equal number of blood vessels originating from the center and the periphery, 83 percent of all vessels displayed a connection to the arteriovenous loop already at 2 weeks. There was a continuous increase of the relative proportion of vessels connected to the arteriovenous loop over time detectable. At 8 weeks, communications between the newly formed vessels and the arteriovenous loop were visible in 97 percent of all vessels. This study demonstrates for the first time the enhancement of angiogenesis in an axially vascularized tissue by an additional extrinsic vascular pathway. By 2 weeks, both pathways showed connections, allowing transplantation of the entire construct using the arteriovenous loop pedicle. This approach will allow for reduction of the time interval between arteriovenous loop implantation and transplantation into the defect site and limitation of operative interventions.

  12. 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

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

    PubMed

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

    2015-10-13

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

  14. 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

  15. 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.

  16. A Combined Epidemiologic and Metabolomic Approach Improves CKD Prediction

    PubMed Central

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

    2013-01-01

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

  17. 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

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

    PubMed

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

    2014-07-01

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

  19. Reduce Manual Curation by Combining Gene Predictions from Multiple Annotation Engines, a Case Study of Start Codon Prediction

    PubMed Central

    Ederveen, Thomas H. A.; Overmars, Lex; van Hijum, Sacha A. F. T.

    2013-01-01

    Nowadays, prokaryotic genomes are sequenced faster than the capacity to manually curate gene annotations. Automated genome annotation engines provide users a straight-forward and complete solution for predicting ORF coordinates and function. For many labs, the use of AGEs is therefore essential to decrease the time necessary for annotating a given prokaryotic genome. However, it is not uncommon for AGEs to provide different and sometimes conflicting predictions. Combining multiple AGEs might allow for more accurate predictions. Here we analyzed the ab initio open reading frame (ORF) calling performance of different AGEs based on curated genome annotations of eight strains from different bacterial species with GC% ranging from 35–52%. We present a case study which demonstrates a novel way of comparative genome annotation, using combinations of AGEs in a pre-defined order (or path) to predict ORF start codons. The order of AGE combinations is from high to low specificity, where the specificity is based on the eight genome annotations. For each AGE combination we are able to derive a so-called projected confidence value, which is the average specificity of ORF start codon prediction based on the eight genomes. The projected confidence enables estimating likeliness of a correct prediction for a particular ORF start codon by a particular AGE combination, pinpointing ORFs notoriously difficult to predict start codons. We correctly predict start codons for 90.5±4.8% of the genes in a genome (based on the eight genomes) with an accuracy of 81.1±7.6%. Our consensus-path methodology allows a marked improvement over majority voting (9.7±4.4%) and with an optimal path ORF start prediction sensitivity is gained while maintaining a high specificity. PMID:23675487

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

    PubMed

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

    2008-10-01

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

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

    PubMed

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

    2016-02-01

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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed Central

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

    2010-01-01

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

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

    PubMed

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

    2011-01-01

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

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

    PubMed

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

    2014-04-01

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

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

    PubMed Central

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

    2014-01-01

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

  7. Combined loss of three DNA damage response pathways renders C. elegans intolerant to light.

    PubMed

    van Bostelen, Ivo; Tijsterman, Marcel

    2017-06-01

    Infliction of DNA damage initiates a complex cellular reaction - the DNA damage response - that involves both signaling and DNA repair networks with many redundancies and parallel pathways. Here, we reveal the three strategies that the simple multicellular eukaryote, C. elegans, uses to deal with DNA damage induced by light. Separately inactivating repair or replicative bypass of photo-lesions results in cellular hypersensitivity towards UV-light, but impeding repair of replication associated DNA breaks does not. Yet, we observe an unprecedented synergistic relationship when these pathways are inactivated in combination. C. elegans mutants that lack nucleotide excision repair (NER), translesion synthesis (TLS) and alternative end joining (altEJ) grow undisturbed in the dark, but become sterile when grown in light. Even exposure to very low levels of normal daylight impedes animal growth. We show that NER and TLS operate to suppress the formation of lethal DNA breaks that require polymerase theta-mediated end joining (TMEJ) for their repair. Our data testifies to the enormous genotoxicity of light and to the demand of multiple layers of protection against an environmental threat that is so common. Copyright © 2017 Elsevier B.V. All rights reserved.

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

    PubMed

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

    2016-11-17

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

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

    PubMed Central

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

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Li, Weihua; Sankarasubramanian, A.

    2012-12-01

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

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

    PubMed Central

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

    2016-01-01

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

  12. 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

  13. 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

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

    DOE PAGES

    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.

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

    SciTech Connect

    Zarkevich, Nickolai A.; Johnson, Duane D.

    2016-01-15

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

  16. Prediction of codeine toxicity in infants and their mothers using a novel combination of maternal genetic markers.

    PubMed

    Sistonen, J; Madadi, P; Ross, C J; Yazdanpanah, M; Lee, J W; Landsmeer, M L A; Nauta, M; Carleton, B C; Koren, G; Hayden, M R

    2012-04-01

    Substantial variation exists in response to standard doses of codeine ranging from poor analgesia to life-threatening central nervous system (CNS) depression. We aimed to discover the genetic markers predictive of codeine toxicity by evaluating the associations between polymorphisms in cytochrome P450 2D6 (CYP2D6), UDP-glucuronosyltransferase 2B7 (UGT2B7), P-glycoprotein (ABCB1), mu-opioid receptor (OPRM1), and catechol O-methyltransferase (COMT) genes, which are involved in the codeine pathway, and the symptoms of CNS depression in 111 breastfeeding mothers using codeine and their infants. A genetic model combining the maternal risk genotypes in CYP2D6 and ABCB1 was significantly associated with the adverse outcomes in infants (odds ratio (OR) 2.68; 95% confidence interval (CI) 1.61-4.48; P(trend) = 0.0002) and their mothers (OR 2.74; 95% CI 1.55-4.84; P(trend) = 0.0005). A novel combination of the genetic and clinical factors predicted 87% of the infant and maternal CNS depression cases with a sensitivity of 80% and a specificity of 87%. Genetic markers can be used to improve the outcome of codeine therapy and are also probably important for other opioids sharing common biotransformation pathways.

  17. Predicting the Folding Pathway of Engrailed Homeodomain with a Probabilistic Roadmap Enhanced Reaction-Path Algorithm☆

    PubMed Central

    Li, Da-wei; Yang, Haijun; Han, Li; Huo, Shuanghong

    2008-01-01

    To predict a protein-folding pathway, we present an alternative to the time-consuming dynamic simulation of atomistic models. We replace the actual dynamic simulation with variational optimization of a reaction path connecting known initial and final protein conformations in such a way as to maximize an estimate of the reactive flux or minimize the mean first passage time at a given temperature, referred to as MaxFlux. We solve the MaxFlux global optimization problem with an efficient graph-theoretic approach, the probabilistic roadmap method (PRM). We employed CHARMM19 and the EEF1 implicit solvation model to describe the protein solution. The effectiveness of our MaxFlux-PRM is demonstrated in our promising simulation results on the folding pathway of the engrailed homeodomain. Our MaxFlux-PRM approach provides the direct evidence to support that the previously reported intermediate state is a genuine on-pathway intermediate, and the demand of CPU power is moderate. PMID:18024496

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

    PubMed Central

    Li, Yan-Hui; Zhang, Gai-Gai

    2017-01-01

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

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

    NASA Astrophysics Data System (ADS)

    Runge, Jakob

    2016-04-01

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

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

    PubMed

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

    2010-03-15

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

  1. 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

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

    PubMed

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

    2016-01-01

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

  3. A decision analysis model predicts the optimal treatment pathway for patients with colorectal cancer and resectable synchronous liver metastases.

    PubMed

    Aloia, Thomas A; Fahy, Bridget N

    2008-05-01

    The estimated 2400 Americans who annually present with colorectal cancer and simultaneous resectable liver metastases encounter a wide array of surgical and medical treatment options. Because of the large number of possible treatment sequences and the absence of clinical trials comparing these various pathways, there is no consensus on the optimal therapeutic strategy. To address this issue, a decision-making model was developed incorporating all possible combinations of the following treatments: colorectal resection, hepatic resection, simultaneous colohepatic resection, and systemic chemotherapy. Transition probabilities associated with each treatment were determined by systematic review of the literature. Variations in complication rates based on the extent of hepatectomy (minor: 1-2 segments vs. major: > 2 segments) were factored into the model. Sensitivity analyses were performed to identify threshold values for study variables that altered the optimal treatment pathway. After 10,000 simulated patient trials with no bias toward any one initial treatment (ie, current practice conditions), the global calculated 5-year survival rate was 21%. For simulated patients with moderate hepatic tumor burden, only treatment sequences that placed systemic therapy before major hepatectomy resulted in improved 5-year survival projections (38% vs. 29%; P = .001; odds ratio, 1.82). Initial treatment with simultaneous colohepatic resection was only favored when the operative mortality rate was adjusted to < 0.5%. This detailed decision-making analysis predicts that the optimal treatment pathway for most patients with colorectal cancer and simultaneous resectable liver metastases is preoperative systemic therapy followed by colohepatectomy or 2-stage resection. In the era of improved systemic therapies, major hepatic resection should be deferred until local and systemic disease can been addressed.

  4. 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

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

    PubMed

    Puttapitakpong, Ploynin; Phupong, Vorapong

    2016-02-01

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

  6. 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…

  7. Enriching for correct prediction of biological processes using a combination of diverse classifiers.

    PubMed

    Ko, Daijin; Windle, Brad

    2011-05-23

    Machine learning models (classifiers) for classifying genes to biological processes each have their own unique characteristics in what genes can be classified and to what biological processes. No single learning model is qualitatively superior to any other model and overall precision for each model tends to be low. The classification results for each classifier can be complementary and synergistic suggesting the benefit of a combination of algorithms, but often the prediction probability outputs of various learning models are neither comparable nor compatible for combining. A means to compare outputs regardless of the model and data used and combine the results into an improved comprehensive model is needed. Gene expression patterns from NCI's panel of 60 cell lines were used to train a Random Forest, a Support Vector Machine and a Neural Network model, plus two over-sampled models for classifying genes to biological processes. Each model produced unique characteristics in the classification results. We introduce the Precision Index measure (PIN) from the maximum posterior probability that allows assessing, comparing and combining multiple classifiers. The class specific precision measure (PIC) is introduced and used to select a subset of predictions across all classes and all classifiers with high precision. We developed a single classifier that combines the PINs from these five models in prediction and found that the PIN Combined Classifier (PINCom) significantly increased the number of correctly predicted genes over any single classifier. The PINCom applied to test genes that were not used in training also showed substantial improvement over any single model. This paper introduces novel and effective ways of assessing predictions by their precision and recall plus a method that combines several machine learning models and capitalizes on synergy and complementation in class selection, resulting in higher precision and recall. Different machine learning models

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

    PubMed

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

    2005-11-15

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2013-04-01

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

  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. 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.

  13. 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.

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

    PubMed

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

    2017-01-20

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

  15. Comparison of the accuracy of three algorithms in predicting accessory pathways among adult Wolff-Parkinson-White syndrome patients.

    PubMed

    Maden, Orhan; Balci, Kevser Gülcihan; Selcuk, Mehmet Timur; Balci, Mustafa Mücahit; Açar, Burak; Unal, Sefa; Kara, Meryem; Selcuk, Hatice

    2015-12-01

    The aim of this study was to investigate the accuracy of three algorithms in predicting accessory pathway locations in adult patients with Wolff-Parkinson-White syndrome in Turkish population. A total of 207 adult patients with Wolff-Parkinson-White syndrome were retrospectively analyzed. The most preexcited 12-lead electrocardiogram in sinus rhythm was used for analysis. Two investigators blinded to the patient data used three algorithms for prediction of accessory pathway location. Among all locations, 48.5% were left-sided, 44% were right-sided, and 7.5% were located in the midseptum or anteroseptum. When only exact locations were accepted as match, predictive accuracy for Chiang was 71.5%, 72.4% for d'Avila, and 71.5% for Arruda. The percentage of predictive accuracy of all algorithms did not differ between the algorithms (p = 1.000; p = 0.875; p = 0.885, respectively). The best algorithm for prediction of right-sided, left-sided, and anteroseptal and midseptal accessory pathways was Arruda (p < 0.001). Arruda was significantly better than d'Avila in predicting adjacent sites (p = 0.035) and the percent of the contralateral site prediction was higher with d'Avila than Arruda (p = 0.013). All algorithms were similar in predicting accessory pathway location and the predicted accuracy was lower than previously reported by their authors. However, according to the accessory pathway site, the algorithm designed by Arruda et al. showed better predictions than the other algorithms and using this algorithm may provide advantages before a planned ablation.

  16. 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

  17. Identifying genotype-dependent efficacy of single and combined PI3K- and MAPK-pathway inhibition in cancer

    PubMed Central

    Sos, Martin L.; Fischer, Stefanie; Ullrich, Roland; Peifer, Martin; Heuckmann, Johannes M.; Koker, Mirjam; Heynck, Stefanie; Stückrath, Isabel; Weiss, Jonathan; Fischer, Florian; Michel, Kathrin; Goel, Aviva; Regales, Lucia; Politi, Katerina A.; Perera, Samanthi; Getlik, Matthäus; Heukamp, Lukas C.; Ansén, Sascha; Zander, Thomas; Beroukhim, Rameen; Kashkar, Hamid; Shokat, Kevan M.; Sellers, William R.; Rauh, Daniel; Orr, Christine; Hoeflich, Klaus P.; Friedman, Lori; Wong, Kwok-Kin; Pao, William; Thomas, Roman K.

    2009-01-01

    In cancer, genetically activated proto-oncogenes often induce “upstream” dependency on the activity of the mutant oncoprotein. Therapeutic inhibition of these activated oncoproteins can induce massive apoptosis of tumor cells, leading to sometimes dramatic tumor regressions in patients. The PI3K and MAPK signaling pathways are central regulators of oncogenic transformation and tumor maintenance. We hypothesized that upstream dependency engages either one of these pathways preferentially to induce “downstream” dependency. Therefore, we analyzed whether downstream pathway dependency segregates by genetic aberrations upstream in lung cancer cell lines. Here, we show by systematically linking drug response to genomic aberrations in non-small-cell lung cancer, as well as in cell lines of other tumor types and in a series of in vivo cancer models, that tumors with genetically activated receptor tyrosine kinases depend on PI3K signaling, whereas tumors with mutations in the RAS/RAF axis depend on MAPK signaling. However, efficacy of downstream pathway inhibition was limited by release of negative feedback loops on the reciprocal pathway. By contrast, combined blockade of both pathways was able to overcome the reciprocal pathway activation induced by inhibitor-mediated release of negative feedback loops and resulted in a significant increase in apoptosis and tumor shrinkage. Thus, by using a systematic chemo-genomics approach, we identify genetic lesions connected to PI3K and MAPK pathway activation and provide a rationale for combined inhibition of both pathways. Our findings may have implications for patient stratification in clinical trials. PMID:19805051

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

    NASA Astrophysics Data System (ADS)

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

    2014-05-01

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

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

    PubMed

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

    2010-01-01

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

  20. 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

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

    PubMed

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

    2014-11-24

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

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

    PubMed

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

    2016-03-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2009-08-01

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

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

    PubMed Central

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

    2017-01-01

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

  5. In silico prediction of specific pathways that regulate mesangial cell proliferation in IgA nephropathy.

    PubMed

    Mirfazeli, Elham Sadat; Marashi, Sayed-Amir; Kalantari, Shiva

    2016-12-01

    IgA nephropathy is one of the most common forms of primary glomerulonephritis worldwide leading to end-stage renal disease. Proliferation of mesangial cells, i.e., the multifunctional cells located in the intracapillary region of glomeruli, after IgA- dominant immune deposition is the major histologic feature in IgA nephropathy. In spite of several studies on molecular basis of proliferation in these cells, specific pathways responsible for regulation of proliferation are still to be discovered. In this study, we predicted a specific signaling pathway started from transferrin receptor (TFRC), a specific IgA1 receptor on mesangial cells, toward a set of proliferation-related proteins. The final constructed subnetwork was presented after filtration and evaluation. The results suggest that estrogen receptor (ESR1) as a hub protein in the significant subnetwork has an important role in the mesangial cell proliferation and is a potential target for IgA nephropathy therapy. In conclusion, this study suggests a novel hypothesis for the mechanism of pathogenesis in IgA nephropathy and is a reasonable start point for the future experimental studies on mesangial proliferation process in this disease. Copyright © 2016 Elsevier Ltd. All rights reserved.

  6. Multiple pathway assessment to predict anti-atherogenic efficacy of drugs targeting macrophages in atherosclerotic plaques.

    PubMed

    Alaarg, Amr; Zheng, Kang He; van der Valk, Fleur M; da Silva, Acarilia Eduardo; Versloot, Miranda; van Ufford, Linda C Quarles; Schulte, Dominik M; Storm, Gert; Metselaar, Josbert M; Stroes, Erik S G; Hamers, Anouk A J

    2016-07-01

    Macrophages play a central role in atherosclerosis development and progression, hence, targeting macrophage activity is considered an attractive therapeutic. Recently, we documented nanomedicinal delivery of the anti-inflammatory compound prednisolone to atherosclerotic plaque macrophages in patients, which did however not translate into therapeutic efficacy. This unanticipated finding calls for in-depth screening of drugs intended for targeting plaque macrophages. We evaluated the effect of several candidate drugs on macrophage activity, rating overall performance with respect to changes in cytokine release, oxidative stress, lipid handling, endoplasmic reticulum (ER) stress, and proliferation of macrophages. Using this in vitro approach, we observed that the anti-inflammatory effect of prednisolone was counterbalanced by multiple adverse effects on other key pathways. Conversely, pterostilbene, T0901317 and simvastatin had an overall anti-atherogenic effect on multiple pathways, suggesting their potential for liposomal delivery. This dedicated assay setup provides a framework for high-throughput assessment. Further in vivo studies are warranted to determine the predictive value of this macrophage-based screening approach and its potential value in nanomedicinal drug development for cardiovascular patients. Copyright © 2016 Elsevier Inc. All rights reserved.

  7. Associative Encoding and Retrieval Are Predicted by Functional Connectivity in Distinct Hippocampal Area CA1 Pathways

    PubMed Central

    Duncan, Katherine; Tompary, Alexa

    2014-01-01

    Determining how the hippocampus supports the unique demands of memory encoding and retrieval is fundamental for understanding the biological basis of episodic memory. One possibility proposed by theoretical models is that the distinct computational demands of encoding and retrieval are accommodated by shifts in the functional interaction between the hippocampal CA1 subregion and its input structures. However, empirical tests of this hypothesis are lacking. To test this in humans, we used high-resolution fMRI to measure functional connectivity between hippocampal area CA1 and regions of the medial temporal lobe and midbrain during extended blocks of associative encoding and retrieval tasks. We found evidence for a double dissociation between the pathways supporting successful encoding and retrieval. Specifically, during the associative encoding task, but not the retrieval task, functional connectivity only between area CA1 and the ventral tegmental area predicted associative long-term memory. In contrast, connectivity between area CA1 and DG/CA3 was greater, on average, during the retrieval task compared with the encoding task, and, importantly, the strength of this connectivity significantly correlated with retrieval success. Together, these findings serve as an important first step toward understanding how the demands of fundamental memory processes may be met by changes in the relative strength of connectivity within hippocampal pathways. PMID:25143600

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

    PubMed Central

    Fumiã, Herman F.; Martins, Marcelo L.

    2013-01-01

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

  9. 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. © FEMS 2015. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

  10. Optimized outcome prediction in breast cancer by combining the 70-gene signature with clinical risk prediction algorithms.

    PubMed

    Drukker, C A; Nijenhuis, M V; Bueno-de-Mesquita, J M; Retèl, V P; van Harten, W H; van Tinteren, H; Wesseling, J; Schmidt, M K; Van't Veer, L J; Sonke, G S; Rutgers, E J T; van de Vijver, M J; Linn, S C

    2014-06-01

    Clinical guidelines for breast cancer treatment differ in their selection of patients at a high risk of recurrence who are eligible to receive adjuvant systemic treatment (AST). The 70-gene signature is a molecular tool to better guide AST decisions. The aim of this study was to evaluate whether adding the 70-gene signature to clinical risk prediction algorithms can optimize outcome prediction and consequently treatment decisions in early stage, node-negative breast cancer patients. A 70-gene signature was available for 427 patients participating in the RASTER study (cT1-3N0M0). Median follow-up was 61.6 months. Based on 5-year distant-recurrence free interval (DRFI) probabilities survival areas under the curve (AUC) were calculated and compared for risk estimations based on the six clinical risk prediction algorithms: Adjuvant! Online (AOL), Nottingham Prognostic Index (NPI), St. Gallen (2003), the Dutch National guidelines (CBO 2004 and NABON 2012), and PREDICT plus. Also, survival AUC were calculated after adding the 70-gene signature to these clinical risk estimations. Systemically untreated patients with a high clinical risk estimation but a low risk 70-gene signature had an excellent 5-year DRFI varying between 97.1 and 100 %, depending on the clinical risk prediction algorithms used in the comparison. The best risk estimation was obtained in this cohort by adding the 70-gene signature to CBO 2012 (AUC: 0.644) and PREDICT (AUC: 0.662). Clinical risk estimations by all clinical algorithms improved by adding the 70-gene signature. Patients with a low risk 70-gene signature have an excellent survival, independent of their clinical risk estimation. Adding the 70-gene signature to clinical risk prediction algorithms improves risk estimations and therefore might improve the identification of early stage node-negative breast cancer patients for whom AST has limited value. In this cohort, the PREDICT plus tool in combination with the 70-gene signature provided the

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

    PubMed Central

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

    2013-01-01

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

  12. 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

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

    PubMed

    Robbins, Daniel W

    2010-11-01

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

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

    NASA Astrophysics Data System (ADS)

    Krawczyk, C. M.; Kolditz, O.

    2013-12-01

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

  15. Combined 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

  16. Long-term prediction of polar motion using a combined SSA and ARMA model

    NASA Astrophysics Data System (ADS)

    Shen, Yi; Guo, Jinyun; Liu, Xin; Kong, Qiaoli; Guo, Linxi; Li, Wang

    2017-09-01

    To meet the need for real-time and high-accuracy predictions of polar motion (PM), the singular spectrum analysis (SSA) and the autoregressive moving average (ARMA) model are combined for short- and long-term PM prediction. According to the SSA results for PM and the SSA prediction algorithm, the principal components of PM were predicted by SSA, and the remaining components were predicted by the ARMA model. In applying this proposed method, multiple sets of PM predictions were made with lead times of two years, based on an IERS 08 C04 series. The observations and predictions of the principal components correlated well, and the SSA + ARMA model effectively predicted the PM. For 360-day lead time predictions, the root-mean-square errors (RMSEs) of PMx and PMy were 20.67 and 20.42 mas, respectively, which were less than the 24.46 and 24.78 mas predicted by IERS Bulletin A. The RMSEs of PMx and PMy in the 720-day lead time predictions were 28.61 and 27.95 mas, respectively.

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

    PubMed

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

    2014-07-01

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

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

    PubMed

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

    2007-07-01

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

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

    PubMed Central

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

    2010-01-01

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

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

    PubMed

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

    2016-06-01

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

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

    PubMed

    Hilser, V J; Freire, E

    1997-02-01

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

  2. Aphasia severity in chronic stroke patients: a combined disconnection in the dorsal and ventral language pathways.

    PubMed

    Rosso, Charlotte; Vargas, Patricia; Valabregue, Romain; Arbizu, Céline; Henry-Amar, François; Leger, Anne; Lehéricy, Stéphane; Samson, Yves

    2015-01-01

    The contribution of lesion size and location in poststroke aphasia is debated, especially the extent to which aphasia severity is affected by damage to specific white matter areas. To identify specific white matter areas critical for poststroke aphasia global severity and to determine whether injury to these areas had more impact on aphasia severity than the infarct volume. Twenty-three chronic poststroke aphasic patients were assessed with the Aphasia Rapid Test (ART) and the Boston Diagnosis Aphasia Examination (BDAE) global severity scales and underwent diffusion tensor and structural imaging. Voxel-based diffusion tensor imaging regression analysis was used to determine in which areas fractional anisotropy (FA) abnormalities were correlated with ART and BDAE severity scales. The relationships between aphasia severity, FA values, and infarct volumes were investigated using global and partial correlations. We found a critical area associated with aphasia severity overlapping with the arcuate and the inferior fronto-occipital fasciculi, resulting in a combined disconnection of the dorsal and ventral pathways. ART scores were inversely correlated with FA values in this region, with greater severity present with lower FA values (correlation coefficient = -0.833, P < .0001). The proportion of variance explained by the FA value was higher than the proportion of variance explained by the infarct volume (R (2) = 68% vs 27%, P = .01). The impact of infarct volume on aphasia severity disappeared when damage to this critical white matter area was taken into account (P = .38). The assessment of the integrity of this region may potentially have a clinical impact in neurorehabilitation and acute decision making. © The Author(s) 2014.

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

    NASA Astrophysics Data System (ADS)

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

    2010-09-01

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

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

    PubMed

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

    2015-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2007-12-01

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

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

    PubMed

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

    2008-01-01

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

  7. Identifying influential metrics in the combined metrics approach of fault prediction.

    PubMed

    Goyal, Rinkaj; Chandra, Pravin; Singh, Yogesh

    2013-01-01

    Fault prediction is a pre-eminent area of empirical software engineering which has witnessed a huge surge over the last couple of decades. In the development of a fault prediction model, combination of metrics results in better explanatory power of the model. Since the metrics used in combination are often correlated, and do not have an additive effect, the impact of a metric on another i.e. interaction should be taken into account. The effect of interaction in developing regression based fault prediction models is uncommon in software engineering; however two terms and three term interactions are analyzed in detail in social and behavioral sciences. Beyond three terms interactions are scarce, because interaction effects at such a high level are difficult to interpret. From our earlier findings (Softw Qual Prof 15(3):15-23) we statistically establish the pertinence of considering the interaction between metrics resulting in a considerable improvement in the explanatory power of the corresponding predictive model. However, in the aforesaid approach, the number of variables involved in fault prediction also shows a simultaneous increment with interaction. Furthermore, the interacting variables do not contribute equally to the prediction capability of the model. This study contributes towards the development of an efficient predictive model involving interaction among predictive variables with a reduced set of influential terms, obtained by applying stepwise regression.

  8. Improved functional prediction of proteins by learning kernel combinations in multilabel settings

    PubMed Central

    Roth, Volker; Fischer, Bernd

    2007-01-01

    Background We develop a probabilistic model for combining kernel matrices to predict the function of proteins. It extends previous approaches in that it can handle multiple labels which naturally appear in the context of protein function. Results Explicit modeling of multilabels significantly improves the capability of learning protein function from multiple kernels. The performance and the interpretability of the inference model are further improved by simultaneously predicting the subcellular localization of proteins and by combining pairwise classifiers to consistent class membership estimates. Conclusion For the purpose of functional prediction of proteins, multilabels provide valuable information that should be included adequately in the training process of classifiers. Learning of functional categories gains from co-prediction of subcellular localization. Pairwise separation rules allow very detailed insights into the relevance of different measurements like sequence, structure, interaction data, or expression data. A preliminary version of the software can be downloaded from . PMID:17493250

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

    PubMed Central

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

    2017-01-01

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

  10. 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. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. 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

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

    PubMed

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

    2014-01-01

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

  13. Integration of prior biological knowledge and epigenetic information enhances the prediction accuracy of the Bayesian Wnt pathway.

    PubMed

    Sinha, Shriprakash

    2014-11-01

    Computational modeling of the Wnt signaling pathway has gained prominence for its use as a diagnostic tool to develop therapeutic cancer target drugs and predict test samples as tumorous/normal. Diagnostic tools entail modeling of the biological phenomena behind the pathway while prediction requires inclusion of factors for discriminative classification. This manuscript develops simple static Bayesian network predictive models of varying complexity by encompassing prior partially available biological knowledge about intra/extracellular factors and incorporating information regarding epigenetic modification into a few genes that are known to have an inhibitory effect on the pathway. Incorporation of epigenetic information enhances the prediction accuracy of test samples in human colorectal cancer. In comparison to the Naive Bayes model where β-catenin transcription complex activation predictions are assumed to correspond to sample predictions, the new biologically inspired models shed light on differences in behavior of the transcription complex and the state of samples. Receiver operator curves and their respective area under the curve measurements obtained from predictions of the state of the test sample and the corresponding predictions of the state of activation of the β-catenin transcription complex of the pathway for the test sample indicate a significant difference between the transcription complex being on (off) and its association with the sample being tumorous (normal). The two-sample Kolmogorov-Smirnov test confirms the statistical deviation between the distributions of these predictions. Hitherto unknown relationship between factors like DKK2, DKK3-1 and SFRP-2/3/5 w.r.t. the β-catenin transcription complex has been inferred using these causal models.

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

    PubMed

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

    2015-02-03

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

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

    PubMed

    Tortora, Giampaolo; Ciardiello, Fortunato; Gasparini, Giampietro

    2008-09-01

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

  16. A combined comorbidity score predicted mortality in elderly patients better than existing scores

    PubMed Central

    Glynn, Robert J.; Avorn, Jerry; Levin, Raisa; Schneeweiss, Sebastian

    2010-01-01

    OBJECTIVE To develop and validate a single numeric comorbidity score for predicting short-and long-term mortality, by combining conditions in the Charlson and Elixhauser measures. STUDY DESIGN AND SETTING In a cohort of 120,679 Pennsylvania Medicare enrollees with drug coverage through a pharmacy assistance program, we developed a single numeric comorbidity score for predicting 1-year mortality, by combining the conditions in the Charlson and Elixhauser measures. We externally validated the combined score in a cohort of New Jersey Medicare enrollees, by comparing its performance to that of both component scores in predicting 1-year mortality, as well as 180-, 90-, and 30-day mortality. RESULTS C-statistics from logistic regression models including the combined score were higher than corresponding c-statistics from models including either the Romano implementation of the Charlson Index or the single numeric version of the Elixhauser system; c-statistics were 0.860 (95% confidence interval [CI]: 0.854, 0.866), 0.839 (95% CI: 0.836, 0.849), and 0.836 (95% CI: 0.834, 0.847), respectively, for the 30-day mortality outcome. The combined comorbidity score also yielded positive values for two recently proposed measures of reclassification. CONCLUSION In similar populations and data settings, the combined score may offer improvements in comorbidity summarization over existing scores. PMID:21208778

  17. 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.

  18. 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.

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

    PubMed

    de Souza, Robson Francisco; Aravind, L

    2012-06-01

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

  20. Integrating publicly-available data to generate computationally-predicted adverse outcome pathways for hepatic steatosis

    EPA Science Inventory

    The adverse outcome pathway (AOP) framework provides a way of organizing knowledge related to the key biological events that result in a particular health outcome. For the majority of environmental chemicals, the availability of curated pathways characterizing potential toxicity ...

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

    PubMed

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

    2014-10-01

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

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

    PubMed

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

    2015-07-01

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

  3. 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

  4. 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),…

  5. 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),…

  6. 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.

  7. Predicting binding sites of hydrolase-inhibitor complexes by combining several methods

    PubMed Central

    Sen, Taner Z; Kloczkowski, Andrzej; Jernigan, Robert L; Yan, Changhui; Honavar, Vasant; Ho, Kai-Ming; Wang, Cai-Zhuang; Ihm, Yungok; Cao, Haibo; Gu, Xun; Dobbs, Drena

    2004-01-01

    Background Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Results In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using Support Vector Machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods. Conclusions We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems. PMID:15606919

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

    PubMed Central

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

    2017-01-01

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

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

    SciTech Connect

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

    2016-08-18

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

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

    DOE PAGES

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

    2016-08-18

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

  11. [ARIA 2016 executive summary: integrated care pathways for predictive medicine across the life cycle].

    PubMed

    Yorgancıoğlu, Ayşe Arzu; Kalaycı, Ömer; Cingi, Cemal; Gemicioğlu, Bilun; Kalyoncu, Ali Fuat; Agache, Iogana; Bachert, Claus; Bedbrook, Anna; Canonica, George Walter; Casale, Thomas; Cruz, Alvaro; Fokkens, Wytsk Ej; Hellings, Peter; Samolinski, Boleslaw; Bousquet, Jean

    2017-03-01

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

  12. 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

    2016-09-08

    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. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

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

    SciTech Connect

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

    2016-08-18

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

  14. 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...

  15. 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...

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

    PubMed

    Kuzmits, Frank E; Adams, Arthur J

    2008-11-01

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

  17. Discrimination of falls and blows in blunt head trauma: assessment of predictability through combined criteria.

    PubMed

    Kremer, Célia; Sauvageau, Anny

    2009-07-01

    The discrimination of falls from homicidal blows in blunt head injuries is a common but difficult problem in both forensic anthropology and pathology. Three criteria have been previously proposed for this distinction: the hat brim line rule, side lateralization of fractures, and number of lacerations. The aim of the present study was to achieve a better distinction rate by combining those criteria and assess the predictability of these combined criteria tools. Over a 6-year period, a total of 114 cases (92 males and 22 females) were studied: 21 cases of downstairs falls, 29 cases of falls from one's own height, and 64 cases of head trauma by a blunt weapon. The results revealed predictability rates varying from 62.5 to 83.3% for criteria pointing towards a fall. As for combined criteria in favor of a blow, the assumption was accurate in all cases (100%).

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

    NASA Astrophysics Data System (ADS)

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

    2012-12-01

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

  19. 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.

  20. Pathway identification combining metabolic flux and functional genomics analyses: acetate and propionate activation by Corynebacterium glutamicum.

    PubMed

    Veit, Andrea; Rittmann, Doris; Georgi, Tobias; Youn, Jung-Won; Eikmanns, Bernhard J; Wendisch, Volker F

    2009-03-10

    Corynebacterium glutamicum can utilize acetic acid and propionic acid for growth and amino acid production. Growth on acetate as sole carbon source requires acetate activation by acetate kinase (AK) and phosphotransacetylase (PTA), encoded in the pta-ack operon. Genetic and enzymatic studies showed that these enzymes also catalyze propionate activation and were required for growth on propionate as sole carbon source. However, when glucose was present as a co-substrate strain lacking the AK-PTA pathway was still able to utilize acetate or propionate for growth indicating that an alternative activation pathway exists. As shown by (13)C-labelling experiments, the carbon skeleton of acetate is conserved during activation to acetyl-CoA in this pathway. Metabolic flux analysis during growth on an acetate-glucose mixture revealed that in the absence of the AK-PTA pathway carbon fluxes in glycolysis, the tricarboxylic acid (TCA) cycle and anaplerosis via PEP carboxylase and/or pyruvate carboxylase were increased, while the glyoxylate cycle flux was decreased. DNA microarray experiments identified cg2840 as a constitutively and highly expressed gene putatively encoding a CoA transferase. Purified His-tagged Cg2840 protein was active as CoA transferase interconverting acetyl-, propionyl- and succinyl-moieties as CoA acceptors and donors. Strains lacking both the CoA transferase and the AK-PTA pathway could neither activate acetate nor propionate in the presence or absence of glucose. Thus, when these short-chain fatty acids are co-metabolized with other carbon sources, CoA transferase and the AK-PTA pathway constitute a redundant system for activation of acetate and propionate.

  1. A unified strategy in selection of the best allometric scaling methods to predict human clearance based on drug disposition pathway.

    PubMed

    Liu, Dongyang; Song, Hanlin; Song, Ling; Liu, Yang; Cao, Yanguang; Jiang, Ji; Hu, Pei

    2016-12-01

    1. It is critical to develop a unified strategy to select the best allometric scaling (AS) method for a given group of drugs. 2. A total of 446 drugs with known human CLiv, clear disposition pathway and animal (rat, dog, monkey) CLiv were analyzed. All drugs were stratified based on their disposition pathway, liver extraction ratio (ERH) and ratios of unbound clearance to renal glomerular filtration rate (RGFR). Up to 22 AS methods were applied and compared in prediction of human CLiv to each group of drugs. 3. AS methods that give the best prediction of human CLiv, were identified for drugs primarily eliminated through liver with a fraction of renal elimination (frenal) within 0.3-0.5 or ERH > 0.3, where human CLiv of more than 80% or 90% drugs could be accurately (within 2- or 3-fold error) predicted. For drugs with ERH < 0.3, acceptable accuracy could be achieved by a two species method TSR,D resulting more than 60% or 75% drugs were predicted within 2- or 3-fold error. 4. By stratified analysis of drugs, according to their disposition pathway and organ extraction ratio, a unified strategy was developed to select the best AS method in prediction of human CLiv.

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

    PubMed Central

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

    2017-01-01

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

  3. 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.

  4. 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

  5. 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).

  6. Fatigue Life Prediction of Metallic Materials Based on the Combined Nonlinear Ultrasonic Parameter

    NASA Astrophysics Data System (ADS)

    Zhang, Yuhua; Li, Xinxin; Wu, Zhenyong; Huang, Zhenfeng; Mao, Hanling

    2017-08-01

    The fatigue life prediction of metallic materials is always a tough problem that needs to be solved in the mechanical engineering field because it is very important for the secure service of mechanical components. In this paper, a combined nonlinear ultrasonic parameter based on the collinear wave mixing technique is applied for fatigue life prediction of a metallic material. Sweep experiments are first conducted to explore the influence of driving frequency on the interaction of two driving signals and the fatigue damage of specimens, and the amplitudes of sidebands at the difference frequency and sum frequency are tracked when the driving frequency changes. Then, collinear wave mixing tests are carried out on a pair of cylindrically notched specimens with different fatigue damage to explore the relationship between the fatigue damage and the relative nonlinear parameters. The experimental results show when the fatigue degree is below 65% the relative nonlinear parameter increases quickly, and the growth rate is approximately 130%. If the fatigue degree is above 65%, the increase in the relative nonlinear parameter is slow, which has a close relationship with the microstructure evolution of specimens. A combined nonlinear ultrasonic parameter is proposed to highlight the relationship of the relative nonlinear parameter and fatigue degree of specimens; the fatigue life prediction model is built based on the relationship, and the prediction error is below 3%, which is below the prediction error based on the relative nonlinear parameters at the difference and sum frequencies. Therefore, the combined nonlinear ultrasonic parameter using the collinear wave mixing method can effectively estimate the fatigue degree of specimens, which provides a fast and convenient method for fatigue life prediction.

  7. Fatigue Life Prediction of Metallic Materials Based on the Combined Nonlinear Ultrasonic Parameter

    NASA Astrophysics Data System (ADS)

    Zhang, Yuhua; Li, Xinxin; Wu, Zhenyong; Huang, Zhenfeng; Mao, Hanling

    2017-07-01

    The fatigue life prediction of metallic materials is always a tough problem that needs to be solved in the mechanical engineering field because it is very important for the secure service of mechanical components. In this paper, a combined nonlinear ultrasonic parameter based on the collinear wave mixing technique is applied for fatigue life prediction of a metallic material. Sweep experiments are first conducted to explore the influence of driving frequency on the interaction of two driving signals and the fatigue damage of specimens, and the amplitudes of sidebands at the difference frequency and sum frequency are tracked when the driving frequency changes. Then, collinear wave mixing tests are carried out on a pair of cylindrically notched specimens with different fatigue damage to explore the relationship between the fatigue damage and the relative nonlinear parameters. The experimental results show when the fatigue degree is below 65% the relative nonlinear parameter increases quickly, and the growth rate is approximately 130%. If the fatigue degree is above 65%, the increase in the relative nonlinear parameter is slow, which has a close relationship with the microstructure evolution of specimens. A combined nonlinear ultrasonic parameter is proposed to highlight the relationship of the relative nonlinear parameter and fatigue degree of specimens; the fatigue life prediction model is built based on the relationship, and the prediction error is below 3%, which is below the prediction error based on the relative nonlinear parameters at the difference and sum frequencies. Therefore, the combined nonlinear ultrasonic parameter using the collinear wave mixing method can effectively estimate the fatigue degree of specimens, which provides a fast and convenient method for fatigue life prediction.

  8. Germline Polymorphisms of the VEGF Pathway Predict Recurrence in Nonadvanced Differentiated Thyroid Cancer.

    PubMed

    Marotta, Vincenzo; Sciammarella, Concetta; Capasso, Mario; Testori, Alessandro; Pivonello, Claudia; Chiofalo, Maria Grazia; Gambardella, Claudio; Grasso, Marica; Antonino, Antonio; Annunziata, Annamaria; Macchia, Paolo Emidio; Pivonello, Rosario; Santini, Luigi; Botti, Gerardo; Losito, Simona; Pezzullo, Luciano; Colao, Annamaria; Faggiano, Antongiulio

    2017-02-01

    Tumor angiogenesis is determined by host genetic background rather than environment. Germline single nucleotide polymorphisms (SNPs) of the vascular endothelial growth factor (VEGF) pathway have demonstrated prognostic value in different tumors. Our main objective was to test the prognostic value of germline SNPs of the VEGF pathway in nonadvanced differentiated thyroid cancer (DTC). Secondarily, we sought to correlate analyzed SNPs with microvessel density (MVD). Multicenter, retrospective, observational study. Four referral centers. Blood samples were obtained from consecutive DTC patients. Genotyping was performed according to the TaqMan protocol, including 4 VEGF-A (-2578C>A, -460T>C, +405G>C, and +936C>T) and 2 VEGFR-2 (+1192 C>T and +1719 T>A) SNPs. MVD was estimated by means of CD34 staining. Rate of recurrent structural disease/disease-free survival (DFS). Difference in MVD between tumors from patients with different genotype. Two hundred four patients with stage I-II DTC (mean follow-up, 73 ± 64 months) and 240 patients with low- to intermediate-risk DTC (mean follow-up, 70 ± 60 months) were enrolled. Two "risk" genotypes were identified by combining VEGF-A SNPs -2578 C>A, -460 T>C, and +405 G>C. The ACG homozygous genotype was protective in both stage I-II (odds ratio [OR], 0.08; 95% confidence interval [CI], 0.01 to 1.43; P = 0.018) and low- to intermediate-risk (OR, 0.14; 95% CI, 0.01 to 1.13; P = 0.035) patients. The CTG homozygous genotype was significantly associated with recurrence in stage I-II (OR, 5.47; 95% CI, 1.15 to 26.04; P = 0.018) and was slightly deleterious in low- to intermediate-risk (OR, 3.39; 95% CI, 0.8 to 14.33; P = 0.079) patients. MVD of primary tumors from patients harboring a protective genotype was significantly lower (median MVD, 76.5 ± 12.7 and 86.7 ± 27.9, respectively; P = 0.024). Analysis of germline VEGF-A SNPs could empower a prognostic approach to DTC.

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

    PubMed Central

    Sasore, Temitope; Kennedy, Breandán

    2014-01-01

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

  10. Comparison of the Combined versus Conventional Apgar Scores in Predicting Adverse Neonatal Outcomes

    PubMed Central

    Dalili, Hosein; Sheikh, Mahdi; Hardani, Amir Kamal; Nili, Firouzeh; Shariat, Mamak; Nayeri, Fatemeh

    2016-01-01

    Objectives Assessing the value of the Combined-Apgar score in predicting neonatal mortality and morbidity compared to the Conventional-Apgar. Methods This prospective cohort study evaluated 942 neonates (166 very preterm, 233 near term, and 543 term) admitted to a tertiary referral hospital. At 1- and 5-minutes after delivery, the Conventional and Combined Apgar scores were recorded. The neonates were followed, and the following information was recorded: the occurrence of severe hyperbilirubinemia requiring medical intervention, the requirement for mechanical ventilation, the occurrence of intraventricular hemorrhage (IVH), and neonatal mortality. Results Before adjusting for the potential confounders, a low Conventional (<7) or Combined (<10) Apgar score at 5-minutes was associated with adverse neonatal outcomes. However, after adjustment for the gestational age, birth weight and the requirement for neonatal resuscitation in the delivery room, a depressed 5-minute Conventional-Apgar score lost its significant associations with all the measured adverse outcomes; after the adjustments, a low 5-minute Combined-Apgar score remained significantly associated with the requirement for mechanical ventilation (OR,18.61; 95%CI,6.75–51.29), IVH (OR,4.8; 95%CI,1.91–12.01), and neonatal mortality (OR,20.22; 95%CI,4.22–96.88). Additionally, using Receiver Operating Characteristics (ROC) curves, the area under the curve was higher for the Combined-Apgar than the Conventional-Apgar for the prediction of neonatal mortality and the measured morbidities among all the admitted neonates and their gestational age subgroups. Conclusions The newly proposed Combined-Apgar score can be a good predictor of neonatal mortality and morbidity in the admitted neonates, regardless of their gestational age and resuscitation status. It is also superior to the Conventional-Apgar in predicting adverse neonatal outcomes in very preterm, near term and term neonates. PMID:26871908

  11. IL-10 combined with procalcitonin improves early prediction of complications of febrile neutropenia in hematological patients.

    PubMed

    Vänskä, Matti; Koivula, Irma; Jantunen, Esa; Hämäläinen, Sari; Purhonen, Anna-Kaisa; Pulkki, Kari; Juutilainen, Auni

    2012-12-01

    Early diagnosis of complicated course in febrile neutropenia is cumbersome due to the non-specificity of clinical and laboratory signs of severe infection. This prospective study included 100 adult hematological patients with febrile neutropenia after intensive chemotherapy at the onset of fever (d0) and for 3 days (d1-d3) thereafter. The study aim was to find early predictors for complicated course of febrile neutropenia, defined as bacteremia or septic shock. Interleukin 6 (IL-6), interleukin 10 (IL-10), procalcitonin (PCT) and C-reactive protein (CRP) all predicted complicated course of febrile neutropenia on d0, but only PCT was predictive throughout the study period. For IL-10 on d0-1 with cut-off 37 ng/L, sensitivity was 0.71, specificity 0.82, positive predictive value 0.52 and negative predictive value 0.92. For PCT on d0-1 with cut-off 0.13 μg/L, the respective measures were 0.95, 0.53, 0.36, and 0.98. For the combination of IL-10 and PCT on d0-1 with the same cut-offs, specificity improved to 0.85 and positive predictive value to 0.56. In conclusion, the present study confirms the high negative predictive value of PCT and provides new evidence for IL-10 as an early predictor for complicated course of febrile neutropenia in hematological patients. Combining IL-10 with PCT improves the early prediction for complicated course of febrile neutropenia. Copyright © 2012 Elsevier Ltd. All rights reserved.

  12. 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

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

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

    2014-01-01

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

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

    PubMed

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

    2006-11-01

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

  16. [Physiological analysis of a mathematical model for predicting somatic eigenstates under combined stresses].

    PubMed

    Yu, X J; Jia, S G; Chen, J S

    1999-06-01

    Objective. To put a mathematical model for predicting human somatic eigenstates (HS) into practical engineering design of countermeasures against combined stresses (hypoxia, heat, noise and vibration) in an aircraft cabin, and confirm the model from the human physiological viewpoint. Method. Published works on these 4 stresses were employed to verify the main and interactive effects which had been previously proved mathematically. Result. The main effects of 4 stresses and the significant interactive effects of 2 from 4 stresses agreed with the published experiments in single or in the same combination of these stresses. Conclusion. The model is reasonable in human physiological consideration and has been adopted in engineering design.

  17. 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.

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

    PubMed Central

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

    2017-01-01

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

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

    PubMed

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

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-04-01

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

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

    PubMed

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

    2014-04-01

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

  2. EPSILON-CP: using deep learning to combine information from multiple sources for protein contact prediction.

    PubMed

    Stahl, Kolja; Schneider, Michael; Brock, Oliver

    2017-06-17

    Accurately predicted contacts allow to compute the 3D structure of a protein. Since the solution space of native residue-residue contact pairs is very large, it is necessary to leverage information to identify relevant regions of the solution space, i.e. correct contacts. Every additional source of information can contribute to narrowing down candidate regions. Therefore, recent methods combined evolutionary and sequence-based information as well as evolutionary and physicochemical information. We develop a new contact predictor (EPSILON-CP) that goes beyond current methods by combining evolutionary, physicochemical, and sequence-based information. The problems resulting from the increased dimensionality and complexity of the learning problem are combated with a careful feature analysis, which results in a drastically reduced feature set. The different information sources are combined using deep neural networks. On 21 hard CASP11 FM targets, EPSILON-CP achieves a mean precision of 35.7% for top- L/10 predicted long-range contacts, which is 11% better than the CASP11 winning version of MetaPSICOV. The improvement on 1.5L is 17%. Furthermore, in this study we find that the amino acid composition, a commonly used feature, is rendered ineffective in the context of meta approaches. The size of the refined feature set decreased by 75%, enabling a significant increase in training data for machine learning, contributing significantly to the observed improvements. Exploiting as much and diverse information as possible is key to accurate contact prediction. Simply merging the information introduces new challenges. Our study suggests that critical feature analysis can improve the performance of contact prediction methods that combine multiple information sources. EPSILON-CP is available as a webservice: http://compbio.robotics.tu-berlin.de/epsilon/.

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

    PubMed

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

    2012-09-01

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

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

    PubMed

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

    2015-09-01

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

  5. 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

  6. Combination antiretroviral therapy (cART) restores HIV-1 infection-mediated impairment of JAK-STAT signaling pathway.

    PubMed

    Liu, Man-Qing; Zhao, Min; Kong, Wen-Hua; Tang, Li; Wang, Fang; Zhu, Ze-Rong; Wang, Xia; Qiu, Hong-Yan; Zhou, Dun-Jin; Wang, Xu; Ho, Wen-Zhe; Zhou, Wang

    2017-04-04

    JAK-STAT signaling pathway has a crucial role in host innate immunity against viral infections, including HIV-1. We therefore examined the impact of HIV-1 infection and combination antiretroviral therapy (cART) on JAK-STAT signaling pathway. Compared to age-matched healthy donors (n = 18), HIV-1-infected subjects (n = 18) prior to cART had significantly lower expression of toll-like receptors (TLR-1/4/6/7/8/9), the IFN regulatory factors (IRF-3/7/9), and the antiviral factors (OAS-1, MxA, A3G, PKR, and Tetherin). Three months' cART partially restores the impaired functions of JAK-STAT-mediated antiviral immunity. We also found most factors had significantly positive correlations (p < 0.05) between each two factors in JAK-STAT pathway in healthy donors (98.25%, 168/171), but such significant positive associations were only found in small part of HIV-1-infected subjects (43.86%, 75/171), and stably increased during the cART (57.31%, 98/171 after 6 months' cART). With regard to the restoration of some HIV-1 restriction factors, HIV-1-infected subjects who had CD4+ T cell counts > 350//μl responded better to cART than those with the counts < 350/μl. These findings indicate that the impairment of JAK-STAT pathway may play a role in the immunopathogenesis of HIV-1 disease.

  7. APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility

    PubMed Central

    2010-01-01

    Background It is well known that most of the binding free energy of protein interaction is contributed by a few key hot spot residues. These residues are crucial for understanding the function of proteins and studying their interactions. Experimental hot spots detection methods such as alanine scanning mutagenesis are not applicable on a large scale since they are time consuming and expensive. Therefore, reliable and efficient computational methods for identifying hot spots are greatly desired and urgently required. Results In this work, we introduce an efficient approach that uses support vector machine (SVM) to predict hot spot residues in protein interfaces. We systematically investigate a wide variety of 62 features from a combination of protein sequence and structure information. Then, to remove redundant and irrelevant features and improve the prediction performance, feature selection is employed using the F-score method. Based on the selected features, nine individual-feature based predictors are developed to identify hot spots using SVMs. Furthermore, a new ensemble classifier, namely APIS (A combined model based on Protrusion Index and Solvent accessibility), is developed to further improve the prediction accuracy. The results on two benchmark datasets, ASEdb and BID, show that this proposed method yields significantly better prediction accuracy than those previously published in the literature. In addition, we also demonstrate the predictive power of our proposed method by modelling two protein complexes: the calmodulin/myosin light chain kinase complex and the heat shock locus gene products U and V complex, which indicate that our method can identify more hot spots in these two complexes compared with other state-of-the-art methods. Conclusion We have developed an accurate prediction model for hot spot residues, given the structure of a protein complex. A major contribution of this study is to propose several new features based on the protrusion index of

  8. 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.

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

    SciTech Connect

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

    1998-10-01

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

  10. 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.

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

    PubMed

    Stamps, Judy A; Krishnan, V V

    2014-11-01

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

  12. 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.

  13. Combination of Cinobufacini and Doxorubicin Increases Apoptosis of Hepatocellular Carcinoma Cells through the Fas- and Mitochondria-Mediated Pathways.

    PubMed

    Xia, Jufeng; Inagaki, Yoshinori; Gao, Jianjun; Qi, Fanghua; Song, Peipei; Han, Guohua; Sawakami, Tatsuo; Gao, Bo; Luo, Chuan; Kokudo, Norihiro; Hasegawa, Kiyoshi; Sakamoto, Yoshihiro; Tang, Wei

    2017-09-25

    Cinobufacini, a traditional Chinese medicine, has been used widely for cancer treatment, such as hepatocellular carcinoma (HCC), sarcoma, and leukemia. Previous studies done by our lab indicated that cinobufacini could suppress HCC cells through mitochondria-mediated and Fas-mediated apoptotic pathways. Here, we use a combination of cinobufacini and doxorubicin to inhibit the growth of HCC cells. The combination group induced more significant apoptosis by affecting proteins and RNA of apoptosis-related elements, such as Bcl-2, Bax, Bid, and cytochrome c. Furthermore, cinobufacini, as a mixture of a number of components, had stronger apoptosis-inducing activity than particular individual components or a simple mixture of a few components. Overall, these results suggested that the combination of cinobufacini and doxorubicin may provide a new strategy for inhibiting the proliferation of HCC cells.

  14. 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.

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

    PubMed Central

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

    1993-01-01

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

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

    PubMed

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

    2015-01-01

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

  17. Multistrain models predict sequential multidrug treatment strategies to result in less antimicrobial resistance than combination treatment.

    PubMed

    Ahmad, Amais; Zachariasen, Camilla; Christiansen, Lasse Engbo; Græsbøll, Kaare; Toft, Nils; Matthews, Louise; Olsen, John Elmerdahl; Nielsen, Søren Saxmose

    2016-06-23

    Combination treatment is increasingly used to fight infections caused by bacteria resistant to two or more antimicrobials. While multiple studies have evaluated treatment strategies to minimize the emergence of resistant strains for single antimicrobial treatment, fewer studies have considered combination treatments. The current study modeled bacterial growth in the intestine of pigs after intramuscular combination treatment (i.e. using two antibiotics simultaneously) and sequential treatments (i.e. alternating between two antibiotics) in order to identify the factors that favor the sensitive fraction of the commensal flora. Growth parameters for competing bacterial strains were estimated from the combined in vitro pharmacodynamic effect of two antimicrobials using the relationship between concentration and net bacterial growth rate. Predictions of in vivo bacterial growth were generated by a mathematical model of the competitive growth of multiple strains of Escherichia coli. Simulation studies showed that sequential use of tetracycline and ampicillin reduced the level of double resistance, when compared to the combination treatment. The effect of the cycling frequency (how frequently antibiotics are alternated in a sequential treatment) of the two drugs was dependent upon the order in which the two drugs were used. Sequential treatment was more effective in preventing the growth of resistant strains when compared to the combination treatment. The cycling frequency did not play a role in suppressing the growth of resistant strains, but the specific order of the two antimicrobials did. Predictions made from the study could be used to redesign multidrug treatment strategies not only for intramuscular treatment in pigs, but also for other dosing routes.

  18. Synergistic target combination prediction from curated signaling networks: Machine learning meets systems biology and pharmacology.

    PubMed

    Chua, Huey Eng; Bhowmick, Sourav S; Tucker-Kellogg, Lisa

    2017-05-25

    Given a signaling network, the target combination prediction problem aims to predict efficacious and safe target combinations for combination therapy. State-of-the-art in silico methods use Monte Carlo simulated annealing (mcsa) to modify a candidate solution stochastically, and use the Metropolis criterion to accept or reject the proposed modifications. However, such stochastic modifications ignore the impact of the choice of targets and their activities on the combination's therapeutic effect and off-target effects, which directly affect the solution quality. In this paper, we present mascot, a method that addresses this limitation by leveraging two additional heuristic criteria to minimize off-target effects and achieve synergy for candidate modification. Specifically, off-target effects measure the unintended response of a signaling network to the target combination and is often associated with toxicity. Synergy occurs when a pair of targets exerts effects that are greater than the sum of their individual effects, and is generally a beneficial strategy for maximizing effect while minimizing toxicity. mascot leverages on a machine learning-based target prioritization method which prioritizes potential targets in a given disease-associated network to select more effective targets (better therapeutic effect and/or lower off-target effects); and on Loewe additivity theory from pharmacology which assesses the non-additive effects in a combination drug treatment to select synergistic target activities. Our experimental study on two disease-related signaling networks demonstrates the superiority of mascot in comparison to existing approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

  19. 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.

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

    PubMed

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

    2011-05-01

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

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

    PubMed Central

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

    2014-01-01

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

  2. Distinct pathways regulated by RET and estrogen receptor in luminal breast cancer demonstrate the biological basis for combination therapy.

    PubMed

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

    2014-04-01

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

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

    PubMed Central

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

    2010-01-01

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

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

    PubMed

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

    2014-08-01

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

  5. 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...

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

    EPA Science Inventory

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

  7. Quantum chemical mass spectrometry: ab initio prediction of electron ionization mass spectra and identification of new fragmentation pathways.

    PubMed

    Cautereels, Julie; Claeys, Magda; Geldof, Davy; Blockhuys, Frank

    2016-08-01

    The electron ionization mass spectra of four organic compounds are predicted based on the results of quantum chemical calculations at the DFT/B3LYP/6-311 + G* level of theory. This prediction is performed 'ab initio', i.e. without any prior knowledge of the thermodynamics or kinetics of the reactions under consideration. Using a set of rules determining which routes will be followed, the fragmentation of the molecules' bonds and the complete resulting fragmentation pathways are studied. The most likely fragmentation pathways are identified based on calculated reaction energies ΔE when bond cleavage is considered and on activation energies ΔE(‡) when rearrangements are taken into account; the final intensities of the peaks in the spectrum are estimated from these values. The main features observed in the experimental mass spectra are correctly predicted, as well as a number of minor peaks. In addition, the results of the calculations allow us to propose fragmentation pathways new to empirical mass spectrometry, which have been experimentally verified using tandem mass spectrometry measurements. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

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

    PubMed

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

    2015-06-01

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

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

    PubMed Central

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

    2014-01-01

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

  10. 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.

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

    PubMed Central

    Buetti-Dinh, Antoine; O’Hare, Thomas

    2016-01-01

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

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

    PubMed

    Lawrenson, Charlotte L; Watson, Thomas C; Apps, Richard

    2016-07-27

    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. 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. During rearing up and down

  13. 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

  14. Drug interaction prediction using ontology-driven hypothetical assertion framework for pathway generation followed by numerical simulation

    PubMed Central

    Arikuma, Takeshi; Yoshikawa, Sumi; Azuma, Ryuzo; Watanabe, Kentaro; Matsumura, Kazumi; Konagaya, Akihiko

    2008-01-01

    Background In accordance with the increasing amount of information concerning individual differences in drug response and molecular interaction, the role of in silico prediction of drug interaction on the pathway level is becoming more and more important. However, in view of the interferences for the identification of new drug interactions, most conventional information models of a biological pathway would have limitations. As a reflection of real world biological events triggered by a stimulus, it is important to facilitate the incorporation of known molecular events for inferring (unknown) possible pathways and hypothetic drug interactions. Here, we propose a new Ontology-Driven Hypothetic Assertion (OHA) framework including pathway generation, drug interaction detection, simulation model generation, numerical simulation, and hypothetic assertion. Potential drug interactions are detected from drug metabolic pathways dynamically generated by molecular events triggered after the administration of certain drugs. Numerical simulation enables to estimate the degree of side effects caused by the predicted drug interactions. New hypothetic assertions of the potential drug interactions and simulation are deduced from the Drug Interaction Ontology (DIO) written in Web Ontology Language (OWL). Results The concept of the Ontology-Driven Hypothetic Assertion (OHA) framework was demonstrated with known interactions between irinotecan (CPT-11) and ketoconazole. Four drug interactions that involved cytochrome p450 (CYP3A4) and albumin as potential drug interaction proteins were automatically detected from Drug Interaction Ontology (DIO). The effect of the two interactions involving CYP3A4 were quantitatively evaluated with numerical simulation. The co-administration of ketoconazole may increase AUC and Cmax of SN-38(active metabolite of irinotecan) to 108% and 105%, respectively. We also estimates the potential effects of genetic variations: the AUC and Cmax of SN-38 may

  15. An integrated anti-arrhythmic target network of a Chinese medicine compound, Wenxin Keli, revealed by combined machine learning and molecular pathway analysis.

    PubMed

    Wang, Taiyi; Lu, Ming; Du, Qunqun; Yao, Xi; Zhang, Peng; Chen, Xiaonan; Xie, Weiwei; Li, Zheng; Ma, Yuling; Zhu, Yan

    2017-05-02

    Wenxin Keli (WK), a Chinese patent medicine, is known to be effective against cardiac arrhythmias and heart failure. Although a number of electrophysiological findings regarding its therapeutic effect have been reported, the active components and system-level characterizations of the component-target interactions of WK have yet to be elucidated. In the current study, we present the first report of a new protective effect of WK on suppressing anti-arrhythmic-agent-induced arrhythmias. In a model of isolated guinea pig hearts, rapid perfusion of quinidine altered the heart rate and prolonged the Q-T interval. Pretreatment with WK significantly prevented quinidine-induced arrhythmias. To explain the therapeutic and protective effects of WK, we constructed an integrated multi-target pharmacological mechanism prediction workflow in combination with machine learning and molecular pathway analysis. This workflow had the ability to predict and rank the probability of each compound interacting with 1715 target proteins simultaneously. The ROC value statistics showed that 97.786% of the values for target prediction were larger than 0.8. We applied this model to carry out target prediction and network analysis for the identified components of 5 herbs in WK. Using the 124 potential anti-arrhythmic components and the 30 corresponding protein targets obtained, an integrative anti-arrhythmic molecular mechanism of WK was proposed. Emerging drug/target networks suggested ion channel and intracellular calcium and autonomic nervous and hormonal regulation had critical roles in WK-mediated anti-arrhythmic activity. A validation of the proposed mechanisms was achieved by demonstrating that calaxin, one of the WK components from Gansong, dose-dependently blocked its predicted target CaV1.2 channel in an electrophysiological assay.

  16. 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

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

    USDA-ARS?s Scientific Manuscript database

    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...

  18. Pathways of Lipid Vesicle Deposition on Solid Surfaces: A Combined QCM-D and AFM Study

    PubMed Central

    Richter, Ralf; Mukhopadhyay, Anneke; Brisson, Alain

    2003-01-01

    Supported lipid bilayers (SLBs) are popular models of cell membranes with potential biotechnological applications, yet the mechanism of SLB formation is only partially understood. In this study, the adsorption and subsequent conformational changes of sonicated unilamellar vesicles on silica supports were investigated by quartz crystal microbalance with dissipation monitoring and atomic force microscopy, using mixtures of zwitterionic, negatively charged, and positively charged lipids, both in the presence and in the absence of Ca2+ ions. Four different pathways of vesicle deposition could be distinguished. Depending on their charge, vesicles i), did not adsorb; ii), formed a stable vesicular layer; or iii), decomposed into an SLB after adsorption at high critical coverage or iv), at low coverage. Calcium was shown to enhance the tendency of SLB formation for negatively charged and zwitterionic vesicles. The role of vesicle-support, interbilayer, and intrabilayer interactions in the formation of SLBs is discussed. PMID:14581204

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

    PubMed Central

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

    2016-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2009-12-01

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

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

    PubMed

    Bidlas, Eva; Lambert, Ronald J W

    2008-11-30

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

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

    PubMed

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

    2012-04-01

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

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

    PubMed

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

    2012-01-01

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

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

    PubMed Central

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

    2017-01-01

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

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

    PubMed Central

    Yuan, Wufeng

    2017-01-01

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

  6. DRUG INTERVENTION RESPONSE PREDICTIONS WITH PARADIGM (DIRPP) IDENTIFIES DRUG RESISTANT CANCER CELL LINES AND PATHWAY MECHANISMS OF RESISTANCE

    PubMed Central

    DIFEO, ANALISA; CHEN, YANWEN; PEARL, TAYLOR; ZHAI, KAIDE; BEBEK, GURKAN; CHANCE, MARK; BARNHOLTZ-SLOAN, JILL

    2014-01-01

    The revolution in sequencing techniques in the past decade has provided an extensive picture of the molecular mechanisms behind complex diseases such as cancer. The Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Project (CGP) have provided an unprecedented opportunity to examine copy number, gene expression, and mutational information for over 1000 cell lines of multiple tumor types alongside IC50 values for over 150 different drugs and drug related compounds. We present a novel pipeline called DIRPP, Drug Intervention Response Predictions with PARADIGM7, which predicts a cell line’s response to a drug intervention from molecular data. PARADIGM (Pathway Recognition Algorithm using Data Integration on Genomic Models) is a probabilistic graphical model used to infer patient specific genetic activity by integrating copy number and gene expression data into a factor graph model of a cellular network. We evaluated the performance of DIRPP on endometrial, ovarian, and breast cancer related cell lines from the CCLE and CGP for nine drugs. The pipeline is sensitive enough to predict the response of a cell line with accuracy and precision across datasets as high as 80 and 88% respectively. We then classify drugs by the specific pathway mechanisms governing drug response. This classification allows us to compare drugs by cellular response mechanisms rather than simply by their specific gene targets. This pipeline represents a novel approach for predicting clinical drug response and generating novel candidates for drug repurposing and repositioning. PMID:24297540

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

    PubMed

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

    2017-01-09

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

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

    PubMed Central

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

    2013-01-01

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

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

    PubMed

    Röttig, Marc; Rausch, Christian; Kohlbacher, Oliver

    2010-01-08

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

  10. Combined Thoracic Ultrasound Assessment during a Successful Weaning Trial Predicts Postextubation Distress.

    PubMed

    Silva, Stein; Ait Aissa, Dalinda; Cocquet, Pierre; Hoarau, Lucille; Ruiz, Jean; Ferre, Fabrice; Rousset, David; Mora, Michel; Mari, Arnaud; Fourcade, Olivier; Riu, Béatrice; Jaber, Samir; Bataille, Bénoît

    2017-10-01

    Recent studies suggest that isolated sonographic assessment of the respiratory, cardiac, or neuromuscular functions in mechanically ventilated patients may assist in identifying patients at risk of postextubation distress. The aim of the present study was to prospectively investigate the value of an integrated thoracic ultrasound evaluation, encompassing bedside respiratory, cardiac, and diaphragm sonographic data in predicting postextubation distress. Longitudinal ultrasound data from 136 patients who were extubated after passing a trial of pressure support ventilation were measured immediately after the start and at the end of this trial. In case of postextubation distress (31 of 136 patients), an additional combined ultrasound assessment was performed while the patient was still in acute respiratory failure. We applied machine-learning methods to improve the accuracy of the related predictive assessments. Overall, integrated thoracic ultrasound models accurately predict postextubation distress when applied to thoracic ultrasound data immediately recorded before the start and at the end of the trial of pressure support ventilation (learning sample area under the curve: start, 0.921; end, 0.951; test sample area under the curve: start, 0.972; end, 0.920). Among integrated thoracic ultrasound data, the recognition of lung interstitial edema and the increased telediastolic left ventricular pressure were the most relevant predictive factors. In addition, the use of thoracic ultrasound appeared to be highly accurate in identifying the causes of postextubation distress. The decision to attempt extubation could be significantly assisted by an integrative, dynamic, and fully bedside ultrasonographic assessment of cardiac, lung, and diaphragm functions.

  11. 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

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

    PubMed

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

    2015-04-01

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

  13. 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.

  14. 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

  15. Prediction of protein-protein interactions by combining structure and sequence conservation in protein interfaces.

    PubMed

    Aytuna, A Selim; Gursoy, Attila; Keskin, Ozlem

    2005-06-15

    Elucidation of the full network of protein-protein interactions is crucial for understanding of the principles of biological systems and processes. Thus, there is a need for in silico methods for predicting interactions. We present a novel algorithm for automated prediction of protein-protein interactions that employs a unique bottom-up approach combining structure and sequence conservation in protein interfaces. Running the algorithm on a template dataset of 67 interfaces and a sequentially non-redundant dataset of 6170 protein structures, 62 616 potential interactions are predicted. These interactions are compared with the ones in two publicly available interaction databases (Database of Interacting Proteins and Biomolecular Interaction Network Database) and also the Protein Data Bank. A significant number of predictions are verified in these databases. The unverified ones may correspond to (1) interactions that are not covered in these databases but known in literature, (2) unknown interactions that actually occur in nature and (3) interactions that do not occur naturally but may possibly be realized synthetically in laboratory conditions. Some unverified interactions, supported significantly with studies found in the literature, are discussed. http://gordion.hpc.eng.ku.edu.tr/prism agursoy@ku.edu.tr; okeskin@ku.edu.tr.

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

    PubMed Central

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

    2017-01-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2014-03-01

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

  18. Combining PSSM and physicochemical feature for protein structure prediction with support vector machine

    NASA Astrophysics Data System (ADS)

    Kurniawan, I.; Haryanto, T.; Hasibuan, L. S.; Agmalaro, M. A.

    2017-05-01

    Protein is one of the giant biomolecules that act as the main component of the organism. Protein is formed from building blocks namely amino acids. Hierarchically, the structure of protein is divided into four levels: primary, secondary, tertiary, and quaternary structure. Protein secondary structure is formed by amino acid sequences that would form three-dimensional structures and have information about the tertiary structure and function of proteins. This study used 277,389 protein residue data from enzyme categories. Position-specific scoring matrix (PSSM) profile and physicochemical are used for features. This study developed support vector machine models to predict the protein secondary structure by recognizing patterns of amino acid sequences. The Q3 results showed that the best scores obtained are 93.16% from the dataset that has 260 features with the radial kernel. Combining PSSM and physicochemical feature additions can be used for prediction.

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

    NASA Technical Reports Server (NTRS)

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

    1975-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  1. Wind gust estimation by combining numerical weather prediction model and statistical post-processing

    NASA Astrophysics Data System (ADS)

    Patlakas, Platon; Drakaki, Eleni; Galanis, George; Spyrou, Christos; Kallos, George

    2017-04-01

    The continuous rise of off-shore and near-shore activities as well as the development of structures, such as wind farms and various offshore platforms, requires the employment of state-of-the-art risk assessment techniques. Such analysis is used to set the safety standards and can be characterized as a climatologically oriented approach. Nevertheless, a reliable operational support is also needed in order to minimize cost drawbacks and human danger during the construction and the functioning stage as well as during maintenance activities. One of the most important parameters for this kind of analysis is the wind speed intensity and variability. A critical measure associated with this variability is the presence and magnitude of wind gusts as estimated in the reference level of 10m. The latter can be attributed to different processes that vary among boundary-layer turbulence, convection activities, mountain waves and wake phenomena. The purpose of this work is the development of a wind gust forecasting methodology combining a Numerical Weather Prediction model and a dynamical statistical tool based on Kalman filtering. To this end, the parameterization of Wind Gust Estimate method was implemented to function within the framework of the atmospheric model SKIRON/Dust. The new modeling tool combines the atmospheric model with a statistical local adaptation methodology based on Kalman filters. This has been tested over the offshore west coastline of the United States. The main purpose is to provide a useful tool for wind analysis and prediction and applications related to offshore wind energy (power prediction, operation and maintenance). The results have been evaluated by using observational data from the NOAA's buoy network. As it was found, the predicted output shows a good behavior that is further improved after the local adjustment post-process.

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

    PubMed Central

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

    2009-01-01

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

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

    PubMed

    Walters, Glenn D

    2011-06-01

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

  4. Correction: An integrated anti-arrhythmic target network of compound Chinese medicine Wenxin Keli revealed by combined machine learning and molecular pathway analysis.

    PubMed

    Wang, Taiyi; Lu, Ming; Du, Qunqun; Yao, Xi; Zhang, Peng; Chen, Xiaonan; Xie, Weiwei; Li, Zheng; Ma, Yuling; Zhu, Yan

    2017-09-08

    Correction for 'An integrated anti-arrhythmic target network of a Chinese medicine compound, Wenxin Keli, revealed by combined machine learning and molecular pathway analysis' by Taiyi Wang et al., Mol. BioSyst., 2017, 13, 1018-1030.

  5. Predictive value of orthopedic evaluation and injury history at the NFL combine.

    PubMed

    Brophy, Robert H; Chehab, Eric L; Barnes, Ronnie P; Lyman, Stephen; Rodeo, Scott A; Warren, Russell F

    2008-08-01

    The National Football League (NFL) holds an annual combine to evaluate college football athletes likely to be drafted for physical skills, to review their medical history, and to perform a physical examination. The athletes receive an orthopedic grade on their ability to participate in the NFL. The purpose of this study was to test the hypothesis that this orthopedic rating at the combine predicts the percent of athletes who play in the NFL and the length of their careers. A database for all athletes reviewed at the combine by the medical staff of one team from 1987 to 2000 was created and linked to a data set containing the number of seasons and the games played in the NFL for each athlete. Players were grouped by orthopedic grade: high, low, and orthopedic failure. The percent of players who played in the NFL and the mean length of their careers was calculated and compared for these groups. The orthopedic grade assigned at the NFL combine correlated with the probability of playing in the league. Whereas 58% of athletes with a high grade and 55% of athletes with a low grade played at least one game, only 36% of athletes given a failing grade did so (P < 0.001). Players with a high grade had a mean career of 41.5 games compared with 34.2 games for players with a low grade and 19.0 games for orthopedic failures. This is the first study to report on the predictive value of a grading system for college athletes before participation in professional sports. Other professional sports may benefit from using a similar grading system for the evaluation of potential players.

  6. Modelling plant interspecific interactions from experiments of perennial crop mixtures to predict optimal combinations.

    PubMed

    Halty, Virginia; Valdés, Matías; Tejera, Mauricio; Picasso, Valentín; Fort, Hugo

    2017-07-28

    The contribution of plant species richness to productivity and ecosystem functioning is a long standing issue in Ecology, with relevant implications for both conservation and agriculture. Both experiments and quantitative modelling are fundamental to the design of sustainable agroecosystems and the optimization of crop production. We modelled communities of perennial crop mixtures by using a generalized Lotka-Volterra model, i.e. a model such that the interspecific interactions are more general than purely competitive. We estimated model parameters -carrying capacities and interaction coefficientsfrom, respectively, the observed biomass of monocultures and bicultures measured in a large diversity experiment of seven perennial forage species in Iowa, United States. The sign and absolute value of the interaction coefficients showed that the biological interactions between species pairs included amensalism, competition, and parasitism (asymmetric positive-negative interaction), with various degrees of intensity. We tested the model fit by simulating the combinations of more than two species and comparing them with the polycultures experimental data. Overall, theoretical predictions are in good agreement with the experiments. Using this model, we also simulated species combinations that were not sown. From all possible mixtures (sown and not sown) we identified which are the most productive species combinations. Our results demonstrate that a combination of experiments and modelling can contribute to the design of sustainable agricultural systems in general and to the optimization of crop production in particular. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  7. 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.

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

    PubMed

    Pitcher, David; Duchaine, Bradley; Walsh, Vincent

    2014-09-08

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

  9. Combination of herbal extracts and platelet-rich plasma induced dermal papilla cell proliferation: involvement of ERK and Akt pathways.

    PubMed

    Rastegar, Hosein; Ahmadi Ashtiani, Hamidreza; Aghaei, Mahmoud; Ehsani, Amirohushang; Barikbin, Behrooz

    2013-06-01

    Recently, platelet-rich plasma (PRP) has attracted attention in various medical fields, including plastic surgery, treatment for problematic wounds, and dermatology. Specifically, PRP has been tested during hair transplantation to reduce swelling and pain and to increase hair density. We examined the effects of PRP and herbal extracts combination in order to identify potential stimulants of hair growth. PRP was prepared using the double-spin method and applied to dermal papilla cells (DPCs). MTT viability test and BrdU cell proliferation assay were used to study the effect of herbal extracts and PRP on proliferation of DPCs. To understand the mechanisms of herbal extracts and PRP involved in the regulation of hair growth, we evaluated signaling pathways and measured the expressions of ERK and Akt, by Western blot. Combination of herbal extracts and PRP was found to induce significant proliferation of human DPCs at concentrations ranging from 1.5% to 4.5%. The present study shows that herbal extracts and PRP affect the expressions of extracellular signal-regulated kinase (ERK) and Akt in DPCs. In this study, we have shown that combination of herbal extracts and PRP plays an active role in promoting the proliferation of human dermal papilla (DP) cells via the regulation of ERK and Akt proteins, and this may be applicable to the future development of herbal extracts and PRP combination therapeutics to enhance hair growth. © 2013 Wiley Periodicals, Inc.

  10. Combined low doses of PPARgamma and RXR ligands trigger an intrinsic apoptotic pathway in human breast cancer cells.

    PubMed

    Bonofiglio, Daniela; Cione, Erika; Qi, Hongyan; Pingitore, Attilio; Perri, Mariarita; Catalano, Stefania; Vizza, Donatella; Panno, Maria Luisa; Genchi, Giuseppe; Fuqua, Suzanne A W; Andò, Sebastiano

    2009-09-01

    Ligand activation of peroxisome proliferator-activated receptor (PPAR)gamma and retinoid X receptor (RXR) induces antitumor effects in cancer. We evaluated the ability of combined treatment with nanomolar levels of the PPARgamma ligand rosiglitazone (BRL) and the RXR ligand 9-cis-retinoic acid (9RA) to promote antiproliferative effects in breast cancer cells. BRL and 9RA in combination strongly inhibit of cell viability in MCF-7, MCF-7TR1, SKBR-3, and T-47D breast cancer cells, whereas MCF-10 normal breast epithelial cells are unaffected. In MCF-7 cells, combined treatment with BRL and 9RA up-regulated mRNA and protein levels of both the tumor suppressor p53 and its effector p21(WAF1/Cip1). Functional experiments indicate that the nuclear factor-kappaB site in the p53 promoter is required for the transcriptional response to BRL plus 9RA. We observed that the intrinsic apoptotic pathway in MCF-7 cells displays an ordinated sequence of events, including disruption of mitochondrial membrane potential, release of cytochrome c, strong caspase 9 activation, and, finally, DNA fragmentation. An expression vector for p53 antisense abrogated the biological effect of both ligands, which implicates involvement of p53 in PPARgamma/RXR-dependent activity in all of the human breast malignant cell lines tested. Taken together, our results suggest that multidrug regimens including a combination of PPARgamma and RXR ligands may provide a therapeutic advantage in breast cancer treatment.

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

    PubMed

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

    2008-10-02

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

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

    PubMed Central

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

    2008-01-01

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

  13. Developing and applying the adverse outcome pathway concept for understanding and predicting neurotoxicity

    EPA Science Inventory

    To support a paradigm shift in regulatory toxicology testing and risk assessment, the Adverse Outcome Pathway (AOP) concept has recently been proposed. This concept is similar to that for Mode of Action (MOA), describing a sequence of measurable key events triggered by a molecula...

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

    NASA Astrophysics Data System (ADS)

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

    2010-07-01

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

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

    PubMed

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

    2016-05-23

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

  16. 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.

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

    PubMed

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

    2016-12-01

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

  18. A Combined High and Low Cycle Fatigue Model for Life Prediction of Turbine Blades.

    PubMed

    Zhu, Shun-Peng; Yue, Peng; Yu, Zheng-Yong; Wang, Qingyuan

    2017-06-26

    Combined high and low cycle fatigue (CCF) generally induces the failure of aircraft gas turbine attachments. Based on the aero-engine load spectrum, accurate assessment of fatigue damage due to the interaction of high cycle fatigue (HCF) resulting from high frequency vibrations and low cycle fatigue (LCF) from ground-air-ground engine cycles is of critical importance for ensuring structural integrity of engine components, like turbine blades. In this paper, the influence of combined damage accumulation on the expected CCF life are investigated for turbine blades. The CCF behavior of a turbine blade is usually studied by testing with four load-controlled parameters, including high cycle stress amplitude and frequency, and low cycle stress amplitude and frequency. According to this, a new damage accumulation model is proposed based on Miner's rule to consider the coupled damage due to HCF-LCF interaction by introducing the four load parameters. Five experimental datasets of turbine blade alloys and turbine blades were introduced for model validation and comparison between the proposed Miner, Manson-Halford, and Trufyakov-Kovalchuk models. Results show that the proposed model provides more accurate predictions than others with lower mean and standard deviation values of model prediction errors.

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

    PubMed Central

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

    2016-01-01

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

  20. A Combined High and Low Cycle Fatigue Model for Life Prediction of Turbine Blades

    PubMed Central

    Yue, Peng; Yu, Zheng-Yong; Wang, Qingyuan

    2017-01-01

    Combined high and low cycle fatigue (CCF) generally induces the failure of aircraft gas turbine attachments. Based on the aero-engine load spectrum, accurate assessment of fatigue damage due to the interaction of high cycle fatigue (HCF) resulting from high frequency vibrations and low cycle fatigue (LCF) from ground-air-ground engine cycles is of critical importance for ensuring structural integrity of engine components, like turbine blades. In this paper, the influence of combined damage accumulation on the expected CCF life are investigated for turbine blades. The CCF behavior of a turbine blade is usually studied by testing with four load-controlled parameters, including high cycle stress amplitude and frequency, and low cycle stress amplitude and frequency. According to this, a new damage accumulation model is proposed based on Miner’s rule to consider the coupled damage due to HCF-LCF interaction by introducing the four load parameters. Five experimental datasets of turbine blade alloys and turbine blades were introduced for model validation and comparison between the proposed Miner, Manson-Halford, and Trufyakov-Kovalchuk models. Results show that the proposed model provides more accurate predictions than others with lower mean and standard deviation values of model prediction errors. PMID:28773064

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

    PubMed

    Wang, Zheng; Cao, Renzhi; Cheng, Jianlin

    2013-01-01

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

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

    PubMed Central

    2013-01-01

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

  3. Interactions between BDNF Val66Met polymorphism and early life stress predict brain and arousal pathways to syndromal depression and anxiety.

    PubMed

    Gatt, J M; Nemeroff, C B; Dobson-Stone, C; Paul, R H; Bryant, R A; Schofield, P R; Gordon, E; Kemp, A H; Williams, L M

    2009-07-01

    Individual risk markers for depression and anxiety disorders have been identified but the explicit pathways that link genes and environment to these markers remain unknown. Here we examined the explicit interactions between the brain-derived neurotrophic factor (BDNF) Val66Met gene and early life stress (ELS) exposure in brain (amygdala-hippocampal-prefrontal gray matter volume), body (heart rate), temperament and cognition in 374 healthy European volunteers assessed for depression and anxiety symptoms. Brain imaging data were based on a subset of 89 participants. Multiple regression analysis revealed main effects of ELS for body arousal (resting heart rate, P=0.005) and symptoms (depression and anxiety, P<0.001) in the absence of main effects for BDNF. In addition, significant BDNF-ELS interactions indicated that BDNF Met carriers exposed to greater ELS have smaller hippocampal and amygdala volumes (P=0.013), heart rate elevations (P=0.0002) and a decline in working memory (P=0.022). Structural equation path modeling was used to determine if this interaction predicts anxiety and depression by mediating effects on the brain, body and cognitive measures. The combination of Met carrier status and exposure to ELS predicted reduced gray matter in hippocampus (P<0.001), and associated lateral prefrontal cortex (P<0.001) and, in turn, higher depression (P=0.005). Higher depression was associated with poorer working memory (P=0.005), and slowed response speed. The BDNF Met-ELS interaction also predicted elevated neuroticism and higher depression and anxiety by elevations in body arousal (P<0.001). In contrast, the combination of BDNF V/V genotype and ELS predicted increases in gray matter of the amygdala (P=0.003) and associated medial prefrontal cortex (P<0.001), which in turn predicted startle-elicited heart rate variability (P=0.026) and higher anxiety (P=0.026). Higher anxiety was linked to verbal memory, and to impulsivity. These effects were specific to the BDNF

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

    PubMed

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

    2015-01-01

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

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

    PubMed

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

    2015-10-06

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

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

    PubMed

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

    2017-02-20

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

  7. Predictions of probable projectile-target combinations for the synthesis of superheavy isotopes of Ts

    NASA Astrophysics Data System (ADS)

    Santhosh, K. P.; Safoora, V.

    2017-06-01

    Taking the Coulomb and proximity potential as the interaction barrier, we have studied the production cross section of probable projectile-target combinations for the synthesis of superheavy element 297Ts. The cold reaction valley of 297Ts is studied to identify the possible projectile-target combinations for the synthesis of 297Ts. The entrance channel Coulomb barrier, the quasifission barrier, and barrier positions for all these combinations are calculated. The fusion probability and survival probability of the excited compound nucleus are evaluated. At energies near and above the Coulomb barrier, the excitation function [capture, fusion, and evaporation residue (ER) cross sections] of these combinations leading to superheavy element (SHE) 297Ts is investigated. It is found that the production cross sections of 297Ts in cold fusion reactions are very small compared to hot fusion reactions. The combinations 40S+257Md , 42S+255Md , 43Cl+254Fm , 44Ar+253Es , 46Ar+251Es , 48Ca+249Bk , 50Ca+247Bk , 67Co+230Th , 68Ni+229Ac , 70Ni+227Ac , and 72Ni+225Ac are observed to be the most favorable projectile-target pairs for the production of SHE 297Ts. Our calculated results are compared with experimental data and with other theoretical studies for the reaction 48Ca+249Bk leading to 297Ts, and are in good agreement with experimental observation of Oganessian et al. Furthermore, the ER cross section for the synthesis of isotopes Ts-296291 and Ts-299298 using 48Ca induced reactions are studied. Among these isotopes, the isotope 298Ts has larger cross section in the 3n channel using the reaction 48Ca+250Bk , and 299Ts has larger cross section in the 4n channel using the reaction 48Ca+251Bk . Therefore, our theoretical predictions to produce isotopes of the element Ts will be helpful for future experiments, as the isotopes have not been synthesized so far.

  8. 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.

  9. 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

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

    PubMed

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

    2009-12-01

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

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

    PubMed

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

    2015-01-29

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

  12. Is the 12-lead electrocardiogram during antidromic circus movement tachycardia helpful in predicting the ablation site in atriofascicular pathways?

    PubMed

    Sternick, Eduardo Back; Lokhandwala, Yash; Bohora, Shomu; Timmermans, Carl; Martins, Priscila Reis; Dias, Liana Valadão; Correia, Frederico Soares; Wellens, Hein J J

    2014-11-01

    Unlike in the Wolff-Parkinson-White syndrome, there has been no systematic study on the role of the pre-excitation pattern in predicting the ablation site in patients with atriofascicular (AF) pathways. We assessed in a large cohort the value of the 12-lead electrocardiogram (ECG) during antidromic tachycardia (ADT) to predict the site of ablation. Forty-five patients were studied, 23 males (51%), mean age of 27 ± 12 years with 46 AF pathways and 48 ADT using the AF pathway for A-V conduction. Inclusion required induction of a sustained ADT and successful ablation. Ablation site was assessed during LAO 45° projection and clockwise classified as hours in posteroseptal, posterolateral, lateral, anterolateral, and anteroseptal tricuspid annulus as follows: 05:00-07:00, >07:00-08:00, >08:00-09:00, >09:00-11:00, and >11:00-13:00 o'clock. The QRS axis was assessed during ADT and classified as normal (>+15°), horizontal (+15° to -30°), and superior (<-30°). During ADT axis was superior (-57° ± 10°) in 15 (31%), horizontal (-11° ± 14°) in 22 (46%), and normal (+45° ± 16°) in 11 (23%) patients. The correct ablation site did not differ between the different groups of QRS axis. QRS width during ADT was narrower in patients with a normal when compared with a horizontal and leftward axis (127 ± 14 vs. 145 ± 12 ms, P < 0.0001), and the V-H interval was shorter (4 ± 3 ms vs. 19 ± 22 ms, P = 0.03). There was no correlation between the AF pathway ablation site and the QRS axis during ADT. The 12-lead ECG during maximal pre-excitation does not predict the proper site of tricuspid annulus ablation in patients with A-V conduction over an AF pathway. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author 2014. For permissions please email: journals.permissions@oup.com.

  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. Predictive inference for best linear combination of biomarkers subject to limits of detection.

    PubMed

    Coolen-Maturi, Tahani

    2017-08-15

    Measuring the accuracy of diagnostic tests is crucial in many application areas including medicine, machine learning and credit scoring. The receiver operating characteristic (ROC) curve is a useful tool to assess the ability of a diagnostic test to discriminate between two classes or groups. In practice, multiple diagnostic tests or biomarkers are combined to improve diagnostic accuracy. Often, biomarker measurements are undetectable either below or above the so-called limits of detection (LoD). In this paper, nonparametric predictive inference (NPI) for best linear combination of two or more biomarkers subject to limits of detection is presented. NPI is a frequentist statistical method that is explicitly aimed at using few modelling assumptions, enabled through the use of lower and upper probabilities to quantify uncertainty. The NPI lower and upper bounds for the ROC curve subject to limits of detection are derived, where the objective function to maximize is the area under the ROC curve. In addition, the paper discusses the effect of restriction on the linear combination's coefficients on the analysis. Examples are provided to illustrate the proposed method. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  15. Prediction of bacterial small RNAs in the RsmA (CsrA) and ToxT pathways: a machine learning approach.

    PubMed

    Fakhry, Carl Tony; Kulkarni, Prajna; Chen, Ping; Kulkarni, Rahul; Zarringhalam, Kourosh

    2017-08-22

    Small RNAs (sRNAs) constitute an important class of post-transcriptional regulators that control critical cellular processes in bacteria. Recent research using high-throughput transcriptomic approaches has led to a dramatic increase in the discovery of bacterial sRNAs. However, it is generally believed that the currently identified sRNAs constitute a limited subset of the bacterial sRNA repertoire. In several cases, sRNAs belonging to a specific class are already known and the challenge is to identify additional sRNAs belonging to the same class. In such cases, machine-learning approaches can be used to predict novel sRNAs in a given class. In this work, we develop novel bioinformatics approaches that integrate sequence and structure-based features to train machine-learning models for the discovery of bacterial sRNAs. We show that features derived from recurrent structural motifs in the ensemble of low energy secondary structures can distinguish the RNA classes with high accuracy. We apply this approach to predict new members in two broad classes of bacterial small RNAs: 1) sRNAs that bind to the RNA-binding protein RsmA/CsrA in diverse bacterial species and 2) sRNAs regulated by the master regulator of virulence, ToxT, in Vibrio cholerae. The involvement of sRNAs in bacterial adaptation to changing environments is an increasingly recurring theme in current research in microbiology. It is likely that future research, combining experimental and computational approaches, will discover many more examples of sRNAs as components of critical regulatory pathways in bacteria. We have developed a novel approach for prediction of small RNA regulators in important bacterial pathways. This approach can be applied to specific classes of sRNAs for which several members have been identified and the challenge is to identify additional sRNAs.

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

    PubMed

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

    2015-12-01

    Predicting in vivo fertility of bull ejaculates using in vitro-assessed semen quality criteria remains challenging for the breeding industry. New technologies such as computer-assisted semen analysis (CASA) and flow cytometry may provide accurate and objective methods to improve semen quality control. The aim of this study was to evaluate the relationship between semen quality parameters and field fertility of bull ejaculates. A total of 153 ejaculates from 19 Holstein bulls have been analyzed using CASA (postthawing semen motility and morphology) and several flow cytometric tests, including sperm DNA integrity, viability (estimated by membrane integrity), acrosomal integrity, mitochondria aerobic functionality and oxidation. Samples were analyzed both immediately after thawing and after 4 hours at 37 °C. A fertility value (FV), based on nonreturn rate at 56 days after insemination and adjusted for environment factors, was calculated for each ejaculate. Simple and multiple regressions have been used to correlate FV with CASA and flow cytometric parameters. Significant simple correlations have been observed between some parameters and FV (e.g., straight line velocity [μm/s], r(2) = -0.12; polarized mitochondria sperm (%), r(2) = 0.07), but the relation between simple parameter and FV was too week to predict the fertility. Partial least square procedure identified several mathematical models combining flow cytometer and CASA variables and had better correlations with FV (adjusted r(2) ranging between 0.24 and 0.40 [P < 0.0001], depending on the number of included variables). In conclusion, this study suggests that quality assessment of thawed bull sperm using CASA and flow cytometry may provide a reasonable prediction of bovine semen fertility. Additional work will be required to increase the prediction reliability and promote this technology in routine artificial insemination laboratory practice. Copyright © 2015 Elsevier Inc. All rights reserved.

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

    PubMed

    Riley, Ellyn A; McFarland, Dennis J

    2017-01-01

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

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

    PubMed

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

    2014-05-01

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

  19. 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

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

    PubMed Central

    Riley, Ellyn A.; McFarland, Dennis J.

    2017-01-01

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

  1. 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. ...

  2. 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. ...

  3. 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.

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed Central

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

    2014-01-01

    Vascular endothelial growth factor receptor (VEGFR) inhibitors are approved for the treatment of several tumor types; however, some tumors show intrinsic resistance to VEGFR inhibitors, and some patients develop acquired resistance to these inhibitors. Therefore, a strategy to overcome VEGFR inhibitor resistance is urgently required. Recent reports suggest that activation of the hepatocyte growth factor (HGF) pathway through its cognate receptor, Met, contributes to VEGFR inhibitor resistance. Here, we explored the effect of the HGF/Met signaling pathway and its inhibitors on resistance to lenvatinib, a VEGFR inhibitor. In in vitro experiments, addition of VEGF plus HGF enhanced cell growth and tube formation of HUVECs when compared with stimulation by either factor alone. Lenvatinib potently inhibited the growth of HUVECs induced by VEGF alone, but cells induced by VEGF plus HGF showed lenvatinib resistance. This HGF-induced resistance was cancelled when the Met inhibitor, golvatinib, was added with lenvatinib. Conditioned medium from tumor cells producing high amounts of HGF also conferred resistance to inhibition by lenvatinib. In s.c. xenograft models based on various tumor cell lines with high HGF expression, treatment with lenvatinib alone showed weak antitumor effects, but treatment with lenvatinib plus golvatinib showed synergistic antitumor effects, accompanied by decreased tumor vessel density. These results suggest that HGF from tumor cells confers resistance to tumor endothelial cells against VEGFR inhibitors, and that combination therapy using VEGFR inhibitors with Met inhibitors may be effective for overcoming resistance to VEGFR inhibitors. Further evaluation in clinical trials is warranted. PMID:24689876

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

    PubMed

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

    2014-06-01

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

  7. 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

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

    PubMed

    Alaimo, Salvatore; Bonnici, Vincenzo; Cancemi, Damiano; Ferro, Alfredo; Giugno, Rosalba; Pulvirenti, Alfredo

    2015-01-01

    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. 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/. 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-values expressing the reliability of

  9. Onsager-Machlup action-based path sampling and its combination with replica exchange for diffusive and multiple pathways.

    PubMed

    Fujisaki, Hiroshi; Shiga, Motoyuki; Kidera, Akinori

    2010-04-07

    For sampling multiple pathways in a rugged energy landscape, we propose a novel action-based path sampling method using the Onsager-Machlup action functional. Inspired by the Fourier-path integral simulation of a quantum mechanical system, a path in Cartesian space is transformed into that in Fourier space, and an overdamped Langevin equation is derived for the Fourier components to achieve a canonical ensemble of the path at a finite temperature. To avoid "path trapping" around an initially guessed path, the path sampling method is further combined with a powerful sampling technique, the replica exchange method. The principle and algorithm of our method is numerically demonstrated for a model two-dimensional system with a bifurcated potential landscape. The results are compared with those of conventional transition path sampling and the equilibrium theory, and the error due to path discretization is also discussed.

  10. Performance of third-trimester combined screening model for prediction of adverse perinatal outcome.

    PubMed

    Miranda, J; Triunfo, S; Rodriguez-Lopez, M; Sairanen, M; Kouru, H; Parra-Saavedra, M; Crovetto, F; Figueras, F; Crispi, F; Gratacós, E

    2017-09-01

    To explore the potential value of third-trimester combined screening for the prediction of adverse perinatal outcome (APO) in the general population and among small-for-gestational-age (SGA) fetuses. This was a nested case-control study within a prospective cohort of 1590 singleton gestations undergoing third-trimester evaluation (32 + 0 to 36 + 6 weeks' gestation). Maternal baseline characteristics, mean arterial blood pressure, fetoplacental ultrasound and circulating biochemical markers (placental growth factor (PlGF), lipocalin-2, unconjugated estriol and inhibin A) were assessed in all women who subsequently had an APO (n = 148) and in a control group without perinatal complications (n = 902). APO was defined as the occurrence of stillbirth, umbilical artery cord blood pH < 7.15, 5-min Apgar score < 7 or emergency operative delivery for fetal distress. Logistic regression models were developed for the prediction of APO in the general population and among SGA cases (defined as customized birth weight < 10(th) centile). The prevalence of APO was 9.3% in the general population and 27.4% among SGA cases. In the general population, a combined screening model including a-priori risk (maternal characteristics), estimated fetal weight (EFW) centile, umbilical artery pulsatility index (UA-PI), estriol and PlGF achieved a detection rate for APO of 26% (area under receiver-operating characteristics curve (AUC), 0.59 (95% CI, 0.54-0.65)), at a 10% false-positive rate (FPR). Among SGA cases, a model including a-priori risk, EFW centile, UA-PI, cerebroplacental ratio, estriol and PlGF predicted 62% of APO (AUC, 0.86 (95% CI, 0.80-0.92)) at a FPR of 10%. The use of fetal ultrasound and maternal biochemical markers at 32-36 weeks provides a poor prediction of APO in the general population. Although it remains limited, the performance of the screening model is improved when applied to fetuses with suboptimal fetal growth. Copyright © 2016 ISUOG. Published by John Wiley & Sons

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

    PubMed Central

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

    2016-01-01

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

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

    PubMed

    Arbiser, Jack L

    2004-04-01

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

  13. Rac3 regulates cell proliferation through cell cycle pathway and predicts prognosis in lung adenocarcinoma.

    PubMed

    Wang, Gebang; Wang, Huan; Zhang, Chenlei; Liu, Tieqin; Li, Qingchang; Lin, Xuyong; Xie, Jingwei; Liu, Hongxu

    2016-09-01

    Lung cancer is still the leading cause of malignant deaths in the world. It is of great importance to find novel functional genes for the tumorigenesis of lung cancer. We demonstrated that Rac3 could promote cell proliferation and inhibit apoptosis in lung adenocarcinoma cell line A549 previously. The aim of this study was to investigate the function and mechanism of Rac3 in lung adenocarcinoma cell lines. Immunohistochemistry staining was performed in 107 lung adenocarcinoma tissues and matched non-tumor tissues. Multivariate analysis and Kaplan-Meier analysis were used to investigate the correlation between Rac3 expression and the clinical outcomes. 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, colony formation assay, and flow cytometry analysis were employed to determine the proliferative ability, cell cycle distribution, and apoptosis in H1299 and H1975 cell lines. Gene expression microarray and pathway analysis between the Rac3-siRNA group and the control group in A549 cells were performed to investigate the pathways and mechanism of Rac3 regulation. Rac3 was shown to be positively expressed in lung adenocarcinoma tissues, and the expression of Rac3 associates with longer survival in lung adenocarcinoma patients. Silencing of Rac3 significantly induced cell growth inhibition, colony formation decrease, cell cycle arrest, and apoptosis of lung adenocarcinoma cell lines, which accompanied by obvious downregulation of CCND1, MYC, and TFDP1 of cell cycle pathway involving in the tumorigenesis of lung adenocarcinoma based on the gene expression microarray. In conclusion, these findings suggest that Rac3 has the potential of being a therapeutic target for lung adenocarcinoma.

  14. 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

  15. 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

  16. 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

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

    SciTech Connect

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

    2013-04-09

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

  18. 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.

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

    PubMed Central

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

    2015-01-01

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

  20. Combining a nontargeted and targeted metabolomics approach to identify metabolic pathways significantly altered in polycystic ovary syndrome.

    PubMed

    Chang, Alice Y; Lalia, Antigoni Z; Jenkins, Gregory D; Dutta, Tumpa; Carter, Rickey E; Singh, Ravinder J; Nair, K Sreekumaran

    2017-06-01

    Polycystic ovary syndrome (PCOS) is a condition of androgen excess and chronic anovulation frequently associated with insulin resistance. We combined a nontargeted and targeted metabolomics approach to identify pathways and metabolites that distinguished PCOS from metabolic syndrome (MetS). Twenty obese women with PCOS were compared with 18 obese women without PCOS. Both groups met criteria for MetS but could not have diabetes mellitus or take medications that treat PCOS or affect lipids or insulin sensitivity. Insulin sensitivity was derived from the frequently sampled intravenous glucose tolerance test. A nontargeted metabolomics approach was performed on fasting plasma samples to identify differentially expressed metabolites, which were further evaluated by principal component and pathway enrichment analysis. Quantitative targeted metabolomics was then applied on candidate metabolites. Measured metabolites were tested for associations with PCOS and clinical variables by logistic and linear regression analyses. This multiethnic, obese sample was matched by age (PCOS, 37±6; MetS, 40±6years) and body mass index (BMI) (PCOS, 34.6±5.1; MetS, 33.7±5.2kg/m(2)). Principal component analysis of the nontargeted metabolomics data showed distinct group separation of PCOS from MetS controls. From the subset of 385 differentially expressed metabolites, 22% were identified by accurate mass, resulting in 19 canonical pathways significantly altered in PCOS, including amino acid, lipid, steroid, carbohydrate, and vitamin D metabolism. Targeted metabolomics identified many essential amino acids, including branched-chain amino acids (BCAA) that were elevated in PCOS compared with MetS. PCOS was most associated with BCAA (P=.02), essential amino acids (P=.03), the essential amino acid lysine (P=.02), and the lysine metabolite α-aminoadipic acid (P=.02) in models adjusted for surrogate variables representing technical variation in metabolites. No significant differences between

  1. Inherent vulnerabilities in monoaminergic pathways predict the emergence of depressive impairments in an animal model of chronic epilepsy.

    PubMed

    Medel-Matus, Jesús-Servando; Shin, Don; Sankar, Raman; Mazarati, Andrey

    2017-08-01

    The objective was to determine whether the depression comorbid with epilepsy could be predicted based on inherent premorbid patterns of monoaminergic transmission. In male Wistar rats, despair-like and anhedonia-like behaviors were examined using forced swimming and taste preference tests, respectively. Serotonergic raphe nucleus (RN)-prefrontal cortex (PFC) and dopaminergic ventral tegmental area (VTA)-nucleus accumbens (NAcc) pathways were interrogated by fast scan cyclic voltammetry (FSCV). The assays were performed before and 2 months after pilocarpine status epilepticus. In a subset of naive rats, FSCV, coupled with the intensity-dependent stimulation paradigm, detected specific deviations in each pathway (six rats for RN-PFC and seven rats for VTA-NAcc, with overlap in two, of 19 total subjects) in the absence of behavioral impairments. During epilepsy, animals with preexisting deviations in RN-PFC invariably developed despair, and rats with deviations in VTA-NAcc developed anhedonia. Serotonergic and dopaminergic pathways, respectively, showed signs of explicit deterioration. We suggest that epilepsy triggers decompensations in the already vulnerable depression-relevant neuronal circuits, which culminate in depression. The established connection between the identified specific signatures in monoamine transmission in naive rats and specific symptoms of epilepsy-associated depression may help in understanding causes of comorbidity and in developing its early biomarkers. Wiley Periodicals, Inc. © 2017 International League Against Epilepsy.

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

    PubMed Central

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

    2013-01-01

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

  3. Combining lymphovascular invasion with reactive stromal grade predicts prostate cancer mortality.

    PubMed

    Saeter, Thorstein; Vlatkovic, Ljiljana; Waaler, Gudmund; Servoll, Einar; Nesland, Jahn M; Axcrona, Karol; Axcrona, Ulrika

    2016-09-01

    Previous studies suggest that lymphovascular invasion (LVI) has a weak and variable effect on prognosis. It is uncertain whether LVI, determined by diagnostic prostate biopsy, predicts prostate cancer death. Data from experimental studies have indicated that carcinoma-associated fibroblasts in the reactive stroma could promote LVI and progression to metastasis. Thus, combining LVI with reactive stromal grade may identify prostate cancer patients at high risk of an unfavorable outcome. The purpose of the present study was to examine if LVI, determined by diagnostic biopsy, alone and in combination with reactive stromal grade could predict prostate cancer death. This population-based study included 283 patients with prostate cancer diagnosed by needle biopsy in Aust-Agder County (Norway) from 1991 to 1999. Clinical data were obtained by medical charts review. Two uropathologists evaluated LVI and reactive stromal grade. The endpoint was prostate cancer death. Patients with LVI had marginally higher risk of prostate cancer death compared to patients without LVI (hazard ratio: 1.8, P-value = 0.04). LVI had a stronger effect on prostate cancer death risk when a high reactive stromal grade was present (hazard ratio: 16.0, P-value <0.001). Therefore, patients with concomitant LVI and high reactive stromal grade were at particularly high risk for prostate cancer death. Evaluating LVI together with reactive stromal grade on diagnostic biopsies could be used to identify patients at high risk of death from prostate cancer. Prostate 76:1088-1094, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

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

    NASA Astrophysics Data System (ADS)

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

    2013-07-01

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

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

    PubMed Central

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

    2010-01-01

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

  6. Pathway-based identification of a smoking associated 6-gene signature predictive of lung cancer risk and survival

    PubMed Central

    Guo, Nancy Lan; Wan, Ying-Wooi

    2012-01-01

    Objective Smoking is a prominent risk factor for lung cancer. However, it is not an established prognostic factor for lung cancer in clinics. To date, no gene test is available for diagnostic screening of lung cancer risk or prognostication of clinical outcome in smokers. This study sought to identify a smoking associated gene signature in order to provide a more precise diagnosis and prognosis of lung cancer in smokers. Methods and materials An implication network based methodology was used to identify biomarkers by modeling crosstalk with major lung cancer signaling pathways. Specifically, the methodology contains the following steps: 1) identifying genes significantly associated with lung cancer survival; 2) selecting candidate genes which are differentially expressed in smokers versus non-smokers from the survival genes identified in Step 1; 3) from these candidate genes, constructing gene coexpression networks based on prediction logic for the smoker group and the non-smoker group, respectively; 4) identifying smoking-mediated differential components, i.e., the unique gene coexpression patterns specific to each group; and 5) from the differential components, identifying genes directly co-expressed with major lung cancer signaling hallmarks. Results A smoking-associated 6-gene signature was identified for prognosis of lung cancer from a training cohort (n=256). The 6-gene signature could separate lung cancer patients into two risk groups with distinct post-operative survival (log-rank P < 0.04, Kaplan-Meier analyses) in three independent cohorts (n=427). The expression-defined prognostic prediction is strongly related to smoking association and smoking cessation (P < 0.02; Pearson’s Chi-squared tests). The 6-gene signature is an accurate prognostic factor (hazard ratio = 1.89, 95% CI: [1.04, 3.43]) compared to common clinical covariates in multivariate Cox analysis. The 6-gene signature also provides an accurate diagnosis of lung cancer with an overall

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

    PubMed

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

    2010-12-13

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

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

    PubMed Central

    2012-01-01

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

  9. 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

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

    PubMed

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

    2015-04-01

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

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

    NASA Astrophysics Data System (ADS)

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

    2012-03-01

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

  12. Combining inferred regulatory and reconstructed metabolic networks enhances phenotype prediction in yeast.

    PubMed

    Wang, Zhuo; Danziger, Samuel A; Heavner, Benjamin D; Ma, Shuyi; Smith, Jennifer J; Li, Song; Herricks, Thurston; Simeonidis, Evangelos; Baliga, Nitin S; Aitchison, John D; Price, Nathan D

    2017-05-01

    Gene regulatory and metabolic network models have been used successfully in many organisms, but inherent differences between them make networks difficult to integrate. Probabilistic Regulation Of Metabolism (PROM) provides a partial solution, but it does not incorporate network inference and underperforms in eukaryotes. We present an Integrated Deduced And Metabolism (IDREAM) method that combines statistically inferred Environment and Gene Regulatory Influence Network (EGRIN) models with the PROM framework to create enhanced metabolic-regulatory network models. We used IDREAM to predict phenotypes and genetic interactions between transcription factors and genes encoding metabolic activities in the eukaryote, Saccharomyces cerevisiae. IDREAM models contain many fewer interactions than PROM and yet produce significantly more accurate growth predictions. IDREAM consistently outperformed PROM using any of three popular yeast metabolic models and across three experimental growth conditions. Importantly, IDREAM's enhanced accuracy makes it possible to identify subtle synthetic growth defects. With experimental validation, these novel genetic interactions involving the pyruvate dehydrogenase complex suggested a new role for fatty acid-responsive factor Oaf1 in regulating acetyl-CoA production in glucose grown cells.

  13. A Bayesian framework for combining heterogeneous data sources for gene function prediction (in Saccharomyces cerevisiae).

    PubMed

    Troyanskaya, Olga G; Dolinski, Kara; Owen, Art B; Altman, Russ B; Botstein, David

    2003-07-08

    Genomic sequencing is no longer a novelty, but gene function annotation remains a key challenge in modern biology. A variety of functional genomics experimental techniques are available, from classic methods such as affinity precipitation to advanced high-throughput techniques such as gene expression microarrays. In the future, more disparate methods will be developed, further increasing the need for integrated computational analysis of data generated by these studies. We address this problem with MAGIC (Multisource Association of Genes by Integration of Clusters), a general framework that uses formal Bayesian reasoning to integrate heterogeneous types of high-throughput biological data (such as large-scale two-hybrid screens and multiple microarray analyses) for accurate gene function prediction. The system formally incorporates expert knowledge about relative accuracies of data sources to combine them within a normative framework. MAGIC provides a belief level with its output that allows the user to vary the stringency of predictions. We applied MAGIC to Saccharomyces cerevisiae genetic and physical interactions, microarray, and transcription factor binding sites data and assessed the biological relevance of gene groupings using Gene Ontology annotations produced by the Saccharomyces Genome Database. We found that by creating functional groupings based on heterogeneous data types, MAGIC improved accuracy of the groupings compared with microarray analysis alone. We describe several of the biological gene groupings identified.

  14. Dereverberation of marine reflection seismic data by a spatial combination of predictive deconvolution and velocity filtering

    NASA Astrophysics Data System (ADS)

    Samson, C.; West, G. F.

    1995-02-01

    Contamination of seismic reflection records at early times by first-order water reverberations can be especially severe during survey operations over hard and flat sea floors on the continental shelf or in lake environments. A new dereverberation scheme based on two classical techniques — predictive deconvolution and velocity filtering — has been developed to address this problem. The techniques are combined spatially to take advantage of their complementary offset- and time-dependent properties. Stage I of the scheme consists of applying predictive deconvolution at short offset. The data are previously conditioned by a normal moveout correction with the water velocity which restores the periodicity of the reverberations in the offset-time plane and enhances the performance of deconvolution. Stage II of the scheme involves velocity filtering in the common-midpoint domain which is particularly effective at long offset where the moveout difference between primary reflections and reverberations is largest. The dereverberation scheme is well suited for the initial processing of large volumes of data due to the general availability of cost-effective deconvolution and velocity filtering algorithms in seismic processing software packages. Practical implementation issues are illustrated by a field example from the GLIMPCE survey in Lake Superior.

  15. Combined Molecular Dynamics Simulation-Molecular-Thermodynamic Theory Framework for Predicting Surface Tensions.

    PubMed

    Sresht, Vishnu; Lewandowski, Eric P; Blankschtein, Daniel; Jusufi, Arben

    2017-08-22

    A molecular modeling approach is presented with a focus on quantitative predictions of the surface tension of aqueous surfactant solutions. The approach combines classical Molecular Dynamics (MD) simulations with a molecular-thermodynamic theory (MTT) [ Y. J. Nikas, S. Puvvada, D. Blankschtein, Langmuir 1992 , 8 , 2680 ]. The MD component is used to calculate thermodynamic and molecular parameters that are needed in the MTT model to determine the surface tension isotherm. The MD/MTT approach provides the important link between the surfactant bulk concentration, the experimental control parameter, and the surfactant surface concentration, the MD control parameter. We demonstrate the capability of the MD/MTT modeling approach on nonionic alkyl polyethylene glycol surfactants at the air-water interface and observe reasonable agreement of the predicted surface tensions and the experimental surface tension data over a wide range of surfactant concentrations below the critical micelle concentration. Our modeling approach can be extended to ionic surfactants and their mixtures with both ionic and nonionic surfactants at liquid-liquid interfaces.

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

    ERIC Educational Resources Information Center

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

    2017-01-01

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

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

    ERIC Educational Resources Information Center

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

    2017-01-01

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

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

    PubMed

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

    2017-02-01

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

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

    PubMed Central

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

    2017-01-01

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

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

    SciTech Connect

    Pierce, Eric M; Bacon, Diana

    2011-01-01

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

  1. 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

  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. In silico prediction of ebolavirus RNA polymerase inhibition by specific combinations of approved nucleotide analogues.

    PubMed

    van Hemert, Formijn J; Zaaijer, Hans L; Berkhout, Ben

    2015-12-01

    The urgency of ebolavirus drug development is obvious in light of the current local epidemic in Western Africa with high morbidity and a risk of wider spread. We present an in silico study as a first step to identify inhibitors of ebolavirus polymerase activity based on approved antiviral nucleotide analogues. Since a structure model of the ebolavirus polymerase is lacking, we performed combined homology and ab initio modeling and report a similarity to known polymerases of human enterovirus, bovine diarrhea virus and foot-and-mouth disease virus. This facilitated the localization of a nucleotide binding domain in the ebolavirus polymerase. We next performed molecular docking studies with nucleotides (ATP, CTP, GTP and UTP) and nucleotide analogues, including a variety of approved antiviral drugs. Specific combinations of nucleotide analogues significantly reduce the ligand-protein interaction energies of the ebolavirus polymerase for natural nucleotides. Any nucleotide analogue on its own did not reduce ligand-protein interaction energies. This prediction encourages specific drug testing efforts and guides future strategies to inhibit ebolavirus replication. Copyright © 2015 Elsevier B.V. All rights reserved.

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

    PubMed

    Krasnopolsky, Vladimir M; Fox-Rabinovitz, Michael S

    2006-03-01

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

  5. 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

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

    PubMed

    Behrmann-Godel, Jasminca

    2013-04-01

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

  7. 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

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

    PubMed Central

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

    2015-01-01

    We describe an innovative protocol for ab initio prediction of ligand crystallographic binding poses and highly effective analysis of large datasets generated for protein-ligand dynamics. We include a procedure for setup and performance of distributed molecular dynamics simulations on cloud computing architectures, a model for efficient analysis of simulation data, and a metric for evaluation of model convergence. We give accurate binding pose predictions for five ligands ranging in affinity from 7 nM to > 200 μM for the immunophilin protein FKBP12, for expedited results in cases where experimental structures are difficult to produce. Our approach goes beyond single, low energy ligand poses to give quantitative kinetic information that can inform protein engineering and ligand design. PMID:25608737

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

    PubMed

    Ferns, Gordon A A

    2008-12-01

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

  10. 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

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

    NASA Astrophysics Data System (ADS)

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

    2017-01-01

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

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

    PubMed Central

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

    2015-01-01

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

  13. High quality protein sequence alignment by combining structural profile prediction and profile alignment using SABERTOOTH

    PubMed Central

    2010-01-01

    Background Protein alignments are an essential tool for many bioinformatics analyses. While sequence alignments are accurate for proteins of high sequence similarity, they become unreliable as they approach the so-called 'twilight zone' where sequence similarity gets indistinguishable from random. For such distant pairs, structure alignment is of much better quality. Nevertheless, sequence alignment is the only choice in the majority of cases where structural data is not available. This situation demands development of methods that extend the applicability of accurate sequence alignment to distantly related proteins. Results We develop a sequence alignment method that combines the prediction of a structural profile based on the protein's sequence with the alignment of that profile using our recently published alignment tool SABERTOOTH. In particular, we predict the contact vector of protein structures using an artificial neural network based on position-specific scoring matrices generated by PSI-BLAST and align these predicted contact vectors. The resulting sequence alignments are assessed using two different tests: First, we assess the alignment quality by measuring the derived structural similarity for cases in which structures are available. In a second test, we quantify the ability of the significance score of the alignments to recognize structural and evolutionary relationships. As a benchmark we use a representative set of the SCOP (structural classification of proteins) database, with similarities ranging from closely related proteins at SCOP family level, to very distantly related proteins at SCOP fold level. Comparing these results with some prominent sequence alignment tools, we find that SABERTOOTH produces sequence alignments of better quality than those of Clustal W, T-Coffee, MUSCLE, and PSI-BLAST. HHpred, one of the most sophisticated and computationally expensive tools available, outperforms our alignment algorithm at family and superfamily levels

  14. The combination of work organizational climate and individual work commitment predicts return to work in women but not in men.

    PubMed

    Holmgren, Kristina; Ekbladh, Elin; Hensing, Gunnel; Dellve, Lotta

    2013-02-01

    To analyze if the combination of organizational climate and work commitment can predict return to work (RTW). This prospective Swedish study was based on 2285 participants, 19 to 64 years old, consecutively selected from the employed population, newly sick-listed for more than 14 days. Data were collected in 2008 through postal questionnaire and from register data. Among women, the combination of good organizational climate and fair work commitment predicted an early RTW with an adjusted relative risk of 2.05 (1.32 to 3.18). Among men, none of the adjusted variables or combinations of variables was found significantly to predict RTW. This study demonstrated the importance of integrative effects of organizational climate and individual work commitment on RTW among women. These factors did not predict RTW in men. More research is needed to understand the RTW process among men.

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

    PubMed Central

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

    2015-01-01

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

  16. Combining qualitative and quantitative operational research methods to inform quality improvement in pathways that span multiple settings

    PubMed Central

    Crowe, Sonya; Brown, Katherine; Tregay, Jenifer; Wray, Jo; Knowles, Rachel; Ridout, Deborah A; Bull, Catherine; Utley, Martin

    2017-01-01

    Background Improving integration and continuity of care across sectors within resource constraints is a priority in many health systems. Qualitative operational research methods of problem structuring have been used to address quality improvement in services involving multiple sectors but not in combination with quantitative operational research methods that enable targeting of interventions according to patient risk. We aimed to combine these methods to augment and inform an improvement initiative concerning infants with congenital heart disease (CHD) whose complex care pathway spans multiple sectors. Methods Soft systems methodology was used to consider systematically changes to services from the perspectives of community, primary, secondary and tertiary care professionals and a patient group, incorporating relevant evidence. Classification and regression tree (CART) analysis of national audit datasets was conducted along with data visualisation designed to inform service improvement within the context of limited resources. Results A ‘Rich Picture’ was developed capturing the main features of services for infants with CHD pertinent to service improvement. This was used, along with a graphical summary of the CART analysis, to guide discussions about targeting interventions at specific patient risk groups. Agreement was reached across representatives of relevant health professions and patients on a coherent set of targeted recommendations for quality improvement. These fed into national decisions about service provision and commissioning. Conclusions When tackling complex problems in service provision across multiple settings, it is important to acknowledge and work with multiple perspectives systematically and to consider targeting service improvements in response to confined resources. Our research demonstrates that applying a combination of qualitative and quantitative operational research methods is one approach to doing so that warrants further

  17. Combining qualitative and quantitative operational research methods to inform quality improvement in pathways that span multiple settings.

    PubMed

    Crowe, Sonya; Brown, Katherine; Tregay, Jenifer; Wray, Jo; Knowles, Rachel; Ridout, Deborah A; Bull, Catherine; Utley, Martin

    2017-08-01

    Improving integration and continuity of care across sectors within resource constraints is a priority in many health systems. Qualitative operational research methods of problem structuring have been used to address quality improvement in services involving multiple sectors but not in combination with quantitative operational research methods that enable targeting of interventions according to patient risk. We aimed to combine these methods to augment and inform an improvement initiative concerning infants with congenital heart disease (CHD) whose complex care pathway spans multiple sectors. Soft systems methodology was used to consider systematically changes to services from the perspectives of community, primary, secondary and tertiary care professionals and a patient group, incorporating relevant evidence. Classification and regression tree (CART) analysis of national audit datasets was conducted along with data visualisation designed to inform service improvement within the context of limited resources. A 'Rich Picture' was developed capturing the main features of services for infants with CHD pertinent to service improvement. This was used, along with a graphical summary of the CART analysis, to guide discussions about targeting interventions at specific patient risk groups. Agreement was reached across representatives of relevant health professions and patients on a coherent set of targeted recommendations for quality improvement. These fed into national decisions about service provision and commissioning. When tackling complex problems in service provision across multiple settings, it is important to acknowledge and work with multiple perspectives systematically and to consider targeting service improvements in response to confined resources. Our research demonstrates that applying a combination of qualitative and quantitative operational research methods is one approach to doing so that warrants further consideration. Published by the BMJ Publishing Group

  18. Machine learning approach to discovering cascade reaction patterns. Application to reaction pathways prediction.

    PubMed

    Nowak, Grazyna; Fic, Grzegorz

    2009-06-01

    We propose a combinatorial learning procedure for discovering graph transformation patterns based on combining transformations that can be used in a consecutive fashion. Application of this kind of pattern to a specified chemical system allows to combine a sequence of consecutive transformations into a one-step operation limiting the complexity of a reaction tree. In a retrosynthetic sense, it provides a global strategy for bond disconnections to plan more efficient convergent syntheses. In a forward direction the approach reduces the number of iterations in order to exploring the courses of complex multistep processes. The procedure enhances the capabilities and applications of the CSB (Chemical Sense Builder) computer program to assist in organic synthesis, biochemistry, or medicinal chemistry. As an example, we present the automatic derivation of the transformation pattern for Ugi-type four-component reactions (reaction sequences) and its application to assist in the designing new cascade transformations for diversity-oriented synthesis (DOS).

  19. 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.

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

    PubMed Central

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

    2013-01-01

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

  1. 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

  2. Wnt pathway curation using automated natural language processing: combining statistical methods with partial and full parse for knowledge extraction.

    PubMed

    Santos, Carlos; Eggle, Daniela; States, David J

    2005-04-15

    Wnt signaling is a very active area of research with highly relevant publications appearing at a rate of more than one per day. Building and maintaining databases describing signal transduction networks is a time-consuming and demanding task that requires careful literature analysis and extensive domain-specific knowledge. For instance, more than 50 factors involved in Wnt signal transduction have been identified as of late 2003. In this work we describe a natural language processing (NLP) system that is able to identify references to biological interaction networks in free text and automatically assembles a protein association and interaction map. A 'gold standard' set of names and assertions was derived by manual scanning of the Wnt genes website (http://www.stanford.edu/~rnusse/wntwindow.html) including 53 interactions involved in Wnt signaling. This system was used to analyze a corpus of peer-reviewed articles related to Wnt signaling including 3369 Pubmed and 1230 full text papers. Names for key Wnt-pathway associated proteins and biological entities are identified using a chi-squared analysis of noun phrases over-represented in the Wnt literature as compared to the general signal transduction literature. Interestingly, we identified several instances where generic terms were used on the website when more specific terms occur in the literature, and one typographic error on the Wnt canonical pathway. Using the named entity list and performing an exhaustive assertion extraction of the corpus, 34 of the 53 interactions in the 'gold standard' Wnt signaling set were successfully identified (64% recall). In addition, the automated extraction found several interactions involving key Wnt-related molecules which were missing or different from those in the canonical diagram, and these were confirmed by manual review of the text. These results suggest that a combination of NLP techniques for information extraction can form a useful first-pass tool for assisting human

  3. 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

  4. 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.

  5. 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

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

    PubMed

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

    2015-01-01

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

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

    SciTech Connect

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

    2016-12-19

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

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

    PubMed Central

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

    2017-01-01

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

  9. Genetic variants in fanconi anemia pathway genes BRCA2 and FANCA predict melanoma survival.

    PubMed

    Yin, Jieyun; Liu, Hongliang; Liu, Zhensheng; Wang, Li-E; Chen, Wei V; Zhu, Dakai; Amos, Christopher I; Fang, Shenying; Lee, Jeffrey E; Wei, Qingyi

    2015-02-01

    Cutaneous melanoma (CM) is the most lethal skin cancer. The Fanconi anemia (FA) pathway involved in DNA crosslink repair may affect CM susceptibility and prognosis. Using data derived from published genome-wide association study, we comprehensively analyzed the associations of 2,339 common single-nucleotide polymorphisms (SNPs) in 14 autosomal FA genes with overall survival (OS) in 858 CM patients. By performing false-positive report probability corrections and stepwise Cox proportional hazards regression analyses, we identified significant associations between CM OS and four putatively functional SNPs: BRCA2 rs10492396 (AG vs. GG: adjusted hazard ratio (adjHR)=1.85, 95% confidence interval (CI)=1.16-2.95, P=0.010), rs206118 (CC vs. TT+TC: adjHR=2.44, 95% CI=1.27-4.67, P=0.007), rs3752447 (CC vs. TT+TC: adjHR=2.10, 95% CI=1.38-3.18, P=0.0005), and FANCA rs62068372 (TT vs. CC+CT: adjHR=1.85, 95% CI=1.27-2.69, P=0.001). Moreover, patients with an increasing number of unfavorable genotypes (NUG) of these loci had markedly reduced OS and melanoma-specific survival (MSS). The final model incorporating with NUG, tumor stage, and Breslow thickness showed an improved discriminatory ability to classify both 5-year OS and 5-year MSS. Additional investigations, preferably prospective studies, are needed to validate our findings.

  10. 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

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

    PubMed

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

    2015-06-16

    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. 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. 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. 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.

  12. 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

  13. Predictive combinations of monitor alarms preceding in-hospital code blue events.

    PubMed

    Hu, Xiao; Sapo, Monica; Nenov, Val; Barry, Tod; Kim, Sunghan; Do, Duc H; Boyle, Noel; Martin, Neil

    2012-10-01

    Bedside monitors are ubiquitous in acute care units of modern healthcare enterprises. However, they have been criticized for generating an excessive number of false positive alarms causing alarm fatigue among care givers and potentially compromising patient safety. We hypothesize that combinations of regular monitor alarms denoted as SuperAlarm set may be more indicative of ongoing patient deteriorations and hence predictive of in-hospital code blue events. The present work develops and assesses an alarm mining approach based on finding frequent combinations of single alarms that are also specific to code blue events to compose a SuperAlarm set. We use 4-way analysis of variance (ANOVA) to investigate the influence of four algorithm parameters on the performance of the data mining approach. The results are obtained from millions of monitor alarms from a cohort of 223 adult code blue and 1768 control patients using a multiple 10-fold cross-validation experiment setup. Using the optimal setting of parameters determined in the cross-validation experiment, final SuperAlarm sets are mined from the training data and used on an independent test data set to simulate running a SuperAlarm set against live regular monitor alarms. The ANOVA shows that the content of a SuperAlarm set is influenced by a subset of key algorithm parameters. Simulation of the extracted SuperAlarm set shows that it can predict code blue events one hour ahead with sensitivity between 66.7% and 90.9% while producing false SuperAlarms for control patients that account for between 2.2% and 11.2% of regular monitor alarms depending on user-supplied acceptable false positive rate. We conclude that even though the present work is still preliminary due to the usage of a moderately-sized database to test our hypothesis it represents an effort to develop algorithms to alleviate the alarm fatigue issue in a unique way. Copyright © 2012 Elsevier Inc. All rights reserved.

  14. Assessment of predictive performance in incomplete data by combining internal validation and multiple imputation.

    PubMed

    Wahl, Simone; Boulesteix, Anne-Laure; Zierer, Astrid; Thorand, Barbara; Avan de Wiel, Mark

    2016-10-26

    Missing values are a frequent issue in human studies. In many situations, multiple imputation (MI) is an appropriate missing data handling strategy, whereby missing values are imputed multiple times, the analysis is performed in every imputed data set, and the obtained estimates are pooled. If the aim is to estimate (added) predictive performance measures, such as (change in) the area under the receiver-operating characteristic curve (AUC), internal validation strategies become desirable in order to correct for optimism. It is not fully understood how internal validation should be combined with multiple imputation. In a comprehensive simulation study and in a real data set based on blood markers as predictors for mortality, we compare three combination strategies: Val-MI, internal validation followed by MI on the training and test parts separately, MI-Val, MI on the full data set followed by internal validation, and MI(-y)-Val, MI on the full data set omitting the outcome followed by internal validation. Different validation strategies, including bootstrap und cross-validation, different (added) performance measures, and various data characteristics are considered, and the strategies are evaluated with regard to bias and mean squared error of the obtained performance estimates. In addition, we elaborate on the number of resamples and imputations to be used, and adopt a strategy for confidence interval construction to incomplete data. Internal validation is essential in order to avoid optimism, with the bootstrap 0.632+ estimate representing a reliable method to correct for optimism. While estimates obtained by MI-Val are optimistically biased, those obtained by MI(-y)-Val tend to be pessimistic in the presence of a true underlying effect. Val-MI provides largely unbiased estimates, with a slight pessimistic bias with increasing true effect size, number of covariates and decreasing sample size. In Val-MI, accuracy of the estimate is more strongly improved by

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

    PubMed

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

    2009-05-05

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

  16. NAViGaTing the micronome--using multiple microRNA prediction databases to identify signalling pathway-associated microRNAs.

    PubMed

    Shirdel, Elize A; Xie, Wing; Mak, Tak W; Jurisica, Igor

    2011-02-25

    MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome--referred to as the micronome--to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal--mirDIP (http://ophid.utoronto.ca/mirDIP). mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05), suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001), to be more studied (p<0.0002), and to have higher degree in the KEGG cancer pathway (p<0.0001), compared to intra-pathway microRNAs. Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level.

  17. NAViGaTing the Micronome – Using Multiple MicroRNA Prediction Databases to Identify Signalling Pathway-Associated MicroRNAs

    PubMed Central

    Shirdel, Elize A.; Xie, Wing; Mak, Tak W.; Jurisica, Igor

    2011-01-01

    Background MicroRNAs are a class of small RNAs known to regulate gene expression at the transcript level, the protein level, or both. Since microRNA binding is sequence-based but possibly structure-specific, work in this area has resulted in multiple databases storing predicted microRNA:target relationships computed using diverse algorithms. We integrate prediction databases, compare predictions to in vitro data, and use cross-database predictions to model the microRNA:transcript interactome – referred to as the micronome – to study microRNA involvement in well-known signalling pathways as well as associations with disease. We make this data freely available with a flexible user interface as our microRNA Data Integration Portal — mirDIP (http://ophid.utoronto.ca/mirDIP). Results mirDIP integrates prediction databases to elucidate accurate microRNA:target relationships. Using NAViGaTOR to produce interaction networks implicating microRNAs in literature-based, KEGG-based and Reactome-based pathways, we find these signalling pathway networks have significantly more microRNA involvement compared to chance (p<0.05), suggesting microRNAs co-target many genes in a given pathway. Further examination of the micronome shows two distinct classes of microRNAs; universe microRNAs, which are involved in many signalling pathways; and intra-pathway microRNAs, which target multiple genes within one signalling pathway. We find universe microRNAs to have more targets (p<0.0001), to be more studied (p<0.0002), and to have higher degree in the KEGG cancer pathway (p<0.0001), compared to intra-pathway microRNAs. Conclusions Our pathway-based analysis of mirDIP data suggests microRNAs are involved in intra-pathway signalling. We identify two distinct classes of microRNAs, suggesting a hierarchical organization of microRNAs co-targeting genes both within and between pathways, and implying differential involvement of universe and intra-pathway microRNAs at the disease level. PMID

  18. 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.

  19. 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.

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

    PubMed Central

    2017-01-01

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

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

    PubMed

    Maeda, Satoshi; Morokuma, Keiji

    2011-08-09

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

  2. SAR202 Genomes from the Dark Ocean Predict Pathways for the Oxidation of Recalcitrant Dissolved Organic Matter.

    PubMed

    Landry, Zachary; Swan, Brandon K; Herndl, Gerhard J; Stepanauskas, Ramunas; Giovannoni, Stephen J

    2017-04-18

    Deep-ocean regions beyond the reach of sunlight contain an estimated 615 Pg of dissolved organic matter (DOM), much of which persists for thousands of years. It is thought that bacteria oxidize DOM until it is too dilute or refractory to support microbial activity. We analyzed five single-amplified genomes (SAGs) from the abundant SAR202 clade of dark-ocean bacterioplankton and found they encode multiple families of paralogous enzymes involved in carbon catabolism, including several families of oxidative enzymes that we hypothesize participate in the degradation of cyclic alkanes. The five partial genomes encoded 152 flavin mononucleotide/F420-dependent monooxygenases (FMNOs), many of which are predicted to be type II Baeyer-Villiger monooxygenases (BVMOs) that catalyze oxygen insertion into semilabile alicyclic alkanes. The large number of oxidative enzymes, as well as other families of enzymes that appear to play complementary roles in catabolic pathways, suggests that SAR202 might catalyze final steps in the biological oxidation of relatively recalcitrant organic compounds to refractory compounds that persist.IMPORTANCE Carbon in the ocean is massively sequestered in a complex mixture of biologically refractory molecules that accumulate as the chemical end member of biological oxidation and diagenetic change. However, few details are known about the biochemical machinery of carbon sequestration in the deep ocean. Reconstruction of the metabolism of a deep-ocean microbial clade, SAR202, led to postulation of new biochemical pathways that may be the penultimate stages of DOM oxidation to refractory forms that persist. These pathways are tied to a proliferation of oxidative enzymes. This research illuminates dark-ocean biochemistry that is broadly consequential for reconstructing the global carbon cycle. Copyright © 2017 Landry